From eee39f3db9a4aa39c09fb21b8091d961962fb971 Mon Sep 17 00:00:00 2001
From: nd-02110114 <nd.12021218@gmail.com>
Date: Sun, 31 May 2020 12:53:10 +0900
Subject: [PATCH] :bug: update tutorials

---
 ...asic_Tools_of_the_Deep_Life_Sciences.ipynb |  327 +-
 .../02_Learning_MNIST_Digit_Classifiers.ipynb |  183 +-
 .../tutorials/03_Modeling_Solubility.ipynb    |  347 +-
 ...4_Introduction_to_Graph_Convolutions.ipynb |  328 +-
 ...5_Putting_Multitask_Learning_to_Work.ipynb |  211 +-
 ...g_Deeper_on_Molecular_Featurizations.ipynb |  332 +-
 .../07_Uncertainty_In_Deep_Learning.ipynb     |  180 +-
 ...troduction_to_Model_Interpretability.ipynb |  381 +-
 ...idelity_model_from_experimental_data.ipynb |  354 +-
 ...ploring_Quantum_Chemistry_with_GDB1k.ipynb |  610 ++-
 ...nsupervised_Embeddings_for_Molecules.ipynb | 1095 ++--
 ...redicting_Ki_of_Ligands_to_a_Protein.ipynb |  148 +-
 ...Modeling_Protein_Ligand_Interactions.ipynb | 2067 +++++---
 ...nteractions_With_Atomic_Convolutions.ipynb |  103 +-
 .../15_Synthetic_Feasibility_Scoring.ipynb    |  125 +-
 ...onal_Generative_Adversarial_Networks.ipynb |  209 +-
 ...erative_Adversarial_Network_on_MNIST.ipynb |  182 +-
 ..._Reinforcement_Learning_to_Play_Pong.ipynb |  654 ++-
 .../21_Introduction_to_Bioinformatics.ipynb   | 4578 +++++++++--------
 19 files changed, 6980 insertions(+), 5434 deletions(-)

diff --git a/examples/tutorials/01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb b/examples/tutorials/01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb
index 677897f95b..8c82ed4245 100644
--- a/examples/tutorials/01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb
+++ b/examples/tutorials/01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb
@@ -89,74 +89,47 @@
       "metadata": {
         "id": "OyxRVW5X5zF0",
         "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "%%capture\n",
-        "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "Jk47QTZ95zF-",
-        "colab_type": "text"
-      },
-      "source": [
-        "You can of course run this tutorial locally if you prefer. In this case, don't run the above cell since it will download and install Anaconda on your local machine. In either case, we can now import `deepchem` the package to play with."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "PDiY03h35zF_",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 253
+          "height": 479
         },
-        "outputId": "7631cf36-ec65-42d9-8457-f3d7adb727fb"
+        "outputId": "c6592413-7694-439c-e233-2a363260d743"
       },
       "source": [
-        "# Run this cell to see if things work\n",
-        "import deepchem as dc"
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 2,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  14732      0 --:--:-- --:--:-- --:--:-- 14732\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "remove current miniconda\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
             "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
             "  warnings.warn(msg, category=FutureWarning)\n"
           ],
           "name": "stderr"
         },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
         {
           "output_type": "stream",
           "text": [
@@ -170,9 +143,48 @@
             "\n"
           ],
           "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 3.28 s, sys: 1.33 s, total: 4.61 s\n",
+            "Wall time: 4min 8s\n"
+          ],
+          "name": "stdout"
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Jk47QTZ95zF-",
+        "colab_type": "text"
+      },
+      "source": [
+        "You can of course run this tutorial locally if you prefer. In this case, don't run the above cell since it will download and install Anaconda on your local machine. In either case, we can now import `deepchem` the package to play with."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PDiY03h35zF_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# Run this cell to see if things work\n",
+        "import deepchem as dc"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -217,11 +229,11 @@
       "metadata": {
         "id": "YEDcUsz35zGO",
         "colab_type": "code",
+        "outputId": "d3cafd3b-ea5f-46d4-b943-51c9fe09612b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 102
-        },
-        "outputId": "4c436d71-7899-4b37-c7f5-0a0e419f41e6"
+        }
       },
       "source": [
         "data, labels"
@@ -232,11 +244,11 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "(array([[0.49479882, 0.88435469, 0.42959074, 0.37081081],\n",
-              "        [0.59314417, 0.63599229, 0.71899725, 0.25277191],\n",
-              "        [0.9648509 , 0.13042426, 0.4710018 , 0.18399865],\n",
-              "        [0.45050728, 0.2351225 , 0.51815274, 0.00368521]]),\n",
-              " array([0.65081079, 0.47392044, 0.31896501, 0.46142403]))"
+              "(array([[0.88854803, 0.85106206, 0.50989582, 0.4604496 ],\n",
+              "        [0.99076893, 0.96688872, 0.17772694, 0.22514947],\n",
+              "        [0.02122289, 0.85833497, 0.59921674, 0.47021954],\n",
+              "        [0.82452021, 0.83123677, 0.74626358, 0.86423226]]),\n",
+              " array([0.57071948, 0.43635559, 0.15942782, 0.07542204]))"
             ]
           },
           "metadata": {
@@ -286,11 +298,11 @@
       "metadata": {
         "id": "LJc90fs_5zGs",
         "colab_type": "code",
+        "outputId": "1553fa2c-6c18-49e8-fc3e-136eebbac879",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "4b32b746-4130-432a-9004-90d66d6e58a7"
+        }
       },
       "source": [
         "dataset"
@@ -301,7 +313,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<deepchem.data.datasets.NumpyDataset at 0x7fd86d000e48>"
+              "<deepchem.data.datasets.NumpyDataset at 0x7fca40fddac8>"
             ]
           },
           "metadata": {
@@ -326,11 +338,11 @@
       "metadata": {
         "id": "HSVqeYox5zGx",
         "colab_type": "code",
+        "outputId": "896d67d0-1c60-4996-d0d3-188b4cc4c293",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 102
-        },
-        "outputId": "e99835ed-4fb4-4601-d2c3-2ac041f8b5a5"
+        }
       },
       "source": [
         "dataset.X, dataset.y"
@@ -341,11 +353,11 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "(array([[0.49479882, 0.88435469, 0.42959074, 0.37081081],\n",
-              "        [0.59314417, 0.63599229, 0.71899725, 0.25277191],\n",
-              "        [0.9648509 , 0.13042426, 0.4710018 , 0.18399865],\n",
-              "        [0.45050728, 0.2351225 , 0.51815274, 0.00368521]]),\n",
-              " array([0.65081079, 0.47392044, 0.31896501, 0.46142403]))"
+              "(array([[0.88854803, 0.85106206, 0.50989582, 0.4604496 ],\n",
+              "        [0.99076893, 0.96688872, 0.17772694, 0.22514947],\n",
+              "        [0.02122289, 0.85833497, 0.59921674, 0.47021954],\n",
+              "        [0.82452021, 0.83123677, 0.74626358, 0.86423226]]),\n",
+              " array([0.57071948, 0.43635559, 0.15942782, 0.07542204]))"
             ]
           },
           "metadata": {
@@ -372,11 +384,11 @@
       "metadata": {
         "id": "k_8IONOw5zHC",
         "colab_type": "code",
+        "outputId": "8778e16c-4ca3-404b-cc28-cf6c15740214",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 85
-        },
-        "outputId": "7040aeec-5087-47f9-c185-460282ec0f92"
+        }
       },
       "source": [
         "for x, y, _, _ in dataset.itersamples():\n",
@@ -387,10 +399,10 @@
         {
           "output_type": "stream",
           "text": [
-            "[0.49479882 0.88435469 0.42959074 0.37081081] 0.6508107895953468\n",
-            "[0.59314417 0.63599229 0.71899725 0.25277191] 0.4739204414199628\n",
-            "[0.9648509  0.13042426 0.4710018  0.18399865] 0.3189650065656162\n",
-            "[0.45050728 0.2351225  0.51815274 0.00368521] 0.46142403321496583\n"
+            "[0.88854803 0.85106206 0.50989582 0.4604496 ] 0.5707194770088277\n",
+            "[0.99076893 0.96688872 0.17772694 0.22514947] 0.43635558550779396\n",
+            "[0.02122289 0.85833497 0.59921674 0.47021954] 0.1594278181500921\n",
+            "[0.82452021 0.83123677 0.74626358 0.86423226] 0.0754220447474171\n"
           ],
           "name": "stdout"
         }
@@ -411,11 +423,11 @@
       "metadata": {
         "id": "1fDXCKv_5zHI",
         "colab_type": "code",
+        "outputId": "7b089c43-6dcc-46a2-ebb8-194cca588e33",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "63509864-b73f-4aaa-970f-f387ffbaa007"
+        }
       },
       "source": [
         "dataset.ids"
@@ -451,11 +463,11 @@
       "metadata": {
         "id": "uffH-1EI5zHR",
         "colab_type": "code",
+        "outputId": "f8cc9d84-67f1-4d44-8937-f0497469e0c2",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "279e0325-4ec5-44c2-9020-9f1a6d7d511f"
+        }
       },
       "source": [
         "dataset.w"
@@ -491,11 +503,11 @@
       "metadata": {
         "id": "JHiBOSJB5zHV",
         "colab_type": "code",
+        "outputId": "2cec0633-6e40-41f9-d1da-c9520b32a4a3",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "6b77e031-63a2-4f38-f16a-9272859ef3a1"
+        }
       },
       "source": [
         "w = np.random.random((4,)) # initializing weights with random vector of size 4x1\n",
@@ -508,7 +520,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "array([0.3758539 , 0.37790286, 0.56012147, 0.79153357])"
+              "array([0.14539906, 0.12112723, 0.66676722, 0.66657104])"
             ]
           },
           "metadata": {
@@ -588,11 +600,11 @@
       "metadata": {
         "id": "lPTLNO6n5zH7",
         "colab_type": "code",
+        "outputId": "cf4ce0c3-cc8e-4335-9f45-7bbb0e16a03f",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "7373e425-066f-4cbf-e8f4-03766b245345"
+        }
       },
       "source": [
         "from tensorflow.examples.tutorials.mnist import input_data\n",
@@ -656,11 +668,11 @@
       "metadata": {
         "id": "MgAfsAdn5zH_",
         "colab_type": "code",
+        "outputId": "d7c5ec0b-ee00-4226-dd58-4a9d5334f454",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "19d601c6-f01a-43dc-af26-199148b3f741"
+        }
       },
       "source": [
         "import matplotlib.pyplot as plt\n",
@@ -675,13 +687,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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PF/e3SHprmvGOvneJvtqiE2GfJumFEY/Xqbvmew9Jd9t+yHZfp5sZxZSIGCjub5A0pZPN\njKLhNN7t9I5pxrvmvWtm+vOq+IHu3Y6JiCMlfVrSucXH1a4Uw9/BumnsdEzTeLfLKNOM/04n37tm\npz+vqhNhXy9p+ojH+xXLukJErC9uN0laqu6binrjWzPoFrebOtzP73TTNN6jTTOuLnjvOjn9eSfC\n/qCkmbYPsL2rpDMk3d6BPt7F9qTihxPZniTpeHXfVNS3SzqruH+WpNs62MvbdMs03mXTjKvD713H\npz+PiLb/STpRw7/I/4+kf+xEDyV9zZD0aPG3utO9SbpRwx/rtmv4t42zJe0jaYWkNZL+XdLkLurt\n3yQ9LukxDQdraod6O0bDH9Efk7Sy+Dux0+9doq+2vG8cLgtkgh/ogEwQdiAThB3IBGEHMkHYgUwQ\ndiAThB3IxP8DY0uEeSQOrDIAAAAASUVORK5CYII=\n",
+            "image/png": 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TeLfLKNOM/04n37tmpz+vqhNhXy9p+ojH+xXLukJErC9uN0laqu6binrjWzPoFrebOtzP73TTNN6jTTOuLnjvOjn9eSfC/qCkmbYPsL2rpDMk3d6BPt7F9qTihxPZniTpeHXfVNS3SzqruH+WpNs62MvbdMs03mXTjKvD713Hpz+PiLb/STpRw7/I/4+kf+xEDyV9zZD0aPG3utO9SbpRwx/rtmv4t42zJe0jaYWkNZL+XdLkLurt3yQ9LukxDQdraod6O0bDH9Efk7Sy+Dux0+9doq+2vG8cLgtkgh/ogEwQdiAThB3IBGEHMkHYgUwQdiAThB3IxP8DY0uEeSQOrDIAAAAASUVORK5CYII=\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -704,11 +717,11 @@
       "metadata": {
         "id": "lhbV376Z5zIN",
         "colab_type": "code",
+        "outputId": "33f1f695-f047-47c4-ef51-a64d2af38548",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "795ce512-e6f2-4293-fe9b-3a298fc9eace"
+        }
       },
       "source": [
         "import tensorflow as tf\n",
@@ -727,13 +740,13 @@
           "text": [
             "Data\n",
             "\n",
-            "[[0.62267598 0.10984498 0.69076553 0.9632871  0.96289973]\n",
-            " [0.08211625 0.47625618 0.49433127 0.4326092  0.91228054]\n",
-            " [0.96577105 0.92293084 0.83923753 0.93247747 0.44985272]\n",
-            " [0.60186817 0.21104548 0.39213392 0.6575507  0.93132051]]\n",
+            "[[0.83418307 0.63209566 0.1512933  0.92250234 0.03714863]\n",
+            " [0.17699677 0.91293785 0.56589149 0.19330683 0.00165008]\n",
+            " [0.32118224 0.10098022 0.45462992 0.30815643 0.85958459]\n",
+            " [0.67693467 0.00927566 0.84660695 0.55743855 0.65285687]]\n",
             "\n",
             " Labels\n",
-            "[0.32892891 0.61337055 0.82170421 0.63212506]\n"
+            "[0.77424749 0.39799986 0.20608597 0.0937734 ]\n"
           ],
           "name": "stdout"
         }
@@ -756,11 +769,11 @@
       "metadata": {
         "id": "e5L_u7YC5zIa",
         "colab_type": "code",
+        "outputId": "cf8a7955-664d-4c5c-e232-da6c337c0267",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "8f40aea8-c26f-4e6e-b179-121de3a4e97a"
+        }
       },
       "source": [
         "iterator = dataset.make_one_shot_iterator() # iterator\n",
@@ -787,13 +800,13 @@
             "Instructions for updating:\n",
             "Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.\n",
             "Numpy Data\n",
-            "[[0.62267598 0.10984498 0.69076553 0.9632871  0.96289973]\n",
-            " [0.08211625 0.47625618 0.49433127 0.4326092  0.91228054]\n",
-            " [0.96577105 0.92293084 0.83923753 0.93247747 0.44985272]\n",
-            " [0.60186817 0.21104548 0.39213392 0.6575507  0.93132051]]\n",
+            "[[0.83418307 0.63209566 0.1512933  0.92250234 0.03714863]\n",
+            " [0.17699677 0.91293785 0.56589149 0.19330683 0.00165008]\n",
+            " [0.32118224 0.10098022 0.45462992 0.30815643 0.85958459]\n",
+            " [0.67693467 0.00927566 0.84660695 0.55743855 0.65285687]]\n",
             "\n",
             " Numpy Label\n",
-            "[0.32892891 0.61337055 0.82170421 0.63212506]\n"
+            "[0.77424749 0.39799986 0.20608597 0.0937734 ]\n"
           ],
           "name": "stdout"
         }
@@ -814,11 +827,11 @@
       "metadata": {
         "id": "c5DV_aLj5zIo",
         "colab_type": "code",
+        "outputId": "3ea4524e-c45a-48d7-efa1-6ddd90c5ff66",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "33221d60-81ef-48c5-c0fb-e278371f1e95"
+        }
       },
       "source": [
         "dataset_ = NumpyDataset(numpy_data, numpy_label) # convert to NumpyDataset\n",
@@ -830,10 +843,10 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "array([[0.62267598, 0.10984498, 0.69076553, 0.9632871 , 0.96289973],\n",
-              "       [0.08211625, 0.47625618, 0.49433127, 0.4326092 , 0.91228054],\n",
-              "       [0.96577105, 0.92293084, 0.83923753, 0.93247747, 0.44985272],\n",
-              "       [0.60186817, 0.21104548, 0.39213392, 0.6575507 , 0.93132051]])"
+              "array([[0.83418307, 0.63209566, 0.1512933 , 0.92250234, 0.03714863],\n",
+              "       [0.17699677, 0.91293785, 0.56589149, 0.19330683, 0.00165008],\n",
+              "       [0.32118224, 0.10098022, 0.45462992, 0.30815643, 0.85958459],\n",
+              "       [0.67693467, 0.00927566, 0.84660695, 0.55743855, 0.65285687]])"
             ]
           },
           "metadata": {
@@ -860,11 +873,11 @@
       "metadata": {
         "id": "hVy39LEe5zJA",
         "colab_type": "code",
+        "outputId": "71ebbeac-aba9-46aa-d5bf-0b39a966ca60",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "f379ec7a-9b51-4b54-b94b-e01ab62f33f9"
+        }
       },
       "source": [
         "iterator_ = dataset_.make_iterator() # Using make_iterator for converting NumpyDataset to tf.data\n",
@@ -885,13 +898,13 @@
           "output_type": "stream",
           "text": [
             "Numpy Data\n",
-            "[[0.08211625 0.47625618 0.49433127 0.4326092  0.91228054]\n",
-            " [0.62267598 0.10984498 0.69076553 0.9632871  0.96289973]\n",
-            " [0.96577105 0.92293084 0.83923753 0.93247747 0.44985272]\n",
-            " [0.60186817 0.21104548 0.39213392 0.6575507  0.93132051]]\n",
+            "[[0.83418307 0.63209566 0.1512933  0.92250234 0.03714863]\n",
+            " [0.32118224 0.10098022 0.45462992 0.30815643 0.85958459]\n",
+            " [0.67693467 0.00927566 0.84660695 0.55743855 0.65285687]\n",
+            " [0.17699677 0.91293785 0.56589149 0.19330683 0.00165008]]\n",
             "\n",
             " Numpy Label\n",
-            "[0.61337055 0.32892891 0.82170421 0.63212506]\n"
+            "[0.77424749 0.20608597 0.0937734  0.39799986]\n"
           ],
           "name": "stdout"
         }
@@ -922,11 +935,11 @@
       "metadata": {
         "id": "I-MBPtBX5zJU",
         "colab_type": "code",
+        "outputId": "b78f6c9b-bfbe-4ffb-e7a8-37c8d2bb22f3",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
-        },
-        "outputId": "234834b7-1473-42f8-8759-a3c8c7414bc4"
+        }
       },
       "source": [
         "!wget https://raw.githubusercontent.com/deepchem/deepchem/master/deepchem/models/tests/example.csv"
@@ -936,7 +949,7 @@
         {
           "output_type": "stream",
           "text": [
-            "--2020-03-27 03:01:02--  https://raw.githubusercontent.com/deepchem/deepchem/master/deepchem/models/tests/example.csv\n",
+            "--2020-05-31 02:33:55--  https://raw.githubusercontent.com/deepchem/deepchem/master/deepchem/models/tests/example.csv\n",
             "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
             "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
             "HTTP request sent, awaiting response... 200 OK\n",
@@ -945,7 +958,7 @@
             "\n",
             "\rexample.csv           0%[                    ]       0  --.-KB/s               \rexample.csv         100%[===================>]     568  --.-KB/s    in 0s      \n",
             "\n",
-            "2020-03-27 03:01:02 (97.0 MB/s) - ‘example.csv’ saved [568/568]\n",
+            "2020-05-31 02:33:55 (30.1 MB/s) - ‘example.csv’ saved [568/568]\n",
             "\n"
           ],
           "name": "stdout"
@@ -983,11 +996,11 @@
       "metadata": {
         "id": "jN1lRtgC5zJi",
         "colab_type": "code",
+        "outputId": "a7960251-8b5e-4b19-dc01-3a6bf727abd4",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 153
-        },
-        "outputId": "f0b49608-7e61-4f55-d51a-5d0af8ebc14e"
+        }
       },
       "source": [
         "import deepchem as dc\n",
@@ -1007,8 +1020,8 @@
             "About to start loading CSV from /content/example.csv\n",
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
-            "TIMING: featurizing shard 0 took 0.053 s\n",
-            "TIMING: dataset construction took 0.138 s\n",
+            "TIMING: featurizing shard 0 took 0.067 s\n",
+            "TIMING: dataset construction took 0.107 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -1062,11 +1075,11 @@
       "metadata": {
         "id": "VkW5MLyL5zKC",
         "colab_type": "code",
+        "outputId": "36a0b0c5-915a-488d-cb2f-4629fc432e27",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "8d0e5268-874b-484a-ee50-67085da6ca52"
+        }
       },
       "source": [
         "len(train_data),len(valid_data),len(test_data)"
@@ -1104,11 +1117,11 @@
       "metadata": {
         "id": "cYeqhEgA5zKH",
         "colab_type": "code",
+        "outputId": "72a0f464-e80e-4ccd-870a-b65056a09852",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "89f162a6-35b9-4e8a-ce1d-a33d14c1529e"
+        }
       },
       "source": [
         "train_data,valid_data,test_data=splitter.split(dataset,frac_train=0.7,frac_valid=0.2,frac_test=0.1)\n",
@@ -1150,11 +1163,11 @@
       "metadata": {
         "id": "kplzieL35zKb",
         "colab_type": "code",
+        "outputId": "3b2538d4-1885-4b82-aef2-e88a8ae9fbcf",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "92991a97-666c-49ab-e4fc-ad33c2ef8767"
+        }
       },
       "source": [
         "!wget https://raw.githubusercontent.com/deepchem/deepchem/master/deepchem/models/tests/user_specified_example.csv"
@@ -1164,7 +1177,7 @@
         {
           "output_type": "stream",
           "text": [
-            "--2020-03-27 03:01:03--  https://raw.githubusercontent.com/deepchem/deepchem/master/deepchem/models/tests/user_specified_example.csv\n",
+            "--2020-05-31 02:33:57--  https://raw.githubusercontent.com/deepchem/deepchem/master/deepchem/models/tests/user_specified_example.csv\n",
             "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
             "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
             "HTTP request sent, awaiting response... 200 OK\n",
@@ -1173,7 +1186,7 @@
             "\n",
             "\r          user_spec   0%[                    ]       0  --.-KB/s               \ruser_specified_exam 100%[===================>]     714  --.-KB/s    in 0s      \n",
             "\n",
-            "2020-03-27 03:01:04 (206 MB/s) - ‘user_specified_example.csv’ saved [714/714]\n",
+            "2020-05-31 02:33:57 (37.8 MB/s) - ‘user_specified_example.csv’ saved [714/714]\n",
             "\n"
           ],
           "name": "stdout"
@@ -1185,11 +1198,11 @@
       "metadata": {
         "id": "s3t_4cEe5zKg",
         "colab_type": "code",
+        "outputId": "244df6c8-0a63-4505-b450-c3b6e7f81ecb",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "3aab5bc9-e85b-433c-a51c-3620c8e6602c"
+        }
       },
       "source": [
         "from deepchem.splits.splitters import SpecifiedSplitter\n",
@@ -1215,8 +1228,8 @@
             "About to start loading CSV from /content/user_specified_example.csv\n",
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
-            "TIMING: featurizing shard 0 took 0.038 s\n",
-            "TIMING: dataset construction took 0.053 s\n",
+            "TIMING: featurizing shard 0 took 0.050 s\n",
+            "TIMING: dataset construction took 0.064 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -1252,11 +1265,11 @@
       "metadata": {
         "id": "JNBpEHmm5zKx",
         "colab_type": "code",
+        "outputId": "01d665c6-eb1b-4eff-ad25-65e0d83e26c3",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "df8c993f-1dc6-4ba9-a01e-f2065d941e46"
+        }
       },
       "source": [
         "train_data,valid_data,test_data"
@@ -1294,11 +1307,11 @@
       "metadata": {
         "id": "zCT3KKQz5zK2",
         "colab_type": "code",
+        "outputId": "c4920252-d2a3-4894-f9a1-a2a3b0616335",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 0
-        },
-        "outputId": "4aa74983-cf8b-4f26-b217-2654818caeb6"
+        }
       },
       "source": [
         "from deepchem.splits.splitters import IndiceSplitter\n",
@@ -1345,11 +1358,11 @@
       "metadata": {
         "id": "es-X6PDQ5zK7",
         "colab_type": "code",
+        "outputId": "66a197fc-72c5-41ea-cacd-ca7043c09adc",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 153
-        },
-        "outputId": "ca654eb0-2606-4340-a9f7-72ac85c8d76f"
+        }
       },
       "source": [
         "from deepchem.splits.splitters import RandomGroupSplitter\n",
@@ -1371,11 +1384,11 @@
           "text": [
             "Loading raw samples now.\n",
             "shard_size: 8192\n",
-            "About to start loading CSV from /usr/local/lib/python3.7/site-packages/deepchem/data/tests/../../models/tests/example.csv\n",
+            "About to start loading CSV from /root/miniconda/lib/python3.6/site-packages/deepchem/data/tests/../../models/tests/example.csv\n",
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
-            "TIMING: featurizing shard 0 took 0.037 s\n",
-            "TIMING: dataset construction took 0.051 s\n",
+            "TIMING: featurizing shard 0 took 0.050 s\n",
+            "TIMING: dataset construction took 0.068 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -1387,11 +1400,11 @@
       "metadata": {
         "id": "sCYn9An75zLK",
         "colab_type": "code",
+        "outputId": "c3a7ea8b-4f4b-412c-c903-a120d3665065",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "ad469ae7-721a-4e61-bab7-7fce98671119"
+        }
       },
       "source": [
         "train_idxs,valid_idxs,test_idxs"
@@ -1402,7 +1415,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "([5, 3, 2, 8, 4, 7], [0, 6, 9], [1])"
+              "([0, 6, 9, 2, 8, 3, 5], [4, 7], [1])"
             ]
           },
           "metadata": {
@@ -1440,11 +1453,11 @@
       "metadata": {
         "id": "Wdiwca-U5zLo",
         "colab_type": "code",
+        "outputId": "a7903880-be1f-4682-d2e0-8193d0c48b09",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 68
-        },
-        "outputId": "5fc58018-a670-46d9-de69-eea813f12def"
+        }
       },
       "source": [
         "print(\"Groups present in the training data =\",train_data)\n",
@@ -1456,8 +1469,8 @@
         {
           "output_type": "stream",
           "text": [
-            "Groups present in the training data = [7, 2, 1, 1, 3, 3]\n",
-            "Groups present in the validation data =  [0, 0, 0]\n",
+            "Groups present in the training data = [0, 0, 0, 1, 1, 2, 7]\n",
+            "Groups present in the validation data =  [3, 3]\n",
             "Groups present in the testing data =  [4]\n"
           ],
           "name": "stdout"
@@ -1491,11 +1504,11 @@
       "metadata": {
         "id": "C8Kkvi5F5zL_",
         "colab_type": "code",
+        "outputId": "9d6caf58-4915-4944-df8b-ecf3f84926f9",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 170
-        },
-        "outputId": "67bf1ecf-f450-4ea3-d7d2-2d9be69a71c7"
+        }
       },
       "source": [
         "from deepchem.splits.splitters import ScaffoldSplitter\n",
@@ -1512,11 +1525,11 @@
           "text": [
             "Loading raw samples now.\n",
             "shard_size: 8192\n",
-            "About to start loading CSV from /usr/local/lib/python3.7/site-packages/deepchem/data/tests/../../models/tests/example.csv\n",
+            "About to start loading CSV from /root/miniconda/lib/python3.6/site-packages/deepchem/data/tests/../../models/tests/example.csv\n",
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
-            "TIMING: featurizing shard 0 took 0.039 s\n",
-            "TIMING: dataset construction took 0.057 s\n",
+            "TIMING: featurizing shard 0 took 0.052 s\n",
+            "TIMING: dataset construction took 0.064 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
diff --git a/examples/tutorials/02_Learning_MNIST_Digit_Classifiers.ipynb b/examples/tutorials/02_Learning_MNIST_Digit_Classifiers.ipynb
index cb728ebea2..0c581442b2 100644
--- a/examples/tutorials/02_Learning_MNIST_Digit_Classifiers.ipynb
+++ b/examples/tutorials/02_Learning_MNIST_Digit_Classifiers.ipynb
@@ -56,67 +56,101 @@
       "metadata": {
         "id": "UXJKRlAv5xFA",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 479
+        },
+        "outputId": "3a8d973a-9b49-44ea-f7b6-4ad80ffcdba3"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  28140      0 --:--:-- --:--:-- --:--:-- 28140\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "remove current miniconda\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.71 s, sys: 996 ms, total: 3.71 s\n",
+            "Wall time: 4min 14s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
         "id": "hbTulXIP5xFN",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 63
-        },
-        "outputId": "ec59ca5d-c872-4de4-af00-fb9ed614d292"
+        "colab": {}
       },
       "source": [
         "from tensorflow.examples.tutorials.mnist import input_data"
       ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "cell_type": "code",
       "metadata": {
         "id": "4u9vY8iu5xFU",
         "colab_type": "code",
+        "outputId": "22a37e01-90de-4bd7-ad54-9b8c42333aed",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 530
-        },
-        "outputId": "e1e5157e-bce2-4b8a-e398-52b95b546dad"
+        }
       },
       "source": [
         "# TODO: This is deprecated. Let's replace with a DeepChem native loader for maintainability.\n",
@@ -127,7 +161,7 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:From <ipython-input-3-a839aeb82f4b>:1: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
+            "WARNING:tensorflow:From <ipython-input-3-227956e9c9c1>:2: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/contrib/learn/python/learn/datasets/mnist.py:260: maybe_download (from tensorflow.contrib.learn.python.learn.datasets.base) is deprecated and will be removed in a future version.\n",
@@ -166,42 +200,15 @@
       "metadata": {
         "id": "MsHJLy-35xFe",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 207
-        },
-        "outputId": "037c5709-8e5f-458f-ccce-8e168174e3ca"
+        "colab": {}
       },
       "source": [
         "import deepchem as dc\n",
         "import tensorflow as tf\n",
         "from tensorflow.keras.layers import Reshape, Conv2D, Flatten, Dense, Softmax"
       ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "cell_type": "code",
@@ -244,11 +251,11 @@
       "metadata": {
         "id": "Xq9T4trd5xGD",
         "colab_type": "code",
+        "outputId": "fc4384c6-730f-4e8c-a60d-d9708d003bdb",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 275
-        },
-        "outputId": "d084c449-863f-45c3-fe62-ac4a91b41d2f"
+        }
       },
       "source": [
         "model.fit(train, nb_epoch=2)"
@@ -258,15 +265,15 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:200: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:200: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
@@ -293,11 +300,11 @@
       "metadata": {
         "id": "ZGP9d70u5xGU",
         "colab_type": "code",
+        "outputId": "7ae20e09-7602-42d7-87a4-2510873146f1",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
-        },
-        "outputId": "896f006d-bfab-43de-8d64-2f4ee5dfc7e5"
+        }
       },
       "source": [
         "from sklearn.metrics import roc_curve, auc\n",
@@ -320,16 +327,16 @@
           "output_type": "stream",
           "text": [
             "Validation\n",
-            "class 0:auc=0.9999057979948827\n",
-            "class 1:auc=0.9999335476621387\n",
-            "class 2:auc=0.9998705637425881\n",
-            "class 3:auc=0.999911789233876\n",
-            "class 4:auc=0.9999623237852037\n",
-            "class 5:auc=0.9998804023326087\n",
-            "class 6:auc=0.9998620230088834\n",
-            "class 7:auc=0.9995460674157303\n",
-            "class 8:auc=0.9998530924048773\n",
-            "class 9:auc=0.9996017892577271\n"
+            "class 0:auc=0.9999473577030227\n",
+            "class 1:auc=0.9999211378882008\n",
+            "class 2:auc=0.9999187049470992\n",
+            "class 3:auc=0.9999000878057167\n",
+            "class 4:auc=0.9999732080250338\n",
+            "class 5:auc=0.9998869625422123\n",
+            "class 6:auc=0.9999294587087216\n",
+            "class 7:auc=0.9998189989785494\n",
+            "class 8:auc=0.9996727967199541\n",
+            "class 9:auc=0.9998515678426888\n"
           ],
           "name": "stdout"
         }
diff --git a/examples/tutorials/03_Modeling_Solubility.ipynb b/examples/tutorials/03_Modeling_Solubility.ipynb
index e14e92c93b..c28cf9a6f3 100644
--- a/examples/tutorials/03_Modeling_Solubility.ipynb
+++ b/examples/tutorials/03_Modeling_Solubility.ipynb
@@ -65,20 +65,76 @@
       "metadata": {
         "id": "hagObl_sc_8_",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "44375acf-0114-483e-ce76-73f07c8a7926"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  97034      0 --:--:-- --:--:-- --:--:-- 97034\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.39 s, sys: 520 ms, total: 2.91 s\n",
+            "Wall time: 4min 6s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -126,7 +182,7 @@
       "metadata": {
         "id": "58FAHaJOc_9D",
         "colab_type": "code",
-        "outputId": "9903c971-0475-43f9-f6be-4011df24806a",
+        "outputId": "4258b01c-157f-407d-bf77-5f76a49b7a8c",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
@@ -140,16 +196,16 @@
         {
           "output_type": "stream",
           "text": [
-            "--2020-03-27 03:22:37--  https://raw.githubusercontent.com/deepchem/deepchem/master/datasets/delaney-processed.csv\n",
+            "--2020-05-31 02:34:48--  https://raw.githubusercontent.com/deepchem/deepchem/master/datasets/delaney-processed.csv\n",
             "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
             "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
             "HTTP request sent, awaiting response... 200 OK\n",
             "Length: 96699 (94K) [text/plain]\n",
             "Saving to: ‘delaney-processed.csv’\n",
             "\n",
-            "\rdelaney-processed.c   0%[                    ]       0  --.-KB/s               \rdelaney-processed.c 100%[===================>]  94.43K  --.-KB/s    in 0.001s  \n",
+            "\rdelaney-processed.c   0%[                    ]       0  --.-KB/s               \rdelaney-processed.c 100%[===================>]  94.43K  --.-KB/s    in 0.003s  \n",
             "\n",
-            "2020-03-27 03:22:37 (70.3 MB/s) - ‘delaney-processed.csv’ saved [96699/96699]\n",
+            "2020-05-31 02:34:49 (31.7 MB/s) - ‘delaney-processed.csv’ saved [96699/96699]\n",
             "\n"
           ],
           "name": "stdout"
@@ -161,10 +217,10 @@
       "metadata": {
         "id": "XXQteOIQc_9G",
         "colab_type": "code",
-        "outputId": "820d03c2-cb3d-4d3a-888a-3de5018aac08",
+        "outputId": "45ce6874-e8f8-41da-b99d-d9dfa0fb9e6c",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 338
+          "height": 102
         }
       },
       "source": [
@@ -180,40 +236,6 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n",
             "Columns of dataset: ['Compound ID' 'ESOL predicted log solubility in mols per litre'\n",
             " 'Minimum Degree' 'Molecular Weight' 'Number of H-Bond Donors'\n",
             " 'Number of Rings' 'Number of Rotatable Bonds' 'Polar Surface Area'\n",
@@ -280,7 +302,7 @@
       "metadata": {
         "id": "iRNwkDU_c_9N",
         "colab_type": "code",
-        "outputId": "da77eae0-fb5a-46ec-bbe4-2e6b22d1e2de",
+        "outputId": "155d4e2c-b2de-4e90-f046-36170259f057",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
@@ -298,7 +320,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -310,7 +332,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -322,7 +344,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -334,7 +356,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -346,7 +368,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -358,7 +380,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -370,7 +392,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -382,7 +404,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -394,7 +416,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -406,7 +428,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -418,7 +440,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -430,7 +452,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -442,7 +464,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": "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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -454,7 +476,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -480,7 +502,7 @@
       "metadata": {
         "id": "t7V7o6x8c_9S",
         "colab_type": "code",
-        "outputId": "2c179f67-979b-4da3-e36f-ecfd0da5e1a3",
+        "outputId": "28cfb813-c73e-425d-a90c-0fad2db9919a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
@@ -505,13 +527,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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L+C7wwYhYUW/5MVn4I2JUhUTSDsDVwLsj4sHmpspnlNnH1DAWIz0GSd8EBr9A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\n",
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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -560,7 +583,7 @@
       "metadata": {
         "id": "UUiC9Z52c_9Z",
         "colab_type": "code",
-        "outputId": "c0d42db9-3901-4b92-a728-537e88d64b00",
+        "outputId": "ded501e1-37e3-48ce-d57e-444fa2abf09b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 170
@@ -583,8 +606,8 @@
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
             "Featurizing sample 1000\n",
-            "TIMING: featurizing shard 0 took 3.792 s\n",
-            "TIMING: dataset construction took 3.869 s\n",
+            "TIMING: featurizing shard 0 took 3.238 s\n",
+            "TIMING: dataset construction took 3.296 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -608,7 +631,7 @@
       "metadata": {
         "id": "_wEJ8mn_c_9c",
         "colab_type": "code",
-        "outputId": "dc712aee-1a86-461e-ccad-1667ec1bdef6",
+        "outputId": "1bec839c-6219-4bcf-b130-6e98b26550e5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
@@ -633,7 +656,7 @@
             "Loading dataset from disk.\n",
             "TIMING: dataset construction took 0.028 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.026 s\n",
+            "TIMING: dataset construction took 0.027 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -656,7 +679,7 @@
         "scrolled": true,
         "id": "koTNAeQ8c_9g",
         "colab_type": "code",
-        "outputId": "a93b3eb8-4ca9-422e-d861-9dd208ead702",
+        "outputId": "20769ae5-5e4f-42f1-b74a-415029b238c4",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
@@ -672,7 +695,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAIAAAD2HxkiAAAGEklEQVR4nO3d0XbiNhRAUdPV//9l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\nSP78AAAAAElFTkSuQmCC\n",
+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -684,7 +707,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -696,7 +719,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -708,7 +731,7 @@
         {
           "output_type": "display_data",
           "data": {
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CQgIR9ejRw+XayFardfDgwUQ0b948p520rEzcv//wv3v3Hr2qS01PvdeBJkIohFi/fr1OpzMYDHq93j5yRqOxV69eU6dO3bBhQ0pKSoNXNLR3+fJlg8Hg7u5+8+bNxp9NHbNnzyaiiIgIdW7jrqys7Nq1KxFt3bpVhXJOtGvXLiIKDAxU8K6GR67q0gRCKIT405/+5Ofnp9frQ0NDo6OjExISUlJSHihzj5u87o2KilLi5E535coVo9FoNBrT09NVK/r+++8TUUBAQFFRkWpFG6moqEhO4v/rX/9StlK1VV1EUwmhvB9qwYIFKtSq6f5DDbJdX6l//RwREUFEcXFxKtdtsDlz5qh5veAsGgrhzJkziWjLli3qlHvttdeIqH///hr/gb3zzjuKX1/V4Ny5c/I5vezsbJVLNwDL9YJTaCiE8nnNDz/8UJ1ytvsPDxw4oE7FBlC5H+NowoQJRPTCCy+wVK87RfoxatFQCHv06EFEGRkZqlXctm0bOdx/qCkq92Mc2fbH/c9//sMygDpSox+jGK2E0Gq1yluu1Fyux2w2y7vJ169fr1rRutPI9dX8+fOJaMiQIYxjqJ16/RhlaCWEeXl5RNSqVSuV63744YdE5O/vn8+0r1tNrFbrwIEDtTCfWVBQIO9qUO2TQn25aD/GRishPH/+PBH98pe/VL/0iBEjiGj+/Pnql64FYz/G0dtvv63ZuXuNXC80hlZCeODAASIaO3as+qXT09PlGrvambu/f/9+69atGfsx1djm7rdt28Y9lv/h0v0YG62EUK5nXm0pO9VMnTqVtPGsqsTej3EkNxdo3bq1pubuXbofY6OVEMrL+rfffpulem5uro+Pj0ae901LS5PLFmvt+krO3S9dupR7IA+5ej/GRishfPbZZ4lpaU1Jriw2YMAA3jcf7fRjHJ09e1ZTc/eu3o+x0UoI5WbXjBs+lpSUyGnxgwcPco1BaKwf4ygqKoocVn9i0QT6MTZaCaF8QI53L76tW7cSUZcuXbjm7rXWj3Gkkbn7ptGPsdFECAsKCojIz8+Pdxhms1nuj8v10VSD/RhHckWCoUOHMo6hafRjbDQRwvT0dCJ64oknuAcikpKS5Ny9+u/Jmu3HVHP//n05d//RRx+xDKDJ9GNsflxLi9GtW7eISO4oyCsyMnL48OEnTpxYtWqVnDVRhxBi9uzZFoslNjZWbmKjWf7+/nFxcbGxsQsWLHj66aftl2OT5DLn9su31fe4dnFxcXfu3ImIiJALQDYF3L8FhBBiw4YNRPSXv/yFeyBC2M3dq7k2scb7MdWYTCY5d799+3bHP7UtNtVg3t7eNV0WNaV+jI0m3gmzs7NJG++ERNS7d+8pU6bs2bNn6dKlchdYpRUUFCxZsoSI1q1bp+gKTs7i7u6+YsWKqKio1157beLEic2aNbP/U51ON378ePul3Gpa1q2m47Kyskeu6SiEmDNnjtlsnjdvnsavF+qH+7eAEEKMGzeOiN5j2uDeUW5urtwf9/Tp0yqUc4l+jKPw8HAiWrZsmdPPXFpa+sgrgibWj7HRRAjlAnVnz57lHsiP5C6iKuyP6yr9GEe2ufvbt2+rUK7p9WNsmNcdlQICAvLz8+/cufOTq4aqprS0tGvXrnfv3p04ceKTTz4pX7RvHvj4+Li7uzse+/r62hao9vPzs/Ut7I+bNWsm18MUQoSHh585c8Z+PV8XEhUVdfDgwWnTpsn3KEXNnTt38+bNERERycnJ9gs6NwXcvwWEvPr39PTU1MWY1WodNGhQQECA0v//3d3dXWtFM3u2ufuLFy8qWqhJ9mNs+Bszcn6iY8eOmvr1FhMTk5qa6uvrO2vWLFuzpKZGQmlpqdykgYhKSkrMZrPjcXFxscVikcdFRUVWq1UeG41GLy8vr5q2PdC2oKCgWbNmbdiwYejQoe3bt6/XFjR1PxZCrFq1qgn2Y2y4fwuIY8eOEdGIESO4B/IjeTO3l5fXyZMnFS1UVVUVEhJCrrlpqbR8+XLHqUKn69KlS7t27Vz0euEn8b8Tamp+gog2b968fPlyg8Gwd+/eIUOGKFrLaDSuXLly7Nixr7/++pQpU1xifsJeXl7e2rVrzWbzzp07Bw4c+MhtZ+p7/Mi5jQEDBsycOdO2rVJTw/1bQCxevJiI3nzzTe6BCCHEnj179Hq9TqfbtWuXakWHDRtGRIsXL1atorPIe1bGjBnDPRDXxh/CSZMmkTb6zkeOHJFXViovvqbmPthOlJKSIqcosrKyuMfi2vhDKJ9hTU5O5h3Gp59+KvsBf/3rX9WvPnnyZCKaMmWK+qUbpqqqSvZI3njjDe6xuDz+ELZt25aIeB/WPnfunJ+fHxHNmTOHZQA5OTnyHp0LFy6wDKC+5KxmcHBweXk591hcHnMITSaTXq83Go2Ma+llZGTIZ3Oio6MtFgvXMBYtWkREv/71r7kGUHd3796VPaSkpCTusTQFzCG8efMmEXXo0IFrANnZ2R06dJDdBd5FNYuLiwMDA4koMTGRcRh1gX6MczGH8Jtvvhk7dqxOp4uMjDx//rzK1fPy8rp160ZEw4YN08Jl1caNG4moe/fulZWV3GOpEfoxTsf/mXDLli3yxkudTjdy5MjPPvtMnbqFhYXyptCnnnpKzQ0waqH9uXv0Y5TAH0IhxJ07d+Lj421T1X369Nm9e7fZbFau4oMHD+QqmmFhYZraheLQoUNEFBAQoM2nddCPUYImQigVFRUlJCTIZikRdenSJSEhoayszOmFTCbTyJEjiahjx47qPIZTL3LufsmSJdwDqQ79GIVoKIRSRUXF7t275Uc1IgoMDIyPjy8oKHDW+c1ms1w8s3Xr1l999ZWzTutEmp27Rz9GIZoLoWSxWI4ePdqvXz8ZRT8/v5iYmNzc3Eae1mq1zpgxg4iaN2/OuNDwT9Lg3D36McrRaAhtUlJSIiMjZRTd3d2jo6OvXbvW4LO9+uqrROTt7Z2SkuLEQTpdTk6Ol5eXdubu0Y9RlNZDKKWlpUVHR8un0fV6fWRkZAPWwli+fLlM8rFjx5QYpHMtXLhQO3P36McoyjVCKGVlZcXExNieFg0PDz969Ggdn8f/+9//TkQGg0E7y0nVrqCg4LHHHtPC3D36MUpzpRBKeXl58fHx/v7+Moq9e/fevXt37Te77Nu3Tz6gtGPHDtXG2XhyOVb2uXv0Y5TmeiGUiouLExIS2rVrJ6MYFBRU03zGJ598IldnWrNmjfrjbIzKykrZJd6yZQvXGNCPUYGrhlAymUy7d+/u0aOHjGJAQEB8fLz9NhKpqak+Pj5EFBcXxzjOBuOdu0c/Rh2uHUJJzmf0799fRtHX1zcmJiYnJ+fy5cvyqnXWrFncY2y4X/3qV1xz9+jHqKMphNDmxIkTI0aMkFF0c3OT74GTJk1ifECp8c6dO6fT6dSfu0c/RjVNKoRSenp6dHS00WgcN27ciBEjuHb8dCK5Akh0dLSaRdGPUY0mVuBWQlZWVvv27eUK89xjaaxbt26FhISYTKbz58/LLQOUdurUqcGDB3t6emZkZHTp0kWFij9neu4BKCU4ONjDw6MJJJCIOnfuPHfuXCHEggULVChnNpvlMh+LFy9GAlXQZN8Jm5jCwsKuXbvm5+cfPXr02WefVbTW+vXr58+fHxwcnJGRYVsMG5SDELqMjRs3vvzyy506dVq9erWPj88j15mvae9b+y1rapeXl9e9e/eioqKkpCTbXbugKITQZchZO4vFcuPGjcacp/Y9pHJzc/Py8saMGZOYmNjYEUPdIISuxGQybdu27fTp03VZQ76m7Wt+0rJly6ZOnRoUFOTUsUONEMKfnZ/cQ6p3795No6HlKhBCAGZNdooCwFUghADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjBDCAGYIYQAzBBCAGYIIQAzhBCAGUIIwAwhBGCGEAIwQwgBmCGEAMwQQgBmCCEAM4QQgBlCCMAMIQRghhACMEMIAZghhADMEEIAZgghADOEEIAZQgjADCEEYIYQAjD7f8l4ls08HQK1AAAAAElFTkSuQmCC\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -720,7 +743,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -732,7 +755,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": "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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -744,7 +767,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": "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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -756,7 +779,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -768,7 +791,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -780,7 +803,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -796,7 +819,7 @@
       "metadata": {
         "id": "wizZIO-Ec_9i",
         "colab_type": "code",
-        "outputId": "9a50501a-981c-4091-ec48-59fae5fd7bea",
+        "outputId": "99170ad2-da4f-42bd-bf9c-72794262a224",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
@@ -812,7 +835,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -824,7 +847,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -836,7 +859,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -848,7 +871,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -860,7 +883,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -872,7 +895,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -884,7 +907,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -896,7 +919,7 @@
         {
           "output_type": "display_data",
           "data": {
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+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -908,7 +931,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -920,7 +943,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAASwAAAEsCAIAAAD2HxkiAAAGaUlEQVR4nO3d0XLaRgBAUej0j9L/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\n",
+            "image/png": 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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -956,7 +979,7 @@
       "metadata": {
         "id": "apAo3BJlc_9o",
         "colab_type": "code",
-        "outputId": "9b05ab5f-8c4b-45eb-f626-03240816dbae",
+        "outputId": "9fa46804-1eda-4fc2-94ae-6eb8e9378683",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 119
@@ -975,11 +998,11 @@
         {
           "output_type": "stream",
           "text": [
-            "TIMING: dataset construction took 0.046 s\n",
+            "TIMING: dataset construction took 0.043 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.011 s\n",
+            "TIMING: dataset construction took 0.010 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.014 s\n",
+            "TIMING: dataset construction took 0.010 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -1030,7 +1053,7 @@
       "metadata": {
         "id": "OG3FfI20c_9u",
         "colab_type": "code",
-        "outputId": "991bf436-1fd6-4ad1-bdf5-bd85253724e9",
+        "outputId": "e051ad14-72f6-485e-9b45-1a52a44d69bc",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 51
@@ -1049,8 +1072,8 @@
         {
           "output_type": "stream",
           "text": [
-            "computed_metrics: [0.1455729054263113]\n",
-            "{'r2_score': 0.1455729054263113}\n"
+            "computed_metrics: [0.15642379593769684]\n",
+            "{'r2_score': 0.15642379593769684}\n"
           ],
           "name": "stdout"
         }
@@ -1071,7 +1094,7 @@
       "metadata": {
         "id": "pT9oo7rUc_9x",
         "colab_type": "code",
-        "outputId": "e567ea92-e5cf-4d00-c4ca-8e0210d2bb88",
+        "outputId": "416935f3-1514-4053-d236-da720aea32f7",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 765
@@ -1099,48 +1122,48 @@
           "text": [
             "Fitting model 1/8\n",
             "hyperparameters: {'n_estimators': 10, 'max_features': 'auto'}\n",
-            "computed_metrics: [0.13866099937883025]\n",
-            "Model 1/8, Metric r2_score, Validation set 0: 0.138661\n",
-            "\tbest_validation_score so far: 0.138661\n",
+            "computed_metrics: [0.09665505290123866]\n",
+            "Model 1/8, Metric r2_score, Validation set 0: 0.096655\n",
+            "\tbest_validation_score so far: 0.096655\n",
             "Fitting model 2/8\n",
             "hyperparameters: {'n_estimators': 10, 'max_features': 'sqrt'}\n",
-            "computed_metrics: [0.2538575265438803]\n",
-            "Model 2/8, Metric r2_score, Validation set 1: 0.253858\n",
-            "\tbest_validation_score so far: 0.253858\n",
+            "computed_metrics: [0.23579449650480744]\n",
+            "Model 2/8, Metric r2_score, Validation set 1: 0.235794\n",
+            "\tbest_validation_score so far: 0.235794\n",
             "Fitting model 3/8\n",
             "hyperparameters: {'n_estimators': 10, 'max_features': 'log2'}\n",
-            "computed_metrics: [0.1297733089466333]\n",
-            "Model 3/8, Metric r2_score, Validation set 2: 0.129773\n",
-            "\tbest_validation_score so far: 0.253858\n",
+            "computed_metrics: [0.34247837589159946]\n",
+            "Model 3/8, Metric r2_score, Validation set 2: 0.342478\n",
+            "\tbest_validation_score so far: 0.342478\n",
             "Fitting model 4/8\n",
             "hyperparameters: {'n_estimators': 10, 'max_features': None}\n",
-            "computed_metrics: [0.1033216553203602]\n",
-            "Model 4/8, Metric r2_score, Validation set 3: 0.103322\n",
-            "\tbest_validation_score so far: 0.253858\n",
+            "computed_metrics: [0.08090864929431707]\n",
+            "Model 4/8, Metric r2_score, Validation set 3: 0.080909\n",
+            "\tbest_validation_score so far: 0.342478\n",
             "Fitting model 5/8\n",
             "hyperparameters: {'n_estimators': 100, 'max_features': 'auto'}\n",
-            "computed_metrics: [0.14669594256297458]\n",
-            "Model 5/8, Metric r2_score, Validation set 4: 0.146696\n",
-            "\tbest_validation_score so far: 0.253858\n",
+            "computed_metrics: [0.16336185759403687]\n",
+            "Model 5/8, Metric r2_score, Validation set 4: 0.163362\n",
+            "\tbest_validation_score so far: 0.342478\n",
             "Fitting model 6/8\n",
             "hyperparameters: {'n_estimators': 100, 'max_features': 'sqrt'}\n",
-            "computed_metrics: [0.2987059189222484]\n",
-            "Model 6/8, Metric r2_score, Validation set 5: 0.298706\n",
-            "\tbest_validation_score so far: 0.298706\n",
+            "computed_metrics: [0.27579222554127303]\n",
+            "Model 6/8, Metric r2_score, Validation set 5: 0.275792\n",
+            "\tbest_validation_score so far: 0.342478\n",
             "Fitting model 7/8\n",
             "hyperparameters: {'n_estimators': 100, 'max_features': 'log2'}\n",
-            "computed_metrics: [0.29910307975871175]\n",
-            "Model 7/8, Metric r2_score, Validation set 6: 0.299103\n",
-            "\tbest_validation_score so far: 0.299103\n",
+            "computed_metrics: [0.24703294022193945]\n",
+            "Model 7/8, Metric r2_score, Validation set 6: 0.247033\n",
+            "\tbest_validation_score so far: 0.342478\n",
             "Fitting model 8/8\n",
             "hyperparameters: {'n_estimators': 100, 'max_features': None}\n",
-            "computed_metrics: [0.1428007486712679]\n",
-            "Model 8/8, Metric r2_score, Validation set 7: 0.142801\n",
-            "\tbest_validation_score so far: 0.299103\n",
-            "computed_metrics: [0.9424213526630559]\n",
-            "Best hyperparameters: (100, 'log2')\n",
-            "train_score: 0.942421\n",
-            "validation_score: 0.299103\n"
+            "computed_metrics: [0.16150468003802176]\n",
+            "Model 8/8, Metric r2_score, Validation set 7: 0.161505\n",
+            "\tbest_validation_score so far: 0.342478\n",
+            "computed_metrics: [0.9294923903685178]\n",
+            "Best hyperparameters: (10, 'log2')\n",
+            "train_score: 0.929492\n",
+            "validation_score: 0.342478\n"
           ],
           "name": "stdout"
         }
@@ -1161,7 +1184,7 @@
       "metadata": {
         "id": "TS0-7gVYc_90",
         "colab_type": "code",
-        "outputId": "c8913bde-27b4-4ee1-9905-4a953c7eac40",
+        "outputId": "add9a488-30f6-4c48-c9c2-2d689f18f875",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 479
@@ -1191,31 +1214,31 @@
           "output_type": "stream",
           "text": [
             "Fitting model 1/1\n",
-            "hyperparameters: {'learning_rate': 0.0002473807343696684, 'decay': 2.0100536048962377e-05, 'nb_epoch': 20}\n",
+            "hyperparameters: {'learning_rate': 8.919202494973016e-05, 'decay': 1.6763602453067613e-06, 'nb_epoch': 20}\n",
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "If using Keras pass *_constraint arguments to layers.\n",
-            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fcf7f5d5048>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fcf7f5d5048>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f5e8feaa588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f5e8feaa588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fcf7f5d5048>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fcf7f5d5048>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f5e8feaa588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f5e8feaa588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
-            "computed_metrics: [0.20106845265671158]\n",
-            "Model 1/1, Metric r2_score, Validation set 0: 0.201068\n",
-            "\tbest_validation_score so far: 0.201068\n",
-            "computed_metrics: [0.6660189147153563]\n",
-            "Best hyperparameters: (0.0002473807343696684, 2.0100536048962377e-05, 20)\n",
-            "train_score: 0.666019\n",
-            "validation_score: 0.201068\n"
+            "computed_metrics: [0.06523513916253021]\n",
+            "Model 1/1, Metric r2_score, Validation set 0: 0.065235\n",
+            "\tbest_validation_score so far: 0.065235\n",
+            "computed_metrics: [0.37744743198346]\n",
+            "Best hyperparameters: (8.919202494973016e-05, 1.6763602453067613e-06, 20)\n",
+            "train_score: 0.377447\n",
+            "validation_score: 0.065235\n"
           ],
           "name": "stdout"
         }
@@ -1236,7 +1259,7 @@
       "metadata": {
         "id": "s8TqBD6pc_94",
         "colab_type": "code",
-        "outputId": "0bba718d-2194-4091-b499-8c935d4cdb0c",
+        "outputId": "1e1cafee-8560-40c9-ec9c-e1fd513bca95",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 51
@@ -1252,8 +1275,8 @@
         {
           "output_type": "stream",
           "text": [
-            "computed_metrics: [0.35314636080523587]\n",
-            "RF Test set R^2 0.353146\n"
+            "computed_metrics: [0.28272032368047584]\n",
+            "RF Test set R^2 0.282720\n"
           ],
           "name": "stdout"
         }
@@ -1264,7 +1287,7 @@
       "metadata": {
         "id": "U-clxvGhc_96",
         "colab_type": "code",
-        "outputId": "2b91e3ef-1c57-4ef6-9c0c-e11cfddc1694",
+        "outputId": "93d7dd8e-b29b-445b-f0e8-f5c79fa577f9",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 51
@@ -1280,8 +1303,8 @@
         {
           "output_type": "stream",
           "text": [
-            "computed_metrics: [0.2861950851545907]\n",
-            "DNN Test set R^2 0.286195\n"
+            "computed_metrics: [0.13437790030940677]\n",
+            "DNN Test set R^2 0.134378\n"
           ],
           "name": "stdout"
         }
@@ -1302,7 +1325,7 @@
       "metadata": {
         "id": "887Zb1-5c_98",
         "colab_type": "code",
-        "outputId": "86693051-f66b-416a-f9f8-2032333bf512",
+        "outputId": "cfa514af-250e-49f9-9ba0-50271d0ab947",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
@@ -1323,13 +1346,14 @@
         {
           "output_type": "display_data",
           "data": {
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TwFmS7pH0XUmHSVq/Q7E559qgE1MqtGuka7Orb+V1ioWiiE0AZva4mV1iZu8DJgE/BHYF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\nZMwunieAchkMM3S+kWhcwRr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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -1339,7 +1363,7 @@
       "metadata": {
         "id": "sai82xRPc_9-",
         "colab_type": "code",
-        "outputId": "083ae71c-541a-4a3b-822f-f3d01ed0ac59",
+        "outputId": "54a2edce-bafb-4f44-dd3c-7174947776c1",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
@@ -1360,13 +1384,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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z+sCxwMp0iuNcdUnj5vWmUda66q1VqfYvG2Dc1NkMk1hVMWGi1Ruwqg1YJ9HI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\nlDcAzjnXo7wBcM65HvX/AZjk8pgn0sNeAAAAAElFTkSuQmCC\n",
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0SitOSZz7c99hthFJCuADwCJgoqS7JX1H0hGS1u2QbI7jZEgZQyWkUVpxSmLRC0s95EYDYhWAmT1uZleZ2buBccD3gT2BWyTdJun0TgnpOE77lDFUQhqllTSC6YWQG3mSJhooZrYC+FN4nS1pU6JNYhzHKRHdDpXQ7GRtGm+jCQeN5ZMxoaCLPL9RBNJsCflV4IvAEuDXwC7AqWb2w5xlcxynh2h1sraR0jpy91FMnHovi5csXe1akec3ikCaaKAHmtkzRF5B84HtgAl5CuU4TjFpJwJonvGDJr5jp9LNbxSBNCagSppDgZ+a2dOSchTJcZxu0Mg802wPvja/eqEjIBszjYeOaI00CuAXku4jMgGdLGkk8GK+YjmO00nSNO7NLCSrl5+ov7K0HTONLwBrj4YmIDM7g2hfgHFmthR4Hjgib8Ecx+kcacwzzSwkq5efAbW2g3bMNL7bWvskhYI4qs656sMb8hDIcZzOk6ZxbyYCaFx+RuR+mqbH3qh3n/d+uf1Akgno8IRrhisAx+kZ0jTuzcToj8tv1Ijh/OGM/RrKk8YkVcbQFkUjKRroCZ0UxHGc7pGmca+daB2xzjDM4NTrZjN52rxBPfR2N3RJCu9QKX+IxPI64ezd9TM9adYBbAicA7w5nLodOM/Mns5TMMdxOkdaL5qKT36jHnq7XjlxHkOLXljKohcif/96jb+7fjZH7IYwKxNI1wP3AFeHU+8DdjWz1eYI8sY3hHGcVXTTA2b8pOltmXgase2Zv6zbwNdjqMQKs9R10I+eQ01vCFPFtmZ2dNXxuZLqr7tOL8xkojmGl4EHgBPMbHE7eTpOP9HtEMh529/TNv4AK8x4aNKhqdJ2u96KRpqVwEskvbFyIGk80ZqAdrgVeG3YVOYfwJlt5uc4fUWeq2rTkHdo6VFN5NNMmd2ut6KRRgGcDFwsab6kh4GLgJPaKdTMbjGzZeHwTmDLdvJznH6jlR54O2Ecask7tHS9/IcNEcOGDl5J0GyZ7jk0mIYmIDObDewqaYNw/EzGMnwQuC7uoqQTgRMBRo8enXHRjlNOmvHJh/ZMH0k287xs6XH5t1JmtfzuOTSYNJPAI4D3A9tQpTDM7OMNvncbsHmdS2eZ2U0hzVlEew0cZY0EwSeBHadCbYMOUW84Lr5/q5O2zZZTNOrJX0uZ7qdV2pkE/iWRmWYusCJtgWZ2QAOBjieKMLp/msbfcZxVNNsDb9X0UZbVtnGjlHryQ/OeQ71KGgWwtpl9KstCJR0MnA68xcxeyDJvx+kG3XAtbGZzl2ZNRhXKYDNPMm/FyZnGc6gf3EXTTAL/QNJHJL1S0saVV5vlXgSsD9wqabakS9rMz3G6RhmCkrU6adutjeSbmbBOGqW0Kn8ZnmkWpFEALwOTibaDnBlebRnizWw7M9vKzHYLr7a8ihynm5TBtbCyH/CI4cNWnlt72Op//9qGd98dRnZ8o5VmG9+kUUqriq8MzzQL0iiATwPbmdk2ZjYmvF6Vt2COUxbKYCap8NKyVdN4i15YOqhhrdfwXj9zgKP3HNXRjeSbbXyTevkVxdes/GV6pu2QZg7gfsDt9I4TQ6v29U7TaEI37vpv7luYSXiHtDTb+DYKPNfMXEmFsjzTdkkzAngemC3pu5IurLzyFsxxykLei6KyolHDGnc9LjBbXjRrt2+1l59EWZ5pu6QZAUwJL8dx6tCJ/Wiz8Ehp1KuNu65Qfqc8YFoJJd1KLz+JftljuOFCsCLhC8GcfiSrxVifmzKXH935yKB9eavzmTJrgFOvm1133964BWN5uUr2gwtmJ2lnIZjjOF0ki8VYU2YNcN1dj67WuB+956qe85G7j+KT19UP9FvPPJRnZM2se/ROfdLMATiO00Wy8EiZOPVelq5YvW//izmPDTqOi8JZz/7eL66SvYyPABwnY7I2XzTjkRJX9uIlS+vmXXu+Gft7v7hK9jJptoTcHpgAbM3gYHCd8wtznBzJssHOwyyStlFOKjstzUx+ximmDYcPY/yk6W6/LwFpooHOAS4hWgG88hdoZjPzFW11fBLYyZqso102G3UzrfJplG7KrAE+/ZM5dUMdjxoxnBdeXrZyL91qNlpnGLPOPrDZ21xZZm3dDRsiECxdvkqOfoi2WXTamQReZmbfyUEmx+k6WUe7bMYs0sxoIWlStJJP3DaKCxYv4YJjd2PCz+YMapiHDRXnHL5T45uKod5ooZ6iKWL0UCcijQL4uaSPAjcCL1VOmtlTuUnlOB0iazt2M/b6rJRPXMjj6rLz8muvVUxjzri5bjqfFygmaRTAB8L7hKpzBng8IKf0ZL3kvxuTqEnpWwmJkMbcFHe9X0Io9AoN3UCrAsCN8WBwTq+R9ZL/ZsISZBVqOS79UKkp2/uUWQPsft4tfPK62bGROBtF6uyXEAq9QuwksKT9zGy6pKPqXTezG3KVrA4+CezkQbdWnWY1AZ1FPo22TqxMYsdNclfSNLNvr6/27RytTAK/BZgOHF7nmgEdVwCOk4ZmG5ZurTrNyi6fRT6N5hEaBYyDVaOB84/auWH00DxXETvpiVUAZnZOeD+hc+I4TnuUrWHJSvm0m0+jeYdGAeMqpJ3ELstew72Oh4JwegoPT9AaSfMO1Tb8fXcYiRrklWYS21cRFwMPBeH0FL3QsNSasPbdYSS/uW9hrrbyet5LACOGD2PiO3ZaGS30+pkDdaOFVpNmEtu9hYqBKwCnpyh7w1LPhPXDOx9ZeT0vk1aaeYRG8wSQ3uOnlZj/TvakiQU0E7gCuMbMFuUvkuO0TlxP9oWXl3V0U5NWSdPI5mUrbzSP0GgU1Yzbab9suFJ00owAjgVOAP4iaQZwJXCLlWknGaerdNLdr5LvxKn3Dop0WdkAvTpNEUlrquqGSavRBPAKs6bqtlbhTJk14EHkOkyahWD3m9lZwPbANUSjgYclnStp47wFdMpNo4VDafMYP2k6Y864mfGTpsd+t5Lu1Otm8+yLy1a7XobJ4LSmqm6YtOot8qqmHZmy+J04zZPKC0jSLsDXgcnA9cC7gGeI1gk4TizteuWkbRhq0yUFRisyjRpZ6J6tvLLKecTwYZnL5N5b3SHtHMBi4HLgDDOrBIT7s6TxeQrnlJ92vXLS+ounsZ1DMSeDa01kR+85apDXTye8gNJSMdtkbdaLMy3FnfdVxNmQZg7gXWb2YPUJSWPM7CEzqxsmwnEqtOuVk1aBpFEoRfQyqef1c/3MgcLHz8969fRQqe6obahWX3VQtsV+RSaNCehnKc85zmqkDQ4WZ+dPGzAtKSBao6Bs3cRNHxFxJrt6573OsiN2BCBpB2AnYMOagHAbAGvnLZjTG6Rx90vq0aX1F49LV8RGv5peWLiWBaNiRor1Nqn3OsuOJBPQWOAwYASDA8I9C3wkT6Gc3qKRuSCpR1cJKtbI3ltWv/KyL1zLimYWhnmdZUdSMLibgJsk7W1mf+qgTE6f0ahHl9be3K2onu3gK2IjmlHgXmfZkWQCOt3Mvgr8t6T31F43s4/nKpnTN7TToyu7N0hWI5ey1wM0p+ihfKO9IpJkAvp7eM9tBxZJnwa+Bow0syfyKscpNq326HrFG6TdkUuv1EMzlHG0V0SSTEA/D+9X51GwpK2AA4FHGqV1eptWe3QeUz7C6yGZXhgd5UWSCejnEB/51cze0WbZFwCnAze1mY/TJkX4g7TSo+tlb5Bmnkkv10O79OPoqBmSTEBfy6tQSUcAA2Y2R3UWetSkPRE4EWD06NF5idS3lPkPkmbuoAjKrVmafSa97hXTzjP00VEySSag29vJWNJtwOZ1Lp0FfJbI/NMQM7sUuBSiTeHbkclZnTL/QRrNHZRVuTX7TLrtFZOnkm33GfroKJkkE9BPzOwYSXMZbAoSYGa2S1LGZnZATL47A2OASu9/S+BuSa8zs8ebvQGnPcr8B2k0d1BW5dbsM+mmV0zeSrbdZ9jro6N2STIBfSK8H5ZlgWY2F9iscixpPjDOvYC6Q9n/IElzB2VVbq08k255xeStZNt9ht0eHRWd2FhAZvZYeH8YeAnYFdgFeCmcc3qAtLF6ykjaOEJFoyjPJM0+DHkr2XafYSWE9agRwwsdE6pbpAkH/WHgbKLY/wK+Lek8M7siCwHMbJss8nFaoxcW1cTZoMva+yvCM0lr2sl7BJnFM/Q1A/Go0c6OkuYB+5jZk+F4E+CPZtbxf9G4ceNsxozc1qU5JaS2oYLBQeDymqAso3dRM4yfND02OFslPhM0rv8s6PW67gSSZprZuNrzafYDeJIoAFyFZ8M5x+k6jWzQefT+yupd1AxpTTudGK14Dz4/kryAPhU+3k+0+9dNRN5ARwB/7YBsTo+QZw+uGxO97U58lqFH24xpxxvo8pI0Alg/vD8QXhV85a6Tmrx7y93wYmpH6STVBxRnLmbCQWOZ8LM5LF0+2EQ8sHgJ4ydNL6TScponaSHYuZ0UxOlN8nYTzGqit5leeTtKJ64+Jk69l5eWrSiWWSlmerAQsjmZ0HBLSEm/kTS99tUJ4Zzyk7eJJgs3v0qvfGDxEoxVDVw9t0doz00zbpPzxUuWdmWbwzhXz8nT5rF0RbyDiG/B2BukmQQ+rerz2sDRwLJ8xHF6jU6YaNq1QTc7Skm7zWXtdQjL6JuQLU5RZjGPkGSOSqOgi76gzmlMQwVgZjNrTv1B0l05yeP0GGXwxW9llJKkdOIa1rWHDanb+AsYsc4wFr2wdLVr9RRlVvMqSYovTnE3ks0pF2lMQBtXvTaVdBCwYQdkc3qAMqzEzHrFcFzDWq+Bh2hEcM7hO6U2K8Xl/+mfzElctVtLkuKrZ+ZKI5tTLtKYgGYS/UZFZPp5CPhQnkI5vUXR3QSzHqU0axoZNWJ4U/70cfkvD4s6044IksxztfKMWGcYZvD0kqVd91BysiONCWhMJwRxnG6R9WKmuIZ1xPBhgzx9YLCiSaso05hn0nhaNVJ8RVfcTvskLQQ7KumLZnZD9uI4TnfIsrGLa1gnvmMnoH1FUy//ejQaiRQh5pDTXZJGAIcnXDPAFYDj1KFRw9puA1ub/xBppfmnmjRzGN7L728aBoMrEh4MznFWpxMB2Zxy03IwOEkbAucAbw6nbgfOM7OnsxXRcZxWcFOO0yppvICuAO4BjgnH7wOuBBLnCBzH6RxuynFaIY0C2NbMjq46PlfS7LwEcpy86WQ0zjJE/uw2XkfdI40CWCLpjWb2ewBJ4wFfA+6Ukk7G8u+HfQPaxeuouzRcCQycDFwsab6kh4GLgJPyFctx8iEp/EGZyyorXkfdJc1CsNnArpI2CMfP5C6V4+REJzeQabesoppGspSrGxv6OKtIEwvoE6Hxfxb4hqS7JR2Yv2iOkz1Zx/3Jq6xmQ1RnSVyI6Dzk6uTzcFYnjQnog6HXfyCwCZEX0KRcpXIKSVLDUBbaieXfybK6ZRpp1MBnLVcnn4ezOmkmgRXe3w5838zulaSkLzi9R69M1nXSZ76dsrplGmm0N0LWcvkahu6SKhqopFuAMcCZktYHVuQrllM08t7asZN00me+1bK6sdcxNFY8ecjlaxi6RxoT0IeAM4C9zOwFYE3ghFylcgqHT9Z1lm6ZRhrZ5N1k01s0VABmtsLM7jazxZImmtmTZvbXTgjnFAefrOss3dpIp1EDX4YNfpz0NBUMTtLdZrZHjvIk4sHguocHHOsfiup+6rROy8HgavPJSB6nZPhkXf/gNvn+oVkFsGcuUjilwBsGx+kt0oSDvrDmGOBpYIaZ3ZSTXE6f42YIx8mfNF5AawO7Af8Mr12ALYEPSfpmjrI5fUo3V8E6Tj+RxgS0CzDezJYDSPoO8DvgjcDcVguW9DHg/wHLgZvN7PRW83J6i26sOchzxNFq3j4KcvImjQLYCFiPyOwDsC6wsZktl/RSK4VK2hc4AtjVzF6StFkr+Ti9SafXHOS5yrnVvHtl5bVTbNKYgL4KzJZ0paSrgFnAZEnrAre1WO7JwCQzewnAzP7TYj5OD9LpNQd5xt1pNW8Pk+x0gjQLwS4H9gGmADcCbzSzy8zseTOb0GK52wNvkvRnSbdL2isuoaQTJc2QNGPhwoUtFueUiU6vNs1zxNFq3p0YBfVCcD+nPdK6ge4FvCl8Xk0iy3AAABB9SURBVAEsaPQFSbcBm9e5dFYod2PgDSHvn0h6ldVZlWZmlwKXQrQQLKW8Tonp9JqDPOPutJp33rGA3MTkQDo30ElEjfSPwqmPS9rbzD6b9D0zOyAhz5OBG0KDf5ekFcCmgHfxHaCzaw4mHDS27irnLEYcreadp0zQW8H9nNZJMwJ4O7Cbma0AkHQ10TxAogJowBRgX+A3krYnCjD3RBv5OU7L5DniqM17w+HDkODU62Yzedo89t1hJL+5b+Fq5eY9CvLgfg6kiAUk6a/AW83sqXC8MfBbM9ul5UKlNYEriNYXvAycZmbTG33PYwE5ZaZePKVaOhVfafyk6XVNTKNGDOcPZ+yXa9lO54mLBZTGC+h8YJakq0LvfybwpXaEMbOXzey9ZvZaM9sjTePvOGWnntmllk55+nhYZwfSbQp/raTfEs0DAHzGzB7PVSrH6UHSmlc6YYbx4H4OJCgASbVhn/8V3reQtIWZ3Z2fWI7Te8R59tRL1wk8uJ+TNAL4esI1A9xQ6DhNUM+zpxY3wzidJFYBmNm+nRTEcXqdemaXOC8gx+kEze4H4DhOG7jZxSkSabyAHMdxnB7EFYDjOE6f0lABKOK9ks4Ox6MlvS5/0RzHcZw8STMH8L9EAeD2A84DngWuZ9W6AKfk+MYjjtOfpFEArzezPSTNAjCzRSGUg9MDeFTI8uKK22mXNHMASyUNJfL9R9JIohGB0wP4xiPlxPdNdrIgjQK4kGgjmM0kfQn4PfDlXKVyOoZHhWyNbm+m4orbyYI0sYB+JGkmsD8g4Egz+3vukjkdIe+NR3qRIpjNXHE7WZDGC2g08ALwc2Aq8Hw45/QAHhWyeYrQ+25n3+Ruj16c4pBmEvhmIvu/gLWBMcA8YKcc5XI6hEeFbJ4i9L5b3TGsCKMXpzikMQHtXH0cooR+NDeJnI7j4Qmaowhms1YVt28F6VTTdCwgM7tb0uvzEMZxykDe+/WmpRXF3czoxd1Me580m8J/qupwCLAHsCA3iZyepp1GpSgNUpnNZmlHL24q6g/SjADWr/q8jGhO4Pp8xHF6mXYalaI1SGU1m6UdvbipqD9IVABhAdj6ZnZah+Rxeph2GhVvkLIh7eilCBPdTv4kbQm5hpktkzS+kwI5vUs7jYo3SNmRZvRShIluJ3+S1gHcFd5nS5oq6X2Sjqq8OiGc01u047vezned5vH1If1BmlAQawNPEkUDPQw4PLw7TlO006h4g9RZjtx9FOcftTOjRgxHwKgRwzn/qJ3d3NZjJM0BbBY8gO5h1UKwCparVE5P0o73TJk9b8pKWSe6nfQkKYChwHoMbvgruAJwWqKdRsUbJMfJliQF8JiZndcxSRzHcZyOkjQHUK/n7ziO4/QISQpg/45J4TiO43ScWBOQmT3VSUEcp9coSugKx4mj6WBwjuM0pmihKxynHmnWATiO0yRF2DTGcRrRlRGApN2AS4gWmS0DPmpmdyV/qzV8GO50Aw9d4ZSBbo0Avgqca2a7AWeH48ypDMMHFi/BWDUM9y3wnLzx0BVOGeiWAjBgg/B5Q3LaX8CH4U638NAVThno1iTwJ4Fpkr5GpIT2iUso6UTgRIDRo5vbi96H4U638NAVThnITQFIug3YvM6ls4jWGJxqZtdLOga4HDigXj5mdilwKcC4ceOaCkHhIW2LSb/My3joCqfo5KYAzKxugw4g6fvAJ8LhT4HL8pChKHu3Oqtw90jHKQ7dmgNYALwlfN4P+GcehXhI2+Lh8zKOUxy6NQfwEeBbktYAXiTY+PPAh+HFwudlHKc4dEUBmNnvgT27UbbTXXxexnGKg68EdjqKu0c6TnHwWEBOR+kl98h+8WZyehdXAE7H6YV5GfdmcnoBVwBO5vRDzzjJm6nX7tXpXVwBOJnSLz1j92ZyegGfBHYyJS8//ymzBhg/aTpjzriZ8ZOmdz2gnwd7c3oBVwBOpuTRMy5iVFf3ZnJ6AVcATqbk0TMu4urhIq8yL9poySkuPgfgZEoe8ZeKam8vojdTv8zBONngIwAnU/LoGbu9PT1FHC05xcVHAE7mZN0z9qiu6SnqaMkpJj4CcApPke3tRcNHS04z+AjAKQVFtLcXER8tOc3gCsBxeoheirXk5I8rAMfpMXy05KTF5wAcx3H6FFcAjuM4fYorAMdxnD7FFYDjOE6f4grAcRynT5GZdVuG1EhaCDycUXabAk9klFencdm7Q5llh3LL77K3x9ZmNrL2ZKkUQJZImmFm47otRyu47N2hzLJDueV32fPBTUCO4zh9iisAx3GcPqWfFcCl3RagDVz27lBm2aHc8rvsOdC3cwCO4zj9Tj+PABzHcfoaVwCO4zh9St8oAEkTJQ1Imh1eb49Jd7CkeZLul3RGp+VMQtKnJZmkTWOuL6+6v6mdli+JFLJ/QNI/w+sDnZavHpK+IOmvoT5vkbRFTLpC1nsT8heq7iVNlnRfkP1GSSNi0s2XNDfc34xOyxlHE/J3v60xs754AROB0xqkGQo8ALwKWBOYA+zYbdmDbFsB04gWwm0ak+a5bsvZiuzAxsCD4X2j8HmjAsi9QdXnjwOXlKzeG8pfxLoHDgTWCJ+/AnwlJt38uP9C0eUvSlvTNyOAlLwOuN/MHjSzl4EfA0d0WaYKFwCnA2WctW8k+0HArWb2lJktAm4FDu6UcHGY2TNVh+tSsrpPKX/h6t7MbjGzZeHwTmDLbsrTLCnlL0Rb028K4JQwLLtC0kZ1ro8CHq06/lc411UkHQEMmNmcBknXljRD0p2SjuyEbI1IKXsh6x1A0pckPQocB5wdk6xw9V4hhfyFrfvAB4FfxVwz4BZJMyWd2EGZmiFO/kLUe0/tCCbpNmDzOpfOAr4DfIHoR/MF4OtED6cQNJD9s0TDykZsbWYDkl4FTJc018weyFLOemQke1dIkt3MbjKzs4CzJJ0JnAKcUydtV+odMpO/4zSSO6Q5C1gG/CgmmzeGet8MuFXSfWZ2Rz4SDyYj+btOTykAMzsgTTpJ3wN+UefSAJG9usKW4VzuxMkuaWdgDDBHUkWmuyW9zswer8ljILw/KOm3wO5EdsZcyUD2AeCtVcdbAr/NRdga0v5miP7Ev6ROA9qteg9ltit/V+q+kdySjgcOA/a3YDSvk0el3v8j6UYis0pHFEAG8netrRlEtydMOvUCXln1+VTgx3XSrEE0CTaGVRMzO3Vb9hoZ51N/InUjYK3weVPgnxRkAjuF7BsDD4V72Ch83rgA8r666vPHgJ+Vqd5Tyl+4uieag/gbMDIhzbrA+lWf/wgc3O06b0L+QrQ1Xa+sDj6UHwBzgb8CUysKAdgC+GVVurcD/yDqwZ3Vbbnr3MfKRhQYB1wWPu8T7m9OeP9Qt2VNK3s4/iBwf3id0G1Zg0zXA/eE38zPgVFlqvc08hex7oMcjwKzw+uScH7lf5XIe2ZOeN1bpP9qGvnDcdfbGg8F4TiO06f0mxeQ4ziOE3AF4DiO06e4AnAcx+lTXAE4juP0Ka4AHMdx+hRXAH1EVdTKeyT9VNI6beR1laR3hs+XSdoxIe1bJe3TQhnz60UPjTufN5J+Kylxc+/qeqk5P07SheHz8ZIuCp9PkvT+qvN1I3YmlJdY91mTsg7eIOl74bn/Ipx7RyXipaQjOymzE48rgP5iiZntZmavBV4GTqq+KKmlleFm9mEz+1tCkrcS+cv3LWY2w8w+Xuf8JWb2/XB4PJGveDP5Nqr7bnAI8OvqE2Y21cwmhcMjgaYUQKu/TScZVwD9y++A7UIv7Xchjv3fJA0N8cz/EgLn/Q+AIi4K8ctvAzarZFTdKwwxzu+WNEfS/0nahkjRnBpGH2+SNFLS9aGMv0gaH767iaK49fdKugxQo5uQ9KkworlH0ierzn8+yPp7SddKOq3Od9eVdHOQ9R5Jx4bz+0uapSjW/BWS1qrz3eeqPr9T0lVVlw9QFBzuH5IOC2lW9oZr8pko6bQwahgH/CjU06GSplSle1sId1D7/eq6f05R8Lc5igLTvSKmvKvDM39Y0lGSvhru9deShqWpg/A7uSrU21xJp1Zd3h+4rSb98eH3sw/wDmByuM9tw+vXioK6/U7SDuE7V0m6RNKfga/W3ovTPq4A+pDQmzqEaOUqwB7AJ8xse+BDwNNmthewF/ARSWOA/wLGEvXc3k+dHr2kkcD3gKPNbFfgXWY2H7gEuCCMPn4HfCsc7wUcDVwWsjgH+L2Z7QTcCIxucB97AicArwfeEGTdXVIl313DfcaZLA4GFpjZrmFU9GtJawNXAcea2c5ES/ZPTpKjDtsQxaU5FLgk5JmImf0MmAEcZ2a7EcXt2SHUKeE+r2iQzbrAnaHu7wA+EpNuW2A/oob4h8Bvwr0uAQ5NWQe7Ea0sfm1IcyWAItPcUjN7OuY+/0i0En9C+D08QLRp+sfMbE/gNOB/q76yJbCPmX2qwb07LeAKoL8YLmk2UUPzCHB5OH+XmT0UPh8IvD+k+zOwCfBq4M3AtWa23MwWANPr5P8G4I5KXmb2VIwcBwAXhTKmAhtIWi+U8cPw3ZuBRQ3u543AjWb2vJk9B9wAvAkYD9xkZi+a2bNEYRDqMRd4m6SvSHpTaLTGAg+Z2T9CmquDXM3wEzNbYWb/JIr3skOT38eiJfo/AN6raEepvYkPi1zhZVYFOZxJpIjq8SszW0p0/0NZZa6ZG76Tpg4eBF4l6duSDgYqew8cCNzSQM6VhOe+D/DT8Hv4LvDKqiQ/NbPlafNzmsPtav3FktC7XImiKJ3PV58i6o1Nq0lXdwvNFhkCvMHMXqwjS25I2opVyuASM7tE0h5EMVm+KOn/gJtSZlcdQ6W2h18bX6XVeCtXEsn7IlFDuKxB+qW2KrbLcuL/3y8BmNkKSdXfWZHwnUGY2SJJuxJtKHMScAxRTKFDgG+kySMwBFhc+7us4vmY804G+AjAqWUacHKVLXh7SesSmRSODbbfVwL71vnuncCbg8kISRuH888C61elu4UoOiUhXeXPfwfw3+HcIUTRKZP4HXCkpHWCjP8Vzv0BOFzS2qGHeRiAmT0azA67hcZ/C+AFM/shMJnIFDYP2EbSdqGM9wG31yn735JeI2lIKLead0kaImlboqBl8xrcR4VB9RRGWguAzxFMLB2iYR0EU88QM7s+yLeHIg2+C1EAtCRW3qdFu5Y9JOldIV8FxeJ0AB8BOLVcRmQGuDv8oRcSeW3cSGQ3/huR+ehPtV80s4WKdma6ITSM/wHeRtSL/Zmi3cE+RrQ/7cWS/kr0G7yDqBd5LnCtpHuJwvs+kiSomd0dJl/vqshuZrMAFE1q/xX4N5Fpo55NemeiycgVwFLgZDN7UdIJRCaJNYC/EM1h1HIGkbllIZFJbb2qa48EmTYATgp5Jt1KhauI5gyWAHub2RKiOP4jzezvaTLIgpR1MAq4MjxngDOBPYFZVSOKOH4MfE/Sx4F3Eu1W9h1JnwOGheuNdr9zMsCjgTo9iaT1zOw5RWsd7gBONLO7uy1XsyhaLzDLzC5vmLjLhAb8fjP7cbdlcdLhCsDpSSRdQ+SxtDZwtZmd32WRmkbSTCIb+NvM7KVuy+P0Hq4AHMdx+hSfBHYcx+lTXAE4juP0Ka4AHMdx+hRXAI7jOH2KKwDHcZw+5f8DUGzWBbmFc9gAAAAASUVORK5CYII=\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
diff --git a/examples/tutorials/04_Introduction_to_Graph_Convolutions.ipynb b/examples/tutorials/04_Introduction_to_Graph_Convolutions.ipynb
index acafc8770f..3db47b723a 100644
--- a/examples/tutorials/04_Introduction_to_Graph_Convolutions.ipynb
+++ b/examples/tutorials/04_Introduction_to_Graph_Convolutions.ipynb
@@ -58,74 +58,47 @@
       "metadata": {
         "id": "EoCLxSnBcj1N",
         "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "%%capture\n",
-        "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "iEMqPVorcj1R",
-        "colab_type": "text"
-      },
-      "source": [
-        "Ok now that we have our environment installed, we can actually import the core `GraphConvModel` that we'll use through this tutorial."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "Ph78CIgAcj1S",
-        "colab_type": "code",
-        "outputId": "82d7839e-0000-4be6-a0ea-370bdc7f5767",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 253
-        }
+          "height": 479
+        },
+        "outputId": "03512dfd-b2a4-4808-8d53-53d5a7d16111"
       },
       "source": [
-        "import deepchem as dc\n",
-        "from deepchem.models.graph_models import GraphConvModel"
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 2,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  93800      0 --:--:-- --:--:-- --:--:-- 93800\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "remove current miniconda\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
             "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
             "  warnings.warn(msg, category=FutureWarning)\n"
           ],
           "name": "stderr"
         },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
         {
           "output_type": "stream",
           "text": [
@@ -139,9 +112,48 @@
             "\n"
           ],
           "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.53 s, sys: 996 ms, total: 3.52 s\n",
+            "Wall time: 4min 10s\n"
+          ],
+          "name": "stdout"
         }
       ]
     },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iEMqPVorcj1R",
+        "colab_type": "text"
+      },
+      "source": [
+        "Ok now that we have our environment installed, we can actually import the core `GraphConvModel` that we'll use through this tutorial."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ph78CIgAcj1S",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import deepchem as dc\n",
+        "from deepchem.models.graph_models import GraphConvModel"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -157,7 +169,7 @@
       "metadata": {
         "id": "JMi2V8Jncj1W",
         "colab_type": "code",
-        "outputId": "3f42b8e3-e959-411e-d419-874fca5916ff",
+        "outputId": "4bc49fff-0338-454d-ac2b-b77c65d50cf5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 476
@@ -185,20 +197,20 @@
             "Featurizing sample 5000\n",
             "Featurizing sample 6000\n",
             "Featurizing sample 7000\n",
-            "TIMING: featurizing shard 0 took 18.303 s\n",
-            "TIMING: dataset construction took 20.896 s\n",
+            "TIMING: featurizing shard 0 took 20.352 s\n",
+            "TIMING: dataset construction took 22.873 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 2.852 s\n",
+            "TIMING: dataset construction took 2.841 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 1.358 s\n",
+            "TIMING: dataset construction took 1.359 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 1.225 s\n",
+            "TIMING: dataset construction took 1.203 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 2.644 s\n",
+            "TIMING: dataset construction took 2.634 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.421 s\n",
+            "TIMING: dataset construction took 0.286 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.314 s\n",
+            "TIMING: dataset construction took 0.286 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -220,7 +232,7 @@
       "metadata": {
         "id": "Y9n3jTNHcj1a",
         "colab_type": "code",
-        "outputId": "c08c5b87-f839-4b37-c744-4cdd943565f4",
+        "outputId": "82ea6005-7ff1-407d-b30c-17bb6d678069",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
@@ -245,47 +257,47 @@
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "If using Keras pass *_constraint arguments to layers.\n",
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ed1a860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ed1a860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529e50a908>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529e50a908>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ca76390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ca76390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529dffeef0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529dffeef0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529cacd860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529cacd860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/layers.py:194: The name tf.unsorted_segment_sum is deprecated. Please use tf.math.unsorted_segment_sum instead.\n",
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28daf06a0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28daf06a0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5e80>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5e80>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28dfc25c0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28dfc25c0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28f9b1f98>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28f9b1f98>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/layers.py:194: The name tf.unsorted_segment_sum is deprecated. Please use tf.math.unsorted_segment_sum instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/layers.py:196: The name tf.unsorted_segment_max is deprecated. Please use tf.math.unsorted_segment_max instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/layers.py:196: The name tf.unsorted_segment_max is deprecated. Please use tf.math.unsorted_segment_max instead.\n",
             "\n",
-            "WARNING:tensorflow:Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7f529cdc4898>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7f529cdc4898>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7fd28ef199b0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7fd28ef199b0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ed1a860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ed1a860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529e50a908>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529e50a908>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ca76390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529ca76390>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529dffeef0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529dffeef0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529cacd860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529cacd860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7f529cdc4898>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7f529cdc4898>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5588>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28daf06a0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28daf06a0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5e80>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd28e0f5e80>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28dfc25c0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28dfc25c0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28f9b1f98>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28f9b1f98>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7fd28ef199b0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method TrimGraphOutput.call of <deepchem.models.graph_models.TrimGraphOutput object at 0x7fd28ef199b0>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
             "\n",
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
@@ -308,16 +320,16 @@
         {
           "output_type": "stream",
           "text": [
-            "Epoch 0 loss: 0.179874\n",
-            "Epoch 1 loss: 0.180224\n",
-            "Epoch 2 loss: 0.171549\n",
-            "Epoch 3 loss: 0.159281\n",
-            "Epoch 4 loss: 0.160095\n",
-            "Epoch 5 loss: 0.153980\n",
-            "Epoch 6 loss: 0.152169\n",
-            "Epoch 7 loss: 0.134809\n",
-            "Epoch 8 loss: 0.142448\n",
-            "Epoch 9 loss: 0.142864\n"
+            "Epoch 0 loss: 0.187494\n",
+            "Epoch 1 loss: 0.179481\n",
+            "Epoch 2 loss: 0.173996\n",
+            "Epoch 3 loss: 0.129795\n",
+            "Epoch 4 loss: 0.165851\n",
+            "Epoch 5 loss: 0.157878\n",
+            "Epoch 6 loss: 0.154927\n",
+            "Epoch 7 loss: 0.128199\n",
+            "Epoch 8 loss: 0.145040\n",
+            "Epoch 9 loss: 0.149751\n"
           ],
           "name": "stdout"
         }
@@ -338,10 +350,10 @@
       "metadata": {
         "id": "qbDXnYs7cj1d",
         "colab_type": "code",
-        "outputId": "2bdbbed2-1844-4808-92ff-5a3c2445987b",
+        "outputId": "e994dfa2-b8b6-486b-fa00-7b5a3845d099",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 279
+          "height": 282
         }
       },
       "source": [
@@ -359,13 +371,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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+            "image/png": "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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -389,7 +402,7 @@
       "metadata": {
         "id": "MeX-9RNWcj1h",
         "colab_type": "code",
-        "outputId": "d8ed150d-6a3f-4234-b16c-d17fdd8b3ffc",
+        "outputId": "5839b650-1bd1-4db4-deda-2f6e00d2cf70",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 122
@@ -411,10 +424,10 @@
           "output_type": "stream",
           "text": [
             "Evaluating model\n",
-            "computed_metrics: [0.8752507599237542, 0.9317176461143224, 0.9216110353258482, 0.9071836603643049, 0.8085335468451595, 0.8950413619597894, 0.9163170299657835, 0.8578393713439281, 0.9150434463367274, 0.8497569522752113, 0.9287126431500636, 0.8930951781618839]\n",
-            "Training ROC-AUC Score: 0.891675\n",
-            "computed_metrics: [0.7691584623756988, 0.8363921957671958, 0.8597597050926138, 0.8286731062531776, 0.6752727272727272, 0.7173707812131225, 0.7484423676012462, 0.8443129242114658, 0.8532611255003532, 0.7628326141675655, 0.8896979568372896, 0.8280792420327303]\n",
-            "Validation ROC-AUC Score: 0.801104\n"
+            "computed_metrics: [0.856695644197583, 0.9277883148683366, 0.9162266517645925, 0.8993123176038667, 0.790893979188138, 0.8848570453915527, 0.9080505953582783, 0.8503534098231695, 0.8895928885115149, 0.8532033377860277, 0.9163754240290927, 0.8774665771661911]\n",
+            "Training ROC-AUC Score: 0.880901\n",
+            "computed_metrics: [0.7864740513530896, 0.7545469576719577, 0.8586219451144587, 0.8444966954753432, 0.6791590909090909, 0.7470301313822674, 0.79932502596054, 0.8434204654876816, 0.848316458676713, 0.7243122977346279, 0.8887398500562889, 0.8223514211886305]\n",
+            "Validation ROC-AUC Score: 0.799733\n"
           ],
           "name": "stdout"
         }
@@ -474,11 +487,11 @@
       "metadata": {
         "id": "71_E0CAUcj1n",
         "colab_type": "code",
+        "outputId": "de35eb15-96e8-4607-9038-9d1d68ab8b0b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 207
-        },
-        "outputId": "6ce899d1-456d-4a75-f060-98acca3297e6"
+        }
       },
       "source": [
         "from deepchem.models.layers import GraphConv, GraphPool, GraphGather\n",
@@ -502,16 +515,16 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529cdff550>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529cdff550>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529cdff208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529cdff208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f52992b5b70>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f52992b5b70>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f5299707c50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f5299707c50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529e03a668>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529e03a668>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n"
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290319860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290319860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd290319208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd290319208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290235080>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290235080>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28b020160>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28b020160>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28b0484a8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28b0484a8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n"
           ],
           "name": "stdout"
         }
@@ -596,7 +609,7 @@
       "metadata": {
         "id": "59WW4rhwcj1w",
         "colab_type": "code",
-        "outputId": "8664b6c2-fd35-4676-cc1a-f098baddb365",
+        "outputId": "daae0b31-08ec-4b2d-b430-d970acc1d61e",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 479
@@ -615,16 +628,16 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529cdff550>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f529cdff550>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529cdff208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f529cdff208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f52992b5b70>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7f52992b5b70>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f5299707c50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7f5299707c50>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529e03a668>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7f529e03a668>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n"
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290319860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290319860>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd290319208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd290319208>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290235080>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphConv.call of <deepchem.models.layers.GraphConv object at 0x7fd290235080>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28b020160>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphPool.call of <deepchem.models.layers.GraphPool object at 0x7fd28b020160>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28b0484a8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method GraphGather.call of <deepchem.models.layers.GraphGather object at 0x7fd28b0484a8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n"
           ],
           "name": "stdout"
         },
@@ -643,16 +656,16 @@
         {
           "output_type": "stream",
           "text": [
-            "Epoch 0 loss: 0.190473\n",
-            "Epoch 1 loss: 0.182911\n",
-            "Epoch 2 loss: 0.176649\n",
-            "Epoch 3 loss: 0.135745\n",
-            "Epoch 4 loss: 0.163365\n",
-            "Epoch 5 loss: 0.161067\n",
-            "Epoch 6 loss: 0.156090\n",
-            "Epoch 7 loss: 0.144639\n",
-            "Epoch 8 loss: 0.149375\n",
-            "Epoch 9 loss: 0.147722\n"
+            "Epoch 0 loss: 0.185946\n",
+            "Epoch 1 loss: 0.178740\n",
+            "Epoch 2 loss: 0.173465\n",
+            "Epoch 3 loss: 0.137110\n",
+            "Epoch 4 loss: 0.159967\n",
+            "Epoch 5 loss: 0.157812\n",
+            "Epoch 6 loss: 0.154875\n",
+            "Epoch 7 loss: 0.145511\n",
+            "Epoch 8 loss: 0.150205\n",
+            "Epoch 9 loss: 0.146304\n"
           ],
           "name": "stdout"
         }
@@ -673,7 +686,7 @@
       "metadata": {
         "id": "SaPi5y8icj11",
         "colab_type": "code",
-        "outputId": "4c48130f-6d3e-4970-9784-a3304f913619",
+        "outputId": "de59a390-73b7-45a9-8613-89ea4bbfd7a5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
@@ -693,13 +706,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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+            "image/png": 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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -720,7 +734,7 @@
         "scrolled": true,
         "id": "f3prNsgGcj14",
         "colab_type": "code",
-        "outputId": "e44e662d-bb34-432d-f91a-47f01feb21f6",
+        "outputId": "330767ae-04b3-4fcd-fc10-2100391f3475",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 102
@@ -757,10 +771,10 @@
           "output_type": "stream",
           "text": [
             "Evaluating model\n",
-            "computed_metrics: [0.8543572433764939]\n",
-            "Training ROC-AUC Score: 0.854357\n",
-            "computed_metrics: [0.7828069015052957]\n",
-            "Valid ROC-AUC Score: 0.782807\n"
+            "computed_metrics: [0.8543051173844746]\n",
+            "Training ROC-AUC Score: 0.854305\n",
+            "computed_metrics: [0.7688187132522871]\n",
+            "Valid ROC-AUC Score: 0.768819\n"
           ],
           "name": "stdout"
         }
diff --git a/examples/tutorials/05_Putting_Multitask_Learning_to_Work.ipynb b/examples/tutorials/05_Putting_Multitask_Learning_to_Work.ipynb
index fabf2884ef..021dc41d23 100644
--- a/examples/tutorials/05_Putting_Multitask_Learning_to_Work.ipynb
+++ b/examples/tutorials/05_Putting_Multitask_Learning_to_Work.ipynb
@@ -53,20 +53,75 @@
       "metadata": {
         "id": "Fc_4bSWJg37l",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "93f0664e-df45-4641-fecd-4e2f1aa8426e"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  65441      0 --:--:-- --:--:-- --:--:-- 65441\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.52 s, sys: 535 ms, total: 3.05 s\n",
+            "Wall time: 4min 19s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -83,10 +138,10 @@
       "metadata": {
         "id": "FGi-ZEfSg37q",
         "colab_type": "code",
-        "outputId": "e75ebc2e-5cf6-4d6b-d2f3-b944103fb5bd",
+        "outputId": "ed7058d7-9de8-4122-c8dc-b11d87c162ea",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 321
+          "height": 85
         }
       },
       "source": [
@@ -111,40 +166,6 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n",
             "Columns of dataset: ['MUV-466' 'MUV-548' 'MUV-600' 'MUV-644' 'MUV-652' 'MUV-689' 'MUV-692'\n",
             " 'MUV-712' 'MUV-713' 'MUV-733' 'MUV-737' 'MUV-810' 'MUV-832' 'MUV-846'\n",
             " 'MUV-852' 'MUV-858' 'MUV-859' 'mol_id' 'smiles']\n",
@@ -169,7 +190,7 @@
       "metadata": {
         "id": "KobfUjlWg37v",
         "colab_type": "code",
-        "outputId": "1ba6dcfc-473d-4114-e900-111f843ae2c4",
+        "outputId": "e472a388-bf6f-441d-fb29-fa580d06cf9b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
@@ -206,7 +227,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -218,7 +239,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -230,7 +251,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -242,7 +263,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -254,7 +275,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -266,7 +287,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -278,7 +299,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -290,7 +311,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -302,7 +323,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -314,7 +335,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -326,7 +347,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -338,7 +359,7 @@
         {
           "output_type": "display_data",
           "data": {
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\n",
             "text/plain": [
               "<IPython.core.display.Image object>"
             ]
@@ -366,7 +387,7 @@
       "metadata": {
         "id": "eqEQiNDpg37z",
         "colab_type": "code",
-        "outputId": "be8df391-37bd-4a0c-fb4a-6e90164addbc",
+        "outputId": "2140755d-0b13-44e9-c103-80ea2731407e",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
@@ -402,12 +423,12 @@
             "Featurizing sample 6000\n",
             "Featurizing sample 7000\n",
             "Featurizing sample 8000\n",
-            "TIMING: featurizing shard 0 took 29.120 s\n",
+            "TIMING: featurizing shard 0 took 26.406 s\n",
             "Loading shard 2 of size 8192.\n",
             "Featurizing sample 0\n",
             "Featurizing sample 1000\n",
-            "TIMING: featurizing shard 1 took 6.476 s\n",
-            "TIMING: dataset construction took 35.992 s\n",
+            "TIMING: featurizing shard 1 took 5.849 s\n",
+            "TIMING: dataset construction took 32.666 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -429,7 +450,7 @@
       "metadata": {
         "id": "-f03zjeIg372",
         "colab_type": "code",
-        "outputId": "e90e1372-0d0e-4def-d9f8-d7f6582bcee1",
+        "outputId": "b806aa40-4293-48f2-ebd5-60a43f4e8c46",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 136
@@ -448,11 +469,11 @@
           "output_type": "stream",
           "text": [
             "Computing train/valid/test indices\n",
-            "TIMING: dataset construction took 0.455 s\n",
+            "TIMING: dataset construction took 0.471 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.222 s\n",
+            "TIMING: dataset construction took 0.236 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.226 s\n",
+            "TIMING: dataset construction took 0.230 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -474,10 +495,10 @@
       "metadata": {
         "id": "BvfbTbsEg376",
         "colab_type": "code",
-        "outputId": "9182b0b7-eec6-4be2-c4cb-d5ac28c3e774",
+        "outputId": "bc3cc023-c3fc-4726-b5ce-42d2c663bb4d",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 887
+          "height": 989
         }
       },
       "source": [
@@ -524,19 +545,19 @@
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "If using Keras pass *_constraint arguments to layers.\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
             "\n"
           ],
           "name": "stdout"
@@ -544,27 +565,33 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n",
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n"
           ],
           "name": "stderr"
@@ -572,20 +599,20 @@
         {
           "output_type": "stream",
           "text": [
-            "computed_metrics: [nan, nan, nan, 0.21584699453551914, nan, nan, 0.5759493670886076, nan, 0.08955223880597016, 0.47850318471337583, nan, 0.6918604651162791, 0.2638121546961326, nan, nan, nan, nan]\n",
-            "Model 1/1, Metric mean-roc_auc_score, Validation set 0: 0.385921\n",
-            "\tbest_validation_score so far: 0.385921\n",
+            "computed_metrics: [nan, nan, 0.8181818181818181, nan, nan, nan, nan, nan, nan, nan, nan, 0.7116477272727273, nan, nan, 0.7777777777777778, nan, nan]\n",
+            "Model 1/1, Metric mean-roc_auc_score, Validation set 0: 0.769202\n",
+            "\tbest_validation_score so far: 0.769202\n",
             "computed_metrics: [1.0, nan, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]\n",
             "Best hyperparameters: ('relu', 0.9, 50, 'glorot_uniform', (1024,), 0.001, 1e-06, 1, False, (0.5,), 1, False, (1000,), (0.1,), (1.0,), 0.0)\n",
             "train_score: 1.000000\n",
-            "validation_score: 0.385921\n"
+            "validation_score: 0.769202\n"
           ],
           "name": "stdout"
         },
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.7/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
+            "/root/miniconda/lib/python3.6/site-packages/deepchem/metrics/__init__.py:368: UserWarning: Error calculating metric mean-roc_auc_score: Only one class present in y_true. ROC AUC score is not defined in that case.\n",
             "  warnings.warn(\"Error calculating metric %s: %s\" % (self.name, e))\n"
           ],
           "name": "stderr"
diff --git a/examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb b/examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
index 76b38f8a59..5606835b51 100644
--- a/examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
+++ b/examples/tutorials/06_Going_Deeper_on_Molecular_Featurizations.ipynb
@@ -22,7 +22,8 @@
     "colab": {
       "name": "06_Going_Deeper_on_Molecular_Featurizations.ipynb",
       "provenance": []
-    }
+    },
+    "accelerator": "GPU"
   },
   "cells": [
     {
@@ -63,20 +64,76 @@
       "metadata": {
         "id": "tS3siM3Ch11-",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "7bbeb07c-f447-499a-8012-efb5759dbd65"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  65441      0 --:--:-- --:--:-- --:--:-- 65441\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.54 s, sys: 549 ms, total: 3.09 s\n",
+            "Wall time: 4min 11s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -93,11 +150,7 @@
       "metadata": {
         "id": "Sp5Hbb4nh12C",
         "colab_type": "code",
-        "outputId": "ada7db94-8aa1-441d-f036-3ee0198ee7a0",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 253
-        }
+        "colab": {}
       },
       "source": [
         "from __future__ import print_function\n",
@@ -112,49 +165,8 @@
         "from deepchem.feat import BPSymmetryFunctionInput, CoulombMatrix, CoulombMatrixEig\n",
         "from deepchem.utils import conformers"
       ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -217,7 +229,7 @@
       "metadata": {
         "id": "3dt_vjtXh12N",
         "colab_type": "code",
-        "outputId": "2cab5c6f-2a3f-41b0-c814-f067d46f8056",
+        "outputId": "08945e4f-19e7-4619-ad20-a92d39492f4c",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
@@ -232,117 +244,117 @@
         {
           "output_type": "stream",
           "text": [
-            "NumSaturatedCarbocycles\n",
-            "MaxAbsPartialCharge\n",
-            "NumSaturatedHeterocycles\n",
-            "SlogP_VSA9\n",
+            "PEOE_VSA11\n",
+            "Kappa2\n",
+            "SlogP_VSA12\n",
+            "SlogP_VSA7\n",
+            "MaxAbsEStateIndex\n",
             "Chi1n\n",
-            "Kappa3\n",
-            "VSA_EState9\n",
-            "NumAliphaticHeterocycles\n",
-            "BalabanJ\n",
-            "SlogP_VSA8\n",
-            "NumAromaticCarbocycles\n",
-            "MinPartialCharge\n",
-            "EState_VSA5\n",
-            "PEOE_VSA14\n",
+            "NumAromaticHeterocycles\n",
             "NumAromaticRings\n",
+            "SMR_VSA5\n",
+            "VSA_EState8\n",
+            "SMR_VSA10\n",
             "PEOE_VSA4\n",
-            "SlogP_VSA10\n",
-            "NumHAcceptors\n",
-            "VSA_EState1\n",
-            "Chi2v\n",
-            "NOCount\n",
-            "Chi1v\n",
-            "PEOE_VSA10\n",
-            "SMR_VSA8\n",
-            "MolMR\n",
-            "SMR_VSA9\n",
-            "SMR_VSA7\n",
-            "Chi4v\n",
-            "EState_VSA4\n",
-            "PEOE_VSA12\n",
-            "Ipc\n",
-            "Chi0\n",
+            "HeavyAtomMolWt\n",
             "Chi4n\n",
-            "RingCount\n",
-            "VSA_EState6\n",
-            "PEOE_VSA3\n",
-            "SMR_VSA1\n",
-            "VSA_EState10\n",
-            "NHOHCount\n",
-            "EState_VSA6\n",
+            "SlogP_VSA5\n",
+            "EState_VSA2\n",
+            "MinEStateIndex\n",
+            "SMR_VSA3\n",
+            "Kappa3\n",
+            "TPSA\n",
+            "PEOE_VSA13\n",
             "EState_VSA10\n",
-            "MaxPartialCharge\n",
-            "FractionCSP3\n",
-            "MolLogP\n",
-            "NumHDonors\n",
-            "EState_VSA9\n",
-            "MinAbsEStateIndex\n",
-            "PEOE_VSA5\n",
+            "NumAromaticCarbocycles\n",
+            "ExactMolWt\n",
+            "BertzCT\n",
+            "PEOE_VSA3\n",
+            "RingCount\n",
+            "BalabanJ\n",
+            "Chi3v\n",
             "MolWt\n",
-            "PEOE_VSA13\n",
+            "PEOE_VSA12\n",
+            "VSA_EState7\n",
+            "SlogP_VSA3\n",
+            "LabuteASA\n",
+            "SlogP_VSA1\n",
+            "SMR_VSA4\n",
+            "EState_VSA9\n",
+            "NumAliphaticCarbocycles\n",
+            "EState_VSA1\n",
             "NumSaturatedRings\n",
+            "SlogP_VSA4\n",
+            "NumSaturatedHeterocycles\n",
+            "SlogP_VSA10\n",
             "NumHeteroatoms\n",
-            "SlogP_VSA3\n",
-            "Kappa1\n",
-            "BertzCT\n",
-            "VSA_EState8\n",
+            "MaxAbsPartialCharge\n",
+            "SlogP_VSA11\n",
+            "VSA_EState4\n",
             "EState_VSA3\n",
-            "NumAromaticHeterocycles\n",
-            "SlogP_VSA2\n",
-            "SMR_VSA5\n",
-            "SlogP_VSA12\n",
-            "SlogP_VSA6\n",
-            "EState_VSA2\n",
-            "ExactMolWt\n",
-            "SMR_VSA4\n",
             "NumAliphaticRings\n",
-            "SlogP_VSA5\n",
+            "Chi2n\n",
+            "PEOE_VSA6\n",
+            "SMR_VSA6\n",
+            "MaxPartialCharge\n",
+            "NHOHCount\n",
             "EState_VSA7\n",
-            "Kappa2\n",
-            "VSA_EState2\n",
-            "MaxAbsEStateIndex\n",
-            "MinEStateIndex\n",
-            "NumValenceElectrons\n",
-            "Chi3n\n",
-            "NumAliphaticCarbocycles\n",
-            "Chi0n\n",
-            "HeavyAtomCount\n",
-            "MinAbsPartialCharge\n",
-            "PEOE_VSA2\n",
-            "HallKierAlpha\n",
-            "TPSA\n",
+            "NumHDonors\n",
+            "NumHAcceptors\n",
             "PEOE_VSA7\n",
-            "VSA_EState7\n",
+            "HallKierAlpha\n",
+            "VSA_EState10\n",
+            "SlogP_VSA9\n",
+            "PEOE_VSA10\n",
+            "MaxEStateIndex\n",
+            "MinAbsEStateIndex\n",
+            "Chi4v\n",
             "EState_VSA8\n",
-            "EState_VSA1\n",
-            "LabuteASA\n",
+            "VSA_EState5\n",
+            "Chi2v\n",
+            "VSA_EState6\n",
             "PEOE_VSA8\n",
-            "SlogP_VSA11\n",
-            "EState_VSA11\n",
-            "Chi1\n",
-            "PEOE_VSA1\n",
-            "PEOE_VSA11\n",
+            "EState_VSA6\n",
             "Chi0v\n",
-            "Chi2n\n",
-            "HeavyAtomMolWt\n",
-            "SMR_VSA10\n",
-            "PEOE_VSA9\n",
-            "VSA_EState4\n",
-            "SlogP_VSA4\n",
-            "SMR_VSA2\n",
+            "Kappa1\n",
+            "MinAbsPartialCharge\n",
             "VSA_EState3\n",
-            "VSA_EState5\n",
-            "SlogP_VSA1\n",
+            "NOCount\n",
+            "SMR_VSA9\n",
+            "Chi0n\n",
+            "NumAliphaticHeterocycles\n",
+            "PEOE_VSA9\n",
+            "VSA_EState9\n",
+            "SlogP_VSA2\n",
+            "FractionCSP3\n",
+            "MinPartialCharge\n",
+            "Chi3n\n",
+            "Chi1\n",
+            "Chi1v\n",
+            "PEOE_VSA5\n",
+            "VSA_EState2\n",
+            "PEOE_VSA2\n",
             "NumRotatableBonds\n",
-            "SlogP_VSA7\n",
-            "SMR_VSA3\n",
-            "Chi3v\n",
-            "MaxEStateIndex\n",
+            "EState_VSA5\n",
+            "MolMR\n",
+            "NumSaturatedCarbocycles\n",
+            "HeavyAtomCount\n",
             "NumRadicalElectrons\n",
-            "PEOE_VSA6\n",
-            "SMR_VSA6\n"
+            "SMR_VSA2\n",
+            "MolLogP\n",
+            "EState_VSA4\n",
+            "NumValenceElectrons\n",
+            "Chi0\n",
+            "PEOE_VSA1\n",
+            "SMR_VSA7\n",
+            "SlogP_VSA6\n",
+            "VSA_EState1\n",
+            "Ipc\n",
+            "SMR_VSA1\n",
+            "SMR_VSA8\n",
+            "SlogP_VSA8\n",
+            "EState_VSA11\n",
+            "PEOE_VSA14\n"
           ],
           "name": "stdout"
         }
@@ -353,7 +365,7 @@
       "metadata": {
         "id": "KfyDpE81h12Q",
         "colab_type": "code",
-        "outputId": "3e971a11-8f7e-4f61-8c4b-10b1d7e7293e",
+        "outputId": "7225bac2-c558-4cd5-b460-339cdfc1d83f",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -434,7 +446,7 @@
       "metadata": {
         "id": "IuPE4MXZh12Y",
         "colab_type": "code",
-        "outputId": "c2ba9d53-4768-4ecd-d6c6-ad57c58ffdbc",
+        "outputId": "4bca2307-208b-4042-c342-42a4fbeec56d",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
@@ -495,7 +507,7 @@
       "metadata": {
         "id": "1rbcGUf6h12c",
         "colab_type": "code",
-        "outputId": "7e024a45-1a86-4555-920e-c256695c9c55",
+        "outputId": "1ce28cc8-cc23-4cb0-9b78-de248699e76f",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -558,7 +570,7 @@
       "metadata": {
         "id": "evLPEI6mh12g",
         "colab_type": "code",
-        "outputId": "c2a45ce2-78e0-4c75-bd85-d9a5024d763c",
+        "outputId": "aa05c66b-643a-4d6c-9cef-f0221e375dd6",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -613,7 +625,7 @@
       "metadata": {
         "id": "ShTPO4wIh12l",
         "colab_type": "code",
-        "outputId": "2cb5e1cc-1253-490d-ebf6-c89f6da141dd",
+        "outputId": "921a1154-438e-4ec9-f3b9-8e7d0dba419c",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -662,7 +674,7 @@
       "metadata": {
         "id": "XnNZB-Kxh12q",
         "colab_type": "code",
-        "outputId": "f7ff7660-f4c6-48a9-86db-2288fe2b1d4b",
+        "outputId": "dfbf6b76-3222-42bc-d521-319c1bde4673",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -707,7 +719,7 @@
       "metadata": {
         "id": "_8PBHQYLh12v",
         "colab_type": "code",
-        "outputId": "bef9414a-0f99-4761-b3c2-2c26145a5709",
+        "outputId": "0bd7d61c-54ed-4c8e-f769-6653bbc07b00",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
diff --git a/examples/tutorials/07_Uncertainty_In_Deep_Learning.ipynb b/examples/tutorials/07_Uncertainty_In_Deep_Learning.ipynb
index debcb3d51e..91b45f9f27 100644
--- a/examples/tutorials/07_Uncertainty_In_Deep_Learning.ipynb
+++ b/examples/tutorials/07_Uncertainty_In_Deep_Learning.ipynb
@@ -54,20 +54,76 @@
       "metadata": {
         "id": "p0MdAUAvkMdD",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "c472e433-bdc4-46af-e6c0-aa5b358e3ca0"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  11725      0 --:--:-- --:--:-- --:--:-- 11725\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.96 s, sys: 773 ms, total: 3.74 s\n",
+            "Wall time: 4min 37s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -84,10 +140,10 @@
       "metadata": {
         "id": "4mHPuoOPkMdH",
         "colab_type": "code",
-        "outputId": "5f0fcd4a-64fa-4d14-ebaf-7f9669ddcb2c",
+        "outputId": "02ca54c8-2de6-43fb-c1ca-0ebcb57df0bb",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 984
+          "height": 768
         }
       },
       "source": [
@@ -107,82 +163,48 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n",
             "Loading raw samples now.\n",
             "shard_size: 8192\n",
             "About to start loading CSV from /tmp/SAMPL.csv\n",
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
-            "TIMING: featurizing shard 0 took 2.384 s\n",
-            "TIMING: dataset construction took 2.476 s\n",
+            "TIMING: featurizing shard 0 took 2.623 s\n",
+            "TIMING: dataset construction took 2.686 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.035 s\n",
+            "TIMING: dataset construction took 0.033 s\n",
             "Loading dataset from disk.\n",
             "TIMING: dataset construction took 0.019 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.021 s\n",
-            "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.032 s\n",
+            "TIMING: dataset construction took 0.019 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.022 s\n",
+            "TIMING: dataset construction took 0.036 s\n",
             "Loading dataset from disk.\n",
             "TIMING: dataset construction took 0.020 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.029 s\n",
+            "TIMING: dataset construction took 0.025 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.009 s\n",
+            "TIMING: dataset construction took 0.028 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.008 s\n",
             "Loading dataset from disk.\n",
             "TIMING: dataset construction took 0.009 s\n",
             "Loading dataset from disk.\n",
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "If using Keras pass *_constraint arguments to layers.\n",
-            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f638b4556d8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f638b4556d8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fdf82923c88>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fdf82923c88>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f638b4556d8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7f638b4556d8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fdf82923c88>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING: Entity <bound method SwitchedDropout.call of <deepchem.models.layers.SwitchedDropout object at 0x7fdf82923c88>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: module 'gast' has no attribute 'Num'\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n"
           ],
           "name": "stdout"
@@ -212,7 +234,7 @@
       "metadata": {
         "id": "iLgia0GVkMdM",
         "colab_type": "code",
-        "outputId": "3cd342eb-1bea-476a-8005-94db738b5829",
+        "outputId": "959036cf-96c2-45bc-d1a3-eba8725578f4",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 265
@@ -233,13 +255,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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4WR+waut+ZlyVycuTRqvMReS06Qg9QI53U8g1w7vz9AcbKdxRxiWZ3bjv8kF0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\n",
+            "image/png": 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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -265,10 +288,10 @@
       "metadata": {
         "id": "hVoRaGn6kMdQ",
         "colab_type": "code",
-        "outputId": "c1e58e58-becf-41d4-afc6-88ad42b943fa",
+        "outputId": "f1aa9bc6-a989-44e0-cebe-1fc63fb10b52",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 215
+          "height": 211
         }
       },
       "source": [
@@ -286,13 +309,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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FuKna7lQVuzk64CcYyt3PSBAmMx6n3VCA3BRvlCQBsmCOoW56BgPkXGk1rSP0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\nmZmZmVmR/wHKQ/mkv8aKugAAAABJRU5ErkJggg==\n",
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\n",
             "text/plain": [
               "<Figure size 864x216 with 3 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -326,7 +350,7 @@
       "metadata": {
         "id": "r3jD4V4rkMdU",
         "colab_type": "code",
-        "outputId": "7ea95f5c-a141-412a-e483-6a719c53a7bc",
+        "outputId": "cdec7cce-0a92-428e-ab88-925b5f6613ea",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 279
@@ -344,13 +368,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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NlnSFpF8D/52k+sms57ndxqyxuiUKSW8gKTmcBjwNLCCZY/uoLsVmljt3wzVrLKuN4hHg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\nZuTbJOn1wJ+QTOM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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -373,7 +398,7 @@
         "scrolled": true,
         "id": "IrD6swafkMdY",
         "colab_type": "code",
-        "outputId": "166c3b20-2b8e-45bb-a619-bbd237985d23",
+        "outputId": "1e797a9f-eaae-433c-dcd8-af84ea526884",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 265
@@ -388,13 +413,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXcAAAD4CAYAAAAXUaZHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nS1JDxl2SGvo/THFWAuNSk7oAAAAASUVORK5CYII=\n",
+            "image/png": "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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
diff --git a/examples/tutorials/08_Introduction_to_Model_Interpretability.ipynb b/examples/tutorials/08_Introduction_to_Model_Interpretability.ipynb
index 9e33cd5a4d..2e5fe77c04 100644
--- a/examples/tutorials/08_Introduction_to_Model_Interpretability.ipynb
+++ b/examples/tutorials/08_Introduction_to_Model_Interpretability.ipynb
@@ -68,20 +68,76 @@
       "metadata": {
         "id": "xdgY3YQLkP1m",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "fadfa806-5cf6-439a-a070-8c77f6427991"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0   7385      0 --:--:-- --:--:-- --:--:--  7385\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.93 s, sys: 770 ms, total: 3.7 s\n",
+            "Wall time: 5min 2s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -100,10 +156,10 @@
       "metadata": {
         "id": "57IdQLKOkP1q",
         "colab_type": "code",
-        "outputId": "3fc5bc43-c000-4aca-a7c7-455fd137e9aa",
+        "outputId": "11624b60-b593-4bf7-ac2f-3985736ece3a",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 712
+          "height": 476
         }
       },
       "source": [
@@ -119,40 +175,6 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n",
             "Loading raw samples now.\n",
             "shard_size: 8192\n",
             "About to start loading CSV from /tmp/tox21.csv.gz\n",
@@ -165,20 +187,20 @@
             "Featurizing sample 5000\n",
             "Featurizing sample 6000\n",
             "Featurizing sample 7000\n",
-            "TIMING: featurizing shard 0 took 19.608 s\n",
-            "TIMING: dataset construction took 19.928 s\n",
+            "TIMING: featurizing shard 0 took 31.873 s\n",
+            "TIMING: dataset construction took 32.182 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.340 s\n",
+            "TIMING: dataset construction took 0.383 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.167 s\n",
+            "TIMING: dataset construction took 0.191 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.168 s\n",
+            "TIMING: dataset construction took 0.186 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.275 s\n",
+            "TIMING: dataset construction took 0.313 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.037 s\n",
+            "TIMING: dataset construction took 0.045 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.036 s\n",
+            "TIMING: dataset construction took 0.041 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -200,7 +222,7 @@
       "metadata": {
         "id": "u0ZLMRiHkP1v",
         "colab_type": "code",
-        "outputId": "e2e0d987-df2d-4aee-b209-7094f18af6dc",
+        "outputId": "50ce1c66-25c9-4d91-dee5-42f2dead6ffc",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 88
@@ -241,7 +263,7 @@
       "metadata": {
         "id": "cnp0tJ2NkP1y",
         "colab_type": "code",
-        "outputId": "90293698-a5d5-449f-e639-f2a22303e534",
+        "outputId": "6ad0e36f-1605-4fcc-a69a-77dff8095bda",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 445
@@ -260,30 +282,30 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:108: The name tf.losses.softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.softmax_cross_entropy instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:109: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
             "\n",
-            "Epoch 0 loss: 0.225389\n",
-            "Epoch 1 loss: 0.148365\n",
-            "Epoch 2 loss: 0.125899\n",
-            "Epoch 3 loss: 0.117782\n",
-            "Epoch 4 loss: 0.111761\n",
-            "Epoch 5 loss: 0.101762\n",
-            "Epoch 6 loss: 0.100397\n",
-            "Epoch 7 loss: 0.095577\n",
-            "Epoch 8 loss: 0.089669\n",
-            "Epoch 9 loss: 0.090393\n"
+            "Epoch 0 loss: 0.229214\n",
+            "Epoch 1 loss: 0.149100\n",
+            "Epoch 2 loss: 0.125665\n",
+            "Epoch 3 loss: 0.113928\n",
+            "Epoch 4 loss: 0.113030\n",
+            "Epoch 5 loss: 0.103234\n",
+            "Epoch 6 loss: 0.100231\n",
+            "Epoch 7 loss: 0.087424\n",
+            "Epoch 8 loss: 0.090101\n",
+            "Epoch 9 loss: 0.090855\n"
           ],
           "name": "stdout"
         }
@@ -304,7 +326,7 @@
       "metadata": {
         "id": "5TWg2RelkP12",
         "colab_type": "code",
-        "outputId": "bac87712-96ef-4afe-d34f-ce5cc582a1e0",
+        "outputId": "ef445811-895c-44e4-fa16-252b7138f425",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 156
@@ -332,12 +354,12 @@
           "output_type": "stream",
           "text": [
             "Evaluating model\n",
-            "computed_metrics: [0.9899623319442414, 0.9965543713872832, 0.9760909005272388, 0.9868944735046623, 0.9267687681697456, 0.9872737351555338, 0.9907162874841502, 0.9385570702886361, 0.99194102642623, 0.9768016931804382, 0.9696714344487396, 0.9838725736980478]\n",
-            "computed_metrics: [0.6137139464700935, 0.8115906084656085, 0.8101363036453829, 0.7339222165734621, 0.6479545454545454, 0.7354850129808828, 0.6837140879196955, 0.7944291787031166, 0.7445726395102426, 0.7318185904350953, 0.8321396919686699, 0.73359173126615]\n",
+            "computed_metrics: [0.9898439434300528, 0.9972844412331405, 0.9767605731065726, 0.9872000198760422, 0.9196999752503592, 0.987017887981579, 0.9914996890622603, 0.9360360857958208, 0.9911223839981069, 0.9774907621046789, 0.9691255829203553, 0.9806645410303743]\n",
+            "computed_metrics: [0.6001583139563647, 0.8021453373015873, 0.8102500796431984, 0.7244375953228266, 0.6429772727272727, 0.7435882306663519, 0.6664719626168225, 0.8010756476197187, 0.7443371791853073, 0.7254809421071557, 0.828522838870392, 0.7356589147286821]\n",
             "Train scores\n",
-            "{'mean-roc_auc_score': 0.9762587221845789}\n",
+            "{'mean-roc_auc_score': 0.9753121571491121}\n",
             "Validation scores\n",
-            "{'mean-roc_auc_score': 0.7394223794494121}\n"
+            "{'mean-roc_auc_score': 0.73542535956214}\n"
           ],
           "name": "stdout"
         }
@@ -362,10 +384,10 @@
       "metadata": {
         "id": "WV50QNwSkP15",
         "colab_type": "code",
-        "outputId": "61fc271e-c414-4c3b-9754-284444074222",
+        "outputId": "3540b982-b77b-4dda-c6b6-d79d5421f925",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 683
+          "height": 688
         }
       },
       "source": [
@@ -377,45 +399,55 @@
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+            "\u001b[?25hRequirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from lime) (0.22.2.post1)\n",
+            "Requirement already satisfied: scikit-image>=0.12 in /usr/local/lib/python3.6/dist-packages (from lime) (0.16.2)\n",
+            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->lime) (2.8.1)\n",
+            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib->lime) (0.10.0)\n",
+            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->lime) (2.4.7)\n",
+            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib->lime) (1.2.0)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->lime) (0.15.1)\n",
+            "Requirement already satisfied: networkx>=2.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.12->lime) (2.4)\n",
+            "Requirement already satisfied: PyWavelets>=0.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.12->lime) (1.1.1)\n",
+            "Requirement already satisfied: imageio>=2.3.0 in /usr/local/lib/python3.6/dist-packages (from scikit-image>=0.12->lime) (2.4.1)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.6/dist-packages (from python-dateutil>=2.1->matplotlib->lime) (1.12.0)\n",
+            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx>=2.0->scikit-image>=0.12->lime) (4.4.2)\n",
+            "Building wheels for collected packages: lime\n",
             "  Building wheel for lime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for lime: filename=lime-0.1.1.37-py3-none-any.whl size=284274 sha256=af66fb1f41bea27172b3148fef22288b682eec2ecf6b58fc9954c7946e839196\n",
-            "  Stored in directory: /root/.cache/pip/wheels/5d/7a/8d/30e0ea15164fb80d5484e83ab991e188d6a9d8febfada307a6\n",
-            "  Building wheel for progressbar (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for progressbar: filename=progressbar-2.5-py3-none-any.whl size=12074 sha256=b69871bd4d43569b75a3238372073a15782ac5a3439d45fcde2d443c0744379e\n",
-            "  Stored in directory: /root/.cache/pip/wheels/f0/fd/1f/3e35ed57e94cd8ced38dd46771f1f0f94f65fec548659ed855\n",
-            "Successfully built lime progressbar\n",
-            "Installing collected packages: progressbar, PyWavelets, imageio, scikit-image, lime\n",
-            "Successfully installed PyWavelets-1.1.1 imageio-2.8.0 lime-0.1.1.37 progressbar-2.5 scikit-image-0.16.2\n"
+            "  Created wheel for lime: filename=lime-0.2.0.0-cp36-none-any.whl size=284181 sha256=d5b05c4c6122f60e3df6048fa1b9e9f022fc8e124e75adcaa5e07887a9ceef14\n",
+            "  Stored in directory: /root/.cache/pip/wheels/22/f2/ec/e5ebd07348b2b1ac722e91c2f549fcc220f7d5f25497a61232\n",
+            "Successfully built lime\n",
+            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
+            "Installing collected packages: pillow, lime\n",
+            "  Found existing installation: Pillow 7.0.0\n",
+            "    Uninstalling Pillow-7.0.0:\n",
+            "      Successfully uninstalled Pillow-7.0.0\n",
+            "Successfully installed lime-0.2.0.0 pillow-5.4.1\n"
           ],
           "name": "stdout"
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.colab-display-data+json": {
+              "pip_warning": {
+                "packages": [
+                  "PIL"
+                ]
+              }
+            }
+          },
+          "metadata": {
+            "tags": []
+          }
         }
       ]
     },
@@ -495,7 +527,7 @@
       "metadata": {
         "id": "VGPZDfmMkP2D",
         "colab_type": "code",
-        "outputId": "e91b03c0-9325-4525-98c9-64dca7d4efc4",
+        "outputId": "820225f0-d8cc-4d66-bf48-2af5dc7e7c64",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 184
@@ -526,9 +558,9 @@
         {
           "output_type": "execute_result",
           "data": {
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WTqNaU6lUDAaDRqOZVHV0zazIrl27UMfSsrKyMrKQ64kTJ1DH0pyzZ88CQLt2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\ntKHuDr/VFAoFmUzJf4uePXs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+            "image/png": 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\n",
             "text/plain": [
-              "<rdkit.Chem.rdchem.Mol at 0x7f5ba158a3f0>"
+              "<rdkit.Chem.rdchem.Mol at 0x7fdf7361c2b0>"
             ]
           },
           "metadata": {
@@ -558,7 +590,7 @@
       "metadata": {
         "id": "BPs0Txu4kP2H",
         "colab_type": "code",
-        "outputId": "9ffd85da-adaf-4312-a837-f926175358b8",
+        "outputId": "c410f052-d115-4aa2-9982-ef29e4ed18b2",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 188
@@ -27966,7 +27998,8 @@
               "\t    this.color = _d2.default.scale.category10();\n",
               "\t    this.color('predicted_value');\n",
               "\t    // + 2 is due to it being a float\n",
-              "\t    var num_digits = Math.floor(Math.max(Math.log10(min_value), Math.log10(max_value))) + 2;\n",
+              "\t    console.log('CREATING THIS');\n",
+              "\t    var num_digits = Math.floor(Math.max(Math.log10(Math.abs(min_value)), Math.log10(Math.abs(max_value)))) + 2;\n",
               "\t    num_digits = Math.max(num_digits, 3);\n",
               "\t\n",
               "\t    var corner_width = 12 * num_digits;\n",
@@ -37664,25 +37697,25 @@
               "/***/ })\n",
               "/******/ ]);\n",
               "//# sourceMappingURL=bundle.js.map </script></head><body>\n",
-              "        <div class=\"lime top_div\" id=\"top_div0U6QB3H7977VBT7\"></div>\n",
+              "        <div class=\"lime top_div\" id=\"top_div00Z6L60CJZSCDMC\"></div>\n",
               "        \n",
               "        <script>\n",
-              "        var top_div = d3.select('#top_div0U6QB3H7977VBT7').classed('lime top_div', true);\n",
+              "        var top_div = d3.select('#top_div00Z6L60CJZSCDMC').classed('lime top_div', true);\n",
               "        \n",
               "            var pp_div = top_div.append('div')\n",
               "                                .classed('lime predict_proba', true);\n",
               "            var pp_svg = pp_div.append('svg').style('width', '100%');\n",
-              "            var pp = new lime.PredictProba(pp_svg, [\"not toxic\", \"toxic\"], [0.00024493917590007186, 0.9997550845146179]);\n",
+              "            var pp = new lime.PredictProba(pp_svg, [\"not toxic\", \"toxic\"], [6.607633986277506e-05, 0.9999339580535889]);\n",
               "            \n",
               "        \n",
               "        var exp_div;\n",
               "            var exp = new lime.Explanation([\"not toxic\", \"toxic\"]);\n",
               "        \n",
               "                exp_div = top_div.append('div').classed('lime explanation', true);\n",
-              "                exp.show([[\"fp_381 <= 0.00\", -0.08158197911568467], [\"fp_7 <= 0.00\", -0.06027144623571045], [\"fp_445 <= 0.00\", -0.05912651866052066], [\"fp_632 <= 0.00\", -0.04878249492701265], [\"fp_751 <= 0.00\", -0.043609001665145974]], 1, exp_div);\n",
+              "                exp.show([[\"fp_445 <= 0.00\", -0.10708463579928981], [\"fp_182 > 0.00\", 0.0930092885863154], [\"fp_381 <= 0.00\", -0.08951146406413682], [\"fp_242 > 0.00\", 0.08201622898521128], [\"fp_956 <= 0.00\", -0.064259362233473]], 1, exp_div);\n",
               "                \n",
               "        var raw_div = top_div.append('div');\n",
-              "            exp.show_raw_tabular([[\"fp_381\", \"0.00\", -0.08158197911568467], [\"fp_7\", \"0.00\", -0.06027144623571045], [\"fp_445\", \"0.00\", -0.05912651866052066], [\"fp_632\", \"0.00\", -0.04878249492701265], [\"fp_751\", \"0.00\", -0.043609001665145974]], 1, raw_div);\n",
+              "            exp.show_raw_tabular([[\"fp_445\", \"0.00\", -0.10708463579928981], [\"fp_182\", \"1.00\", 0.0930092885863154], [\"fp_381\", \"0.00\", -0.08951146406413682], [\"fp_242\", \"1.00\", 0.08201622898521128], [\"fp_956\", \"0.00\", -0.064259362233473]], 1, raw_div);\n",
               "        \n",
               "        </script>\n",
               "        </body></html>"
@@ -37712,7 +37745,7 @@
       "metadata": {
         "id": "4ja4_jCKkP2N",
         "colab_type": "code",
-        "outputId": "72bf5f41-ccdd-4639-8fa1-1022564cdfdd",
+        "outputId": "5b286770-a5b3-4796-8da2-8e914a803982",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
@@ -37754,7 +37787,7 @@
         {
           "output_type": "stream",
           "text": [
-            "RDKit WARNING: [04:14:03] WARNING: not removing hydrogen atom without neighbors\n"
+            "RDKit WARNING: [02:54:11] WARNING: not removing hydrogen atom without neighbors\n"
           ],
           "name": "stderr"
         }
@@ -37765,7 +37798,7 @@
       "metadata": {
         "id": "PAe3ZOhUkP2Q",
         "colab_type": "code",
-        "outputId": "ffe66084-d27e-4e18-eb7b-6a58d428274a",
+        "outputId": "2d65a950-4a4d-4611-c93b-03f83eb8dde3",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 167
@@ -37781,9 +37814,9 @@
         {
           "output_type": "execute_result",
           "data": {
-            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcIAAACWCAIAAADCEh9HAAAABmJLR0QA/wD/AP+gvaeTAAAJkUlE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\nibjtQgAAAABJRU5ErkJggg==\n",
+            "image/png": "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\n",
             "text/plain": [
-              "<rdkit.Chem.rdchem.Mol at 0x7f5b900ebee0>"
+              "<rdkit.Chem.rdchem.Mol at 0x7fdf16533440>"
             ]
           },
           "metadata": {
@@ -37808,10 +37841,10 @@
       "metadata": {
         "id": "0_kZg3NCkP2T",
         "colab_type": "code",
-        "outputId": "fb41b461-7334-41bb-c35a-c7df70608f86",
+        "outputId": "e7674b3a-33af-41da-8113-1ba381675bb8",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 167
+          "height": 34
         }
       },
       "source": [
@@ -37820,17 +37853,11 @@
       "execution_count": 14,
       "outputs": [
         {
-          "output_type": "execute_result",
-          "data": {
-            "image/png": 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++uKLLzo7O8tksscee4xiErBaqFEYKKFQuHbt2sbGRkJIdHT03r17vby8aIcihJCoqKhv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\nCWoUAGBIUKMAAEPyfy5p+qPwKm6OAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<rdkit.Chem.rdchem.Mol at 0x7f5b900ebd50>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 14
+          "output_type": "stream",
+          "text": [
+            "RDKit ERROR: [02:54:18] non-ring atom 0 marked aromatic\n"
+          ],
+          "name": "stderr"
         }
       ]
     },
@@ -37839,10 +37866,10 @@
       "metadata": {
         "id": "Tp5vzQj7kP2V",
         "colab_type": "code",
-        "outputId": "b166daf4-43bd-4f57-bd02-5eddf40e0bad",
+        "outputId": "b4222508-ab82-47e6-c58c-46557ae00ca1",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 167
+          "height": 34
         }
       },
       "source": [
@@ -37851,17 +37878,11 @@
       "execution_count": 15,
       "outputs": [
         {
-          "output_type": "execute_result",
-          "data": {
-            "image/png": 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pg8dGKR6s0dRw+DBWrMCHHyISwa23oqwMU6bAbsfWrfD5EArB4YAsw++Hx4NQCC4XAgEE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\nSpT/B3YzNK9+5fqDAAAAAElFTkSuQmCC\n",
-            "text/plain": [
-              "<rdkit.Chem.rdchem.Mol at 0x7f5b900ebb70>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 15
+          "output_type": "stream",
+          "text": [
+            "RDKit ERROR: [02:54:18] non-ring atom 0 marked aromatic\n"
+          ],
+          "name": "stderr"
         }
       ]
     },
@@ -37870,10 +37891,10 @@
       "metadata": {
         "id": "bgzEgQrikP2X",
         "colab_type": "code",
-        "outputId": "40dcba41-3ea2-4640-c54a-4c6b78204899",
+        "outputId": "af8d8715-ba2f-43e2-d224-e0eb3aced572",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 167
+          "height": 34
         }
       },
       "source": [
@@ -37882,17 +37903,11 @@
       "execution_count": 16,
       "outputs": [
         {
-          "output_type": "execute_result",
-          "data": {
-            "image/png": 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PsiesUbKC8+cRE3Omf//ZTU25eXlBQUGiA4nU3NycnZ3t6+sbFBTEB+ztEmuULO0//0FM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\nIX8AAAAASUVORK5CYII=\n",
-            "text/plain": [
-              "<rdkit.Chem.rdchem.Mol at 0x7f5b44380da0>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          },
-          "execution_count": 16
+          "output_type": "stream",
+          "text": [
+            "RDKit ERROR: [02:54:18] non-ring atom 0 marked aromatic\n"
+          ],
+          "name": "stderr"
         }
       ]
     },
@@ -37901,10 +37916,10 @@
       "metadata": {
         "id": "UStW3HMakP2c",
         "colab_type": "code",
-        "outputId": "4ae56cdc-40d8-4488-ced7-476ef0f0318b",
+        "outputId": "7539acf8-29b7-42b4-9d02-532d5afa7d4d",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 34
+          "height": 167
         }
       },
       "source": [
@@ -37913,11 +37928,17 @@
       "execution_count": 17,
       "outputs": [
         {
-          "output_type": "stream",
-          "text": [
-            "RDKit ERROR: [04:14:09] non-ring atom 5 marked aromatic\n"
-          ],
-          "name": "stderr"
+          "output_type": "execute_result",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<rdkit.Chem.rdchem.Mol at 0x7fdf16374760>"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 17
         }
       ]
     },
@@ -37926,7 +37947,7 @@
       "metadata": {
         "id": "z5o3gkGxkP2f",
         "colab_type": "code",
-        "outputId": "9ad1bc04-8fd4-47ce-ac0a-7e200e9ab9f5",
+        "outputId": "eea5235d-48b4-4c76-ac8a-12ae770c57cb",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 167
@@ -37940,9 +37961,9 @@
         {
           "output_type": "execute_result",
           "data": {
-            "image/png": 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+            "image/png": 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\n",
             "text/plain": [
-              "<rdkit.Chem.rdchem.Mol at 0x7f5b44568080>"
+              "<rdkit.Chem.rdchem.Mol at 0x7fdf16374850>"
             ]
           },
           "metadata": {
diff --git a/examples/tutorials/09_Creating_a_high_fidelity_model_from_experimental_data.ipynb b/examples/tutorials/09_Creating_a_high_fidelity_model_from_experimental_data.ipynb
index ced8c7eeb7..a20ab3639f 100644
--- a/examples/tutorials/09_Creating_a_high_fidelity_model_from_experimental_data.ipynb
+++ b/examples/tutorials/09_Creating_a_high_fidelity_model_from_experimental_data.ipynb
@@ -102,20 +102,76 @@
       "metadata": {
         "id": "tbLbuh6wl8tX",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "9b09fbf5-13a7-4fd1-fa5d-9932f28b120b"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  21813      0 --:--:-- --:--:-- --:--:-- 21813\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.79 s, sys: 609 ms, total: 3.4 s\n",
+            "Wall time: 3min 40s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -144,11 +200,11 @@
       "metadata": {
         "id": "fYBi59mkl56F",
         "colab_type": "code",
+        "outputId": "172f8a13-7050-406b-fdc0-db4a58ec2858",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 190
-        },
-        "outputId": "65b62378-47c4-4713-bc0a-668773dbc315"
+        }
       },
       "source": [
         "!pip install pubchempy"
@@ -159,11 +215,11 @@
           "output_type": "stream",
           "text": [
             "Collecting pubchempy\n",
-            "  Downloading PubChemPy-1.0.4.tar.gz (29 kB)\n",
+            "  Downloading https://files.pythonhosted.org/packages/aa/fb/8de3aa9804b614dbc8dc5c16ed061d819cc360e0ddecda3dcd01c1552339/PubChemPy-1.0.4.tar.gz\n",
             "Building wheels for collected packages: pubchempy\n",
             "  Building wheel for pubchempy (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
-            "  Created wheel for pubchempy: filename=PubChemPy-1.0.4-py3-none-any.whl size=13828 sha256=a376d8fd8ab07e38e0db777a6485020d2c1dc3e1630e52ded176bda397359297\n",
-            "  Stored in directory: /root/.cache/pip/wheels/7c/3d/8c/8192697412e9899dc55bbbb08bbc1197bef333caaa2a71c448\n",
+            "  Created wheel for pubchempy: filename=PubChemPy-1.0.4-cp36-none-any.whl size=13825 sha256=bd54eb755f3e83b75a2579701aadc27284d998fe33b1fc1e22342e2c109939d8\n",
+            "  Stored in directory: /root/.cache/pip/wheels/10/4d/51/6b843681a9a5aef35f0d0fbce243de46f85080036e16118752\n",
             "Successfully built pubchempy\n",
             "Installing collected packages: pubchempy\n",
             "Successfully installed pubchempy-1.0.4\n"
@@ -260,11 +316,11 @@
       "metadata": {
         "id": "hVJDAGT8mbl1",
         "colab_type": "code",
+        "outputId": "3665e5d9-91c2-4804-b6e3-a61562471d4a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 309
-        },
-        "outputId": "62d4b33e-2981-48f0-cfe7-fa989240b9f7"
+        }
       },
       "source": [
         "!wget https://github.com/deepchem/deepchem/raw/master/datasets/Positive%20Modulators%20Summary_%20918.TUC%20_%20v1.xlsx"
@@ -274,12 +330,12 @@
         {
           "output_type": "stream",
           "text": [
-            "--2020-03-27 06:12:34--  https://github.com/deepchem/deepchem/raw/master/datasets/Positive%20Modulators%20Summary_%20918.TUC%20_%20v1.xlsx\n",
-            "Resolving github.com (github.com)... 140.82.114.4\n",
-            "Connecting to github.com (github.com)|140.82.114.4|:443... connected.\n",
+            "--2020-05-31 02:53:46--  https://github.com/deepchem/deepchem/raw/master/datasets/Positive%20Modulators%20Summary_%20918.TUC%20_%20v1.xlsx\n",
+            "Resolving github.com (github.com)... 140.82.112.4\n",
+            "Connecting to github.com (github.com)|140.82.112.4|:443... connected.\n",
             "HTTP request sent, awaiting response... 302 Found\n",
             "Location: https://raw.githubusercontent.com/deepchem/deepchem/master/datasets/Positive%20Modulators%20Summary_%20918.TUC%20_%20v1.xlsx [following]\n",
-            "--2020-03-27 06:12:34--  https://raw.githubusercontent.com/deepchem/deepchem/master/datasets/Positive%20Modulators%20Summary_%20918.TUC%20_%20v1.xlsx\n",
+            "--2020-05-31 02:53:46--  https://raw.githubusercontent.com/deepchem/deepchem/master/datasets/Positive%20Modulators%20Summary_%20918.TUC%20_%20v1.xlsx\n",
             "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
             "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
             "HTTP request sent, awaiting response... 200 OK\n",
@@ -288,7 +344,7 @@
             "\n",
             "\r          Positive    0%[                    ]       0  --.-KB/s               \rPositive Modulators 100%[===================>]  41.85K  --.-KB/s    in 0.02s   \n",
             "\n",
-            "2020-03-27 06:12:34 (1.66 MB/s) - ‘Positive Modulators Summary_ 918.TUC _ v1.xlsx’ saved [42852/42852]\n",
+            "2020-05-31 02:53:47 (1.69 MB/s) - ‘Positive Modulators Summary_ 918.TUC _ v1.xlsx’ saved [42852/42852]\n",
             "\n"
           ],
           "name": "stdout"
@@ -318,11 +374,11 @@
         "scrolled": true,
         "id": "ei2QwtnVl57D",
         "colab_type": "code",
+        "outputId": "42c4aa3e-4247-4794-e6c6-c1b884753eb8",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
-        },
-        "outputId": "87434c60-b799-452c-8937-d3afa040efeb"
+        }
       },
       "source": [
         "# preview 5 rows of raw dataframe\n",
@@ -456,11 +512,11 @@
         "scrolled": true,
         "id": "adUjxQF2l57Z",
         "colab_type": "code",
+        "outputId": "acdaa261-e58b-43a3-f2f4-6869ae4ea364",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 119
-        },
-        "outputId": "77ded6f9-9d76-4cae-fa40-af2044096698"
+        }
       },
       "source": [
         "# remove column labels (rows 0 and 1), as we will replace them\n",
@@ -497,11 +553,11 @@
       "metadata": {
         "id": "_AmIYJGjl57j",
         "colab_type": "code",
+        "outputId": "d9cdb418-295a-4ec8-e757-07747257ad81",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
-        },
-        "outputId": "90ccc8b2-cf45-429c-e06a-cf80314ba4de"
+        }
       },
       "source": [
         "# preview cleaner dataframe\n",
@@ -633,11 +689,11 @@
       "metadata": {
         "id": "yfCp2htdl570",
         "colab_type": "code",
+        "outputId": "847e66c8-eb78-42e8-fc03-e35c496685d5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "7f92b5c2-557e-472c-dae4-9c6fcf7d2411"
+        }
       },
       "source": [
         "get_compounds(drugs[1], 'name')"
@@ -663,11 +719,11 @@
       "metadata": {
         "id": "rsesx-l8l58L",
         "colab_type": "code",
+        "outputId": "fd80dfc8-b365-4844-ef9f-f1dc91afef41",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 34
-        },
-        "outputId": "9b135c7f-f4f9-452e-85ea-8dca4a7998a5"
+        }
       },
       "source": [
         "get_compounds(drugs[1], 'name')[0].canonical_smiles"
@@ -780,11 +836,11 @@
         "scrolled": true,
         "id": "PMlMlVJTl59t",
         "colab_type": "code",
+        "outputId": "75129524-31a7-4e54-913a-f262f35d06a3",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 85
-        },
-        "outputId": "6ab99bf6-54b0-4e3b-b077-18d346aa889f"
+        }
       },
       "source": [
         "smiles_map = {}\n",
@@ -801,10 +857,10 @@
         {
           "output_type": "stream",
           "text": [
-            "Errored on 90\n",
             "Errored on 162\n",
-            "Errored on 176\n",
-            "Errored on 303\n"
+            "Errored on 237\n",
+            "Errored on 303\n",
+            "Errored on 399\n"
           ],
           "name": "stdout"
         }
@@ -830,11 +886,11 @@
       "metadata": {
         "id": "xV3mQWwrl5-v",
         "colab_type": "code",
+        "outputId": "af23230b-9000-4e62-f6e7-13195c0f25c2",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 204
-        },
-        "outputId": "281093cd-868d-4ea5-d355-5a4b3524da46"
+        }
       },
       "source": [
         "# preview smiles data\n",
@@ -987,7 +1043,11 @@
       "metadata": {
         "id": "Xe0sqLZ0l5-6",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 0
+        },
+        "outputId": "4e0be4fd-83a4-40c8-d636-153bc81943ae"
       },
       "source": [
         "import matplotlib.pyplot as plt\n",
@@ -996,8 +1056,17 @@
         "import seaborn as sns\n",
         "sns.set_style('white')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
+            "  import pandas.util.testing as tm\n"
+          ],
+          "name": "stderr"
+        }
+      ]
     },
     {
       "cell_type": "code",
@@ -1058,11 +1127,11 @@
       "metadata": {
         "id": "HZjb8u_fl5_S",
         "colab_type": "code",
+        "outputId": "d4ff3ed7-3580-4f50-f1b5-1a04b35bef1d",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 300
-        },
-        "outputId": "4c61f9b4-ad8b-489f-ed47-0ce49e8af281"
+          "height": 0
+        }
       },
       "source": [
         "smiles_lens = [len(i) if i is not None else 0 for i in smiles_data['drug']]\n",
@@ -1087,7 +1156,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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wk5/8hCNHjvAv//Ivnqyvk5ycHP72t7/xpz/9ic8//5xt27YN6Nf3Z4UVFlKjgr0+oqpd\ne1+L9HMIMbj0alr1+++/n1OnTjF79mxeeukl4uPjAZgxYwbz5s3rch+j0Uh5ebn7sdlsxmg0dtqm\nrKyMhIQEHA4HFouFqKju14BoP0ZoaCi33347BQUFzJkzpzenIXpQZLYwyjg4rjYAokP0xIcZJDiE\nGGR6dcXxgx/8gHfffZd//dd/dYeGzda2tOfmzZu73CczM5Pi4mLOnDmDzWYjNzeXrKysDttkZWWx\nZcsWALZv386kSZNQFOWidTgcDmpq2tZ/sNvt7N69m4yMjN6cguiB3eniVNXgGFF1obFJ4Rwpk+AQ\nYjDp1RXH2rVrmTZtWofnfvjDH7rf9Ls8sE7HqlWrWLJkCU6nk/nz55ORkcG6desYN24c06dPZ8GC\nBaxcuRKTyURERARr1qxx75+VlUVjYyN2u53333+fV155haSkJJYsWYLdbsflcnHDDTfwgx/84Due\nurhQcVUTdqc6qK44AMYkhZNfVEWr3Tng658LIbrWbXBUVlZiNptpbW3lyJEj7o7oxsZGWlp6XqFt\n2rRpnQJn+fLl7s8NBgPPP/98l/vu2rWry+cvdoUjLk1RRdscVRmD4Oa/C41NisDpUikyN5KZEuHt\ncoQQ9BAcH374IZs3b6a8vJz/+q//cj8fEhLCQw895PHixMApNFvQDKIRVe3GJIYDcKSsXoJDiEGi\n2+CYO3cuc+fOZfv27WRnZw9UTcILisyNDIsOHlTNQQ6nC41GIViv5R+nargxPbbD62EGHRHBei9V\nJ8TQ1W1wbNu2jdmzZ3P27FleffXVTq8vXrzYY4WJgVVotgy6jvEWu4sDJXXEhRn4vLiW/MKqDq9P\nHRUrwSGEF3QbHO39GM3NzQNSjPAOm6NtRJVpjLHnjb0gKSKIz0tqcakqmm5G3QkhBka3wXHHHXcA\nbXNVCf9VXN2Ew6UOmqlGvi0xIhCbw0VNk43YUJmGRghv6zY4nnrqqW53fuKJJ/q1GOEd7Uu0Zgyy\nobjtks6vzXGurkWCQ4hBoNvgGDt27EDVIbyo0NyIRoGRcYMzOOLDDGgUKKtv5coUb1cjhOhxVJXw\nf0VmC2kxIYNqRNWFdFoNxvBAyup7vndICOF53QbH008/zc9//nN++tOfdvn6Sy+95JGixMAqNFsG\nzRocF5MYEUiRudHbZQgh6CE4Zs+eDTDgM+CKgWN1OCmubua2cYneLqVbiRFBfFFSh6XVTlhggLfL\nEWJI6zY4xo0bB8B1112HzWbj5MmTKIrC8OHD0etl/Lwvq2+2YbE6OFHZiNOlEhOqp7T2m2HXVrvT\ni9V1lhgZCLT1c0hwCOFdvZrkcPfu3fziF79g2LBhqKpKaWkp//mf/9lpHirhOyxWB/mFVRSU1gFQ\n02TrcIPdhGGR3iqtS0kR34ysGqzDhoUYKnoVHL/61a94/fXXSUtLA6CkpIR7771XgsMPmBusKEDc\nIB/mGhigJSo4gLL6Vm+XIsSQ16v1OEJCQtyhAZCamkpISIjHihIDp8LSSkyoHp2214tBek1SZBDn\n6mRklRDe1u0Vx44dO4C2vo577rmH2267DUVReO+998jMzByQAoVnmRusxIcFeruMXkmMCOTIuQas\ndieGQTp0WIihoNvg+OCDD9yfx8bG8umnnwIQHR2N1Wr1bGXC4xxOFzVNVjKTw71dSq8kRQShAuUN\nraTFyBWvEN7SbXBcuAaH8D+VjVZcKsSH+8YVR/vUI2frWiQ4hPCiXnWOW61WNm3aRFFRUYcrDQkW\n31bR0Pa9NPpIU1V4UADhgTpKa6WfQwhv6lWP6MqVK6msrOTDDz/kuuuuw2w2S+e4HzBbWtEoEBvq\nO/fkJEcFS3AI4WW9Co6SkhJWrFhBUFAQc+fOZf369RQUFHi6NuFhFQ1WYkIMPjGiql1KVBBVjVZa\nB9kNikIMJb16x9Dp2lq0wsPDKSwsxGKxUF1d7dHChOeZG1qJDx/c9298W8r5fg656hDCe3rVx/HD\nH/6Q+vp6li9fztKlS2lubmb58uWerk14kNXhpKbJxvjUwXWHeE+So853kNfKqpRCeEuvgmPhwoVA\n25xVeXl5Hi1IDIyS6mZU2ta68CXBeh3RIXpK5UZAIbymV8FRW1vL//zP//DFF1+gKAoTJ07kvvvu\nIyoqytP1CQ85Vd32F7uvDMW9UEpUEKer5YpDCG/pVR/HQw89RHR0NM8//zzr1q0jKiqKBx980NO1\nCQ86VdXkcyOq2qVEBVPfYqemyebtUoQYknoVHJWVldx///2kpqaSmprKfffdJ53jPq64qomYUAM6\nje+MqGrX3kF+tKzBy5UIMTT16l3jxhtvJDc3F5fLhcvl4t1332XKlCmerk140PGKRhJ8sJkK2u4g\nV5DgEMJbuu3jmDBhAoqioKoqr732GitXrgTA5XIRHBzMI488MiBFiv5labVTVt9KZnKEt0v5TvS6\ntjXIj5RZvF2KEENSt8Fx4MCBgapDDKBj5W1vuAkRvnnFAZAaHcyRcw24XCoajeLtcoQYUno1qgog\nLy+Pzz77DGgblnvLLbd4rCjhWUfbg8NHm6oA0mKC+bS4hsIKC5cn+MbsvkL4i171ceTk5PD6668z\ncuRIRo4cyeuvv85zzz3n6dqEhxwtayDUoCMiyHfX7k6LDgbgs+JaL1cixNDTqyuOPXv2sG3bNjTn\nR+DMnTuXOXPm8LOf/cyjxQnP+LqsgfT4UBTFd5t4okP0RIfo+fx0LT+elNbzDkKIftPrsZgNDd+M\nYLFYpFPSV7lcKl+XW0iP9+3ZjRVFITM5gs9O13i7FCGGnF5dcfz0pz9l7ty5XH/99aiqyqeffsq/\n//u/e7o24QFnaptptjlJjwv1dimXLDM5nD2FlVQ0tPrkHfBC+KoerzhcLheKovDWW29hMpm49dZb\neeutt5gxY0aPB8/Pzyc7OxuTycTLL7/c6XWbzcaKFSswmUwsXLiQ0tJSoG2Kk0WLFjFhwgRWr17d\nYZ9Dhw4xa9YsTCYTTz31FKqq9vZcBXD0/BDWkfF+EBwpbcOJPzst/RxCDKQeg0Oj0bBhwwbi4+OZ\nPn0606dPJy4urscDO51OVq9ezYYNG8jNzeWdd97h+PHjHbbZuHEj4eHh7Ny5k7vuuoucnBwADAYD\ny5cv5+GHH+503CeffJJf/vKX7Nixg+LiYvLz83t7roK2jnGNAsNjfbupCmCUMQyDTiMd5EIMsF71\ncUyePJk//OEPlJWVUVdX5/7oTkFBAWlpaaSmpqLX65k5c2anmXV37drF3LlzAcjOzmb//v2oqkpw\ncDDXXHMNBkPHmVsrKipobGzkqquuQlEU5syZI7P19tHX5Q1cFhtCYIDW26VcsgCthvGpkXwu/RxC\nDKhe9XG8++67KIrCn//85w7Pd/embTabSUhIcD82Go2dVg00m80kJia2FaLTERYWRm1tLdHR0b06\nZkJCAmazuTenIM47Wmbx2TvGu3JNWhQv55+k2eYgWN/r25KEEJegV1cc7777Lj/60Y+4/PLLueKK\nK1i0aBG5ubmerk30s0arg5KaZq5IDPN2Kf3muuHROFwqn0s/hxADplfB8cgjj3DixAkWLVrEj3/8\nY44fP97jPFVGo5Hy8nL3Y7PZjNFo7LRNWVkZAA6HA4vF0u0aH98+Znl5eadjios7cq5tSPWYJP+5\n0/ray6LRaRQ+OiGzNQsxUHp1bV9UVMS7777rfjxp0qQeR1VlZmZSXFzMmTNnMBqN5ObmdrrbPCsr\niy1btjBhwgS2b9/OpEmTur0pLT4+ntDQUA4ePMj48ePZunUrixYt6s0pCKCgtK1falxyBDaHy8vV\n9I8Qg46rUiMlOIQYQL264hgzZgwHDx50P/7yyy8ZN25ct/vodDpWrVrFkiVLmDFjBrfddhsZGRms\nW7fO3TeyYMEC6urqMJlMvPrqqx3uDcnKyuJXv/oVW7ZsYerUqe4RWb/4xS944oknMJlMDBs2jKlT\np/b5pIeqQ2frSQgPJD7Mv+55mJwey1elddS32L1dihBDQq+uOA4fPswdd9xBUlISAOfOnWP48OHM\nmjULgL/97W9d7jdt2jSmTZvW4bnly5e7PzcYDDz//PNd7rtr164un8/MzOSdd97pTdniWwrO1rvv\nffAnk0fG8HxeEZ+cqsE0RpouhfC0XgXHhg0bPF2H8DBLq51TVU3MuSrZ26X0uwnDIjHoNOw7XiXB\nIcQA6FVwJCf735vNUHP4XAOqil9ecRh0Wq69LJr90s8hxIDwvQWnxXdy6Gw9gF/dw3GhyekxHDNb\nqLRYvV2KEH5PgmOIKCitJykikNhQQ88b+6DJI2MB2H9SrjqE8DQJjiHiq7P1jPPTqw2AcUnhhAfq\n2FtY6e1ShPB7EhxDQMP5jvEr/bB/o51Oq+GmUXHsLqzE5ZIZk4XwJAmOIaC9f8OfrzgAskbHU2mx\ncvhcQ88bCyG+MwmOIeCrUv/uGG83bXQcigIfHKvwdilC+DUJjiHgQEkdqdFBxPhpx3i72FADV6ZE\nsutrCQ4hPEmCw8+pqsoXJbVcPezik0f6k6zR8XxZWkd1owzLFcJTJDj83Nm6Fios1qETHJfHo6qw\nR0ZXCeExEhx+qr7ZRmltM+8faVvoKiUqiNLaZveH1e70coWeMTYpnLgwgzRXCeFBsmSan7JYHeQX\nVrH9iJkArcK5ulbMDd8030wYFunF6jxHo1G4ZXQcfz9Ujs3hQq+Tv42E6G/yW+XnSqqbSYkKRqu5\n+Don/ub74xKwtDrYd7zK26UI4ZfkisOP2Z0uyupbuCkjztuleITD6aK0trnT88NjQwg16Nhy4Cy3\nXB7vhcqE8G8SHH6stLYFlwrDooO9XYpHtNhdHDhR0+VrGfGh7Pq6QpqrhPAA+Y3yY2dq2v4aT/XT\n4OjOuOQIGq0OPjohzVVC9DcJDj9WUtNMTIieUMPQu7DMiA8lRK/l3a/KvF2KEH5HgsNPqarK6Zpm\nv22m6olOq2FKRiw7jpixO13eLkcIvyLB4afO1LTQZHVwWWyIt0vxmptHx1HXbOdDGV0lRL+S4PBT\nB8/UATA8ZugGx/XDY4gMDuDtz0u9XYoQfkWCw099WVpHqEFHTKje26V4jV6nYfb4JHYcMVPfbPd2\nOUL4DQkOP6SqKgdK6rgsNgRFGTo3/nVl4TWp2Bwu/vrlWW+XIoTfkODwQ6W1bRMbDo8Zmh3jFxqb\nFM7lCWFskuYqIfqNBIcf+uRU201xw2NDvVyJ9ymKwsJrUvmytJ5Cs8Xb5QjhFyQ4/NAnp2oIC9QR\nH+7fCzf11pyrktBpFP7v0zPeLkUIvyDB4Yc+Ka5hfEokmiHev9EuJtSAaYyRTV+U0mLzz+nkhRhI\nEhx+pqKhlVNVTYxP9e/1xfvqrsmXUddsZ+tB6SQX4lJJcPiZj8/3b0xI9c/1Nr6r64ZHMyYxnD/u\nK0ZVVW+XI4RPk+DwMx8dryIsUEeGMczbpQwqiqJw142XccxsYf/Jam+XI4RPk+DwM/tOVDFpRMyQ\nWript/5pfBLRIXpe3Vfs7VKE8GkSHH7kTE0zZ2pauHFkjLdLGZQCA7T883XDeP+omVNVTd4uRwif\nJcHhR9rXnrgxPdbLlQwO7SsEXvhx61gjAVoNz+04RmltM/XNNm+XKYTPGXoLNfixfceriQszkB4f\nytm6Fm+X43UXWyHw6mGR/P2rcsYkhvNPVyURETx05/MS4ruQKw4/oaoqH52oZvLImCE/P1VPbsqI\nQ0Vlr0y3LsR34tHgyM/PJzs7G5PJxMsvv9zpdZvNxooVKzCZTCxcuJDS0m/mE1q/fj0mk4ns7Gz2\n7t3rfj4rK4tZs2Yxe/Zs5s2b58nyfUqhuZGqRis3jpRmqp5EBeu5KjWKz4prqG2Spioh+spjTVVO\np5PVq1fz6quvYjQaWbBgAVlZWaSnp7u32bhxI+Hh4ezcuZPc3FxycnJYu3Ytx48fJzc3l9zcXMxm\nM4sXL2b79u1otVoAXnvtNaKjoz1Vuk/ad/6v58np0jHeG9NGxXGgpJY3PykhM0XueRGiLzx2xVFQ\nUEBaWhqpqano9XpmzpxJXl5eh2127drF3LlzAcjOzmb//v2oqkpeXh4zZ85Er9eTmppKWloaBQUF\nnirVL3x0ooph0cGkRMmMuL0RF2ZgfGokb39xloqGVm+XI4RP8VhwmM1mEhIS3I+NRiNms7nTNomJ\niQDodDrCwsKora3tcd+773PQelYAABcZSURBVL6befPm8dZbb3mqfJ9idTj56EQ1U0dJM1VfTL88\nHodL5X8+OO7tUoTwKT43qurNN9/EaDRSXV3N4sWLGTFiBNdee623y/Kqz4trabY5mTYq3tul+JSY\nUAMzxiXw53+UMGt8IokRQZ22CTPoZNSVEN/isSsOo9FIeXm5+7HZbMZoNHbapqysDACHw4HFYiEq\nKqrbfdv/jYmJwWQySRMWsKewkgCtwg1y41+fLbwmFYBfvXuM/MKqTh8Wq8PLFQox+HgsODIzMyku\nLubMmTPYbDZyc3PJysrqsE1WVhZbtmwBYPv27UyaNAlFUcjKyiI3NxebzcaZM2coLi7myiuvpLm5\nmcbGRgCam5vZt28fGRkZnjoFn7GnsJJr0qIJNfjcBaTXxYUZuH54NF+U1FJhkb4OIXrDY+80Op2O\nVatWsWTJEpxOJ/PnzycjI4N169Yxbtw4pk+fzoIFC1i5ciUmk4mIiAjWrFkDQEZGBrfddhszZsxA\nq9WyatUqtFot1dXV3H///UDbqK3bb7+dqVOneuoUBrX6ZhsWq4NKi5Wvyy3cd/NISmub3a9b7bLu\nRG9NGx3Pp6dryTtawZ3XDfN2OUIMeh79E3XatGlMmzatw3PLly93f24wGHj++ee73Hfp0qUsXbq0\nw3Opqan89a9/7f9CfZDF6iC/sIrPitvujNZpNOQXfnND24RhMsS0t0INOm4cGcMHxyqZVtdCUmTn\nvg4hxDfkznEfV1jRSHigDqMsE3tJpqTHERigYecRc88bCzHESXD4MKdL5XiFhQxjmEwzcomC9Fqm\nZcRxzGzhdLXMnCtEdyQ4fNjp6iZa7S5GyaJN/eKGkbGEBer4+6FyWSVQiG5IcPiwI2UN6DQKo4yh\n3i7FL+h1GkxXGCmpaebQuQZvlyPEoCXB4aNUVeXIuQbS40Mx6LTeLsdvXJ0WhTHcwPbD5ThcLm+X\nI8SgJMHhowrNjdS12BmbFO7tUvyKRlG4bVwiNU02/nGy81oeQggJDp+VX1iJAoxOkODobxnxoWTE\nh5L3tZkamXZdiE4kOHzU3qIqLosNkbvFPUBRFG6/Mgm7Q+XF3Se8XY4Qg44Ehw8qrmriZFUTYxLl\nasNT4sIMTMmI5e+Hyvm0WJqshLiQBIcP+vuhtgkgx0j/hkfdMjoeY7iB/9h6CLtTOsqFaCfB4WNU\nVWXzF6VkJocTJdN9e5Rep+HB743i63ILa98v9HY5QgwaEhw+5quz9RRVNPL9cYneLmVImJIRyw+v\nSeV3u0/wj5PV3i5HiEFBgsPHbPq8FINOQ9blcd4uZchYNWsMadHBPPjWQeqb7d4uRwivk+DwIVaH\nk20Hz5E9NoGwwABvlzNkhBh0rLtjAhUWK/f9+XOsDpmyXgxtEhw+JO9oBfUtduZPTPF2KUPO+NRI\nnp1/JfuOV7PiLwdxumQuKzF0SXD4kE2fl2IMNzAlPdbbpQxJ8yem8B+3j+Hvh8p5eFMBNoeMtBJD\nk9w95iOOVzTywbEKlt2SjlYjU6h7y91ThtPY6mDN+4WcqGzkdz+6utuFn9pXaryYMIOOCBkdJ3yM\nBIePeGnPCQw6DXdNvszbpQx5y7+XwShjKCs3FTDz+b08/P3LWTgxBZ228wV8+0qNFzN1VKwEh/A5\n0lTlA0prm9l64Cx3XjeMmFBZ6W8wuC0zkb8uu5ERcaE8tvkrstfm807BOen7EEOCBIcPeDn/JIoC\n99w0wtuliAuMiAtl009vYP2iiQAs+/MBbs75gNf3F9Nql5FXwn9JcAxyFQ2t/OXTM8ybkNJtW7rw\nDkVRyB6bwI4Hp/HSjycSF2pg1bbD3Pyb3fzvx6dlqhLhl6SPYxBTVZVV2w6DCktvHuntckQ3tBqF\n749L4PvjEvjoRBW/3VHIf2w9xMi4EL4/NpHkKAl94T/kimMQy/2qjPcOl/OgaRSXxYZ4uxzRS5NH\nxrLxpzfw+59cQ32LnRf3HGfnETMuWcdc+AkJjkGqqtHKqm2HGZ8ayT03Dfd2OaKPFEXBNMbI/959\nHeNTIvngWAVvfHwaq/R9CD8gwTEItdqdPPjWQRpbHeQsuLLLYZ7CN4QFBrBgYgqzxidRaLbw4p4T\n1DbLqoLCt0kfh5dc7MYwq93JY5u/4tPiWn694EoyjGFeqE60czhdlNY2X/R1nQa6u4HcaneiKAo3\njIghLtTAnz85zUt7TvD/brhMBjsInyXB4SVd3RhmabWz8bNSTlQ28uhtl7PwmlQvVSfatdhdHDhx\n8RUAJwyL5EBJXbevt0uPD+Vfp47kjx8V8/u9J/nR9WlMHSXTxwjfI20gg4Cqqnx+uoa17xdxqrqJ\n+VenMPNKWW/DHxnDA/nptJFEBev540en2H643NslCdFncsXhRVaHkwMldXx8spoKi5XLYoKZMyGZ\n+LBAb5cmPCgiKIB7p47gjY9P88t3jtJqd7F02kg0MgeZ8BESHF5wurqJ331wnG1fnqPV7iI5MoiF\nE1MYnxqJRml78+ipbV1G5/i2wAAtd02+jD1Flfxm+zH2Ha/i2flXkhod3GE7mSSxf8j/Y/+S4BhA\nX5TUsn7PCXYcMaNRFMYmhTN5RAyp0cEoSse/NnvTti58m06r4clZY/jeFUaezj1K9tp8fjwpjYUT\nU9yDIi7sC3OpKg0tdmqabDS0OmhstZMWG0x0sIFgg5akiCCSIoNIiwkmMEDrzVMbdGSyyf4lwTEA\nDpTU8pvtx/joRDXhgTruu3kkpiuMHCmzeLs04WWKonDndcOYOiqOZ3KP8sqHp3g5/yRpMcEkRgQS\nqNNSUttMY6uD2mYbdmfPNxEqCqRGBZMeH8rIuBDSYkKICzMQG2ogNlRPbKiBEIP//+o7XSpnapop\nqmiktLaZ4qomQg0BJEQEytIEl8j/f3q86HiFhd9sP8b2w2ZiQvQ8MfMK7rxuGCEGXVszlASHOC85\nMogXfnQ1VY1Wth44y4GSOiosrZyqakIFYkMNZMSHEhtmICbEQHigjrDAACaPjEarUbBYHVQ0WClv\naKWkppmS6mZO1zTz4fGqLhecCgzQEBNiICZUT0J4IMaIQNLjQhiXHIExvGMfmy8149idLvYWVfLX\ng+d4/2gFjV00Txl0GobHhjAmMZzM5AgMcnXWZxIcHnCmppnn84p4+4tSgvU6HjKN4l+mDCd0CPyV\nJy5NbKiBJRfMglxa29xtEwuKwmenvxkObNBpyYgPIyO+ranLpaqMTQonQKuhqtFKVaONqkYr1Y1W\nqptsVFqsHClr4P2jZtpnhI8ICmBkXAgj40IZGRfK7eMTB31wVDdaefOTEv7349OYG6xEBAUwMzOR\nq9MiyTCG0Wx18OHxauqabZysaqLIbOHrcgt/KzjH2KQIggI0JEYEyZVIL3n0nSw/P5+nn34al8vF\nwoULuffeezu8brPZePjhhzl8+DCRkZGsWbOGlJS29bTXr1/Ppk2b0Gg0PPHEE9x00029OqY3Ha9o\n5KU9J9hy4CxajcK/3Dic+25JJzpkcP/SCf+lURTiwgykRAVfdJvS2mY++LqSsvoWSmqaKa5u5uty\nC1+cvz/lL5+WMG1UHJPTY5k0IoaIoICBKr9Hh87W88ePivnrl+ewOVzclBHLL2eP4+bR8eh139xt\nUFrbTElNC8Oig7kyJRJVbWvG+qKkjoKzdTz4f1/y7HvHmHt1MvOvTiE9PtSLZzX4eSw4nE4nq1ev\n5tVXX8VoNLJgwQKysrJIT093b7Nx40bCw8PZuXMnubm55OTksHbtWo4fP05ubi65ubmYzWYWL17M\n9u3bAXo85kCraGjlg2MVbPyslM9O1xIYoOEnN6Rx79QRJEbIncHC+3ozQk+rUUiJCiYlKpjJI9uu\nVMrrWzlR2UhNk43/+6yU1/afBsAYbiAtJoTYUD1BATp0mrbZgQMDtAQGaDHoNBh0GvQBWoIDtCRH\nBjEiPpS4UEOHN/PvQlVVTlQ2seNIOX89eI6vyy0E67XMvSqJWeOT3JOBVlhaO53jhRRFYVhMCMNi\nQtz3TO0+VsHL+Sd5cfcJRsSGcGVKBFckhhMbaiAqJABFUbA7XNidKnanC5vThd3pwu5w0dBqp9Hq\nwOlS0SgKGo2CVlHQKKDRKOi1CqBc8Frb8xpFQatRCNFrCQsMQKtRCNJriQ8zEBcWSHigrtPAmcHA\nY8FRUFBAWloaqaltdz/PnDmTvLy8Dm/yu3btYtmyZQBkZ2ezevVqVFUlLy+PmTNnotfrSU1NJS0t\njYKCAoAej9mfVFXFYnXQ0GKnocWBpdVOXYud4qomTlQ2cqCkjqKKRgBGxIXw6G2Xs2BiCrGySp8Y\nRL7LCD2NopAU2TZKa+qoWOLDAjlQUstnp2s5WdnE6eomjpVbaLE5abQ6aLY5cfRi9cOo4ACM4YHE\nhwdiDDNgDA/EGG4gKkRPqEFHsF5H29RsCla7k4ZWB9VNVkqqmzlZ1cQXp2upbmqb6+vqYZE8OWsM\nc69OwdJqJ7+wipKall6fY7sArYapo2L5f5Mvo8LSyl8PnuPjkzV8fLKGrQfP9XhOnmTQaYgLM5wP\nEgPxYYHffB5uIC40kMjggPNhrUWv06DXaTze5Oax4DCbzSQkJLgfG41G95v/hdskJralvU6nIyws\njNraWsxmM+PHj++wr9lsBujxmP3p3zcW8PYXpV2+Fhuq54rEcOZPTGFKeixjk8IH5V8GQvQHvU7D\n9SNiuH5ETKfX2vthXGrbX+J2p4rj/L9Wh5O0mGBcKlQ0WDFbWqloaKXCYqWw3EJlo7VXy+3qtRpS\no4O4eXQ81w2P4sb02A7Nb5ZWe7+cZ3xYIEtuGuHuZ2potVPTaKPm/MSUeq2GAG3bm7NOo2DQtT2u\ntLTy8ckaNBoFl6qiqnT4d2xSOF+W1nd6vu2jLdjiQg04XSpNVgeVjVYqLVYqLFYqGlqpbLRysrKJ\nf5yqoa659+c6Ljmcd/7tpn75v7nQkOitPXv2LPPmzftO+17dzWtNwI7zH/1t0yB/fTDUMBTOoSeD\n4Rx78nEXz+mApPMffXHq/MdbfdzP0+fYk3d7eP3DXhwj7PxHnxTCvA/W9HUvAKKiooiKiuryNY8F\nh9FopLz8m3l4zGYzRqOx0zZlZWUkJCTgcDiwWCxERUV1u29Px+zKP/7xj0s9HSGEEOd5bJLDzMxM\niouLOXPmDDabjdzcXLKysjpsk5WVxZYtWwDYvn07kyZNQlEUsrKyyM3NxWazcebMGYqLi7nyyit7\ndUwhhBCe5bErDp1Ox6pVq1iyZAlOp5P58+eTkZHBunXrGDduHNOnT2fBggWsXLkSk8lEREQEa9a0\nXVJlZGRw2223MWPGDLRaLatWrUKrbbtJp6tjCiGEGDiKqspCyEIIIXpP1uMQQgjRJxIcQggh+kSC\nY4Dl5+eTnZ2NyWTi5Zdf9nY5HpeVlcWsWbOYPXu2e0h0XV0dixcv5tZbb2Xx4sXU19d7ucpL99hj\nj3HDDTdw++23u5+72HmqqspTTz2FyWRi1qxZHD582FtlX7Kuzvu///u/uemmm5g9ezazZ89mz549\n7tfWr1+PyWQiOzubvXv3eqPkflNWVsaiRYuYMWMGM2fO5LXXXgOGxvcdVQwYh8OhTp8+XS0pKVGt\nVqs6a9YstaioyNtledQtt9yiVldXd3ju2WefVdevX6+qqqquX79e/fWvf+2N0vrVJ598oh46dEid\nOXOm+7mLnefu3bvVu+++W3W5XOqBAwfUBQsWeKXm/tDVeT///PPqhg0bOm1bVFSkzpo1S7VarWpJ\nSYk6ffp01eFwDGS5/cpsNquHDh1SVVVVLRaLeuutt6pFRUVD4vsuVxwD6MJpWPR6vXvKlKEmLy+P\nOXPmADBnzhzef/99L1d06a699loiIiI6PHex82x/XlEUrrrqKhoaGqioqBjwmvtDV+d9Md1NJeSL\n4uPjGTt2LAChoaGMGDECs9k8JL7vEhwDqKtpWNqnUvFnd999N/PmzeOtt9ru962uriY+Ph6AuLg4\nqqurvVmex1zsPL/9c5CQkOB3Pwd/+tOfmDVrFo899pi7qcaff/5LS0s5evQo48ePHxLfdwkO4VFv\nvvkmW7Zs4fe//z1/+tOf+PTTTzu8rijKkJjja6icJ8Cdd97Jzp072bZtG/Hx8fzqV7/ydkke1dTU\nxAMPPMDjjz9OaGjH6dj99fsuwTGAejMNi79pP7+YmBhMJhMFBQXExMS4L9ErKiqIjo72Zokec7Hz\n/PbPQXl5uV/9HMTGxqLVatFoNCxcuJCvvvoK8M+ff7vdzgMPPMCsWbO49dZbgaHxfZfgGEBDbcqU\n5uZmGhsb3Z/v27ePjIwMsrKy2Lp1KwBbt25l+vTp3izTYy52nu3Pq6rKwYMHCQsLczdt+IML2+3f\nf/999+wOF5tKyFepqsrPf/5zRowYweLFi93PD4Xvu9w5PsD27NnDM888454yZenSpd4uyWPOnDnD\n/fffD7Qt7HX77bezdOlSamtrWbFiBWVlZSQlJbF27VoiIy++XoIveOihh/jkk0+ora0lJiaGf/u3\nf+N73/tel+epqiqrV69m7969BAUF8cwzz5CZmentU/hOujrvTz75hK+//hqA5ORkVq9e7X6DfPHF\nF3n77bfRarU8/vjjTJs2zZvlX5LPPvuMH/3oR4waNQqNpu1v8Iceeogrr7zS77/vEhxCCCH6RJqq\nhBBC9IkEhxBCiD6R4BBCCNEnEhxCCCH6RIJDCCFEn0hwCCGE6BMJDiG6MGHChEva/+mnn+40vcp3\ncc8999DQ0HBJNT344IMUFxdfci1CtJPgEKKf1dbW8uWXX3Lttdde8rF+//vfEx4efknHuPPOO9mw\nYcMl1yJEO523CxBisNuwYQN///vfsdlsmEwmHnjgAUpLS7nnnnuYOHEiBw4cwGg08rvf/Y7AwEB2\n7NjBTTfd5N4/JyeHXbt2odVqmTJlCo888giPPvooBoOBo0ePUl1dzTPPPMPWrVs5ePAg48ePd08M\nmJWVxaZNmzrN59VVTc3NzaxYsYLy8nJcLhf33XcfM2bM4JprruHRRx/F4XCg08mvvLh08lMkRDc+\n/PBDTp8+zaZNm1BVlaVLl/Lpp5+SmJjI6dOn+e1vf8tTTz3F8uXL2b59O7Nnz+aLL74gOzsbaLv6\n2LlzJ++99x6KoribnQAaGhp46623yMvLY+nSpbz55ptkZGSwYMECjh49yhVXXNGnmmpqaoiPj3ev\nLGmxWADQaDSkpaXx9ddfM27cOA//j4mhQJqqhOjGvn372LdvH3PmzGHu3LmcPHnS3V+QkpLifnMf\nO3YsZ8+eBaCystJ9hRAWFobBYODxxx9nx44dBAYGuo99yy23oCgKo0ePJjY2ltGjR6PRaEhPT3cf\nqy81jRo1io8++ojf/OY3fPbZZ4SFhbn3iY6O9tlFg8TgI1ccQnRDVVXuvfde7rjjjg7Pl5aWotfr\n3Y+1Wi1WqxUAg8Hg/lyn07Fp0yb279/Pe++9xxtvvMHrr78O4N5fUZQOx9JoNDgcjj7XBLB582b2\n7NnD2rVrmTRpEsuWLQPAZrN1CC0hLoVccQjRjSlTpvD222/T1NQEtK0h0dOKhSNHjqSkpARoW+TH\nYrEwbdo0Hn/8cY4dO+axmsxmM0FBQcyePZu7776bI0eOuPcpLi52T28uxKWSKw4hujFlyhROnDjh\n/us+ODiY3/zmN+5ptLty880385e//IWFCxfS1NTEfffd574CefTRRz1W0+nTp/n1r3+NRqNBp9Px\n5JNPAlBVVYXBYCAuLu6Sv7YQINOqC+ERd955J+vXr7/kobT94Y9//CMhISEsXLjQ26UIPyFNVUJ4\nwKOPPsq5c+e8XQbQ1kE/d+5cb5ch/IhccQghhOgTueIQQgjRJxIcQggh+kSCQwghRJ9IcAghhOgT\nCQ4hhBB98v8BnLWiiLdEExkAAAAASUVORK5CYII=\n",
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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1130,11 +1199,11 @@
       "metadata": {
         "id": "FDX7tagnl5_e",
         "colab_type": "code",
+        "outputId": "c8ff34b3-0299-4302-ec80-5e2dfde22606",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 210
-        },
-        "outputId": "6bea8243-2342-419f-8d74-f808077248f2"
+          "height": 0
+        }
       },
       "source": [
         "# look\n",
@@ -1145,9 +1214,9 @@
         {
           "output_type": "execute_result",
           "data": {
-            "image/png": 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27FcPUlFR4OgIUimYmMCmTdClS+6WJ0+etGrVKiUlJV9iHGNj4169evn7+6ui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\nJIQQQgghhBAN9T/sQ73b2oxTjAAAAABJRU5ErkJggg==\n",
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\n",
             "text/plain": [
-              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1200x200 at 0x7FA195789278>"
+              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=1200x200 at 0x7F460CEF1EB8>"
             ]
           },
           "metadata": {
@@ -1200,11 +1269,11 @@
       "metadata": {
         "id": "H5wkbrWgl5_n",
         "colab_type": "code",
+        "outputId": "243bea73-449c-4d9a-a99a-edbf1fea1492",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 452
-        },
-        "outputId": "8fe2d8bc-9201-457a-b9ef-3f2695a408e3"
+          "height": 0
+        }
       },
       "source": [
         "nan_rows = smiles_data[smiles_data.isnull().T.any().T]\n",
@@ -1245,11 +1314,6 @@
               "      <td>-7.8266</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>90</th>\n",
-              "      <td>-5.49647</td>\n",
-              "      <td>-9.7844</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
               "      <th>162</th>\n",
               "      <td>-12.8456</td>\n",
               "      <td>-11.4627</td>\n",
@@ -1260,11 +1324,6 @@
               "      <td>-6.61225</td>\n",
               "    </tr>\n",
               "    <tr>\n",
-              "      <th>176</th>\n",
-              "      <td>-13.4039</td>\n",
-              "      <td>-11.3516</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
               "      <th>187</th>\n",
               "      <td>NaN</td>\n",
               "      <td>-8.23326</td>\n",
@@ -1275,6 +1334,11 @@
               "      <td>NaN</td>\n",
               "    </tr>\n",
               "    <tr>\n",
+              "      <th>237</th>\n",
+              "      <td>30.8369</td>\n",
+              "      <td>6.16932</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
               "      <th>262</th>\n",
               "      <td>NaN</td>\n",
               "      <td>-12.8788</td>\n",
@@ -1304,6 +1368,11 @@
               "      <td>NaN</td>\n",
               "      <td>-8.78722</td>\n",
               "    </tr>\n",
+              "    <tr>\n",
+              "      <th>399</th>\n",
+              "      <td>-1.45559</td>\n",
+              "      <td>-6.47666</td>\n",
+              "    </tr>\n",
               "  </tbody>\n",
               "</table>\n",
               "</div>"
@@ -1311,18 +1380,18 @@
             "text/plain": [
               "          n1       n2\n",
               "62       NaN  -7.8266\n",
-              "90  -5.49647  -9.7844\n",
               "162 -12.8456 -11.4627\n",
               "175      NaN -6.61225\n",
-              "176 -13.4039 -11.3516\n",
               "187      NaN -8.23326\n",
               "233 -8.21781      NaN\n",
+              "237  30.8369  6.16932\n",
               "262      NaN -12.8788\n",
               "288      NaN -2.34264\n",
               "300      NaN -8.19936\n",
               "301      NaN -10.4633\n",
               "303 -5.61374  8.42267\n",
-              "311      NaN -8.78722"
+              "311      NaN -8.78722\n",
+              "399 -1.45559 -6.47666"
             ]
           },
           "metadata": {
@@ -1362,11 +1431,11 @@
       "metadata": {
         "id": "txAjPzOAl5_2",
         "colab_type": "code",
+        "outputId": "a4564f8e-817d-4df7-9ce1-2becb341767c",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 458
-        },
-        "outputId": "420c6feb-ee4b-40ea-e567-c837a2ce5980"
+          "height": 0
+        }
       },
       "source": [
         "# seaborn jointplot will allow us to compare n1 and n2, and plot each marginal\n",
@@ -1378,7 +1447,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<seaborn.axisgrid.JointGrid at 0x7fa1951d6eb8>"
+              "<seaborn.axisgrid.JointGrid at 0x7f460e332a58>"
             ]
           },
           "metadata": {
@@ -1389,7 +1458,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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+            "image/png": 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I5/PpvvvuU0FBgf7qr/7K6dKQIKbtM1dRlBc2DCQpzZJ67b4Fv//n+mtUWdMU8fOjlWZZunFt9aBnXJFaw01fZIwrxdI9GGByF6ErpwePHDmiKVOmaPLkycrMzFRxcbFqa2udLgsJZNo+c6X5ucryZoT8mKW+wJL62swP/emzqEdlQ+mx7YjPuEJNsYY7w8uURcZIba4MLb/fr5ycnODvfT6f/H6/gxUh0cL91O/m0cCme2aGPFsrHp2EoQx8LhWYYu3fvPHQc4dV/txhfSUjTVnejCvO8HLjCBYYyJWhBZg4Gggc6Dh+bOgR12iJ9hDHUFOsgQA9d7lLHd29+snfzdWhtQUEFozhytDy+XxqbW0N/t7v98vn8zlYERIt3Im+TtxcY+liLM3P1djM+D4qPrS2IGxw9R+JDjWVSscgTOTKRoxZs2apublZJ06ckM/nU3V1tZ588kmny0KCuWGfueF0Mcb7udujVUd154yJ+nXD8UEfu3PGxOCvo9k+auDehjBPLN2DAeH2JpTcvz+hK0MrPT1dGzdu1He/+1319PRo2bJlmjZtmtNlIQWF62L84SvHwt7kI7W/eyxLt00dr+az7cGdLWJtyvhNw/Gwz/be+MNfJPWF7aXO7qi+ntuXEyCy4XQPRuL2zkJXTg9K0vz581VTU6MDBw5o9erVTpeDFFTV2BI2UM5d7go7TRjqeVxAj23r3U/O6XKUgRJKoKkilMAor7Km6YqjSobCVCFM4drQApwUmBaM5IevHAt5vf/zuFC6em2du9wVMXyGy5Z0x/a6YX1dty4nAPojtIAQotlLMNJoy0mBXeZDyc3yRtXEAbiVK59pAU6LdtRR/txh/fCVY7Jt6Xx7V/AAxhfebxnWBrqjxZauOB5FunLJgMknGCO1EVpACLE0SJy7/GWHXktbu37TcDxhC4ojsdU3sgp3lAnH1ieH4XQPRhKps3Aoieg8JLSAECqK8gaNRqLlhsCS+gLr0NqCkB9zw3ICjI7R7h4ciUR0HhJaSAr9j7cfjZFD4M+Oxqa2TmC6D8mKRgwYL9Qee6NxqGFpfm7ELZPcJnBUVt82UrbKnzusG9ZWK3/za65sGAGGg9CC8SIdYzJSodZchTtI0Wn/b1uxHrjtep273KX2ri9P6Tp3uUsV+z4guJAUmB6E8eJ5jElgmjBwBpXHstTV65anVlf63z/4nS53hT5SsqvHdu1ZZBiZ0W7EGIlYmziG07hBaMF44Tr9RmvdUeBGP9zGjEQJF1gBLB5OTm5qxIjVcBo3mB6E8RJxjEk0i43djsXDSAaMtGC8/lN48Vp3NNrbLSVahseimxBJgdBCUoj3uiOPZanHduezrKFY+vKZlsRO7jAb04NAFEwNrDR9udh5tJYCAE5ipIWUFOti5NxhnHvltLEZaYOaMwJLARhtJQ83dQ/GKn0YwyZCCylnOKcRVxTl6aHnDrtmi6ZohOsmpIswuZjePRgrpgeRcoZajFzV2KI7ttfpxrXVumN7naoaW1San2tUYEVCFyFMxkgLKSfSYuRIozATpwgHYk9CmI7QQsqJtBg53Chs08vHZLlz96aIsrwZ+l9j0ke8FGC0NyQGhovQQsoJdeyIN8OjO2dM1K8bjof8M23tXSGvu4U3I03dvba6eux+1zzadM/MEYfLcJ4BAvFCaCHlhFqMHDht2BSBdWO5/UY98RoNRXoGSGg5z+TuwYF7FUazFyGhhZQ0cDHyHdvrjNqm6U/bFgd/HWgcCYTVT/5u7qiGSTw3JE5miZpSNbl7cKBo9iIktACZdQP29Hu4loipu3hvSJyMmFKNH1reAZl1A+6/O0ekxpHRkogNiZNNPM94S3WEFqDQN2YnWZLGRNguILB+LNwIsa29a9S2ayrNz9W2pbOUm+WVpb7W/21LZzFiiIAp1fhhehDQ4OaMsZkeXep05hlXmiXdPnWC3v3kXNjPCUw3XePNCNvZOJqNEvHekDjZJHJK1YlGjEyPpTFx+CFv3JihI4nQQsoK9aD80NoCVTW26OHnDjtWV68tHfrTZ0N+XntXj76SEX40xk/1zqhqbNGlju5B1+M1pepEI8a86dfpq+PHJvQ1AwgtpKRID8ora5oU+Qxg92i73KXxYzN07vLg0ZZJz+mSxcB/VwHjx2bosZKRr5kDz7SQoiI9KDdphDIpy6vHSmaGbJS4c8bEQXsoIr7CnXA9NjOdwBoljLSQkiI9KA/3PCLRLCniJr2B6aZIi6VpuU4sGjDij5EWUlK4qbPAsy03/I/x7duuV+4XdQ7c9tCStOzmL5sjSvNzdWhtgT7ZXqxDawv0xh/+Qsu1AyL9u8LoYKSFlBRu/8H+I5d1+4+ofcCZVIHtk+Ltgduu1+OlsyT1tbcPHPnZkt74w18G/blAc0m4kSI/8cdXpH9X8TKc7sGRdv9F0+UXL4QWjDGa2+KEmlLr//XCtXjfuLZ6+N9ACGlW300t0F6f5c0YtMlttFNO4ZoA+uMn/vga6t9VPAyne9DJ7r+RIrRghGi2xYk11Iaz9igrTKfecH0lPU1P3Bt5oW60a37CNQEEsItFYrCmLb7cMHUPDCma04bX7T+qlrZ22foy1EazY66qsUWf/8/g9Tcjcbmrd8g6o91GKdLUH7tYIFkQWjDCUFNko7XXW2DH9FBt4pU1TerqHf3nWUPVGe02SuGm/nKzvDq0toDAQlJgehBGGGqKbDitxgOnE4dqE49nE8NQXzuaKScnmgCARHMktHbs2KE33nhDGRkZuv7667Vt2zZdffXVkqTdu3dr3759SktL06OPPqq/+Zu/caJEuMxQN+RY93oL9Yws1KnF/Q87DPcauVlenfpiWnK4RqNBwokmADivf/dgtF2BTnb/jZQjld9xxx165JFHlJ6ersrKSu3evVsVFRX6+OOPVV1drerqavn9fpWVlammpkYej3t234YzhrohxzrKGKppob/AKCjSa0RqMx/KaI6GaAJIPf27B03uCoyWI6H1zW9+M/jruXPn6r/+678kSbW1tSouLlZmZqYmT56sKVOm6MiRI8rPz3eiTLhMpBtyrKOMWKb6AqOgoV5jYKBleCxlpFm6/MVar0A7eyx1ArhSxND6/PPPtXv3brW2tmrevHkqKSkJfmzTpk3atGnTiAt44YUXtGjRIkmS3+/XnDlzgh/z+Xzy+/0jfg2khlhGGdFu1TRwFBTuNWINTUIKGJ6IobVu3TpNmTJFRUVF2rdvn1577TU9+eSTyszM1AcffBDxC69YsUJnzpwZdL28vFwLFiyQJO3atUsej0f33HPPCL4FIHahpvoG8lhWTG3iTM0B8RcxtI4fP66f/vSnkqQFCxZo165d+vu//3vt2rVryC+8d+/eiB/fv3+/Dh48qL1798qy+nZW8/l8am1tDX6O3++Xz+cb8rWAWAXC5YevHAu5WDgjzVLl8jmEEOAyEddpdXZ2qrf3y73XVq9erfvvv18PPPCA2tqGf+hYfX29fv7zn2vXrl3yer/smiooKFB1dbU6Ozt14sQJNTc3a/bs2cN+HSCS0vxcjc0M/XPbVV9x7iiJSGvFgIEC3YP512cpPQVW3kYcad15551qaGjQN77xjeC1pUuX6rrrrtPjjz8+7BfdsmWLOjs7VVZWJkmaM2eONm/erGnTpmnRokVavHixPB6PNm7cSOcgRl3/9Vnh2tTbRnGrplhEs10V0N/A7sFkFzG0/uVf/kVS34irpqZGLS0t6u7u28ZmJM+hXn/99bAfW716tVavXj3srw1EEs2mspJzG8tG2tmD0AKibHlfvXq1xo0bp5kzZyozM1OSgs+hAJNEsz7LyV0kOEQQiCyq0PL7/XrmmWfiXQswItHs8j7UzT/3iz8n9Z1jlei1VLHu7AGkmqge2+Xn56upiRNP4V7R7vI+1M3/VFu7Nr18TBX7PojrjvHhRLujOxBAI0YI77//vl588UXl5uYGpwcl6ZVXXolbYUAson0WVFGUp4eeOxy2AcOW1NY+uAkjUc+V2D8QsaIRI4R//dd/jXcdwIiEm/ZraWsfNM337duu128ajse8wW2iniuxSBkIL6rQys3lfyC4W7hnQZYUvN7S1h4cZWV5M2RZfa3t0YYXz5UA56XADChSQahnQZY0KJACv29r79L/dPXqJ383V7lRhBHPlQB3ILSQFEKd7jvUCCrwnCpU4GWkWRo/NiPiScEAEs/ck8CAAQY+C7pje92QO7mfamun+QFGC3QPZnosow93jFbyf4dIWdHs5N7/rCy3h1Q069CQegLdg/OmX6drxmYO+fmmI7SQtPqPoFra2gc94zLpORV7EgJ9CC0ktf4jKJNHKuxJCPQhtGCc4YZPoqcARzMk2ZMQ6ENowSimTJONdp3sSQj0oeUdRok0TeYmo10nexIinED3YHdPr85f7nS6nLhjpAWjmDJNNtp10paPcAbuPZjsHYSEFowy3GmyRDdhhKvTVt/6seG8vglt+UC8MT0IowxnmizaY0viXWdAIo86AZINoQWjhNquaagtlpx4Dta/zlDc+BwOMAHTgzBOrNNkbn0O5vTrIzlYkm69cbzGZHjYxglIBk60iw9seQ9XFzBStqQxGR59dfxYp0tJCKYHkfScaBcPNSWZyNcHkhUjLSQ9J9rFI0395dKuDgwboYWUkOh28XBTkrlZXh1aW5CwOoBkw/QgEAfsYAHEByMtIA7YwQKJkmalxuGPAanznQIJxg4WSARPmpX0Wzf1x/QgAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGI6G1p49e5SXl6fPPvtMkmTbth5//HEVFhaqpKREx44dc7I8AIDLOBZan376qQ4dOqRJkyYFr9XX16u5uVmvvfaatmzZok2bNjlVHgDAhRwLrW3btqmiokKWZQWv1dbWqrS0VJZlae7cubpw4YJOnz7tVIkAAJdxJLQOHDig7OxszZgx44rrfr9fOTk5wd/n5OTI7/cnujwAgEvFbZf3FStW6MyZM4Oul5eXa/fu3dqzZ0+8XhoAkKTiFlp79+4Neb2pqUknT57UkiVLJEmtra1aunSpnn/+efl8PrW2tgY/t7W1VT6fL14lAgAMk/DztPLy8vT2228Hf19QUKB9+/ZpwoQJKigo0K9//WsVFxfrgw8+0Lhx45SdnZ3oEgEALuWqQyDnz5+vN998U4WFhfJ6vdq6davTJQEAXMTx0Kqrqwv+2rIsPfbYYw5WAwBwM3bEAAAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABgj3ekCMLqqGltUWdOkU23tmpTlVUVRnkrzc50uC0Cc9Nq2zl/u1DVjM50uJSEIrSRS1diidfuPqr2rR5LU0taudfuPShLBBSSpnl5bFzu6Uya0mB5MIpU1TcHACmjv6lFlTZNDFQHA6CK0ksiptvaYrgOAaQitJDIpyxvTdQAwDaGVRCqK8uTN8FxxzZvhUUVRnkMVAcDoohEjiQSaLegeBFJHmmVp3JjUuZWnzneaIkrzcwkpIIV40qyU6RyUmB4EABiE0AIAGIPQAgAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AIAGMOx0PrVr36lu+++W8XFxdq5c2fw+u7du1VYWKiioiK99dZbTpXnOlWNLbpje51uXFutO7bXqaqxxemSACDhHNkRo6GhQbW1tXr55ZeVmZmps2fPSpI+/vhjVVdXq7q6Wn6/X2VlZaqpqZHH4xniKyY3zskCEE6qHQLpyEjr2Wef1apVq5SZ2fcmX3vttZKk2tpaFRcXKzMzU5MnT9aUKVN05MgRJ0p0Fc7JAhBO4BDIVOFIaDU3N+u9997T8uXL9cADDwSDye/3KycnJ/h5Pp9Pfr/fiRJdhXOyAKBP3KYHV6xYoTNnzgy6Xl5erp6eHp0/f16//e1vdfToUZWXl6u2tjZepRhvUpZXLSECinOyAKSauIXW3r17w37s2WefVWFhoSzL0uzZs5WWlqZz587J5/OptbU1+Hl+v18+ny9eJRqjomRLzyUAAAvmSURBVCjvimdaEudkAUhNjkwPLliwQO+8844k6ZNPPlFXV5fGjx+vgoICVVdXq7OzUydOnFBzc7Nmz57tRImuUpqfq21LZyk3yytLUm6WV9uWzqIJA0DKcaR7cNmyZVq/fr2+9a1vKSMjQ9u3b5dlWZo2bZoWLVqkxYsXy+PxaOPGjSnfORjAOVkAQrEkpafQiltHQiszM1M/+tGPQn5s9erVWr16dYIrAgAz2ZK6e52uInE4udgwVY0tqqxp0qm2dk3K8qqiKI8RGICUQWgZhEXGAFJdCs2Emo9FxgBSHaFlEBYZA0h1hJZBwi0mZpExkLrSLEvjxqTOkx5CyyAVRXnyZly5BIBFxkBq86RZKbNZrkQjhlECzRZ0DwJIVYSWYVhkDCCVMT0IADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBi3vLsDO7QAQHULLYezcDmAkem1b5y93psyuGEwPOoyd2wGMRE+vrYsd3U6XkTCElsPYuR0AokdoOYyd2wEgeoSWw9i5HQCiRyOGw9i5HQCiR2i5ADu3AxguDoEEABgj1Q6BJLQAAMYgtAAAxiC0AADGILQAAMYgtAAAxiC0AADGILQAAMYgtAAAxiC0AADGILQAAMYgtAAAxiC0AADGILQAAMZwJLQ+/PBD3X///VqyZImWLl2qI0eOSJJs29bjjz+uwsJClZSU6NixY06UBwBwKUdCq7KyUt/73vf00ksv6Z//+Z9VWVkpSaqvr1dzc7Nee+01bdmyRZs2bXKiPACASzkSWpZl6dKlS5KkixcvKjs7W5JUW1ur0tJSWZaluXPn6sKFCzp9+rQTJQIAXMiR4y7Xr1+vlStXaseOHert7dV//Md/SJL8fr9ycnKCn5eTkyO/3x8MNQBAaotbaK1YsUJnzpwZdL28vFwNDQ1at26dioqK9Oqrr2rDhg3au3dvvEoBACSJuIVWpBD6/ve/rw0bNkiSFi1apEcffVSS5PP51NraGvy81tZW+Xy+eJUIADCMI8+0srOz9e6770qSGhoadMMNN0iSCgoKVFVVJdu2dfjwYY0bN46pQQBAkCPPtLZs2aKtW7equ7tbY8aM0ebNmyVJ8+fP15tvvqnCwkJ5vV5t3brVifIAAC7lSGjdcsst2r9//6DrlmXpsccec6AiADBTr23r/OVOXTM20+lSEoIdMQDAYD29ti52dDtdRsIQWgAAYzgyPei0qsYWVdY06VRbuyZleVVRlKfS/FynywIADCHlQquqsUXr9h9Ve1ePJKmlrV3r9h+VJIILAFwu5aYHK2uagoEV0N7Vo8qaJocqAgBEK+VC61Rbe0zXAcDN0ixL48akzqRZyoXWpCxvTNcBwM08aVbKtLtLKRhaFUV58mZ4rrjmzfCooijPoYoAANFKnTHlFwLNFnQPAoB5Ui60pL7gIqQAwDwpNz0IADAXoQUAMAahBQAwBqEFADAGoQUAMAahBQAwBqEFADAGoQUAMAahBQAwRlLsiNHS0qKlS5c6XQYAjJrx48frmWeeierzUoll27btdBEAAESD6UEAgDEILQCAMQgtAIAxCC0AgDEILQCAMQgtAIAxCK0offjhh7r//vu1ZMkSLV26VEeOHJEk2batxx9/XIWFhSopKdGxY8ccq/FXv/qV7r77bhUXF2vnzp3B67t371ZhYaGKior01ltvOVZfwJ49e5SXl6fPPvtMkrvewx07dujuu+9WSUmJvve97+nChQvBj7nlfayvr1dRUZEKCwv19NNPO1ZHf59++qm+853vaPHixSouLtYvf/lLSVJbW5vKysq0cOFClZWV6fz5847W2dPTo9LSUj344IOSpBMnTmj58uUqLCxUeXm5Ojs7Ha0PUbARlbKyMvvgwYO2bdv2wYMH7QceeCD465UrV9q9vb12Y2Ojfd999zlS39tvv23/wz/8g93R0WHbtm2fOXPGtm3b/uijj+ySkhK7o6PDPn78uH3XXXfZ3d3djtRo27Z96tQp+x//8R/tv/3bv7XPnj1r27Z73kPbtu233nrL7urqsm3btnfu3Gnv3LnTtm33vI/d3d32XXfdZR8/ftzu6OiwS0pK7I8++ijhdQzk9/vt//7v/7Zt27YvXrxoL1y40P7oo4/sHTt22Lt377Zt27Z3794dfD+dsmfPHvvhhx+2V61aZdu2ba9Zs8b+z//8T9u2bfsHP/iB/Zvf/MbJ8hAFRlpRsixLly5dkiRdvHhR2dnZkqTa2lqVlpbKsizNnTtXFy5c0OnTpxNe37PPPqtVq1YpMzNTknTttdcG6ysuLlZmZqYmT56sKVOmBEeJTti2bZsqKipkWVbwmlveQ0n65je/qfT0vo1i5s6dq9bW1mCNbngfjxw5oilTpmjy5MnKzMxUcXGxamtrE17HQNnZ2Zo5c6Yk6aqrrtLUqVPl9/uDf7eSVFpaqgMHDjhWY2trqw4ePKj77rtPUt8Iv6GhQUVFRZKke++91xXvJSIjtKK0fv167dy5U/Pnz9eOHTv08MMPS5L8fr9ycnKCn5eTkyO/35/w+pqbm/Xee+9p+fLleuCBB4I31IH1+Xw+R+qTpAMHDig7O1szZsy44rpb3sOBXnjhBc2bN0+Se95Ht9QRycmTJ/Xhhx9qzpw5Onv2bPAHvIkTJ+rs2bOO1bV161ZVVFQoLa3vtnfu3DldffXVwR9S3PLvDpElxd6Do2XFihU6c+bMoOvl5eVqaGjQunXrVFRUpFdffVUbNmzQ3r17XVNfT0+Pzp8/r9/+9rc6evSoysvLHfmpMVKNu3fv1p49exJe00CRalywYIEkadeuXfJ4PLrnnnsSXZ7RLl26pDVr1mj9+vW66qqrrviYZVlXjLAT6Y033tCECRP09a9/Xe+8844jNWB0EFr9RAqh73//+9qwYYMkadGiRXr00Ucl9f2kG5hCkvqmIHw+X8Lre/bZZ1VYWCjLsjR79mylpaXp3Llzg+rz+/1xqy9SjU1NTTp58qSWLFkiqe99Wrp0qZ5//vmEvoeRagzYv3+/Dh48qL179wZvsol+H8NxSx2hdHV1ac2aNSopKdHChQsl9U1Tnz59WtnZ2Tp9+rQmTJjgSG2///3vVVdXp/r6enV0dOjzzz/XE088oQsXLqi7u1vp6elx/3eH0cH0YJSys7P17rvvSpIaGhp0ww03SJIKCgpUVVUl27Z1+PBhjRs3LjgdkkgLFiwI/gT5ySefqKurS+PHj1dBQYGqq6vV2dmpEydOqLm5WbNnz054fXl5eXr77bdVV1enuro65eTkaP/+/Zo4caJr3kOprzPv5z//uXbt2iWv1xu87pb3cdasWWpubtaJEyfU2dmp6upqFRQUJLyOgWzb1oYNGzR16lSVlZUFrwf+biWpqqpKd911lyP1PfLII6qvr1ddXZ1+/OMf67bbbtOTTz6pW2+9VTU1NZKkF1980RXvJSJjpBWlLVu2aOvWreru7taYMWO0efNmSdL8+fP15ptvqrCwUF6vV1u3bnWkvmXLlmn9+vX61re+pYyMDG3fvl2WZWnatGlatGiRFi9eLI/Ho40bN8rj8ThSYzhueQ+lvr/nzs7O4I13zpw52rx5s2vex/T0dG3cuFHf/e531dPTo2XLlmnatGkJr2Og999/Xy+99JKmT58eHE0//PDDWrVqlcrLy7Vv3z5NmjRJTz31lMOVXqmiokIPPfSQnnrqKX3ta1/T8uXLnS4JQ+BoEgCAMZgeBAAYg9ACABiD0AIAGIPQAgAYg9ACABiD0AJi8Lvf/U7FxcWaMWOGjh496nQ5QMohtIAYTJ8+XT/96U/113/9106XAqQkFhcDIZw8eVL/9E//pJtvvlmNjY3y+Xz62c9+pptuusnp0oCUxkgLCOPPf/6zvv3tb6u6ulrjxo0LbvcDwDmEFhDGV7/6VX3ta1+TJM2cOVMtLS0OVwSA0ALCCByoKUkej0c9PT0OVgNAIrQAAAYhtIAYvP7665o3b54aGxv14IMPauXKlU6XBKQUdnkHABiDkRYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBj/H6mi1Vp2nJmIAAAAAElFTkSuQmCC\n",
             "text/plain": [
               "<Figure size 432x432 with 3 Axes>"
             ]
@@ -1419,11 +1488,11 @@
       "metadata": {
         "id": "guGcilXIl5_9",
         "colab_type": "code",
+        "outputId": "3c8985b4-af4d-475f-bd0a-d5aa77f595f1",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 296
-        },
-        "outputId": "d731d6fd-9b7c-44b6-8c6b-400fe11c86f4"
+          "height": 0
+        }
       },
       "source": [
         "diff_df = df['n1'] - df['n2']\n",
@@ -1449,7 +1518,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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PZ/HixR4tUPiPk1bbkO8Dca6oM1NdTU020pI8eiohRrx+BcR3v/vdHrOR7HY7Wq2WTZs2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\nQmCC\n",
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ut0Jds2PIziCqGyUghLiYXs8gzpw5g8Vioa2tjUOHDnl2Wm1qaqK1tXVIChSjQ22THZeieHUGE0CYTkOAWoVFziCEuKheA+LDDz9k69atVFdX88wzz3juDw0N5aGHHvJ6cWL06PyL3ttnEGqVisjgQCyNdq++jhAjQa8BkZ2dTXZ2Nvn5+bKyWXhV55iAt7bZOFdESKB0MQnRB73+NG7fvp2srCwqKip45ZVXuj2+bNkyrxUmRheLl7fZOFdUsJaSOlkLIcTF9BoQneMMLS0tQ1KMGL0sDW0EqFSEDfK1qHsSGRJIbakDu9OFThPg9dcTYrjq9afx+9//PtCxF5MQ3lTd2EZ0qNYrlxo9n2cmU0MbiTGhXn89IYarXgPiySef7PXJv/jFLwa1GDF6WRrbGBOuHZLX6lwLUWFtlYAQohe9BsT06dOHqg4xylU3tGGMCBqS14o8O5W2vF6magvRm4vOYhJiKFga20gZHzEkrxURHIgKqJSAEKJXvQbEU089xeOPP869997b4+O/+93vvFKUGF1aHS4a25yMDdMNyetpAtREh2mpsEpACNGbXgMiKysLQHZsFV7VuSZhTPjQBASAUR9EhZxBCNGrXvdiSk5OBuDaa69l5syZ6PV6IiIimDlzJtdee+1FD15UVERGRgYmk4mNGzd2e9zhcLBy5UpMJhNLliyhvLy8y+OVlZXMmjWLl19+uT/vSQwznYvkxoQNzSA1dASEdDEJ0bs+bda3e/duTCYTTz31FP/5n//J3Llzef/993t9jsvlIjc3l02bNmE2m9mxYwcnTpzo0mbLli3o9Xp27tzJ0qVLycvL6/L4s88+y3e+851+viUx3Jy2dQTEUHUxARj0Oirr23C7lSF7TSGGmz6tSnr22Wd59dVXSUxMBKC0tJS777672xbg5youLiYxMZGEhAQAMjMzKSgoYPLkyZ42hYWFnjUWGRkZ5ObmoigKKpWK9957j/j4eEJCQgb85sTw0HkGMTZcR2nd0PxVb4gIwuFyU9NkJ1Y/NLOnhBhu+nQGERoa6gkHgISEBEJDe58/brFYMBqNntsGgwGLxdKtTVxcHAAajYbw8HCsVivNzc289NJLskBvlKhubCNUG0CozvurqDsZz4aCTHUV4sJ6/Yn829/+BnSMRfz4xz/m1ltvRaVS8e6775KSkuK1on7961/zwx/+8KIhJEYGS2MbhiH+K75zzUVlfSvfmBA1pK8txHDRa0Ds2rXL8/WYMWPYu3cvANHR0djtvW+XbDAYqK6u9ty2WCwYDIZubaqqqjAajTidTmw2G1FRUXz55Zfk5+eTl5dHY2MjarUanU7H7bff3u83KPyfpdE+5AHR+Xoy1VWIC+s1IM69BkR/paSkUFJSQllZGQaDAbPZzK9+9asubdLT09m2bRuzZs0iPz+f1NRUVCoVf/nLXzxtXnjhBUJCQiQcRrDqhjauvTx6SF8zTKchPEgjU12F6EWfOn3tdjuvv/46x48f73Lm0FuAaDQaVq9ezfLly3G5XCxevJikpCTWrVtHcnIyc+bMIScnh1WrVmEymYiIiGDt2rWX/o7EsOJ2K5y2DX0XE0B8ZLBMdRWiF30KiFWrVjFx4kQ+/PBD7r//ft566y0mTpx40eelpaV1m+m0YsUKz9c6nY7169f3eoyf/vSnfSlRDFN1LQ7aXQpG/dBNce0UHxlMuXQxCXFBfZrFVFpaysqVKwkODiY7O5sNGzZQXFzs7drEKNA5xdUnZxBRwdLFJEQv+hQQGk3HiYZer+fYsWPYbDZqa2u9WpgYHToXyRmGaCfXc8VHBmNrc9LY1j7kry3EcNCnLqbbbruNhoYGVqxYwU9+8hNaWlq6dBUJMVCV9R0BERcRhGuIVzXHRwWfraEVvdH7lzoVYrjpU0AsWbIE6NiTqaCgwKsFidGlsr4VjVpFbHgQVQ1D290zLrIjIMrrWplm1A/pawsxHPQpIKxWK7/+9a/5/PPPUalUXH311dx3331ERckCI3FpKutbMUYEEaD2/qVGz5cY3bGNS2mdXHNdiJ70aQzioYceIjo6mvXr17Nu3TqioqL42c9+5u3axChQWd/m+Ut+qEWHagnXafi6ttknry+Ev+tTQJw5c4b777+fhIQEEhISuO+++2SQWgyKivpW4n0UECqVisQxIZTUyhmEED3pU0B8+9vfxmw243a7cbvdvP3229xwww3erk2McE6Xm+rGNp8FBEBiTKicQQhxAb2OQcyaNQuVSoWiKPzhD39g1apVALjdbkJCQnjssceGpEgxMp222XG5FZ91MQFcFhNC/oFq2l1uAgP69PeSEKNGrwGxb9++oapDjEKd21yMi/Td9RgSY0JxuhUq61tJjJHdg4U4V5834C8oKODTTz8FOqa73nTTTV4rSowOnauYfdnFdNnZUCipbZGAEOI8fTqnzsvL49VXX2XSpElMmjSJV199tdvOrEL0V4XnDMK3XUyAjEMI0YM+nUG8//77bN++HbW6I0+ys7NZuHAhDz/8sFeLEyNbZX0rkSGBQ3olufONDdcRHBhASY3MZBLifH0elWtsbPR8bbPZvFKMGF0q69sYF+G7swc4O9U1JkTOIIToQZ/+dLv33nvJzs7muuuuQ1EU9u7dyyOPPOLt2sQIV2FtJeHsamZfSowJ4cTpJl+XIYTfuWhAuN1uVCoV//d//8f+/fsBeOSRRxg7dqzXixMjW2V9K6kTh/ZKcj25LCaUXUfO4HIrPtnyQwh/ddGAUKvVbNq0iXnz5jFnzpyhqEmMAo1t7djsTs+Oqr6UGBOKww8W7Qnhb/o0BnH99dfz8ssvU1VVRX19veefEANV6QczmDp5ZjLVyDiEEOfq0xjE22+/jUql4i9/+UuX+2XrbzFQ/hQQiWP+uRbi+sk+LkYIP9KnM4i3336bH/zgB0ybNo0rrriCO+64A7PZfNHnFRUVkZGRgclkYuPGjd0edzgcrFy5EpPJxJIlSygvLweguLiYrKwssrKyWLBgATt37uzn2xL+rsLq+0VyneL0QWg1apnJJMR5+hQQjz32GF999RV33HEHt99+OydOnLjoPkwul4vc3Fw2bdqE2Wxmx44dnDhxokubLVu2oNfr2blzJ0uXLiUvLw+ApKQk3njjDbZv386mTZtYvXo1TqdzgG9R+KOK+jYCA1SMDdP5uhTUahUTokM4JV1MQnTRpy6m48eP8/bbb3tup6amMm/evF6fU1xcTGJiIgkJCQBkZmZSUFDA5Mn/PIcvLCzkgQceACAjI4Pc3FwURSE4+J9/VdrtdlQqmVky0lTWtxIXEYzaT2YNTRobynGLTHUV4lx9OoO48sor+eKLLzy3v/zyS5KTk3t9jsViwWg0em4bDAYsFku3NnFxcQBoNBrCw8OxWq2e18jMzGTBggU88cQTaDS+W20rBldDi4OvzjQxJkxLubXF88/e7hqyGpwud5fXjosIoqS2mdONQ3vZUyH8WZ9+6x48eJDvf//7jBs3DoDKykouv/xy5s+fD8Bbb7016IXNmDEDs9nMV199xWOPPcbs2bPR6XzfHSEunc3u5OSZZlLiIyg6VuO5f9aEyCGrobXdzb6v6jy329rduBU4WNlIrN734yJC+IM+BcSmTZv6fWCDwUB1dbXntsViwWAwdGtTVVWF0WjE6XRis9m6Xed60qRJhISEcOzYMVJSUvpdh/A/9S0OWttdjAn3n8A36Du2HD9V04zsUyxEhz4FRHx8fL8PnJKSQklJCWVlZRgMBsxmc7cdYNPT09m2bRuzZs0iPz+f1NRUVCoVZWVlxMXFodFoqKio4OTJkwOqQfinsrqObpwxYVofV/JPMWFa1Co4KQPVQnh4rWNfo9GwevVqli9fjsvlYvHixSQlJbFu3TqSk5OZM2cOOTk5rFq1CpPJREREBGvXrgXgs88+46WXXkKj0aBWq/mP//gPoqN9vyWDGByldR07p/rDDKZOGrWaMWE6mckkxDm8OvKblpZGWlpal/tWrFjh+Vqn07F+/fpuz1u4cCELFy70ZmnCh8qsLQSoVESG+M8ZBHR0M508IwEhRCe5CK8YcqV1LUSHav1uYzyDXkdVQxstDllzIwRIQAgfKKtr9asB6k6x4R0D1bIeQogOEhBiSLncChXWVr8aoO5kPDuT6ZhFLoglBEhAiCFWYW3F4XL71QB1p+gwLdoAtQSEEGdJQIghdbKmo/smxg8DQn328qPHpItJCEACQgyxzllCY/1wDALg8jGhcgYhxFkSEGJInappJkynIVQb4OtSenT52FCqGtpoaG33dSlC+JwEhBhSJ2uaSIgO9tsdeiePDQPgcFWjjysRwvckIMSQOnWmmQnRIb4u44KmGsMBOFDR4ONKhPA9CQgxZFocTiob2kjw44CIDtVi1AexXwJCCAkIMXROnO6YHXRZjP8GBEByfIQEhBBIQIgh1PlLd4oh3MeVXJjT5SYxJphTZ5o5Vt1IubWFhhaHr8sSwickIMSQOVDRQERwIHERQb4u5YJa2920uxQUYMtnFRQdq8Fml72ZxOgkASGGTHF5AynxEX47g6lTfGTHFeUq6+Xyo2J0k4AQQ8LudHHMYiM5PsLXpVxUeFAg+iANFRIQYpSTgBBD4mi1jXaXQsowCAiAcZHBEhBi1JOAEEOic4B6OAVEjc2O3enydSlC+IwEhBgSnQPUCdHBvi6lT+Ijg1GAqvo2X5cihM9IQIghsb+igeR4vd8PUHfqHKiWbiYxmnk1IIqKisjIyMBkMrFx48ZujzscDlauXInJZGLJkiWUl5cD8NFHH7Fo0SLmz5/PokWL2LNnjzfLFF5md7o4Wj08Bqg76YNloFoIrwWEy+UiNzeXTZs2YTab2bFjBydOnOjSZsuWLej1enbu3MnSpUvJy8sDICoqihdffJG33nqLZ599lkcffdRbZYohcKy6aVgNUHcaHxVCWV2Lr8sQwme8FhDFxcUkJiaSkJCAVqslMzOTgoKCLm0KCwvJzs4GICMjgz179qAoCldeeSUGgwGApKQk7HY7DoesZh2uhtsAdaeE6BBqmx3Uy0pqMUp5LSAsFgtGo9Fz22AwYLFYurWJi4sDQKPREB4ejtVq7dImPz+fK6+8Eq3W/65hLPpmX6mVyJBAv97FtScJUR3jEIer5AJCYnTS+LqA3hw/fpy8vDw2b97s61LEJdhzspbrLo8eNgPUneKjglEBhyrl2hBidPLaGYTBYKC6utpz22KxeLqNzm1TVVUFgNPpxGazERUVBUB1dTUPPPAAa9asYcKECd4qU3hZWV0L5dZWvjUxxtel9JtOE4BBH8RBuXiQGKW8FhApKSmUlJRQVlaGw+HAbDaTnp7epU16ejrbtm0DOrqSUlNTUalUNDY2cvfdd/Pwww9z9dVXe6tEMQT2nKwF4FuTxvi4koEZHxXM4apGFEXxdSlCDDmvBYRGo2H16tUsX76cefPmceutt5KUlMS6des8g9U5OTnU19djMpl45ZVXeOSRRwD405/+RGlpKb/5zW/IysoiKyuL2tpab5UqvOjjr2qJDtUyxRDm61IGJCE6BFubk1M1zb4uRYgh59UxiLS0NNLS0rrct2LFCs/XOp2O9evXd3vefffdx3333efN0sQQUBSFj0/Wkjpx+I0/dEqI6hhY/6Ksnoljh2fICTFQspJaeE1pXQuVDW3DcvyhU6xeR7A2gC/K6n1dihBDTgJCeM2erzrHH4ZvQKhVKqYZwyUgxKgkASG8Zs/JWsaG65g0zLtmrozTc7iqkbZ22dlVjC4SEMIrFEVhz1e1pE6MGbbjD52uHKen3aVwUNZDiFFGAkJ4xcHKRk7b7MxOGp7TW881PU4PIN1MYtSRgBBeUXjkNCoV3Dg11telXLIx4TriIoIkIMSo49dbbYjho6HFgc3u9Nx+50AVVxj1aAOGd/dSp5kJkXxRZr14QyFGEAkIMShsdidFx2o6vm5r53CVDdOVBmx2JxEhw3+jxZkJkbxzoJqaJjtjwnS+LkeIISFdTGLQHbN07H46zRju40oGz8yESAC+KJVuJjF6SECIQXek2kZEcCBGfZCvSxk0KeMjCFCrZBxCjCrSxSQGldPl5vjpJmYmRKJSqXC63JRbu4quicsAABclSURBVF6VzT7M1hM4XW7qmh1MHBPKxydrPe8nXKcZEd1nQlyIBIQYVCdrmnE43Z7updZ2N/u+quvSZtaESF+UNmCd7yEqRMuX5fXsPnoGtUrF7CljJCDEiCZdTGJQ7S9vQKdRD/vV0z1JiA7G7nRTY7P7uhQhhoQEhBg07S43ByobmD4ugsCAkfet1bmza2ldy0VaCjEyjLyfYuEzR6tt2J1uZiRE+LoUrxgbriNEG8DXtRIQYnSQgBCD5svyesJ0GiaOGXndSwAqlYrE6BBKauXiQWJ0kIAQg6LZ7uRotY2U+I7poCNVYkwotc0ObG3tvi5FCK+TgBCDoujYGZxuhRkJw2uGUn9dFtMxDiHdTGI0kIAQg8K8v5qokEASooJ9XYpXjYsKRqNW8bV0M4lRwKsBUVRUREZGBiaTiY0bN3Z73OFwsHLlSkwmE0uWLKG8vBwAq9XKHXfcwaxZs8jNzfVmiWIQfPa1lS/K6vnWCLj2w8Vo1GoSokP4WmYyiVHAawHhcrnIzc1l06ZNmM1mduzYwYkTJ7q02bJlC3q9np07d7J06VLy8vIA0Ol0rFixgkcffdRb5YlB9NtdJ4gIDuTay4fvpUX7IzEmhMr6Vlodw2tFuBD95bWAKC4uJjExkYSEBLRaLZmZmRQUFHRpU1hYSHZ2NgAZGRns2bMHRVEICQnhmmuuQaeTXTP93aHKRgqOnGbJNePRakZHj+VlMaG4FThUJVeYEyOb136iLRYLRqPRc9tgMGCxWLq1iYuLA0Cj0RAeHo7VKnvuDye/3X2CMJ2GRd+I93UpQ2ZCdAgqoLhcNu4TI5vsxSQu6PyLAHXq3KTuH6fqeHt/FfekTUIfFOiDCn0jKDAAY0QQn30tASFGNq8FhMFgoLq62nPbYrFgMBi6tamqqsJoNOJ0OrHZbERFRXmrJNFP514E6Fyzp4yh2eHivj9/xmUxofzkxkk0to6udQFTDOF8eLyGhtZ2IoJHTziK0cVrXUwpKSmUlJRQVlaGw+HAbDaTnp7epU16ejrbtm0DID8/n9TU1BE/C2YksLe7uOePn9HW7mbjndeMqrOHTlcYw3EpCkXHzvi6FCG8xmtnEBqNhtWrV7N8+XJcLheLFy8mKSmJdevWkZyczJw5c8jJyWHVqlWYTCYiIiJYu3at5/np6ek0NTXR3t7Oe++9x+bNm5k8ebK3yhV91Opw8a9b97O/ooGX7ryGybEjc1uNixkfHUJkcCCFR04zf8Y4X5cjhFd4dQwiLS2NtLS0LvetWLHC87VOp2P9+vU9PrewsNCbpYkBqG2y84c9X1Pf4uC5nKswXWm4+JNGKLVKReqkGHYdPY3LrYzo7UXE6DU65iWKS3bGZud3739Fi8PJ2ttm8L1rEnxdks9dPymG+pZ29pXKzDsxMklAiIuqb3Gw+aNToFJxz+xJzJogEwkArr08Go1aRcGR074uRQivkIAQvWqxO9n8UQlt7S6WXX8ZY8Nl8WKnMJ2Gay+PpvCwBIQYmSQgRK/ePlBFXbOdO791GeMiR/ZGfAMx5woDRy02DsuqajECSUCIC/q81MrnpfV8J2ksl48J9XU5fmnxN+IJ0Qawseikr0sRYtBJQIge2Z0u8vKPERUSyE1TY7s85nS5Kbe2dPlnbx+dG9dFhmj5f9dO4K9fVlJulR1excgiASF6tPH9k5TWtbBgRny3Tfha290UHavp8s/hUnxUqe/ddcPlqICXPzzl61KEGFQSEKKbmiY7L77/FbOnjGGqMdzX5fi9cZHBZM2M53//UYa12eHrcoQYNBIQopvf7DpBW7uLe2ZP9HUpw8a9aRNpbXfxxFsHKatr7tYF19AiwSGGH9nNVXRRbm3hzx+XknP1eBJjQvm6ttXXJQ0LSYZwHrhpMr/edYIWh4sbzxu3mT1lDBEhWh9VJ8TASECILta9dxyAFTdPQVFG77jCQDw8dwpHLTb+dshCRHCgLCgUw550MQmPw1WNvPF5ObenJhIvax76TaVS8W+3TuPyMaFs+aycrZ+Xy2VJxbAmZxAjwMUu7NMXiqLwyzcPEBEcyE/TZdfcgdJq1Cy9/jIKj5zmg+NnOGqxcfM0A9dPiu7zMXr6PPvzWQoxWCQgRoDeLuzT118qb3xewadfW1mzOIWoUPlFdCkCA9RkTDeSPC6Cv35ZwbYvKvj4VC333TSZhTPHEd7L9TNaHE4+PlVL/gELKhVEhWgZG64j86o4CQgx5CQgBA0t7Tzz9mG+MSGSJVfLLq2DJT4qmHvTJnHMYuOTU3X88s0DPG0+zC3JRpLjI5gcG4bT5aamyc7R6ib+UVLLocpG3OcN/ahVUFzewL/eOo2E6BDfvBkxKklA+NildA/Z2to5Um3jk5O17Cutp8nuJCgwgDFhOuIigvo0yOxwuvnZa19gbXHwhx9di1qua9BnnSvKz3X+inKVSsVUo57l37mc2uZ2/vcfpfztkIVt+yq6tNNq1CSP0/PDb13GZWNCaGzt+J6wtrRzpLqRnYcs/O1QNT+5cTI/TZ9MYMCFhw8Ho8tReNdw+YwkIHzI6XLzeamVnYdO09DaTmu7i47f6QqTY8Mw6IPQBQYQHBhAgBoaW51YWxwctzRxsLKBktquv5yCAwNwON24zgbDjuJKbk9NJHtWPDFh3XdhbXe5+en/fE7hkdM8uTCZ5PiIIXjXI0dru5t9X9V1uW/WhMge26pUKmYmRDIzIZJnFinUNDn4+GQNhypthAVpCA/SoFGrPcfYV1oPQEyYjsmxYTwydwp//LiU9QXHef/YGdZ+bwYTx/Z8Nb/B6HIU3jVcPiMJiCF22tbGO/urKThyms9K6mg+b5aLClCAXUcvfK3jhOhgpsdFsPgb45keryciOJBDlTYC1CpcboX6FgcnzzRz1GLjSfNh1rx7BNOVBjJTxpEcr2dsuI6iYzX86eOv+fBEDf8+/0puT0307hsXHiqVirHhOmZNiMLW1rdZTrH6INbeNpM5V8Ty+LYD3PL8B9z5rUQeSJ9MpB/9QhEjiwTEJerLjJPaJjvvHKjmzX0VfPa1FQVIjA7BNN3A9Dg9Da1OIoIDCdEGoFKpUBSFay+PwulWsDvd2Ns7zgrCdRoiQzSoVF27F+ztLs8lLwPUKmLCdMSE6Vhx82RK61oxF1fy7kELb++v7vK8yJBAnlgwnR9ef1mP72O0bsDnDX3pjurL82cmRPKHZd/kpQ9O8fKHp/jfvWVcnRhFcrwebUAAFlsbX9c2U1LTgq2tHU2AmqBANVEhWupa7GTNiJdxDB9zON3sPVVH4RELFdZWbHYngQFqdBo1J2uauDU5jhnjI9D00o04VLwaEEVFRTz11FO43W6WLFnC3Xff3eVxh8PBo48+ysGDB4mMjGTt2rWMHz8egA0bNvD666+jVqv5xS9+wXe+8x1vltonLrfC17XNHLPYsDTaOWOzU9Nkp6K+FRUq1CpQqeCyMaFEBmux2Nr4/GsrRy02FAUmRIdw07RYUuIjMOiDgK7dCZ1UKhVON+wrbehWQ0f7vnVrtLa7Kbe2MiMhiunxEVQ3tBEepKHF4SJ1YgzXXR7t+Sbs6ZT3QscV/def7qi+PP/bk8cwaWwYJ2ua+OpMMxveP4nTrRATqiUqVEuoLgCjPgiXotDqcFFubSUv/xh5+cdIiY8ga+Y4MqYbJSyGSLPdye6jZ8g/WM2uI6ex2Z2ogDFhOiJDAnG6FeqaHWz64BSbPjhFTKiWW1OMZKaM45rLonodc/ImrwWEy+UiNzeXV155BYPBQE5ODunp6Uye/M859lu2bEGv17Nz507MZjN5eXk8//zznDhxArPZjNlsxmKxsGzZMvLz8wkICPBWubjcCm3tLtraXbS2u2hsdVLV0Eq5tZWjFhuHKhs5Wm2j9Zy/+gLUKrQBapxuN26lYy2BooBytnsoXKdh5oRIbkk2MvdKI+FBAXxwvNZr76E3GrWa8VEhzJ4yhvFR8kthJDBGBPG9b45nfFQIdqcLFSq0GjXl1pZuYa8oCpePCeVgZSNvFVfypPkwT5oPM2lsKN+e3LEpY1JsOLHhHb+wQrQaAtSdf/TIxIW+cLrctLS7aGxtx9Jop7K+lYOVjeyvqGdviRWH00302V/8VydG0Wx3ERTY9XfaVeP1lNS28O6Bal7/rJw/fVxKuE7D9ZNjuGp8JEmxYUyICSEqREtEcCDaALVXJ5Z4LSCKi4tJTEwkIaFj2mRmZiYFBQVdAqKwsJAHHngAgIyMDHJzc1EUhYKCAjIzM9FqtSQkJJCYmEhxcTGzZs0a9DrrWxxkPF+EpdF+wTb6IA1XxOn5/rUJXBGnZ5oxnHGRwUSFaKlqaO32w/idpBjiI0NQnffDJdcLEN6i0/T+x5NKpWJCTAjXTx7Dj2dP5FRNM4VHTrP76Gm2fFre5Q+f7s+FAJUK9dnAUPcSGBebOKfQe4OLP3+gD3r3tRVF6TY9GSAwQMVUYzi3X5dIxnQD11wWTYBa1WOIQ8f1Rb4bH8l3rxpHs93JB8fP8P6xGj48cYb8g5YeX1ujVvHL717JD6+/rPc3MABeCwiLxYLRaPTcNhgMFBcXd2sTFxfXUYhGQ3h4OFarFYvFwowZM7o812Lp+T+nU0VFBYsWLRpQrfFn//WmvRi+pOPfxbzej9e+UNv+3D8Ybf2hBmk7OG0v5EJtr+jHMcQAHIYv6Ph3MRf6jMae/Xch24/A9n4X1qGiouKCj42YQepPPvnE1yUIIcSI4rWRD4PBQHX1P2fNWCwWDAZDtzZVVVUAOJ1ObDYbUVFRfXquEEII7/JaQKSkpFBSUkJZWRkOhwOz2Ux6enqXNunp6Wzbtg2A/Px8UlNTUalUpKenYzabcTgclJWVUVJSwlVXXeWtUoUQQvTAa11MGo2G1atXs3z5clwuF4sXLyYpKYl169aRnJzMnDlzyMnJYdWqVZhMJiIiIli7di0ASUlJ3HrrrcybN4+AgABWr17t1RlMQgghulMpclUYIYQQPfD9Uj0hhBB+SQJCCCFEjyQgztq8eTNTp06lrq5jOwNFUXjyyScxmUzMnz+fgwcP+rjC/luzZg233HIL8+fP5/7776exsdHz2IYNGzCZTGRkZPDBBx/4sMqBKSoqIiMjA5PJxMaNG31dzoBVVVVxxx13MG/ePDIzM/nDH/4AQH19PcuWLWPu3LksW7aMhobu264MFy6Xi4ULF3LPPfcAUFZWxpIlSzCZTKxcuRKHw+HjCvuvsbGRBx98kFtuuYVbb72Vffv2jajPzEMRSmVlpfKjH/1IufHGG5Xa2lpFURRl9+7dyl133aW43W5l3759Sk5Ojo+r7L8PPvhAaW9vVxRFUZ577jnlueeeUxRFUY4fP67Mnz9fsdvtSmlpqTJnzhzF6XT6stR+cTqdypw5c5TS0lLFbrcr8+fPV44fP+7rsgbEYrEoBw4cUBRFUWw2mzJ37lzl+PHjypo1a5QNGzYoiqIoGzZs8Hx2w9HmzZuVhx56SLn77rsVRVGUBx98UNmxY4eiKIryy1/+Uvnzn//sy/IG5NFHH1Vee+01RVEUxW63Kw0NDSPqM+skZxDAM888w6pVq7psi1FQUMDChQs79vGfOZPGxkZOnz7twyr774YbbkCj6ZioNnPmTM/akgttZTJcnLuNi1ar9WzjMhzFxsYyffp0AMLCwpg4cSIWi8Xz/QewcOFC3nvvPV+WOWDV1dXs3r2bnJwcoOPM/OOPPyYjIwOA7OzsYffZ2Ww29u7d63lPWq0WvV4/Yj6zc436gHjvvfeIjY1l2rRpXe4/f6sQo9F40e0+/Nkbb7zB7NmzgZ63QRlO7224138h5eXlHD58mBkzZlBbW0tsbCwAY8eOpbbWN5s8Xqqnn36aVatWoT57MSSr1Yper/f84TIcf67Ky8uJjo7m3/7t31i4cCGPP/44LS0tI+YzO9eI2WqjN0uXLqWmpvvGWCtXrmTDhg1s3rzZB1UNjt7e28033wzAiy++SEBAAAsWLBjq8kQfNTc38+CDD/Lzn/+csLCuV4pTqVTDckfVXbt2ER0dTXJy8ojaCsfpdHLo0CF++ctfMmPGDJ588slu42DD9TM736gIiN///vc93n/06FHKy8vJysoCOk6HFy1axJYtW7pt91FdXe2X231c6L112rp1K7t37+b3v/+95xt2uG9lMtzrP197ezsPPvgg8+fPZ+7cuQDExMRw+vRpYmNjOX36NNHR0T6usv8+//xzCgsLKSoqwm6309TUxFNPPUVjYyNOpxONRuO3P1e9MRqNGI1Gz4ait9xyCxs3bhwRn9n5RnUX09SpU9mzZw+FhYUUFhZiNBrZunUrY8eOJT09nTfffBNFUfjiiy8IDw/3nD4OF0VFRWzatIkXX3yR4OBgz/3DfSuTvmzjMlwoisLjjz/OxIkTWbZsmef+zu8/gDfffJM5c+b4qsQBe/jhhykqKqKwsJD//u//JjU1lV/96ldcd9115OfnA7Bt27Zh99mNHTsWo9HIyZMnAdizZw+TJk0aEZ/Z+WQl9TnS09N5/fXXiY6ORlEUcnNz+eCDDwgODubpp58mJSXF1yX2i8lkwuFwEBnZceWyGTNmkJubC3R0O73xxhsEBATw85//nLS0NF+W2m/vv/8+Tz/9tGcbl5/85Ce+LmlAPv30U37wgx8wZcoUTz/9Qw89xFVXXcXKlSupqqpi3LhxPP/8857PcTj65JNP2Lx5Mxs2bKCsrIyf/exnNDQ0cMUVV5CXl4dWO7yuq3348GEef/xx2tvbSUhI4JlnnsHtdo+ozwwkIIQQQlzAqO5iEkIIcWESEEIIIXokASGEEKJHEhBCCCF6JAEhhBCiRxIQYsR74YUXePnllwFYt24df//734GOKaaZmZlkZWXR1tbGmjVryMzMZM2aNb4s94L279/Pk08+6esyxCgi01zFiPfCCy8QEhLCXXfd1eX+1atXc/XVV3tW0l999dX84x//6PPlbTtXAwsxUsl3txiRXnzxRd58802io6OJi4vz7Jj6r//6r9x4443YbDbeffddPvzwQ4qKimhubqalpYVFixZxzz33kJqayr//+79TWVkJwM9//nOuvvpqXnjhBUpLSykrK2PcuHH84he/uGC7yspKysvLqays5Ic//CF33nkn0LHK9uWXX0alUjF16lT+67/+i7q6uh6Pc65zF5v1dvxzzZo1izvvvJNdu3YRFBTEb3/7W8aMGeO1/3cxwvhqn3EhvGX//v3Kd7/7XaWlpUWx2WzKzTffrGzatElRFEV57LHHlHfeeafb14qiKDNnzvR8/dBDDyl79+5VFEVRKioqlFtuuUVRFEVZv369kp2drbS2tl603W233abY7XaltrZWufbaaxWHw6EcO3ZMmTt3rue6I1artdfjnOvjjz/2XFPhQsc/35QpU5SCggJFURRlzZo1ym9+85v+/4eKUUvOIMSI8+mnn3LzzTd79p8ayF4/f//73zlx4oTndlNTE83NzZ7jBQUFXbRdWloaWq2W6OhooqOjqa2t5eOPP+aWW27xbOTWuRXDhY4TGhp6wRp7Ov6526ADBAYGctNNNwGQnJzMRx991O//CzF6SUAI0QO3281rr72GTqfr9ti5Gx/21u7c/YUCAgJwOp0Der0L6cvxAwMDPbv4qtVqXC5Xn48vhMxiEiPON7/5Td577z3a2tpoampi165d/T7GDTfcwB//+EfP7cOHD19Su06pqam8++67WK1WoOPa0wM5jhBDQQJCjDjTp09n3rx5ZGVl8eMf/3hAu/A+/vjjHDhwgPnz5zNv3jz+53/+55LadUpKSuLee+/ljjvuYMGCBTz77LMDOo4QQ0GmuQohhOiRnEEIIYTokQSEEEKIHklACCGE6JEEhBBCiB5JQAghhOiRBIQQQogeSUAIIYTo0f8HlP4RyS644fMAAAAASUVORK5CYII=\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1501,11 +1570,11 @@
       "metadata": {
         "id": "PcBDorCcl6AS",
         "colab_type": "code",
+        "outputId": "3dad2352-5006-4258-fb6f-de2097813079",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 34
-        },
-        "outputId": "b862a1b9-a68c-404f-b25d-92cc215299d4"
+          "height": 0
+        }
       },
       "source": [
         "ci_95 = std*2\n",
@@ -1517,7 +1586,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "17.7912540435791"
+              "17.629011154174805"
             ]
           },
           "metadata": {
@@ -1558,11 +1627,11 @@
       "metadata": {
         "id": "qR8D_BKel6Ay",
         "colab_type": "code",
+        "outputId": "de0902ae-167b-4168-a566-fb3b26d7e2fc",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 458
-        },
-        "outputId": "505dd2a5-20ad-4ae0-b146-d91c112c32e3"
+          "height": 0
+        }
       },
       "source": [
         "sns.jointplot('n1', 'n2', data=df) "
@@ -1573,7 +1642,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<seaborn.axisgrid.JointGrid at 0x7fa19512ed68>"
+              "<seaborn.axisgrid.JointGrid at 0x7f460e332390>"
             ]
           },
           "metadata": {
@@ -1584,7 +1653,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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2AV0HAFMQWkloQqZvQNcBwBSEVhIqL8yVz+u55JrP61F5Ya5DFQHA0KARIwmF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\nrkJggg==\n",
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V6amnnnK6LCCi983C4TUjSXFHJSPRfVf+0sf6X9ePjTrauuPm8ZFfVzYEYh4m2VOgpU2zNtdyjpZLDbR7sL+9CSV370/oytBKT0/XmjVr9IMf/EChUEgLFy7U5MmTnS4LiIg2rdbWEdIjL34sKXZwTYgzdSd1dw+OSk/T/3R09fvYWDq6bNX/v9NRP7fvD3+S9HXoJtKGLyUeyhh5A+0eTISb9yd0ZWhJ0pw5czRnzhynywCiihUmIduO+8092tEhPXXZUmfIVuZor463tCV8f1e0OqIJj/QGs0NHeKqQ0IKTXLmmBbhZZUNA0feM6NbWEdITrx+O+rmSvBxtKp2qnEyfLHXfdNxbR5et063dbe+DCay4LOn6R6vijuBirYdJ3FwM5xFawABtq27ss2VSb6dbOxLap2/IQ6kftq24tedk+vTuo/kJN3IAI82104OAWyU62ih74YCeeP2wbFs609ahCZk+3XHzeL38YWDE7s0aiJ43D0ebxuTmYrgBoQUM0EAaJHrekxVoadPz9Uf6HaWNNEvqs81TrBOQWc9yn4F2DyYikQ7DaEai65DQAgaov2aKeNwWWOHpwGji7U0I9xiO7sHBGomuQ0ILGKDwN/JYO6Gbguk+mIhGDGAQSvJy4nbZudWfZ3hkXfz//3SEVPbCAd248g09VnnI6dKAhBBawCBF2wQ31gGKbvD0387Q4XV36ds3jtP59lBkqjJk2/pN/RGCC0ZgehAYpGgHKUa57coVPJalshcO6OEXD6grxsLarvePakPJ1JEtDJdtOBoxBmswDRwDbd4gtIDLdKHz63Ov2kNua7XoFr4fLFZg9XwMzOKmRozBGGjzBtODwGUYzHZIbhVtdw7AbQgt4DIk07ZG/3vmRKdLAPpFaAGXIVm2NfKmSc/XH0noXDDASYQWcBlMv89ptDdN3jRLHV3dayPhI0gILrgVjRhAD5UNgQFtXVSSl3NJ96BJMn1eWZbU2nFp7RxBYhY3dQ8ORmeoS2da2xNuxiC0gIsGexrx2nun6KEXDrhui6b+xAvaZFqrS3amdw9KA+sgZHoQuCjWacTbqhsjH1c2BDRrc62uf7Qqsv5TkpdjXGD1J1nW6pB8GGkBF8UaXYSvxxuJDfaEYTdiT0K4GSMt4KJYo4vw9VgjsbWvHTY6sDJ93shJyjmZPm0qncp6FlyLkRZwUayDD++4ebxmba6NeYaW25swLHWve2T6vDrf3qmOHrt2+Lwerb13CiEFYxBawEXRDj5080nD8YSnK3N6dUAOtDsS7md696D09Z6FiexDSGgBPfQ++HDW5lrjAkuS/rjp7sivw80jBFVySobuwbBEuggJLSAOE1u/e+4hONg2fsCtaMQA4jCx9btnU0is5pFHXvyYXS9gJEILiCPaQY9uYEkalR77n2/4HrJYI8WQbbNdE4zE9CAQR+/mjMzRXp1p7VBXP183nNIs6fYbxumDz0/HfEx4GvAqnzdmdyPbNSUHpxoxMjyWRg3xD3RjRvUfSYQW0Eu0Drt3H82XJH3vF+/p3T9+6Wh9XbYSqqGtI6Q/86bJ5/XEbCYxcc0Ol3KqEWP2TdfoG2NHj/jrMj0I9BBuXAi0tEXd9dzpwBqoltYObSqdGvOARxPX7JDaCC2gh0T2HzTJhEyfSvJy9NT90/uszbFdE0zE9CDQQ3/7D7pJeKeLWHqGUrQbp7lfCyYitIAeJmT6om7XFJ5Gm3XjONdMEX7vtmu17w9/UqClrU+AWZIW3nLpjdK9b5wGTERoAT3E2n8wPGJ5/oe3R23GGOld3h+47VptKJkqSVH3RbQl7fvDn0asHjhnoN2DQ9X1l0in33AgtGC0od5LL5FptOd/eHufr7v+0apBv2Y8s24cp//7xTmdbu1uW8/0eftscGvSlCaG3kC7B53q+hsqhBaMlegWRQMNtsFMo8WaVrxc14+/ImpIJvLadAYiGdE9CGMletJwvBb2oXLHzeOH9PnCnq8/0m+t0XbtoDMQyYrQgrESmRYbqhb28E7p1z9aFdkiqafhWj+ypX5rLcnL0abSqRzkiJTA9CCMlci02GDWe3pPJ/Y+UyvaNORwrh8l8tx0BiJVOBJaW7Zs0b59++T1enXttddq06ZNuvLKKyVJFRUV2r17t9LS0vTYY4/pr//6r50oEQbor9NPGvh6T7R1st/UH+nzuN779sV6nZxMn45fnJocLNamEE/P7sFEOgOd6vobKo5MD86aNUu//e1v9frrr+u6665TRUWFJOmzzz5TVVWVqqqq9Oyzz+qJJ55QKGTeAXwYGYlMiw10vSfadGIsPUdA8V7nckKHtSn0J9w92HCkRaO8Hn1j7Oi4//V3yKLbORK53/nOdyK/njFjhv7jP/5DklRTU6OioiJlZGRo4sSJmjRpkg4ePKi8vDwnyoQB+psWG+hOEAOZ5usZRv29Tu8RoddjyZtmqbWje7/4cCv7QGoFUlHc0Prqq69UUVGh5uZmzZ49W8XFxZHPrV27VmvXrr3sAl5++WXNnz9fkhQMBjV9+vTI5/x+v4LB4GW/BlLbQNZ7BtK63nsEFOt1BhqchBQQW9zQWrlypSZNmqTCwkLt3r1be/fu1VNPPaWMjAx9/PHHcZ948eLFOnnyZJ/rZWVlmjt3riRp+/bt8ng8uvfeey/jtwAMnWjrZNGMHe0dULjQKAEMjbihdeTIEf385z+XJM2dO1fbt2/X3//932v79u39PvHOnTvjfn7Pnj3av3+/du7cKevisQl+v1/Nzc2RxwSDQfn9/n5fCxgq4WB54vXDkV0oevN5PXq8eMpIlgXgoriNGO3t7erq+vqM1uXLl+v+++/XAw88oJaWwR86VldXp2effVbbt2+Xz/f1ukB+fr6qqqrU3t6uo0ePqqmpSdOmTRv06wCDUZKXo9EZ0X+e81gW90DBVcLdg3nXZio9Be68jTvSuuOOO1RfX69vf/vbkWulpaW65pprtGHDhkG/6Pr169Xe3q4lS5ZIkqZPn65169Zp8uTJmj9/vu6++255PB6tWbNGHs/QHucMxNLz/qxYLepdtk1gwVV67j04+6ZrnC1mBMQNrR//+MeSukdc1dXVCgQC6uzslKTLWod68803Y35u+fLlWr58+aCfGxiM3vdnxcI9U4CzEmp5X758ucaMGaMpU6YoI6O7x9+KcXw3YKJE7s/ininAeQmFVjAY1HPPPTfctQDDIpFd3vu7Pyu8liV1n1/FfVSAMxIKrby8PDU2Nio3l58yYZZEjy/p7/6skG1r7WuHdb69Ux0hO+5zASOp5zZOKd+IEfbhhx/qlVdeUU5OTmR6UJJef/31YSsMGArxdnnvvd1Tf2taLW19W+CjPRcwkmjEiOIXv/jFcNcBDItY036BlrY+03ybSqdq7WuHo4bTYF4DwNBLKLRycvgpEmaKNe1nSZHrgZY2PfTCAdnq3nT3nul/oX1/+FPC2znRUQiMnBSYAUUqi7b7uiX1uQ8r/HGgpU0vfxhQeWGuchIIIzoKgZFFaCGpRTu+pL+zrcLrVNECz5tmaexoLycEAw4x+zQwIAG9N6udtbm236m/4y1tA96dHXCCJWnm9WM1yusx/oDHRCT/7xDoJZFOwfA6Fbuzw+1sKXL4YyogtJByeo6gAi1tfda4WKcC3IvQQkrqOYJKZMcMAO5AaCEpXE7wMAUImIPQgvES3aoJgPloeYfx4m3VBCQ7S1JnqEtnWtudLmVEEFowXqxtlNheCanAlvS7P36pcxc6nS5lRDA9COPF2qopke2VaMIAzMJIC8aLtnNFIm3r4bWwQEubbH29FlbZEBjGagFcDkILxou2VVMi2yuxFgaYh+lBJIXBtK2zFoZkkGZZmn3TNSmxhZPESAspLNaaF0eNwCSeNEvfGDtaV43O6P/BSYDQQsoa7FoYAOekxngSiIJd3AHzEFpIaWzhBJiF6UEAgDEILQCAMQgtAIAxCC0AgDEILQCAMQgtAIAxCC0AgDEILQCAMQgtAIAxCC0AgDEILQCAMQgtAIAxHA2tHTt2KDc3V19++aUkybZtbdiwQQUFBSouLtbhw4edLA8A4DKOhdYXX3yhd999VxMmTIhcq6urU1NTk/bu3av169dr7dq1TpUHAHAhx0Jr06ZNKi8vl2VZkWs1NTUqKSmRZVmaMWOGzp49qxMnTjhVIgDAZRwJrbfeektZWVm6+eabL7keDAaVnZ0d+Tg7O1vBYHCkywMAuNSwHQK5ePFinTx5ss/1srIyVVRUaMeOHcP10gCAJDVsobVz586o1xsbG3Xs2DEtWLBAktTc3KzS0lK99NJL8vv9am5ujjy2ublZfr9/uEoEABhm2EIrltzcXL333nuRj/Pz87V7926NGzdO+fn5+s1vfqOioiJ9/PHHGjNmjLKyska6RACAS414aMUzZ84cvf322yooKJDP59PGjRudLgkA4CKOh1ZtbW3k15Zl6fHHH3ewGgCAm7EjBgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAY6U4XgOFR2RDQtupGHW9p04RMn8oLc1WSl+N0WQCGWJdt60xru64aneF0KSOC0EpClQ0BrdxzSG0dIUlSoKVNK/cckiSCC0gyoS5b5y50pkxoMT2YhLZVN0YCK6ytI6Rt1Y0OVQQAQ4PQSkLHW9oGdB0ATEFoJaEJmb4BXQcAUxBaSai8MFc+r+eSaz6vR+WFuQ5VBABDg0aMJBRutqB7EEh+aZalMaNS51t56vxOU0xJXg4hBaQAT5qVMp2DEtODAACDEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGM4Flq//vWvddddd6moqEhbt26NXK+oqFBBQYEKCwv1zjvvOFWea1U2BDRrc62uf7RKszbXqrIh4HRJADBiHNkRo76+XjU1NXrttdeUkZGhU6dOSZI+++wzVVVVqaqqSsFgUEuWLFF1dbU8Hk8/z5gaOCcLQG+pdgikIyOtXbt2admyZcrI6H6Tr776aklSTU2NioqKlJGRoYkTJ2rSpEk6ePCgEyW6EudkAegtfAhkqnAktJqamvT73/9eixYt0gMPPBAJpmAwqOzs7Mjj/H6/gsGgEyW6EudkAUh1wzY9uHjxYp08ebLP9bKyMoVCIZ05c0YvvviiDh06pLKyMtXU1AxXKUljQqZPgSgBxTlZAFLFsIXWzp07Y35u165dKigokGVZmjZtmtLS0nT69Gn5/X41NzdHHhcMBuX3+4erROOUF+ZesqYlcU4WgNTiyPTg3Llz9f7770uSPv/8c3V0dGjs2LHKz89XVVWV2tvbdfToUTU1NWnatGlOlOhKJXk52lQ6VTmZPlmScjJ92lQ6lSYMACnDke7BhQsXatWqVbrnnnvk9Xq1efNmWZalyZMna/78+br77rvl8Xi0Zs0aOgd74ZwsAD1ZktJT6I5bR0IrIyNDP/3pT6N+bvny5Vq+fPkIVwQAZrIldXY5XcXI4eRiQ1U2BLStulHHW9o0IdOn8sJcRmAAkh6hZSBuMgaQqlJoJjR5cJMxgFRFaBmIm4wBpCpCy0CxbibmJmMg9aRZlsaMSp2VHkLLQOWFufJ5L70VgJuMgdTkSbNSZrNciUYMI4WbLegeBJBqCC1DcZMxgFTE9CAAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYtLy7CDu3A0B8hJZLsHM7gMHosm2daW1PmV0xmB50CXZuBzAYoS5b5y50Ol3GiCG0XIKd2wGgf4SWS7BzOwD0j9ByCXZuB4D+0YjhEuzcDgD9I7RchJ3bAQwUh0ACAIyRaodAEloAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGMQWgAAYxBaAABjEFoAAGM4ElqffPKJ7r//fi1YsEClpaU6ePCgJMm2bW3YsEEFBQUqLi7W4cOHnSgPAOBSjoTWtm3b9KMf/Uivvvqq/vEf/1Hbtm2TJNXV1ampqUl79+7V+vXrtXbtWifKAwC4lCOhZSIyGHYAAAmpSURBVFmWzp8/L0k6d+6csrKyJEk1NTUqKSmRZVmaMWOGzp49qxMnTjhRIgDAhRw57nLVqlVaunSptmzZoq6uLv3bv/2bJCkYDCo7OzvyuOzsbAWDwUioAQBS27CF1uLFi3Xy5Mk+18vKylRfX6+VK1eqsLBQb7zxhlavXq2dO3cOVykAgCQxbKEVL4R+8pOfaPXq1ZKk+fPn67HHHpMk+f1+NTc3Rx7X3Nwsv98/XCUCAAzjyJpWVlaWPvjgA0lSfX29rrvuOklSfn6+KisrZdu2Dhw4oDFjxjA1CACIcGRNa/369dq4caM6Ozs1atQorVu3TpI0Z84cvf322yooKJDP59PGjRudKA8A4FKOhNatt96qPXv29LluWZYef/xxByoCADN12bbOtLbrqtEZTpcyItgRAwAMFuqyde5Cp9NljBhCCwBgDEemB51W2RDQtupGHW9p04RMn8oLc1WSl+N0WQCAfqRcaFU2BLRyzyG1dYQkSYGWNq3cc0iSCC4AcLmUmx7cVt0YCaywto6QtlU3OlQRACBRKRdax1vaBnQdANwszbI0ZlTqTJqlXGhNyPQN6DoAuJknzUqZdncpBUOrvDBXPq/nkms+r0flhbkOVQQASFTqjCkvCjdb0D0IAOZJudCSuoOLkAIA86Tc9CAAwFyEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYSbEjRiAQUGlpqdNlAMCQGTt2rJ577rmEHpdKLNu2baeLAAAgEUwPAgCMQWgBAIxBaAEAjEFoAQCMQWgBAIxBaAEAjEFoJeiTTz7R/fffrwULFqi0tFQHDx6UJNm2rQ0bNqigoEDFxcU6fPiww5V2+/Wvf6277rpLRUVF2rp1a+R6RUWFCgoKVFhYqHfeecfBCr+2Y8cO5ebm6ssvv5Tkzvd0y5Ytuuuuu1RcXKwf/ehHOnv2bORzbnxP6+rqVFhYqIKCAj3zzDNOlxPxxRdf6O/+7u909913q6ioSL/61a8kSS0tLVqyZInmzZunJUuW6MyZMw5X+rVQKKSSkhI9+OCDkqSjR49q0aJFKigoUFlZmdrb2x2uMMXYSMiSJUvs/fv327Zt2/v377cfeOCByK+XLl1qd3V12Q0NDfZ9993nZJm2bdv2e++9Z3//+9+3L1y4YNu2bZ88edK2bdv+9NNP7eLiYvvChQv2kSNH7DvvvNPu7Ox0slT7+PHj9j/8wz/Yf/M3f2OfOnXKtm13vqfvvPOO3dHRYdu2bW/dutXeunWrbdvufE87OzvtO++80z5y5Ih94cIFu7i42P70008drSksGAza//mf/2nbtm2fO3fOnjdvnv3pp5/aW7ZssSsqKmzbtu2KiorI++sGO3bssB9++GF72bJltm3b9ooVK+zf/va3tm3b9j/90z/Zzz//vJPlpRxGWgmyLEvnz5+XJJ07d05ZWVmSpJqaGpWUlMiyLM2YMUNnz57ViRMnnCxVu3bt0rJly5SRkSFJuvrqqyV111pUVKSMjAxNnDhRkyZNiowYnbJp0yaVl5fLsqzINTe+p9/5zneUnt69gcyMGTPU3NwsyZ3v6cGDBzVp0iRNnDhRGRkZKioqUk1NjaM1hWVlZWnKlCmSpCuuuEI33HCDgsFg5M9ckkpKSvTWW285WWZEc3Oz9u/fr/vuu09S9yxAfX29CgsLJUnf/e53XfPepgpCK0GrVq3S1q1bNWfOHG3ZskUPP/ywJCkYDCo7OzvyuOzsbAWDQafKlCQ1NTXp97//vRYtWqQHHngg8k20d61+v9/RWt966y1lZWXp5ptvvuS6G9/Tnl5++WXNnj1bkvveU8mdNUVz7NgxffLJJ5o+fbpOnToV+UFw/PjxOnXqlMPVddu4caPKy8uVltb9rfL06dO68sorIz/AuO3vZipIir0Hh8rixYt18uTJPtfLyspUX1+vlStXqrCwUG+88YZWr16tnTt3jnyRF8WrNRQK6cyZM3rxxRd16NAhlZWVOfbTYLw6KyoqtGPHDgeqii5erXPnzpUkbd++XR6PR/fee+9Il5dUzp8/rxUrVmjVqlW64oorLvmcZVmXjLydsm/fPo0bN07f+ta39P777ztdDi4itHqIF0I/+clPtHr1aknS/Pnz9dhjj0nq/ik2PFUkdU8n+P3+Ya1Til/rrl27VFBQIMuyNG3aNKWlpen06dN9ag0Gg8Nea6w6GxsbdezYMS1YsEBS9/tWWlqql156yZXvqSTt2bNH+/fv186dOyPfVJ14T/vjxpp66ujo0IoVK1RcXKx58+ZJ6p7CPnHihLKysnTixAmNGzfO4Sqljz76SLW1taqrq9OFCxf01Vdf6cknn9TZs2fV2dmp9PT0Efu7ia8xPZigrKwsffDBB5Kk+vp6XXfddZKk/Px8VVZWyrZtHThwQGPGjIlMczhl7ty5kZ8MP//8c3V0dGjs2LHKz89XVVWV2tvbdfToUTU1NWnatGmO1Jibm6v33ntPtbW1qq2tVXZ2tvbs2aPx48e78j2tq6vTs88+q+3bt8vn80Wuu+k9DZs6daqampp09OhRtbe3q6qqSvn5+Y7WFGbbtlavXq0bbrhBS5YsiVwP/5lLUmVlpe68806nSox45JFHVFdXp9raWv3sZz/TbbfdpqeeekozZ85UdXW1JOmVV15xzXubKhhpJWj9+vXauHGjOjs7NWrUKK1bt06SNGfOHL399tsqKCiQz+fTxo0bHa5UWrhwoVatWqV77rlHXq9XmzdvlmVZmjx5subPn6+7775bHo9Ha9askcfjcbrcPtz4nq5fv17t7e2Rb7TTp0/XunXrXPmepqena82aNfrBD36gUCikhQsXavLkyY7WFPbhhx/q1Vdf1U033RQZZT/88MNatmyZysrKtHv3bk2YMEFPP/20w5XGVl5eroceekhPP/20vvnNb2rRokVOl5RSOJoEAGAMpgcBAMYgtAAAxiC0AADGILQAAMYgtAAAxiC0gAH493//dxUVFenmm2/WoUOHnC4HSDmEFjAAN910k37+85/rr/7qr5wuBUhJ3FwMRHHs2DH98Ic/1C233KKGhgb5/X798z//s2688UanSwNSGiMtIIb//u//1ve+9z1VVVVpzJgxka17ADiH0AJi+MY3vqFvfvObkqQpU6YoEAg4XBEAQguIIXyIpiR5PB6FQiEHqwEgEVoAAIMQWsAAvPnmm5o9e7YaGhr04IMPaunSpU6XBKQUdnkHABiDkRYAwBiEFgDAGIQWAMAYhBYAwBiEFgDAGIQWAMAYhBYAwBj/H8slXs3h7RpZAAAAAElFTkSuQmCC\n",
             "text/plain": [
               "<Figure size 432x432 with 3 Axes>"
             ]
@@ -1612,11 +1681,11 @@
       "metadata": {
         "id": "7NsMKc6Nl6A3",
         "colab_type": "code",
+        "outputId": "cee13ea9-a9a6-4e76-81b6-8eae6dbb0f22",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 204
-        },
-        "outputId": "9dbd217a-c6fb-4146-c841-4d51023588b4"
+          "height": 0
+        }
       },
       "source": [
         "avg_df = df[['label', 'drug']]\n",
@@ -1633,12 +1702,12 @@
             "A value is trying to be set on a copy of a slice from a DataFrame.\n",
             "Try using .loc[row_indexer,col_indexer] = value instead\n",
             "\n",
-            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
             "  This is separate from the ipykernel package so we can avoid doing imports until\n",
             "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
             "A value is trying to be set on a copy of a slice from a DataFrame\n",
             "\n",
-            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
             "  after removing the cwd from sys.path.\n"
           ],
           "name": "stderr"
@@ -1660,11 +1729,11 @@
       "metadata": {
         "id": "YN1DgKJNl6BD",
         "colab_type": "code",
+        "outputId": "b162d0ab-ab94-46ae-f09e-5cb1bfecdc5a",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 296
-        },
-        "outputId": "e87426b6-65d3-465e-c796-9ca357f66468"
+          "height": 0
+        }
       },
       "source": [
         "plt.errorbar(np.arange(avg_df.shape[0]), avg_df['n'], yerr=ci_95, fmt='o')\n",
@@ -1688,7 +1757,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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mzJkAAD8/P+zfvx8+Pj44e/YsFixYgLy8PHh6enb488VY8a3tW9ez24rsiQPDJCdWA6Sg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\n",
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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1719,11 +1788,11 @@
         "scrolled": false,
         "id": "MQPUH1ogl6BH",
         "colab_type": "code",
+        "outputId": "b3117e20-e8d6-43b1-a9eb-e4281d32d0f5",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 282
-        },
-        "outputId": "7146010d-48a1-41df-88a5-b40fe661e9bd"
+          "height": 0
+        }
       },
       "source": [
         "actives = avg_df[abs(avg_df['n'])-ci_95 > 25]['n']\n",
@@ -1747,7 +1816,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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+            "image/png": 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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
@@ -1763,11 +1832,11 @@
       "metadata": {
         "id": "9rz2KjJ8l6BS",
         "colab_type": "code",
+        "outputId": "1d0ab9c5-fe4c-4789-a585-c4e530e3d23b",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 34
-        },
-        "outputId": "ddf862ff-81e9-42b1-a5b9-ab0943b3b089"
+          "height": 0
+        }
       },
       "source": [
         "# summary\n",
@@ -1778,7 +1847,7 @@
         {
           "output_type": "stream",
           "text": [
-            "(430, 5) (390, 3) 6\n"
+            "(430, 5) (391, 3) 6\n"
           ],
           "name": "stdout"
         }
@@ -1829,11 +1898,11 @@
       "metadata": {
         "id": "WwcvCbigl6BX",
         "colab_type": "code",
+        "outputId": "5154a1d4-2a56-4cbf-bfe7-e89c5a1baeda",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 119
-        },
-        "outputId": "76354129-03de-4030-bd52-911f3be07ff7"
+          "height": 0
+        }
       },
       "source": [
         "# 1 if condition for active is met, 0 otherwise\n",
@@ -1844,12 +1913,12 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
+            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
             "A value is trying to be set on a copy of a slice from a DataFrame.\n",
             "Try using .loc[row_indexer,col_indexer] = value instead\n",
             "\n",
-            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-            "  \"\"\"Entry point for launching an IPython kernel.\n"
+            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+            "  \n"
           ],
           "name": "stderr"
         }
@@ -1893,69 +1962,24 @@
       "metadata": {
         "id": "GBneufPbl6Bq",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 253
-        },
-        "outputId": "9ba52033-f1f4-4875-d239-49de0963e9fc"
+        "colab": {}
       },
       "source": [
         "import deepchem as dc"
       ],
-      "execution_count": 44,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "cell_type": "code",
       "metadata": {
         "id": "NRpnbgyAl6Bv",
         "colab_type": "code",
+        "outputId": "5a9a02f8-81eb-4669-dd83-61dd0ad27523",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 153
-        },
-        "outputId": "52195c65-ffbc-4f58-a911-25b4f61015e1"
+          "height": 0
+        }
       },
       "source": [
         "dataset_file = 'modulators.csv'\n",
@@ -1975,8 +1999,8 @@
             "About to start loading CSV from modulators.csv\n",
             "Loading shard 1 of size 8192.\n",
             "Featurizing sample 0\n",
-            "TIMING: featurizing shard 0 took 1.464 s\n",
-            "TIMING: dataset construction took 1.650 s\n",
+            "TIMING: featurizing shard 0 took 1.601 s\n",
+            "TIMING: dataset construction took 1.774 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -2002,11 +2026,11 @@
       "metadata": {
         "id": "-Ll5i93il6B1",
         "colab_type": "code",
+        "outputId": "fc14f85f-3775-4027-ce04-1e1dd6019f89",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 51
-        },
-        "outputId": "ce4923b9-7db0-403f-f647-143ece548814"
+          "height": 0
+        }
       },
       "source": [
         "transformer = dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)\n",
@@ -2017,7 +2041,7 @@
         {
           "output_type": "stream",
           "text": [
-            "TIMING: dataset construction took 0.211 s\n",
+            "TIMING: dataset construction took 0.200 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
diff --git a/examples/tutorials/10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb b/examples/tutorials/10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb
index 0ab8426364..a191b0fd3f 100644
--- a/examples/tutorials/10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb
+++ b/examples/tutorials/10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb
@@ -1,273 +1,341 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "# Tutorial Part 10: Exploring Quantum Chemistry with GDB1k"
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    },
+    "colab": {
+      "name": "10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb",
+      "provenance": []
+    }
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Most of the tutorials we've walked you through so far have focused on applications to the drug discovery realm, but DeepChem's tool suite works for molecular design problems generally. In this tutorial, we're going to walk through an example of how to train a simple molecular machine learning for the task of predicting the atomization energy of a molecule. (Remember that the atomization energy is the energy required to form 1 mol of gaseous atoms from 1 mol of the molecule in its standard state under standard conditions).\n",
-    "\n",
-    "## Colab\n",
-    "\n",
-    "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb)\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!chmod +x Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local\n",
-    "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-    "import sys\n",
-    "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "With our setup in place, let's do a few standard imports to get the ball rolling."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "import unittest\n",
-    "import numpy as np\n",
-    "import deepchem as dc\n",
-    "import numpy.random\n",
-    "from deepchem.utils.evaluate import Evaluator\n",
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "from sklearn.kernel_ridge import KernelRidge"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The ntext step we want to do is load our dataset. We're using a small dataset we've prepared that's pulled out of the larger GDB benchmarks. The dataset contains the atomization energies for 1K small molecules."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tasks = [\"atomization_energy\"]\n",
-    "dataset_file = \"../../datasets/gdb1k.sdf\"\n",
-    "smiles_field = \"smiles\"\n",
-    "mol_field = \"mol\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We now need a way to transform molecules that is useful for prediction of atomization energy. This representation draws on foundational work [1] that represents a molecule's 3D electrostatic structure as a 2D matrix $C$ of distances scaled by charges, where the $ij$-th element is represented by the following charge structure.\n",
-    "\n",
-    "$C_{ij} = \\frac{q_i q_j}{r_{ij}^2}$\n",
-    "\n",
-    "If you're observing carefully, you might ask, wait doesn't this mean that molecules with different numbers of atoms generate matrices of different sizes? In practice the trick to get around this is that the matrices are \"zero-padded.\" That is, if you're making coulomb matrices for a set of molecules, you pick a maximum number of atoms $N$, make the matrices $N\\times N$ and set to zero all the extra entries for this molecule. (There's a couple extra tricks that are done under the hood beyond this. Check out reference [1] or read the source code in DeepChem!)\n",
-    "\n",
-    "DeepChem has a built in featurization class `dc.feat.CoulombMatrixEig` that can generate these featurizations for you."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "featurizer = dc.feat.CoulombMatrixEig(23, remove_hydrogens=False)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that in this case, we set the maximum number of atoms to $N = 23$. Let's now load our dataset file into DeepChem. As in the previous tutorials, we use a `Loader` class, in particular `dc.data.SDFLoader` to load our `.sdf` file into DeepChem. The following snippet shows how we do this:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "loader = dc.data.SDFLoader(\n",
-    "      tasks=[\"atomization_energy\"], smiles_field=\"smiles\",\n",
-    "      featurizer=featurizer,\n",
-    "      mol_field=\"mol\")\n",
-    "dataset = loader.featurize(dataset_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For the purposes of this tutorial, we're going to do a random split of the dataset into training, validation, and test. In general, this split is weak and will considerably overestimate the accuracy of our models, but for now in this simple tutorial isn't a bad place to get started."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "random_splitter = dc.splits.RandomSplitter()\n",
-    "train_dataset, valid_dataset, test_dataset = random_splitter.train_valid_test_split(dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "One issue that Coulomb matrix featurizations have is that the range of entries in the matrix $C$ can be large. The charge $q_1q_2/r^2$ term can range very widely. In general, a wide range of values for inputs can throw off learning for the neural network. For this, a common fix is to normalize the input values so that they fall into a more standard range. Recall that the normalization transform applies to each feature $X_i$ of datapoint $X$\n",
-    "\n",
-    "$\\hat{X_i} = \\frac{X_i - \\mu_i}{\\sigma_i}$\n",
-    "\n",
-    "where $\\mu_i$ and $\\sigma_i$ are the mean and standard deviation of the $i$-th feature. This transformation enables the learning to proceed smoothly. A second point is that the atomization energies also fall across a wide range. So we apply an analogous transformation normalization transformation to the output to scale the energies better. We use DeepChem's transformation API to make this happen:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "transformers = [\n",
-    "    dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset),\n",
-    "    dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]\n",
-    "\n",
-    "for dataset in [train_dataset, valid_dataset, test_dataset]:\n",
-    "  for transformer in transformers:\n",
-    "      dataset = transformer.transform(dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {
-    "collapsed": true
-   },
-   "source": [
-    "Now that we have the data cleanly transformed, let's do some simple machine learning. We'll start by constructing a random forest on top of the data. We'll use DeepChem's hyperparameter tuning module to do this."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def rf_model_builder(model_params, model_dir):\n",
-    "  sklearn_model = RandomForestRegressor(**model_params)\n",
-    "  return dc.models.SklearnModel(sklearn_model, model_dir)\n",
-    "params_dict = {\n",
-    "    \"n_estimators\": [10, 100],\n",
-    "    \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n",
-    "}\n",
-    "\n",
-    "metric = dc.metrics.Metric(dc.metrics.mean_absolute_error)\n",
-    "optimizer = dc.hyper.HyperparamOpt(rf_model_builder)\n",
-    "best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(\n",
-    "    params_dict, train_dataset, valid_dataset, transformers,\n",
-    "    metric=metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's build one more model, a kernel ridge regression, on top of this raw data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def krr_model_builder(model_params, model_dir):\n",
-    "  sklearn_model = KernelRidge(**model_params)\n",
-    "  return dc.models.SklearnModel(sklearn_model, model_dir)\n",
-    "\n",
-    "params_dict = {\n",
-    "    \"kernel\": [\"laplacian\"],\n",
-    "    \"alpha\": [0.0001],\n",
-    "    \"gamma\": [0.0001]\n",
-    "}\n",
-    "\n",
-    "metric = dc.metrics.Metric(dc.metrics.mean_absolute_error)\n",
-    "optimizer = dc.hyper.HyperparamOpt(krr_model_builder)\n",
-    "best_krr, best_krr_hyperparams, all_krr_results = optimizer.hyperparam_search(\n",
-    "    params_dict, train_dataset, valid_dataset, transformers,\n",
-    "    metric=metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Congratulations! Time to join the Community!\n",
-    "\n",
-    "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
-    "\n",
-    "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
-    "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
-    "\n",
-    "## Join the DeepChem Gitter\n",
-    "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!\n",
-    "\n",
-    "# Bibliography:\n",
-    "\n",
-    "[1] https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.146401"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 1
-}
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "Rqb9ef8F2UJW",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Tutorial Part 10: Exploring Quantum Chemistry with GDB1k"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IcM5fm932UJY",
+        "colab_type": "text"
+      },
+      "source": [
+        "Most of the tutorials we've walked you through so far have focused on applications to the drug discovery realm, but DeepChem's tool suite works for molecular design problems generally. In this tutorial, we're going to walk through an example of how to train a simple molecular machine learning for the task of predicting the atomization energy of a molecule. (Remember that the atomization energy is the energy required to form 1 mol of gaseous atoms from 1 mol of the molecule in its standard state under standard conditions).\n",
+        "\n",
+        "## Colab\n",
+        "\n",
+        "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb)\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hiRnnJpG2UJY",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ub1J6G5w2UJd",
+        "colab_type": "text"
+      },
+      "source": [
+        "With our setup in place, let's do a few standard imports to get the ball rolling."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "19IsqJhx2UJe",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import os\n",
+        "import unittest\n",
+        "import numpy as np\n",
+        "import deepchem as dc\n",
+        "import numpy.random\n",
+        "from deepchem.utils.evaluate import Evaluator\n",
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "from sklearn.kernel_ridge import KernelRidge"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "AssRCAgB2UJi",
+        "colab_type": "text"
+      },
+      "source": [
+        "The ntext step we want to do is load our dataset. We're using a small dataset we've prepared that's pulled out of the larger GDB benchmarks. The dataset contains the atomization energies for 1K small molecules."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "j5PUW7452UJi",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "tasks = [\"atomization_energy\"]\n",
+        "dataset_file = \"../../datasets/gdb1k.sdf\"\n",
+        "smiles_field = \"smiles\"\n",
+        "mol_field = \"mol\""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hs0RDgHN2UJm",
+        "colab_type": "text"
+      },
+      "source": [
+        "We now need a way to transform molecules that is useful for prediction of atomization energy. This representation draws on foundational work [1] that represents a molecule's 3D electrostatic structure as a 2D matrix $C$ of distances scaled by charges, where the $ij$-th element is represented by the following charge structure.\n",
+        "\n",
+        "$C_{ij} = \\frac{q_i q_j}{r_{ij}^2}$\n",
+        "\n",
+        "If you're observing carefully, you might ask, wait doesn't this mean that molecules with different numbers of atoms generate matrices of different sizes? In practice the trick to get around this is that the matrices are \"zero-padded.\" That is, if you're making coulomb matrices for a set of molecules, you pick a maximum number of atoms $N$, make the matrices $N\\times N$ and set to zero all the extra entries for this molecule. (There's a couple extra tricks that are done under the hood beyond this. Check out reference [1] or read the source code in DeepChem!)\n",
+        "\n",
+        "DeepChem has a built in featurization class `dc.feat.CoulombMatrixEig` that can generate these featurizations for you."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Yadcs27f2UJn",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "featurizer = dc.feat.CoulombMatrixEig(23, remove_hydrogens=False)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Z9BJKEmd2UJq",
+        "colab_type": "text"
+      },
+      "source": [
+        "Note that in this case, we set the maximum number of atoms to $N = 23$. Let's now load our dataset file into DeepChem. As in the previous tutorials, we use a `Loader` class, in particular `dc.data.SDFLoader` to load our `.sdf` file into DeepChem. The following snippet shows how we do this:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "t-OldF822UJr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "loader = dc.data.SDFLoader(\n",
+        "      tasks=[\"atomization_energy\"], smiles_field=\"smiles\",\n",
+        "      featurizer=featurizer,\n",
+        "      mol_field=\"mol\")\n",
+        "dataset = loader.featurize(dataset_file)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gQ_zcAz92UJt",
+        "colab_type": "text"
+      },
+      "source": [
+        "For the purposes of this tutorial, we're going to do a random split of the dataset into training, validation, and test. In general, this split is weak and will considerably overestimate the accuracy of our models, but for now in this simple tutorial isn't a bad place to get started."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "GNhuNAZY2UJu",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "random_splitter = dc.splits.RandomSplitter()\n",
+        "train_dataset, valid_dataset, test_dataset = random_splitter.train_valid_test_split(dataset)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7ouN5JxO2UJ0",
+        "colab_type": "text"
+      },
+      "source": [
+        "One issue that Coulomb matrix featurizations have is that the range of entries in the matrix $C$ can be large. The charge $q_1q_2/r^2$ term can range very widely. In general, a wide range of values for inputs can throw off learning for the neural network. For this, a common fix is to normalize the input values so that they fall into a more standard range. Recall that the normalization transform applies to each feature $X_i$ of datapoint $X$\n",
+        "\n",
+        "$\\hat{X_i} = \\frac{X_i - \\mu_i}{\\sigma_i}$\n",
+        "\n",
+        "where $\\mu_i$ and $\\sigma_i$ are the mean and standard deviation of the $i$-th feature. This transformation enables the learning to proceed smoothly. A second point is that the atomization energies also fall across a wide range. So we apply an analogous transformation normalization transformation to the output to scale the energies better. We use DeepChem's transformation API to make this happen:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "eN7aqR042UJ0",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "transformers = [\n",
+        "    dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset),\n",
+        "    dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]\n",
+        "\n",
+        "for dataset in [train_dataset, valid_dataset, test_dataset]:\n",
+        "  for transformer in transformers:\n",
+        "      dataset = transformer.transform(dataset)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "collapsed": true,
+        "id": "IerJqoXo2UJ5",
+        "colab_type": "text"
+      },
+      "source": [
+        "Now that we have the data cleanly transformed, let's do some simple machine learning. We'll start by constructing a random forest on top of the data. We'll use DeepChem's hyperparameter tuning module to do this."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "scrolled": true,
+        "id": "UNG8EXtg2UJ6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def rf_model_builder(model_params, model_dir):\n",
+        "  sklearn_model = RandomForestRegressor(**model_params)\n",
+        "  return dc.models.SklearnModel(sklearn_model, model_dir)\n",
+        "params_dict = {\n",
+        "    \"n_estimators\": [10, 100],\n",
+        "    \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n",
+        "}\n",
+        "\n",
+        "metric = dc.metrics.Metric(dc.metrics.mean_absolute_error)\n",
+        "optimizer = dc.hyper.HyperparamOpt(rf_model_builder)\n",
+        "best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(\n",
+        "    params_dict, train_dataset, valid_dataset, transformers,\n",
+        "    metric=metric)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "FdhT0zDD2UJ-",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's build one more model, a kernel ridge regression, on top of this raw data."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LYTzmcyy2UJ-",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def krr_model_builder(model_params, model_dir):\n",
+        "  sklearn_model = KernelRidge(**model_params)\n",
+        "  return dc.models.SklearnModel(sklearn_model, model_dir)\n",
+        "\n",
+        "params_dict = {\n",
+        "    \"kernel\": [\"laplacian\"],\n",
+        "    \"alpha\": [0.0001],\n",
+        "    \"gamma\": [0.0001]\n",
+        "}\n",
+        "\n",
+        "metric = dc.metrics.Metric(dc.metrics.mean_absolute_error)\n",
+        "optimizer = dc.hyper.HyperparamOpt(krr_model_builder)\n",
+        "best_krr, best_krr_hyperparams, all_krr_results = optimizer.hyperparam_search(\n",
+        "    params_dict, train_dataset, valid_dataset, transformers,\n",
+        "    metric=metric)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IS9JTDyi2UKD",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Congratulations! Time to join the Community!\n",
+        "\n",
+        "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
+        "\n",
+        "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
+        "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
+        "\n",
+        "## Join the DeepChem Gitter\n",
+        "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!\n",
+        "\n",
+        "# Bibliography:\n",
+        "\n",
+        "[1] https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.98.146401"
+      ]
+    }
+  ]
+}
\ No newline at end of file
diff --git a/examples/tutorials/11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb b/examples/tutorials/11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb
index cb3f4b0c77..9e15381aac 100644
--- a/examples/tutorials/11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb
+++ b/examples/tutorials/11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb
@@ -1,523 +1,594 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial Part 11: Learning Unsupervised Embeddings for Molecules\n",
-    "\n",
-    "\n",
-    "In this example, we will use a `SeqToSeq` model to generate fingerprints for classifying molecules.  This is based on the following paper, although some of the implementation details are different: Xu et al., \"Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery\" (https://doi.org/10.1145/3107411.3107424).\n",
-    "\n",
-    "Many types of models require their inputs to have a fixed shape.  Since molecules can vary widely in the numbers of atoms and bonds they contain, this makes it hard to apply those models to them.  We need a way of generating a fixed length \"fingerprint\" for each molecule.  Various ways of doing this have been designed, such as Extended-Connectivity Fingerprints (ECFPs).  But in this example, instead of designing a fingerprint by hand, we will let a `SeqToSeq` model learn its own method of creating fingerprints.\n",
-    "\n",
-    "A `SeqToSeq` model performs sequence to sequence translation.  For example, they are often used to translate text from one language to another.  It consists of two parts called the \"encoder\" and \"decoder\".  The encoder is a stack of recurrent layers.  The input sequence is fed into it, one token at a time, and it generates a fixed length vector called the \"embedding vector\".  The decoder is another stack of recurrent layers that performs the inverse operation: it takes the embedding vector as input, and generates the output sequence.  By training it on appropriately chosen input/output pairs, you can create a model that performs many sorts of transformations.\n",
-    "\n",
-    "In this case, we will use SMILES strings describing molecules as the input sequences.  We will train the model as an autoencoder, so it tries to make the output sequences identical to the input sequences.  For that to work, the encoder must create embedding vectors that contain all information from the original sequence.  That's exactly what we want in a fingerprint, so perhaps those embedding vectors will then be useful as a way to represent molecules in other models!\n",
-    "\n",
-    "\n",
-    "## Colab\n",
-    "\n",
-    "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb)\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. This notebook will take a few hours to run on a GPU machine, so we encourage you to run it on Google colab unless you have a good GPU machine available."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!chmod +x Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local\n",
-    "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-    "import sys\n",
-    "sys.path.append('/usr/local/lib/python3.7/site-packages/')\n",
-    "import deepchem as dc"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's start by loading the data.  We will use the MUV dataset.  It includes 74,501 molecules in the training set, and 9313 molecules in the validation set, so it gives us plenty of SMILES strings to work with."
-   ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    },
+    "colab": {
+      "name": "11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb",
+      "provenance": []
+    }
   },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-      "  warnings.warn(msg, category=FutureWarning)\n",
-      "RDKit WARNING: [15:40:18] Enabling RDKit 2019.09.3 jupyter extensions\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hzpae9-r2aoK",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Tutorial Part 11: Learning Unsupervised Embeddings for Molecules\n",
+        "\n",
+        "\n",
+        "In this example, we will use a `SeqToSeq` model to generate fingerprints for classifying molecules.  This is based on the following paper, although some of the implementation details are different: Xu et al., \"Seq2seq Fingerprint: An Unsupervised Deep Molecular Embedding for Drug Discovery\" (https://doi.org/10.1145/3107411.3107424).\n",
+        "\n",
+        "Many types of models require their inputs to have a fixed shape.  Since molecules can vary widely in the numbers of atoms and bonds they contain, this makes it hard to apply those models to them.  We need a way of generating a fixed length \"fingerprint\" for each molecule.  Various ways of doing this have been designed, such as Extended-Connectivity Fingerprints (ECFPs).  But in this example, instead of designing a fingerprint by hand, we will let a `SeqToSeq` model learn its own method of creating fingerprints.\n",
+        "\n",
+        "A `SeqToSeq` model performs sequence to sequence translation.  For example, they are often used to translate text from one language to another.  It consists of two parts called the \"encoder\" and \"decoder\".  The encoder is a stack of recurrent layers.  The input sequence is fed into it, one token at a time, and it generates a fixed length vector called the \"embedding vector\".  The decoder is another stack of recurrent layers that performs the inverse operation: it takes the embedding vector as input, and generates the output sequence.  By training it on appropriately chosen input/output pairs, you can create a model that performs many sorts of transformations.\n",
+        "\n",
+        "In this case, we will use SMILES strings describing molecules as the input sequences.  We will train the model as an autoencoder, so it tries to make the output sequences identical to the input sequences.  For that to work, the encoder must create embedding vectors that contain all information from the original sequence.  That's exactly what we want in a fingerprint, so perhaps those embedding vectors will then be useful as a way to represent molecules in other models!\n",
+        "\n",
+        "\n",
+        "## Colab\n",
+        "\n",
+        "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb)\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. This notebook will take a few hours to run on a GPU machine, so we encourage you to run it on Google colab unless you have a good GPU machine available."
+      ]
     },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading raw samples now.\n",
-      "shard_size: 8192\n",
-      "About to start loading CSV from /var/folders/st/ds45jcqj2232lvhr0y9qt5sc0000gn/T/muv.csv.gz\n",
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-      "TIMING: featurizing shard 11 took 3.824 s\n",
-      "TIMING: dataset construction took 121.359 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 3.393 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.770 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 1.871 s\n",
-      "Loading dataset from disk.\n"
-     ]
-    }
-   ],
-   "source": [
-    "import deepchem as dc\n",
-    "tasks, datasets, transformers = dc.molnet.load_muv()\n",
-    "train_dataset, valid_dataset, test_dataset = datasets\n",
-    "train_smiles = train_dataset.ids\n",
-    "valid_smiles = valid_dataset.ids"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We need to define the \"alphabet\" for our `SeqToSeq` model, the list of all tokens that can appear in sequences.  (It's also possible for input and output sequences to have different alphabets, but since we're training it as an autoencoder, they're identical in this case.)  Make a list of every character that appears in any training sequence."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "tokens = set()\n",
-    "for s in train_smiles:\n",
-    "  tokens = tokens.union(set(c for c in s))\n",
-    "tokens = sorted(list(tokens))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Create the model and define the optimization method to use.  In this case, learning works much better if we gradually decrease the learning rate.  We use an `ExponentialDecay` to multiply the learning rate by 0.9 after each epoch."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "ci69aRSm2aoO",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "WARNING:tensorflow:Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
-      "WARNING: Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
-      "WARNING:tensorflow:Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
-      "WARNING: Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n"
-     ]
-    }
-   ],
-   "source": [
-    "from deepchem.models.optimizers import Adam, ExponentialDecay\n",
-    "max_length = max(len(s) for s in train_smiles)\n",
-    "batch_size = 100\n",
-    "batches_per_epoch = len(train_smiles)/batch_size\n",
-    "model = dc.models.SeqToSeq(tokens,\n",
-    "                           tokens,\n",
-    "                           max_length,\n",
-    "                           encoder_layers=2,\n",
-    "                           decoder_layers=2,\n",
-    "                           embedding_dimension=256,\n",
-    "                           model_dir='fingerprint',\n",
-    "                           batch_size=batch_size,\n",
-    "                           learning_rate=ExponentialDecay(0.004, 0.9, batches_per_epoch))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's train it!  The input to `fit_sequences()` is a generator that produces input/output pairs.  On a good GPU, this should take a few hours or less."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6bm1iYbw2aoT",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's start by loading the data.  We will use the MUV dataset.  It includes 74,501 molecules in the training set, and 9313 molecules in the validation set, so it gives us plenty of SMILES strings to work with."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ending global_step 999: Average loss 72.0029\n",
-      "Ending global_step 1999: Average loss 40.7221\n",
-      "Ending global_step 2999: Average loss 31.5364\n",
-      "Ending global_step 3999: Average loss 26.4576\n",
-      "Ending global_step 4999: Average loss 22.814\n",
-      "Ending global_step 5999: Average loss 19.5248\n",
-      "Ending global_step 6999: Average loss 16.4594\n",
-      "Ending global_step 7999: Average loss 18.8898\n",
-      "Ending global_step 8999: Average loss 13.476\n",
-      "Ending global_step 9999: Average loss 11.5528\n",
-      "Ending global_step 10999: Average loss 10.1594\n",
-      "Ending global_step 11999: Average loss 10.6434\n",
-      "Ending global_step 12999: Average loss 6.57057\n",
-      "Ending global_step 13999: Average loss 6.46177\n",
-      "Ending global_step 14999: Average loss 7.53559\n",
-      "Ending global_step 15999: Average loss 4.95809\n",
-      "Ending global_step 16999: Average loss 4.35039\n",
-      "Ending global_step 17999: Average loss 3.39137\n",
-      "Ending global_step 18999: Average loss 3.5216\n",
-      "Ending global_step 19999: Average loss 3.08579\n",
-      "Ending global_step 20999: Average loss 2.80738\n",
-      "Ending global_step 21999: Average loss 2.92217\n",
-      "Ending global_step 22999: Average loss 2.51032\n",
-      "Ending global_step 23999: Average loss 1.86265\n",
-      "Ending global_step 24999: Average loss 1.67088\n",
-      "Ending global_step 25999: Average loss 1.87016\n",
-      "Ending global_step 26999: Average loss 1.61166\n",
-      "Ending global_step 27999: Average loss 1.40708\n",
-      "Ending global_step 28999: Average loss 1.4488\n",
-      "Ending global_step 29801: Average loss 1.33917\n",
-      "TIMING: model fitting took 5619.924 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "def generate_sequences(epochs):\n",
-    "  for i in range(epochs):\n",
-    "    for s in train_smiles:\n",
-    "      yield (s, s)\n",
-    "\n",
-    "model.fit_sequences(generate_sequences(40))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see how well it works as an autoencoder.  We'll run the first 500 molecules from the validation set through it, and see how many of them are exactly reproduced."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "YnAnjl9d2aoU",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "672ec5a4-9d90-44f1-d503-98e9d9fbb40d"
+      },
+      "source": [
+        "import deepchem as dc\n",
+        "tasks, datasets, transformers = dc.molnet.load_muv()\n",
+        "train_dataset, valid_dataset, test_dataset = datasets\n",
+        "train_smiles = train_dataset.ids\n",
+        "valid_smiles = valid_dataset.ids"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n",
+            "RDKit WARNING: [15:40:18] Enabling RDKit 2019.09.3 jupyter extensions\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
+            "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
+            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading raw samples now.\n",
+            "shard_size: 8192\n",
+            "About to start loading CSV from /var/folders/st/ds45jcqj2232lvhr0y9qt5sc0000gn/T/muv.csv.gz\n",
+            "Loading shard 1 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 0 took 10.486 s\n",
+            "Loading shard 2 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 1 took 10.458 s\n",
+            "Loading shard 3 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 2 took 10.235 s\n",
+            "Loading shard 4 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 3 took 10.636 s\n",
+            "Loading shard 5 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 4 took 10.483 s\n",
+            "Loading shard 6 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 5 took 10.145 s\n",
+            "Loading shard 7 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 6 took 9.811 s\n",
+            "Loading shard 8 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 7 took 10.585 s\n",
+            "Loading shard 9 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 8 took 10.481 s\n",
+            "Loading shard 10 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 9 took 11.081 s\n",
+            "Loading shard 11 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "Featurizing sample 4000\n",
+            "Featurizing sample 5000\n",
+            "Featurizing sample 6000\n",
+            "Featurizing sample 7000\n",
+            "Featurizing sample 8000\n",
+            "TIMING: featurizing shard 10 took 10.569 s\n",
+            "Loading shard 12 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "TIMING: featurizing shard 11 took 3.824 s\n",
+            "TIMING: dataset construction took 121.359 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 3.393 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 1.770 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 1.871 s\n",
+            "Loading dataset from disk.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "reproduced 363 of 500 validation SMILES strings\n"
-     ]
-    }
-   ],
-   "source": [
-    "predicted = model.predict_from_sequences(valid_smiles[:500])\n",
-    "count = 0\n",
-    "for s,p in zip(valid_smiles[:500], predicted):\n",
-    "  if ''.join(p) == s:\n",
-    "    count += 1\n",
-    "print('reproduced', count, 'of 500 validation SMILES strings')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now we'll trying using the encoder as a way to generate molecular fingerprints.  We compute the embedding vectors for all molecules in the training and validation datasets, and create new datasets that have those as their feature vectors.  The amount of data is small enough that we can just store everything in memory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": [
-    "train_embeddings = model.predict_embeddings(train_smiles)\n",
-    "train_embeddings_dataset = dc.data.NumpyDataset(train_embeddings,\n",
-    "                                                train_dataset.y,\n",
-    "                                                train_dataset.w,\n",
-    "                                                train_dataset.ids)\n",
-    "\n",
-    "valid_embeddings = model.predict_embeddings(valid_smiles)\n",
-    "valid_embeddings_dataset = dc.data.NumpyDataset(valid_embeddings,\n",
-    "                                                valid_dataset.y,\n",
-    "                                                valid_dataset.w,\n",
-    "                                                valid_dataset.ids)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "For classification, we'll use a simple fully connected network with one hidden layer."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "EslVHE2m2aoY",
+        "colab_type": "text"
+      },
+      "source": [
+        "We need to define the \"alphabet\" for our `SeqToSeq` model, the list of all tokens that can appear in sequences.  (It's also possible for input and output sequences to have different alphabets, but since we're training it as an autoencoder, they're identical in this case.)  Make a list of every character that appears in any training sequence."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Ending global_step 999: Average loss 829.805\n",
-      "Ending global_step 1999: Average loss 450.42\n",
-      "Ending global_step 2999: Average loss 326.079\n",
-      "Ending global_step 3999: Average loss 265.199\n",
-      "Ending global_step 4999: Average loss 246.724\n",
-      "Ending global_step 5999: Average loss 224.64\n",
-      "Ending global_step 6999: Average loss 202.624\n",
-      "Ending global_step 7460: Average loss 213.885\n",
-      "TIMING: model fitting took 19.780 s\n"
-     ]
-    }
-   ],
-   "source": [
-    "classifier = dc.models.MultitaskClassifier(n_tasks=len(tasks),\n",
-    "                                                      n_features=256,\n",
-    "                                                      layer_sizes=[512])\n",
-    "classifier.fit(train_embeddings_dataset, nb_epoch=10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Find out how well it worked.  Compute the ROC AUC for the training and validation datasets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "nsE8e9xn2aoa",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "tokens = set()\n",
+        "for s in train_smiles:\n",
+        "  tokens = tokens.union(set(c for c in s))\n",
+        "tokens = sorted(list(tokens))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vgzyJ1-42aog",
+        "colab_type": "text"
+      },
+      "source": [
+        "Create the model and define the optimization method to use.  In this case, learning works much better if we gradually decrease the learning rate.  We use an `ExponentialDecay` to multiply the learning rate by 0.9 after each epoch."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NHKrymnM2aoh",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "fe3a80bd-9432-469c-d1ef-7bf0c39e42eb"
+      },
+      "source": [
+        "from deepchem.models.optimizers import Adam, ExponentialDecay\n",
+        "max_length = max(len(s) for s in train_smiles)\n",
+        "batch_size = 100\n",
+        "batches_per_epoch = len(train_smiles)/batch_size\n",
+        "model = dc.models.SeqToSeq(tokens,\n",
+        "                           tokens,\n",
+        "                           max_length,\n",
+        "                           encoder_layers=2,\n",
+        "                           decoder_layers=2,\n",
+        "                           embedding_dimension=256,\n",
+        "                           model_dir='fingerprint',\n",
+        "                           batch_size=batch_size,\n",
+        "                           learning_rate=ExponentialDecay(0.004, 0.9, batches_per_epoch))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
+            "WARNING:tensorflow:Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
+            "WARNING: Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
+            "WARNING:tensorflow:Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n",
+            "WARNING: Entity <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>> could not be transformed and will be executed as-is. Please report this to the AutgoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: converting <bound method Stack.call of <deepchem.models.layers.Stack object at 0x1a3cc7b0f0>>: AssertionError: Bad argument number for Name: 3, expecting 4\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hSr7FkSW2aok",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's train it!  The input to `fit_sequences()` is a generator that produces input/output pairs.  On a good GPU, this should take a few hours or less."
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computed_metrics: [0.97828427249789751, 0.98705973960125326, 0.966007068438685, 0.9874401066031584, 0.97794394675150698, 0.98021719680962449, 0.95318452689781941, 0.97185747562764213, 0.96389538770053473, 0.96798988621997473, 0.9690779239145807, 0.98544402211472004, 0.97762497271338133, 0.96843239633294886, 0.97753648081489997, 0.96504683675485614, 0.93547151958366914]\n",
-      "computed_metrics: [0.90790686952512678, 0.79891461649782913, 0.61900937081659968, 0.75241212956581671, 0.58678903240426017, 0.72765072765072758, 0.34929006085192693, 0.83986814712005553, 0.82379943502824859, 0.61844636844636847, 0.863620199146515, 0.68106930272108857, 0.98020477815699669, 0.85073580939032944, 0.781015678254942, 0.75399733510992673, nan]\n",
-      "Training set ROC AUC: {'mean-roc_auc_score': 0.97132433878689139}\n",
-      "Validation set ROC AUC: {'mean-roc_auc_score': 0.74592061629292239}\n"
-     ]
+      "cell_type": "code",
+      "metadata": {
+        "id": "NZ5l_g1E2aok",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "8db60a71-2724-4342-d513-13d7bcbad3f9"
+      },
+      "source": [
+        "def generate_sequences(epochs):\n",
+        "  for i in range(epochs):\n",
+        "    for s in train_smiles:\n",
+        "      yield (s, s)\n",
+        "\n",
+        "model.fit_sequences(generate_sequences(40))"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Ending global_step 999: Average loss 72.0029\n",
+            "Ending global_step 1999: Average loss 40.7221\n",
+            "Ending global_step 2999: Average loss 31.5364\n",
+            "Ending global_step 3999: Average loss 26.4576\n",
+            "Ending global_step 4999: Average loss 22.814\n",
+            "Ending global_step 5999: Average loss 19.5248\n",
+            "Ending global_step 6999: Average loss 16.4594\n",
+            "Ending global_step 7999: Average loss 18.8898\n",
+            "Ending global_step 8999: Average loss 13.476\n",
+            "Ending global_step 9999: Average loss 11.5528\n",
+            "Ending global_step 10999: Average loss 10.1594\n",
+            "Ending global_step 11999: Average loss 10.6434\n",
+            "Ending global_step 12999: Average loss 6.57057\n",
+            "Ending global_step 13999: Average loss 6.46177\n",
+            "Ending global_step 14999: Average loss 7.53559\n",
+            "Ending global_step 15999: Average loss 4.95809\n",
+            "Ending global_step 16999: Average loss 4.35039\n",
+            "Ending global_step 17999: Average loss 3.39137\n",
+            "Ending global_step 18999: Average loss 3.5216\n",
+            "Ending global_step 19999: Average loss 3.08579\n",
+            "Ending global_step 20999: Average loss 2.80738\n",
+            "Ending global_step 21999: Average loss 2.92217\n",
+            "Ending global_step 22999: Average loss 2.51032\n",
+            "Ending global_step 23999: Average loss 1.86265\n",
+            "Ending global_step 24999: Average loss 1.67088\n",
+            "Ending global_step 25999: Average loss 1.87016\n",
+            "Ending global_step 26999: Average loss 1.61166\n",
+            "Ending global_step 27999: Average loss 1.40708\n",
+            "Ending global_step 28999: Average loss 1.4488\n",
+            "Ending global_step 29801: Average loss 1.33917\n",
+            "TIMING: model fitting took 5619.924 s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_lxf1lmX2aoo",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's see how well it works as an autoencoder.  We'll run the first 500 molecules from the validation set through it, and see how many of them are exactly reproduced."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "NXDBtIvn2aop",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "59d18b07-0945-4bbb-ecf0-9860ed140e62"
+      },
+      "source": [
+        "predicted = model.predict_from_sequences(valid_smiles[:500])\n",
+        "count = 0\n",
+        "for s,p in zip(valid_smiles[:500], predicted):\n",
+        "  if ''.join(p) == s:\n",
+        "    count += 1\n",
+        "print('reproduced', count, 'of 500 validation SMILES strings')"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "reproduced 363 of 500 validation SMILES strings\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Rt9GLy502aou",
+        "colab_type": "text"
+      },
+      "source": [
+        "Now we'll trying using the encoder as a way to generate molecular fingerprints.  We compute the embedding vectors for all molecules in the training and validation datasets, and create new datasets that have those as their feature vectors.  The amount of data is small enough that we can just store everything in memory."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kdUfsbtZ2aov",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "train_embeddings = model.predict_embeddings(train_smiles)\n",
+        "train_embeddings_dataset = dc.data.NumpyDataset(train_embeddings,\n",
+        "                                                train_dataset.y,\n",
+        "                                                train_dataset.w,\n",
+        "                                                train_dataset.ids)\n",
+        "\n",
+        "valid_embeddings = model.predict_embeddings(valid_smiles)\n",
+        "valid_embeddings_dataset = dc.data.NumpyDataset(valid_embeddings,\n",
+        "                                                valid_dataset.y,\n",
+        "                                                valid_dataset.w,\n",
+        "                                                valid_dataset.ids)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lVvfGr562aoz",
+        "colab_type": "text"
+      },
+      "source": [
+        "For classification, we'll use a simple fully connected network with one hidden layer."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "tFmnnVNm2aoz",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "e4efa887-24ac-4fab-e17b-fe27fc905a2b"
+      },
+      "source": [
+        "classifier = dc.models.MultitaskClassifier(n_tasks=len(tasks),\n",
+        "                                                      n_features=256,\n",
+        "                                                      layer_sizes=[512])\n",
+        "classifier.fit(train_embeddings_dataset, nb_epoch=10)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Ending global_step 999: Average loss 829.805\n",
+            "Ending global_step 1999: Average loss 450.42\n",
+            "Ending global_step 2999: Average loss 326.079\n",
+            "Ending global_step 3999: Average loss 265.199\n",
+            "Ending global_step 4999: Average loss 246.724\n",
+            "Ending global_step 5999: Average loss 224.64\n",
+            "Ending global_step 6999: Average loss 202.624\n",
+            "Ending global_step 7460: Average loss 213.885\n",
+            "TIMING: model fitting took 19.780 s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "khdB2v7R2ao2",
+        "colab_type": "text"
+      },
+      "source": [
+        "Find out how well it worked.  Compute the ROC AUC for the training and validation datasets."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZlilhPvm2ao2",
+        "colab_type": "code",
+        "colab": {},
+        "outputId": "7ee4c5d3-2647-401a-ce5f-65ded20daaee"
+      },
+      "source": [
+        "import numpy as np\n",
+        "metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode=\"classification\")\n",
+        "train_score = classifier.evaluate(train_embeddings_dataset, [metric], transformers)\n",
+        "valid_score = classifier.evaluate(valid_embeddings_dataset, [metric], transformers)\n",
+        "print('Training set ROC AUC:', train_score)\n",
+        "print('Validation set ROC AUC:', valid_score)"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "computed_metrics: [0.97828427249789751, 0.98705973960125326, 0.966007068438685, 0.9874401066031584, 0.97794394675150698, 0.98021719680962449, 0.95318452689781941, 0.97185747562764213, 0.96389538770053473, 0.96798988621997473, 0.9690779239145807, 0.98544402211472004, 0.97762497271338133, 0.96843239633294886, 0.97753648081489997, 0.96504683675485614, 0.93547151958366914]\n",
+            "computed_metrics: [0.90790686952512678, 0.79891461649782913, 0.61900937081659968, 0.75241212956581671, 0.58678903240426017, 0.72765072765072758, 0.34929006085192693, 0.83986814712005553, 0.82379943502824859, 0.61844636844636847, 0.863620199146515, 0.68106930272108857, 0.98020477815699669, 0.85073580939032944, 0.781015678254942, 0.75399733510992673, nan]\n",
+            "Training set ROC AUC: {'mean-roc_auc_score': 0.97132433878689139}\n",
+            "Validation set ROC AUC: {'mean-roc_auc_score': 0.74592061629292239}\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ixqbRXnW2ao6",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Congratulations! Time to join the Community!\n",
+        "\n",
+        "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
+        "\n",
+        "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
+        "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
+        "\n",
+        "## Join the DeepChem Gitter\n",
+        "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
+      ]
     }
-   ],
-   "source": [
-    "import numpy as np\n",
-    "metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean, mode=\"classification\")\n",
-    "train_score = classifier.evaluate(train_embeddings_dataset, [metric], transformers)\n",
-    "valid_score = classifier.evaluate(valid_embeddings_dataset, [metric], transformers)\n",
-    "print('Training set ROC AUC:', train_score)\n",
-    "print('Validation set ROC AUC:', valid_score)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Congratulations! Time to join the Community!\n",
-    "\n",
-    "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
-    "\n",
-    "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
-    "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
-    "\n",
-    "## Join the DeepChem Gitter\n",
-    "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git a/examples/tutorials/12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb b/examples/tutorials/12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb
index c8697e88cb..00eee1f706 100644
--- a/examples/tutorials/12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb
+++ b/examples/tutorials/12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb
@@ -56,14 +56,10 @@
         "colab": {}
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
       "execution_count": 0,
       "outputs": []
@@ -73,11 +69,11 @@
       "metadata": {
         "id": "9uKkg6iXeYtb",
         "colab_type": "code",
+        "outputId": "8a41594b-a80f-4008-964b-2c10132278bf",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 304
-        },
-        "outputId": "8a41594b-a80f-4008-964b-2c10132278bf"
+        }
       },
       "source": [
         "import os\n",
@@ -104,7 +100,7 @@
         "print(\"Number of examples in dataset: %s\" % str(dataset.shape[0]))\n",
         "print(\"Number of examples in crystal dataset: %s\" % str(crystal_dataset.shape[0]))"
       ],
-      "execution_count": 2,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -207,11 +203,11 @@
       "metadata": {
         "id": "qEaaVKbKeYtz",
         "colab_type": "code",
+        "outputId": "e31aadd2-7663-4f00-815e-33f37bd0f828",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
-        },
-        "outputId": "e31aadd2-7663-4f00-815e-33f37bd0f828"
+        }
       },
       "source": [
         "num_to_display = 12\n",
@@ -220,7 +216,7 @@
         "    molecules.append(Chem.MolFromSmiles(data[\"mol\"]))\n",
         "display_images(mols_to_pngs(molecules, basename=\"dataset\"))"
       ],
-      "execution_count": 4,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "display_data",
@@ -383,11 +379,11 @@
       "metadata": {
         "id": "dBa2xXeNeYt7",
         "colab_type": "code",
+        "outputId": "1925e077-7b30-4812-c4fb-4e0619a31cce",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
-        },
-        "outputId": "1925e077-7b30-4812-c4fb-4e0619a31cce"
+        }
       },
       "source": [
         "num_to_display = 12\n",
@@ -396,7 +392,7 @@
         "    molecules.append(Chem.MolFromSmiles(data[\"mol\"]))\n",
         "display_images(mols_to_pngs(molecules, basename=\"crystal_dataset\"))"
       ],
-      "execution_count": 5,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "display_data",
@@ -559,11 +555,11 @@
       "metadata": {
         "id": "z_N2_csYeYuG",
         "colab_type": "code",
+        "outputId": "5b95d5e3-14c9-4e2e-9f68-dbb425a5e08e",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
-        },
-        "outputId": "5b95d5e3-14c9-4e2e-9f68-dbb425a5e08e"
+        }
       },
       "source": [
         "%matplotlib inline\n",
@@ -581,7 +577,7 @@
         "plt.grid(True)\n",
         "plt.show()"
       ],
-      "execution_count": 6,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "display_data",
@@ -625,11 +621,11 @@
       "metadata": {
         "id": "op-ucdRNeYuT",
         "colab_type": "code",
+        "outputId": "e310a830-7de8-4655-9367-dfdaa766c5f3",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 323
-        },
-        "outputId": "e310a830-7de8-4655-9367-dfdaa766c5f3"
+        }
       },
       "source": [
         "import deepchem as dc\n",
@@ -642,7 +638,7 @@
         "dataset = loader.featurize(dataset_file)\n",
         "crystal_dataset = loader.featurize(crystal_dataset_file)"
       ],
-      "execution_count": 8,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -697,11 +693,11 @@
       "metadata": {
         "id": "XISgZKsYeYuc",
         "colab_type": "code",
+        "outputId": "0ef562a7-6460-4d31-dff8-eeb6c0cbe302",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 119
-        },
-        "outputId": "0ef562a7-6460-4d31-dff8-eeb6c0cbe302"
+        }
       },
       "source": [
         "splitter = dc.splits.SpecifiedSplitter(dataset_file, \"Model\")\n",
@@ -710,7 +706,7 @@
         "#NOTE THE RENAMING:\n",
         "valid_dataset, test_dataset = test_dataset, valid_dataset"
       ],
-      "execution_count": 9,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -741,11 +737,11 @@
       "metadata": {
         "id": "-l8uMJpueYuj",
         "colab_type": "code",
+        "outputId": "7692477d-3e18-41b1-870e-7e5b8dc3b8a0",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 1000
-        },
-        "outputId": "7692477d-3e18-41b1-870e-7e5b8dc3b8a0"
+        }
       },
       "source": [
         "print(valid_dataset.ids)\n",
@@ -753,7 +749,7 @@
         "              for compound in islice(valid_dataset.ids, num_to_display)]\n",
         "display_images(mols_to_pngs(valid_mols, basename=\"valid_set\"))"
       ],
-      "execution_count": 10,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -928,11 +924,11 @@
       "metadata": {
         "id": "lT7PxXreeYut",
         "colab_type": "code",
+        "outputId": "53ec6ecc-0751-4ebb-ce8c-130e4fdce051",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 153
-        },
-        "outputId": "53ec6ecc-0751-4ebb-ce8c-130e4fdce051"
+        }
       },
       "source": [
         "print(\"Number of compounds in train set\")\n",
@@ -944,7 +940,7 @@
         "print(\"Number of compounds in crystal set\")\n",
         "print(len(crystal_dataset))"
       ],
-      "execution_count": 11,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -978,11 +974,11 @@
       "metadata": {
         "id": "lKQfu5pveYuy",
         "colab_type": "code",
+        "outputId": "260f065a-1fe1-4339-a9b5-9953bf6b0007",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
-        },
-        "outputId": "260f065a-1fe1-4339-a9b5-9953bf6b0007"
+        }
       },
       "source": [
         "transformers = [\n",
@@ -995,7 +991,7 @@
         "      datasets[i] = transformer.transform(dataset)\n",
         "train_dataset, valid_dataset, test_dataset, crystal_dataset = datasets"
       ],
-      "execution_count": 12,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1052,11 +1048,11 @@
       "metadata": {
         "id": "jU49euh3eYvC",
         "colab_type": "code",
+        "outputId": "ea62e7d9-9824-4228-9aad-6c127a7107d5",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 765
-        },
-        "outputId": "ea62e7d9-9824-4228-9aad-6c127a7107d5"
+        }
       },
       "source": [
         "from sklearn.ensemble import RandomForestClassifier\n",
@@ -1075,7 +1071,7 @@
         "    params_dict, train_dataset, valid_dataset, transformers,\n",
         "    metric=metric)"
       ],
-      "execution_count": 13,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1134,11 +1130,11 @@
       "metadata": {
         "id": "jqjBgMxHeYvO",
         "colab_type": "code",
+        "outputId": "950f98ca-aaeb-4386-e5b8-f302925fdd74",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 479
-        },
-        "outputId": "950f98ca-aaeb-4386-e5b8-f302925fdd74"
+        }
       },
       "source": [
         "import numpy.random\n",
@@ -1158,7 +1154,7 @@
         "    params_dict, train_dataset, valid_dataset, transformers,\n",
         "    metric=metric)"
       ],
-      "execution_count": 14,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1209,11 +1205,11 @@
       "metadata": {
         "id": "VeINkC9ReYvW",
         "colab_type": "code",
+        "outputId": "6e8fe05b-5bca-4790-fc2a-8986c37cc43b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 207
-        },
-        "outputId": "6e8fe05b-5bca-4790-fc2a-8986c37cc43b"
+        }
       },
       "source": [
         "from deepchem.utils.evaluate import Evaluator\n",
@@ -1246,7 +1242,7 @@
         "    [metric], rf_crystal_csv_out, rf_crystal_stats_out)\n",
         "print(\"RF Crystal set R^2 %f\" % (rf_crystal_score[\"roc_auc_score\"]))"
       ],
-      "execution_count": 15,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1277,11 +1273,11 @@
       "metadata": {
         "id": "LMDBBUtJeYvb",
         "colab_type": "code",
+        "outputId": "c27f8711-bd89-4930-efcc-2556453a533c",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 207
-        },
-        "outputId": "c27f8711-bd89-4930-efcc-2556453a533c"
+        }
       },
       "source": [
         "dnn_train_csv_out = \"dnn_train_classifier.csv\"\n",
@@ -1312,7 +1308,7 @@
         "    [metric], dnn_crystal_csv_out, dnn_crystal_stats_out)\n",
         "print(\"DNN Crystal set AUC %f\" % (dnn_crystal_score[\"roc_auc_score\"]))"
       ],
-      "execution_count": 16,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1353,11 +1349,11 @@
       "metadata": {
         "id": "NqEbvd2ZeYvg",
         "colab_type": "code",
+        "outputId": "0259bdef-9184-4214-a101-64f12490857b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 323
-        },
-        "outputId": "0259bdef-9184-4214-a101-64f12490857b"
+        }
       },
       "source": [
         "#Make directories to store the raw and featurized datasets.\n",
@@ -1368,7 +1364,7 @@
         "dataset = loader.featurize(dataset_file)\n",
         "crystal_dataset = loader.featurize(crystal_dataset_file)"
       ],
-      "execution_count": 17,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1413,11 +1409,11 @@
       "metadata": {
         "id": "dPEHZbTreYvo",
         "colab_type": "code",
+        "outputId": "3cbf271f-db6a-4c1e-bc39-524c3d992765",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 119
-        },
-        "outputId": "3cbf271f-db6a-4c1e-bc39-524c3d992765"
+        }
       },
       "source": [
         "splitter = dc.splits.SpecifiedSplitter(dataset_file, \"Model\")\n",
@@ -1426,7 +1422,7 @@
         "#NOTE THE RENAMING:\n",
         "valid_dataset, test_dataset = test_dataset, valid_dataset"
       ],
-      "execution_count": 18,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1447,11 +1443,11 @@
       "metadata": {
         "id": "leu2sy1HeYvx",
         "colab_type": "code",
+        "outputId": "e42b9fe4-2b19-41a3-f23a-22062f278583",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 153
-        },
-        "outputId": "e42b9fe4-2b19-41a3-f23a-22062f278583"
+        }
       },
       "source": [
         "print(\"Number of compounds in train set\")\n",
@@ -1463,7 +1459,7 @@
         "print(\"Number of compounds in crystal set\")\n",
         "print(len(crystal_dataset))"
       ],
-      "execution_count": 19,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1486,11 +1482,11 @@
       "metadata": {
         "id": "NmlQz-9ZeYv2",
         "colab_type": "code",
+        "outputId": "67060adc-8c11-4386-a679-c7a871f84db0",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 357
-        },
-        "outputId": "67060adc-8c11-4386-a679-c7a871f84db0"
+        }
       },
       "source": [
         "transformers = [\n",
@@ -1503,7 +1499,7 @@
         "      datasets[i] = transformer.transform(dataset)\n",
         "train_dataset, valid_dataset, test_dataset, crystal_dataset = datasets"
       ],
-      "execution_count": 20,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1550,11 +1546,11 @@
       "metadata": {
         "id": "BgB88N9leYv7",
         "colab_type": "code",
+        "outputId": "d7099322-f193-401e-9e9a-242ee410a47c",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 765
-        },
-        "outputId": "d7099322-f193-401e-9e9a-242ee410a47c"
+        }
       },
       "source": [
         "from sklearn.ensemble import RandomForestRegressor\n",
@@ -1573,7 +1569,7 @@
         "    params_dict, train_dataset, valid_dataset, transformers,\n",
         "    metric=metric)"
       ],
-      "execution_count": 21,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1632,11 +1628,11 @@
       "metadata": {
         "id": "qEhs3pUueYv_",
         "colab_type": "code",
+        "outputId": "b5abdeba-a769-4b35-f02d-4e71a9661c47",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 717
-        },
-        "outputId": "b5abdeba-a769-4b35-f02d-4e71a9661c47"
+        }
       },
       "source": [
         "import numpy.random\n",
@@ -1656,7 +1652,7 @@
         "    params_dict, train_dataset, valid_dataset, transformers,\n",
         "    metric=metric)"
       ],
-      "execution_count": 22,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1711,11 +1707,11 @@
       "metadata": {
         "id": "1c-1CX5weYwC",
         "colab_type": "code",
+        "outputId": "fe8b926e-ac8c-4e97-df5d-e28ee2d91caf",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 153
-        },
-        "outputId": "fe8b926e-ac8c-4e97-df5d-e28ee2d91caf"
+        }
       },
       "source": [
         "from deepchem.utils.evaluate import Evaluator\n",
@@ -1748,7 +1744,7 @@
         "    [metric], rf_crystal_csv_out, rf_crystal_stats_out)\n",
         "print(\"RF Crystal set R^2 %f\" % (rf_crystal_score[\"r2_score\"]))"
       ],
-      "execution_count": 23,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1771,11 +1767,11 @@
       "metadata": {
         "id": "D7g92mUweYwF",
         "colab_type": "code",
+        "outputId": "890443b8-87b8-4f5d-9187-60cc1c4924c4",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 153
-        },
-        "outputId": "890443b8-87b8-4f5d-9187-60cc1c4924c4"
+        }
       },
       "source": [
         "dnn_train_csv_out = \"dnn_train_regressor.csv\"\n",
@@ -1806,7 +1802,7 @@
         "    [metric], dnn_crystal_csv_out, dnn_crystal_stats_out)\n",
         "print(\"DNN Crystal set R^2 %f\" % (dnn_crystal_score[\"r2_score\"]))\n"
       ],
-      "execution_count": 24,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "stream",
@@ -1829,11 +1825,11 @@
       "metadata": {
         "id": "fPpZmZbqeYwK",
         "colab_type": "code",
+        "outputId": "16590d66-2014-4aff-e522-3cfb446faa92",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
-        },
-        "outputId": "16590d66-2014-4aff-e522-3cfb446faa92"
+        }
       },
       "source": [
         "task = \"pIC50\"\n",
@@ -1848,7 +1844,7 @@
         "plt.plot([2, 11], [2, 11], color='k')\n",
         "plt.show()"
       ],
-      "execution_count": 25,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "display_data",
@@ -1869,11 +1865,11 @@
       "metadata": {
         "id": "OBCPydPleYwO",
         "colab_type": "code",
+        "outputId": "af86365a-230e-4ba5-ba94-a1de0a888007",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 295
-        },
-        "outputId": "af86365a-230e-4ba5-ba94-a1de0a888007"
+        }
       },
       "source": [
         "task = \"pIC50\"\n",
@@ -1888,7 +1884,7 @@
         "plt.plot([2, 11], [2, 11], color='k')\n",
         "plt.show()"
       ],
-      "execution_count": 26,
+      "execution_count": 0,
       "outputs": [
         {
           "output_type": "display_data",
diff --git a/examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb b/examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb
index 2b0904fa16..bbae5b600d 100644
--- a/examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb
+++ b/examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb
@@ -1,785 +1,1290 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial Part 13: Modeling Protein-Ligand Interactions\n",
-    "\n",
-    "In this tutorial, we'll walk you through the use of machine learning methods to predict the binding energy of a protein-ligand complex. Recall that a ligand is some small molecule which interacts (usually non-covalently) with a protein. As you work through the tutorial, you'll trace an arc from loading a raw dataset to fitting a random forest model to predict binding affinities. We'll take the following steps to get there:\n",
-    "\n",
-    "1. Loading a chemical dataset, consisting of a series of protein-ligand complexes.\n",
-    "2. Featurizing each protein-ligand complexes with various featurization schemes. \n",
-    "3. Fitting a series of models with these featurized protein-ligand complexes.\n",
-    "4. Visualizing the results.\n",
-    "\n",
-    "To start the tutorial, we'll use a simple pre-processed dataset file that comes in the form of a gzipped file. Each row is a molecular system, and each column represents a different piece of information about that system. For instance, in this example, every row reflects a protein-ligand complex, and the following columns are present: a unique complex identifier; the SMILES string of the ligand; the binding affinity (Ki) of the ligand to the protein in the complex; a Python `list` of all lines in a PDB file for the protein alone; and a Python `list` of all lines in a ligand file for the ligand alone.\n",
-    "\n",
-    "## Colab\n",
-    "\n",
-    "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb)\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!chmod +x Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local\n",
-    "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-    "import sys\n",
-    "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import deepchem as dc\n",
-    "from deepchem.utils import download_url\n",
-    "\n",
-    "import os\n",
-    "\n",
-    "data_dir = dc.utils.get_data_dir()\n",
-    "dataset_file = os.path.join(data_dir, \"pdbbind_core_df.csv.gz\")\n",
-    "\n",
-    "if not os.path.exists(dataset_file):\n",
-    "    print('File does not exist. Downloading file...')\n",
-    "    download_url(\"https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/pdbbind_core_df.csv.gz\")\n",
-    "    print('File downloaded...')\n",
-    "\n",
-    "raw_dataset = dc.utils.save.load_from_disk(dataset_file)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what `dataset` looks like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Type of dataset is: <class 'pandas.core.frame.DataFrame'>\n",
-      "  pdb_id                                             smiles  \\\n",
-      "0   2d3u        CC1CCCCC1S(O)(O)NC1CC(C2CCC(CN)CC2)SC1C(O)O   \n",
-      "1   3cyx  CC(C)(C)NC(O)C1CC2CCCCC2C[NH+]1CC(O)C(CC1CCCCC...   \n",
-      "2   3uo4        OC(O)C1CCC(NC2NCCC(NC3CCCCC3C3CCCCC3)N2)CC1   \n",
-      "3   1p1q                         CC1ONC(O)C1CC([NH3+])C(O)O   \n",
-      "4   3ag9  NC(O)C(CCC[NH2+]C([NH3+])[NH3+])NC(O)C(CCC[NH2...   \n",
-      "\n",
-      "                                          complex_id  \\\n",
-      "0    2d3uCC1CCCCC1S(O)(O)NC1CC(C2CCC(CN)CC2)SC1C(O)O   \n",
-      "1  3cyxCC(C)(C)NC(O)C1CC2CCCCC2C[NH+]1CC(O)C(CC1C...   \n",
-      "2    3uo4OC(O)C1CCC(NC2NCCC(NC3CCCCC3C3CCCCC3)N2)CC1   \n",
-      "3                     1p1qCC1ONC(O)C1CC([NH3+])C(O)O   \n",
-      "4  3ag9NC(O)C(CCC[NH2+]C([NH3+])[NH3+])NC(O)C(CCC...   \n",
-      "\n",
-      "                                         protein_pdb  \\\n",
-      "0  ['HEADER    2D3U PROTEIN\\n', 'COMPND    2D3U P...   \n",
-      "1  ['HEADER    3CYX PROTEIN\\n', 'COMPND    3CYX P...   \n",
-      "2  ['HEADER    3UO4 PROTEIN\\n', 'COMPND    3UO4 P...   \n",
-      "3  ['HEADER    1P1Q PROTEIN\\n', 'COMPND    1P1Q P...   \n",
-      "4  ['HEADER    3AG9 PROTEIN\\n', 'COMPND    3AG9 P...   \n",
-      "\n",
-      "                                          ligand_pdb  \\\n",
-      "0  ['COMPND    2d3u ligand \\n', 'AUTHOR    GENERA...   \n",
-      "1  ['COMPND    3cyx ligand \\n', 'AUTHOR    GENERA...   \n",
-      "2  ['COMPND    3uo4 ligand \\n', 'AUTHOR    GENERA...   \n",
-      "3  ['COMPND    1p1q ligand \\n', 'AUTHOR    GENERA...   \n",
-      "4  ['COMPND    3ag9 ligand \\n', 'AUTHOR    GENERA...   \n",
-      "\n",
-      "                                         ligand_mol2  label  \n",
-      "0  ['### \\n', '### Created by X-TOOL on Thu Aug 2...   6.92  \n",
-      "1  ['### \\n', '### Created by X-TOOL on Thu Aug 2...   8.00  \n",
-      "2  ['### \\n', '### Created by X-TOOL on Fri Aug 2...   6.52  \n",
-      "3  ['### \\n', '### Created by X-TOOL on Thu Aug 2...   4.89  \n",
-      "4  ['### \\n', '### Created by X-TOOL on Thu Aug 2...   8.05  \n",
-      "Shape of dataset is: (193, 7)\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(\"Type of dataset is: %s\" % str(type(raw_dataset)))\n",
-    "print(raw_dataset[:5])\n",
-    "print(\"Shape of dataset is: %s\" % str(raw_dataset.shape))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Visualizing what these proteins and ligands look like will help us build intuition and understanding about these systems. Let's write a bit of code to help us view our molecules. We'll use the `nglview` library to help us do this. You can install this library by calling `pip install nglview`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "af7deefebf5f4f9b885c5e18fcf7a81f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "_ColormakerRegistry()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "import nglview\n",
-    "import tempfile\n",
-    "import os\n",
-    "import mdtraj as md\n",
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use the `mdtraj` library to help us manipulate both ligand and protein objects. We'll use the following convenience function to parse in the ligand and protein representations above into mdtraj."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def convert_lines_to_mdtraj(molecule_lines):\n",
-    "  molecule_lines = molecule_lines.strip('[').strip(']').replace(\"'\",\"\").replace(\"\\\\n\", \"\").split(\", \")\n",
-    "  tempdir = tempfile.mkdtemp()\n",
-    "  molecule_file = os.path.join(tempdir, \"molecule.pdb\")\n",
-    "  with open(molecule_file, \"w\") as f:\n",
-    "    for line in molecule_lines:\n",
-    "        f.write(\"%s\\n\" % line)\n",
-    "  molecule_mdtraj = md.load(molecule_file)\n",
-    "  return molecule_mdtraj"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at the first protein ligand pair in our dataset:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "first_protein, first_ligand = raw_dataset.iloc[0][\"protein_pdb\"], raw_dataset.iloc[0][\"ligand_pdb\"]\n",
-    "protein_mdtraj = convert_lines_to_mdtraj(first_protein)\n",
-    "ligand_mdtraj = convert_lines_to_mdtraj(first_ligand)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use the convenience function `nglview.show_mdtraj` in order to view our proteins and ligands."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "813bd1db316e4fe6a687e0c3607bb1e3",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "NGLWidget()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "v = nglview.show_mdtraj(ligand_mdtraj)\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that we have an idea of what the ligand looks like, let's take a look at our protein:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "82bdffe1b5cb4fa7a674f49338aecc29",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "NGLWidget()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "view = nglview.show_mdtraj(protein_mdtraj)\n",
-    "view"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Can we view the complex with both protein and ligand? Yes, but we'll need the following helper function to join the two mdtraj files for the protein and ligand."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def combine_mdtraj(protein, ligand):\n",
-    "  chain = protein.topology.add_chain()\n",
-    "  residue = protein.topology.add_residue(\"LIG\", chain, resSeq=1)\n",
-    "  for atom in ligand.topology.atoms:\n",
-    "      protein.topology.add_atom(atom.name, atom.element, residue)\n",
-    "  protein.xyz = np.hstack([protein.xyz, ligand.xyz])\n",
-    "  protein.topology.create_standard_bonds()\n",
-    "  return protein\n",
-    "complex_mdtraj = combine_mdtraj(protein_mdtraj, ligand_mdtraj)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now visualize our complex"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9f2875889cfa4756b93ecd768fbba575",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "NGLWidget()"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "v = nglview.show_mdtraj(complex_mdtraj)\n",
-    "v"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can see that the ligand slots into a groove on the outer edge of the protein. Ok, now that we've got our basic visualization tools up and running, let's see if we can use some machine learning to understand our dataset of protein-ligand systems better.\n",
-    "\n",
-    "In order to do this, we'll need a way to transform our protein-ligand complexes into representations which can be used by learning algorithms. Ideally, we'd have neural protein-ligand complex fingerprints, but DeepChem doesn't yet have a good learned fingerprint of this sort. We do however have well tuned manual featurizers that can help us with our challenge here.\n",
-    "\n",
-    "We'll make of two types of fingerprints in the rest of the tutorial, the circular fingerprints and the grid descriptors. The grid descriptors convert a 3D volume containing an arragment of atoms into a fingerprint. This is really useful for understanding protein-ligand complexes since it will allow us to transfer protein-ligand complexes into vectors that can be passed into a simple machine learning algorithms. Let's see how we can create such a fingerprint in DeepChem. We'll make use of the `dc.feat.RdkitGridFeaturizer` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "grid_featurizer = dc.feat.RdkitGridFeaturizer(\n",
-    "    voxel_width=16.0, feature_types=[\"ecfp\", \"splif\", \"hbond\", \"pi_stack\", \"cation_pi\", \"salt_bridge\"], \n",
-    "    ecfp_power=5, splif_power=5, parallel=True, flatten=True, sanitize=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we'll create circular fingerprints. These convert small molecules into a vector of fragments. You can create these fingerprints with the `dc.feat.CircularFingerprint` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "compound_featurizer = dc.feat.CircularFingerprint(size=128)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The convenience loader `dc.molnet.load_pdbbind_grid` will take care of performing featurizing the pdbbind dataset under the hood for us. We'll use this helper method to perform our featurization for us. We'll featurize the \"refined\" subset of the PDBBIND dataset (which consists of only a couple thousand protein-ligand complexes) to keep this task manageable."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading raw samples now.\n",
-      "shard_size: 8192\n",
-      "About to start loading CSV from /var/folders/st/ds45jcqj2232lvhr0y9qt5sc0000gn/T/refined_smiles_labels.csv\n",
-      "Loading shard 1 of size 8192.\n",
-      "Featurizing sample 0\n",
-      "Featurizing sample 1000\n",
-      "Featurizing sample 2000\n",
-      "Featurizing sample 3000\n",
-      "TIMING: featurizing shard 0 took 3.487 s\n",
-      "TIMING: dataset construction took 3.594 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.116 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.061 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.068 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.089 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.016 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.014 s\n",
-      "Loading dataset from disk.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid(\n",
-    "    featurizer=\"ECFP\", subset=\"refined\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now, we're ready to do some learning! \n",
-    "\n",
-    "To fit a deepchem model, first we instantiate one of the provided (or user-written) model classes. In this case, we have a created a convenience class to wrap around any ML model available in Sci-Kit Learn that can in turn be used to interoperate with deepchem. To instantiate an ```SklearnModel```, you will need (a) task_types, (b) model_params, another ```dict``` as illustrated below, and (c) a ```model_instance``` defining the type of model you would like to fit, in this case a ```RandomForestRegressor```."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from sklearn.ensemble import RandomForestRegressor\n",
-    "\n",
-    "seed=23 # Set a random seed to get stable results\n",
-    "sklearn_model = RandomForestRegressor(n_estimators=10, max_features='sqrt')\n",
-    "sklearn_model.random_state = seed\n",
-    "model = dc.models.SklearnModel(sklearn_model)\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computed_metrics: [0.8426371735461211]\n",
-      "RF Train set R^2 0.842637\n",
-      "computed_metrics: [0.4889401215203093]\n",
-      "RF Valid set R^2 0.488940\n"
-     ]
-    }
-   ],
-   "source": [
-    "from deepchem.utils.evaluate import Evaluator\n",
-    "import pandas as pd\n",
-    "\n",
-    "metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
-    "\n",
-    "evaluator = Evaluator(model, train_dataset, transformers)\n",
-    "train_r2score = evaluator.compute_model_performance([metric])\n",
-    "print(\"RF Train set R^2 %f\" % (train_r2score[\"r2_score\"]))\n",
-    "\n",
-    "evaluator = Evaluator(model, valid_dataset, transformers)\n",
-    "valid_r2score = evaluator.compute_model_performance([metric])\n",
-    "print(\"RF Valid set R^2 %f\" % (valid_r2score[\"r2_score\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is decent performance on a validation set! It's interesting to note that a trivial prediction from just the ligand can do reasonably on the task of predicting the binding energy."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[-0.698915   -0.2038876  -0.83584868 -0.19569178 -0.52297811 -1.12700989\n",
-      " -0.24781718 -0.95717433 -1.04587129  0.01657991]\n"
-     ]
-    }
-   ],
-   "source": [
-    "predictions = model.predict(test_dataset)\n",
-    "print(predictions[:10])"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# The protein-ligand complex view."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In the previous section, we featurized only the ligand. The signal we observed in R^2 reflects the ability of grid fingerprints and random forests to learn general features that make ligands \"drug-like.\" This time, let's see if we can do something sensible with our protein-ligand fingerprints that make use of our structural information. To start with, we need to re-featurize the dataset but using the \"grid\" fingerprints this time."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.167 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.056 s\n",
-      "Loading dataset from disk.\n",
-      "TIMING: dataset construction took 0.067 s\n",
-      "Loading dataset from disk.\n"
-     ]
-    }
-   ],
-   "source": [
-    "pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid(\n",
-    "    featurizer=\"grid\", subset=\"refined\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now train a simple random forest model on this dataset."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "seed=23 # Set a random seed to get stable results\n",
-    "sklearn_model = RandomForestRegressor(n_estimators=10, max_features='sqrt')\n",
-    "sklearn_model.random_state = seed\n",
-    "model = dc.models.SklearnModel(sklearn_model)\n",
-    "model.fit(train_dataset)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's see what our accuracies looks like!"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "computed_metrics: [0.8981027537791372]\n",
-      "RF Train set R^2 0.898103\n",
-      "computed_metrics: [0.457958594329243]\n",
-      "RF Valid set R^2 0.457959\n"
-     ]
-    }
-   ],
-   "source": [
-    "metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
-    "\n",
-    "evaluator = Evaluator(model, train_dataset, transformers)\n",
-    "train_r2score = evaluator.compute_model_performance([metric])\n",
-    "print(\"RF Train set R^2 %f\" % (train_r2score[\"r2_score\"]))\n",
-    "\n",
-    "evaluator = Evaluator(model, valid_dataset, transformers)\n",
-    "valid_r2score = evaluator.compute_model_performance([metric])\n",
-    "print(\"RF Valid set R^2 %f\" % (valid_r2score[\"r2_score\"]))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ok, there's some predictive performance here, but it looks like we have lower accuracy than the ligand-only dataset. What gives? There might be a few things going on. It's possible that for this particular dataset the pure ligand only features are quite predictive. But nonetheless, it's probably still useful to have a protein-ligand model since it's likely to learn different features than the the pure ligand-only model."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Doing Some Hyperparameter Optimization"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ok, now that we've built a few models, let's do some hyperparameter optimization to see if we can get our numbers to be a little better. We'll use the `dc.hyper` module to do this for us."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Fitting model 1/12\n",
-      "hyperparameters: {'n_estimators': 10, 'max_features': 'auto'}\n",
-      "computed_metrics: [0.4764324138132032]\n",
-      "Model 1/12, Metric r2_score, Validation set 0: 0.476432\n",
-      "\tbest_validation_score so far: 0.476432\n",
-      "Fitting model 2/12\n",
-      "hyperparameters: {'n_estimators': 10, 'max_features': 'sqrt'}\n",
-      "computed_metrics: [0.457958594329243]\n",
-      "Model 2/12, Metric r2_score, Validation set 1: 0.457959\n",
-      "\tbest_validation_score so far: 0.476432\n",
-      "Fitting model 3/12\n",
-      "hyperparameters: {'n_estimators': 10, 'max_features': 'log2'}\n",
-      "computed_metrics: [0.4490199575272553]\n",
-      "Model 3/12, Metric r2_score, Validation set 2: 0.449020\n",
-      "\tbest_validation_score so far: 0.476432\n",
-      "Fitting model 4/12\n",
-      "hyperparameters: {'n_estimators': 10, 'max_features': None}\n",
-      "computed_metrics: [0.4764324138132032]\n",
-      "Model 4/12, Metric r2_score, Validation set 3: 0.476432\n",
-      "\tbest_validation_score so far: 0.476432\n",
-      "Fitting model 5/12\n",
-      "hyperparameters: {'n_estimators': 50, 'max_features': 'auto'}\n",
-      "computed_metrics: [0.5233787062200923]\n",
-      "Model 5/12, Metric r2_score, Validation set 4: 0.523379\n",
-      "\tbest_validation_score so far: 0.523379\n",
-      "Fitting model 6/12\n",
-      "hyperparameters: {'n_estimators': 50, 'max_features': 'sqrt'}\n",
-      "computed_metrics: [0.5199897039641606]\n",
-      "Model 6/12, Metric r2_score, Validation set 5: 0.519990\n",
-      "\tbest_validation_score so far: 0.523379\n",
-      "Fitting model 7/12\n",
-      "hyperparameters: {'n_estimators': 50, 'max_features': 'log2'}\n",
-      "computed_metrics: [0.49432826062655066]\n",
-      "Model 7/12, Metric r2_score, Validation set 6: 0.494328\n",
-      "\tbest_validation_score so far: 0.523379\n",
-      "Fitting model 8/12\n",
-      "hyperparameters: {'n_estimators': 50, 'max_features': None}\n",
-      "computed_metrics: [0.5233787062200923]\n",
-      "Model 8/12, Metric r2_score, Validation set 7: 0.523379\n",
-      "\tbest_validation_score so far: 0.523379\n",
-      "Fitting model 9/12\n",
-      "hyperparameters: {'n_estimators': 100, 'max_features': 'auto'}\n",
-      "computed_metrics: [0.5329568053100839]\n",
-      "Model 9/12, Metric r2_score, Validation set 8: 0.532957\n",
-      "\tbest_validation_score so far: 0.532957\n",
-      "Fitting model 10/12\n",
-      "hyperparameters: {'n_estimators': 100, 'max_features': 'sqrt'}\n",
-      "computed_metrics: [0.513292992168339]\n",
-      "Model 10/12, Metric r2_score, Validation set 9: 0.513293\n",
-      "\tbest_validation_score so far: 0.532957\n",
-      "Fitting model 11/12\n",
-      "hyperparameters: {'n_estimators': 100, 'max_features': 'log2'}\n",
-      "computed_metrics: [0.48901397189410456]\n",
-      "Model 11/12, Metric r2_score, Validation set 10: 0.489014\n",
-      "\tbest_validation_score so far: 0.532957\n",
-      "Fitting model 12/12\n",
-      "hyperparameters: {'n_estimators': 100, 'max_features': None}\n",
-      "computed_metrics: [0.5329568053100839]\n",
-      "Model 12/12, Metric r2_score, Validation set 11: 0.532957\n",
-      "\tbest_validation_score so far: 0.532957\n",
-      "computed_metrics: [0.9336377471642344]\n",
-      "Best hyperparameters: (100, None)\n",
-      "train_score: 0.933638\n",
-      "validation_score: 0.532957\n"
-     ]
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+                  {
+                    "data": "MODEL        0\nATOM      1  C1  CCT A   1      12.930  30.871  73.555  1.00  0.00          C   \nATOM      2  C2  CCT A   1      12.904  29.594  72.915  1.00  0.00          C   \nATOM      3  C3  CCT A   1      11.675  28.876  72.876  1.00  0.00          C   \nATOM      4  C4  CCT A   1      10.504  29.416  73.455  1.00  0.00          C   \nATOM      5  C5  CCT A   1      10.520  30.692  74.092  1.00  0.00          C   \nATOM      6  C6  CCT A   1      11.753  31.411  74.134  1.00  0.00          C   \nATOM      7  C7  CCT A   1       6.884  31.797  75.111  1.00  0.00          C   \nATOM      8  C8  CCT A   1       7.733  32.392  76.033  1.00  0.00          C   \nATOM      9  C9  CCT A   1       9.108  32.061  75.768  1.00  0.00          C   \nATOM     10  C10 CCT A   1       9.277  31.234  74.669  1.00  0.00          C   \nATOM     11  S11 CCT A   1       7.757  30.874  73.982  1.00  0.00          S   \nATOM     12  C12 CCT A   1      14.083  29.040  72.318  1.00  0.00          C   \nATOM     13  N13 CCT A   1      15.043  28.581  71.811  1.00  0.00          N   \nATOM     14  C14 CCT A   1       5.402  31.944  75.107  1.00  0.00          C   \nATOM     15  O15 CCT A   1       4.768  31.230  74.313  1.00  0.00          O   \nATOM     16  O16 CCT A   1       4.868  32.787  75.871  1.00  0.00          O   \nATOM     17  N17 CCT A   1       7.301  33.237  77.137  1.00  0.00          N   \nATOM     18  S18 CCT A   1       8.282  34.502  77.689  1.00  0.00          S   \nATOM     19  C19 CCT A   1       7.775  36.467  75.711  1.00  0.00          C   \nATOM     20  C20 CCT A   1       8.733  35.682  76.427  1.00  0.00          C   \nATOM     21  C21 CCT A   1      10.125  35.830  76.146  1.00  0.00          C   \nATOM     22  C22 CCT A   1      10.578  36.740  75.166  1.00  0.00          C   \nATOM     23  C23 CCT A   1       9.635  37.525  74.450  1.00  0.00          C   \nATOM     24  C24 CCT A   1       8.246  37.387  74.720  1.00  0.00          C   \nATOM     25  C25 CCT A   1       6.286  36.367  75.952  1.00  0.00          C   \nATOM     26  O26 CCT A   1       9.481  33.897  78.252  1.00  0.00          O   \nATOM     27  O27 CCT A   1       7.483  35.183  78.686  1.00  0.00          O   \nATOM     28  H   CCT A   1       5.399  33.298  76.450  1.00  0.00          H   \nATOM     29  H   CCT A   1       6.431  33.071  77.566  1.00  0.00          H   \nTER      30      CCT A   1\nENDMDL\nCONECT    1    2    6\nCONECT    2    1    3   12\nCONECT    3    2    4\nCONECT    4    3    5\nCONECT    5    4    6   10\nCONECT    6    1    5\nCONECT    7    8   11   14\nCONECT    8    7    9   17\nCONECT    9    8   10\nCONECT   10    5    9   11\nCONECT   11    7   10\nCONECT   12    2   13\nCONECT   13   12\nCONECT   14    7   15   16\nCONECT   15   14\nCONECT   16   14   28\nCONECT   17    8   18   29\nCONECT   18   17   20   26   27\nCONECT   19   20   24   25\nCONECT   20   18   19   21\nCONECT   21   20   22\nCONECT   22   21   23\nCONECT   23   22   24\nCONECT   24   19   23\nCONECT   25   19\nCONECT   26   18\nCONECT   27   18\nCONECT   28   16\nCONECT   29   17\nEND\n",
+                    "type": "blob",
+                    "binary": false
+                  }
+                ],
+                "kwargs": {
+                  "ext": "pdb",
+                  "defaultRepresentation": true
+                },
+                "type": "call_method"
+              }
+            ]
+          }
+        },
+        "655b10a5e1d8422ea4555272e0411fee": {
+          "model_module": "nglview-js-widgets",
+          "model_name": "NGLModel",
+          "state": {
+            "_view_name": "NGLView",
+            "_scene_rotation": {},
+            "_ngl_view_id": [],
+            "_synced_model_ids": [],
+            "_camera_orientation": [],
+            "frame": 0,
+            "_view_module": "nglview-js-widgets",
+            "_ibtn_fullscreen": "IPY_MODEL_99cb361beb24462daabde6237a04a892",
+            "_camera_str": "orthographic",
+            "_ngl_serialize": false,
+            "picked": {},
+            "_model_module": "nglview-js-widgets",
+            "_igui": null,
+            "_iplayer": "IPY_MODEL_c7a6c7e665574f12af61e3cd5b6a189d",
+            "layout": "IPY_MODEL_7e7f661fbc2b4651a60c3f74c9e7da47",
+            "_view_width": "",
+            "_ngl_coordinate_resource": {},
+            "_view_module_version": "2.7.5",
+            "_player_dict": {},
+            "_synced_repr_model_ids": [],
+            "_ngl_version": "",
+            "max_frame": 0,
+            "_dom_classes": [],
+            "_model_name": "NGLModel",
+            "_scene_position": {},
+            "_model_module_version": "2.7.5",
+            "gui_style": null,
+            "background": "white",
+            "_view_count": null,
+            "_view_height": "",
+            "_ngl_repr_dict": {},
+            "_ngl_original_stage_parameters": {},
+            "_ngl_full_stage_parameters": {},
+            "n_components": 0,
+            "_ngl_color_dict": {},
+            "_gui_theme": null,
+            "_ngl_msg_archive": [
+              {
+                "reconstruc_color_scheme": false,
+                "methodName": "loadFile",
+                "target": "Stage",
+                "args": [
+                  {
+                    "data": "MODEL        0\nATOM      1  N   SER A   1      15.324  37.268  22.693  1.00  0.00          N   \nATOM      2  CA  SER A   1      15.651  38.380  23.628  1.00  0.00          C   \nATOM      3  C   SER A   1      16.340  37.889  24.890  1.00  0.00          C   \nATOM      4  O   SER A   1      15.662  37.449  25.835  1.00  0.00          O   \nATOM      5  CB  SER A   1      14.375  39.133  24.030  1.00  0.00          C   \nATOM      6  OG  SER A   1      14.718  40.337  24.714  1.00  0.00          O   \nATOM      7  N   MET A   2      17.676  37.947  24.893  1.00  0.00          N   \nATOM      8  CA  MET A   2      18.467  37.535  26.056  1.00  0.00          C   \nATOM      9  C   MET A   2      18.329  38.628  27.108  1.00  0.00          C   \nATOM     10  O   MET A   2      18.402  39.819  26.772  1.00  0.00          O   \nATOM     11  CB  MET A   2      19.951  37.383  25.685  1.00  0.00          C   \nATOM     12  CG  MET A   2      20.246  36.266  24.686  1.00  0.00          C   \nATOM     13  SD  MET A   2      19.701  34.709  25.344  1.00  0.00          S   \nATOM     14  CE  MET A   2      20.967  34.406  26.652  1.00  0.00          C   \nATOM     15  N   SER A   3      18.132  38.228  28.363  1.00  0.00          N   \nATOM     16  CA  SER A   3      17.986  39.184  29.453  1.00  0.00          C   \nATOM     17  C   SER A   3      19.224  40.075  29.525  1.00  0.00          C   \nATOM     18  O   SER A   3      19.119  41.279  29.800  1.00  0.00          O   \nATOM     19  CB  SER A   3      17.758  38.436  30.779  1.00  0.00          C   \nATOM     20  OG  SER A   3      18.692  37.371  30.959  1.00  0.00          O   \nATOM     21  N   TYR A   4      20.387  39.483  29.246  1.00  0.00          N   \nATOM     22  CA  TYR A   4      21.667  40.201  29.244  1.00  0.00          C   \nATOM     23  C   TYR A   4      22.617  39.643  28.188  1.00  0.00          C   \nATOM     24  O   TYR A   4      22.530  38.475  27.821  1.00  0.00          O   \nATOM     25  CB  TYR A   4      22.400  40.032  30.583  1.00  0.00          C   \nATOM     26  CG  TYR A   4      21.694  40.566  31.800  1.00  0.00          C   \nATOM     27  CD1 TYR A   4      21.799  41.911  32.153  1.00  0.00          C   \nATOM     28  CD2 TYR A   4      20.910  39.728  32.597  1.00  0.00          C   \nATOM     29  CE1 TYR A   4      21.139  42.410  33.274  1.00  0.00          C   \nATOM     30  CE2 TYR A   4      20.243  40.227  33.726  1.00  0.00          C   \nATOM     31  CZ  TYR A   4      20.366  41.566  34.049  1.00  0.00          C   \nATOM     32  OH  TYR A   4      19.710  42.075  35.136  1.00  0.00          O   \nATOM     33  N   THR A   5      23.526  40.486  27.719  1.00  0.00          N   \nATOM     34  CA  THR A   5      24.591  40.084  26.800  1.00  0.00          C   \nATOM     35  C   THR A   5      25.816  40.634  27.541  1.00  0.00          C   \nATOM     36  O   THR A   5      25.823  41.802  27.951  1.00  0.00          O   \nATOM     37  CB  THR A   5      24.494  40.753  25.418  1.00  0.00          C   \nATOM     38  OG1 THR A   5      24.368  42.164  25.584  1.00  0.00          O   \nATOM     39  CG2 THR A   5      23.312  40.227  24.654  1.00  0.00          C   \nATOM     40  N   TRP A   6      26.834  39.802  27.736  1.00  0.00          N   \nATOM     41  CA  TRP A   6      28.022  40.216  28.478  1.00  0.00          C   \nATOM     42  C   TRP A   6      29.258  40.373  27.601  1.00  0.00          C   \nATOM     43  O   TRP A   6      29.378  39.696  26.594  1.00  0.00          O   \nATOM     44  CB  TRP A   6      28.299  39.201  29.583  1.00  0.00          C   \nATOM     45  CG  TRP A   6      27.121  38.979  30.493  1.00  0.00          C   \nATOM     46  CD1 TRP A   6      26.265  37.906  30.498  1.00  0.00          C   \nATOM     47  CD2 TRP A   6      26.661  39.861  31.523  1.00  0.00          C   \nATOM     48  NE1 TRP A   6      25.303  38.067  31.476  1.00  0.00          N   \nATOM     49  CE2 TRP A   6      25.525  39.261  32.119  1.00  0.00          C   \nATOM     50  CE3 TRP A   6      27.101  41.103  32.006  1.00  0.00          C   \nATOM     51  CZ2 TRP A   6      24.827  39.858  33.165  1.00  0.00          C   \nATOM     52  CZ3 TRP A   6      26.405  41.696  33.051  1.00  0.00          C   \nATOM     53  CH2 TRP A   6      25.281  41.073  33.618  1.00  0.00          C   \nATOM     54  N   THR A   7      30.167  41.273  27.974  1.00  0.00          N   \nATOM     55  CA  THR A   7      31.389  41.462  27.188  1.00  0.00          C   \nATOM     56  C   THR A   7      32.529  40.626  27.765  1.00  0.00          C   \nATOM     57  O   THR A   7      33.474  40.291  27.063  1.00  0.00          O   \nATOM     58  CB  THR A   7      31.867  42.927  27.175  1.00  0.00          C   \nATOM     59  OG1 THR A   7      32.096  43.360  28.516  1.00  0.00          O   \nATOM     60  CG2 THR A   7      30.837  43.833  26.520  1.00  0.00          C   \nATOM     61  N   GLY A   8      32.437  40.286  29.043  1.00  0.00          N   \nATOM     62  CA  GLY A   8      33.501  39.518  29.660  1.00  0.00          C   \nATOM     63  C   GLY A   8      34.160  40.354  30.740  1.00  0.00          C   \nATOM     64  O   GLY A   8      34.856  39.832  31.600  1.00  0.00          O   \nATOM     65  N   ALA A   9      33.952  41.666  30.689  1.00  0.00          N   \nATOM     66  CA  ALA A   9      34.506  42.549  31.705  1.00  0.00          C   \nATOM     67  C   ALA A   9      33.832  42.157  33.020  1.00  0.00          C   \nATOM     68  O   ALA A   9      32.645  41.824  33.047  1.00  0.00          O   \nATOM     69  CB  ALA A   9      34.201  43.992  31.373  1.00  0.00          C   \nATOM     70  N   LEU A  10      34.590  42.203  34.106  1.00  0.00          N   \nATOM     71  CA  LEU A  10      34.068  41.822  35.412  1.00  0.00          C   \nATOM     72  C   LEU A  10      33.234  42.901  36.104  1.00  0.00          C   \nATOM     73  O   LEU A  10      33.382  44.089  35.839  1.00  0.00          O   \nATOM     74  CB  LEU A  10      35.226  41.428  36.341  1.00  0.00          C   \nATOM     75  CG  LEU A  10      36.135  40.284  35.885  1.00  0.00          C   \nATOM     76  CD1 LEU A  10      37.324  40.165  36.832  1.00  0.00          C   \nATOM     77  CD2 LEU A  10      35.340  38.984  35.834  1.00  0.00          C   \nATOM     78  N   ILE A  11      32.352  42.454  36.994  1.00  0.00          N   \nATOM     79  CA  ILE A  11      31.521  43.343  37.785  1.00  0.00          C   \nATOM     80  C   ILE A  11      32.437  43.578  38.975  1.00  0.00          C   \nATOM     81  O   ILE A  11      32.647  42.698  39.808  1.00  0.00          O   \nATOM     82  CB  ILE A  11      30.227  42.644  38.225  1.00  0.00          C   \nATOM     83  CG1 ILE A  11      29.373  42.330  36.990  1.00  0.00          C   \nATOM     84  CG2 ILE A  11      29.486  43.511  39.216  1.00  0.00          C   \nATOM     85  CD1 ILE A  11      28.181  41.424  37.266  1.00  0.00          C   \nATOM     86  N   THR A  12      33.000  44.770  39.030  1.00  0.00          N   \nATOM     87  CA  THR A  12      33.962  45.116  40.058  1.00  0.00          C   \nATOM     88  C   THR A  12      33.421  45.785  41.304  1.00  0.00          C   \nATOM     89  O   THR A  12      32.416  46.496  41.262  1.00  0.00          O   \nATOM     90  CB  THR A  12      35.007  46.030  39.467  1.00  0.00          C   \nATOM     91  OG1 THR A  12      34.350  47.186  38.933  1.00  0.00          O   \nATOM     92  CG2 THR A  12      35.763  45.313  38.352  1.00  0.00          C   \nATOM     93  N   PRO A  13      34.098  45.562  42.441  1.00  0.00          N   \nATOM     94  CA  PRO A  13      33.675  46.162  43.707  1.00  0.00          C   \nATOM     95  C   PRO A  13      34.058  47.636  43.694  1.00  0.00          C   \nATOM     96  O   PRO A  13      34.976  48.032  42.976  1.00  0.00          O   \nATOM     97  CB  PRO A  13      34.466  45.364  44.744  1.00  0.00          C   \nATOM     98  CG  PRO A  13      35.744  45.042  44.008  1.00  0.00          C   \nATOM     99  CD  PRO A  13      35.238  44.644  42.639  1.00  0.00          C   \nATOM    100  N   CYS A  14      33.363  48.458  44.469  1.00  0.00          N   \nATOM    101  CA  CYS A  14      33.711  49.872  44.492  1.00  0.00          C   \nATOM    102  C   CYS A  14      34.445  50.217  45.774  1.00  0.00          C   \nATOM    103  O   CYS A  14      34.518  51.385  46.164  1.00  0.00          O   \nATOM    104  CB  CYS A  14      32.470  50.748  44.365  1.00  0.00          C   \nATOM    105  SG  CYS A  14      32.885  52.457  43.973  1.00  0.00          S   \nATOM    106  N   ALA A  15      34.990  49.187  46.414  1.00  0.00          N   \nATOM    107  CA  ALA A  15      35.731  49.319  47.665  1.00  0.00          C   \nATOM    108  C   ALA A  15      35.921  47.928  48.260  1.00  0.00          C   \nATOM    109  O   ALA A  15      35.303  46.960  47.809  1.00  0.00          O   \nATOM    110  CB  ALA A  15      34.966  50.208  48.649  1.00  0.00          C   \nATOM    111  N   ALA A  16      36.770  47.829  49.277  1.00  0.00          N   \nATOM    112  CA  ALA A  16      37.039  46.548  49.921  1.00  0.00          C   \nATOM    113  C   ALA A  16      35.745  45.884  50.379  1.00  0.00          C   \nATOM    114  O   ALA A  16      34.830  46.548  50.872  1.00  0.00          O   \nATOM    115  CB  ALA A  16      37.979  46.744  51.108  1.00  0.00          C   \nATOM    116  N   GLU A  17      35.677  44.568  50.207  1.00  0.00          N   \nATOM    117  CA  GLU A  17      34.497  43.804  50.595  1.00  0.00          C   \nATOM    118  C   GLU A  17      34.858  42.718  51.599  1.00  0.00          C   \nATOM    119  O   GLU A  17      35.829  42.003  51.411  1.00  0.00          O   \nATOM    120  CB  GLU A  17      33.867  43.128  49.370  1.00  0.00          C   \nATOM    121  CG  GLU A  17      33.337  44.058  48.298  1.00  0.00          C   \nATOM    122  CD  GLU A  17      32.922  43.292  47.043  1.00  0.00          C   \nATOM    123  OE1 GLU A  17      33.800  42.688  46.389  1.00  0.00          O   \nATOM    124  OE2 GLU A  17      31.720  43.286  46.716  1.00  0.00          O   \nATOM    125  N   GLU A  18      34.081  42.599  52.666  1.00  0.00          N   \nATOM    126  CA  GLU A  18      34.309  41.552  53.652  1.00  0.00          C   \nATOM    127  C   GLU A  18      33.254  40.467  53.405  1.00  0.00          C   \nATOM    128  O   GLU A  18      32.092  40.786  53.188  1.00  0.00          O   \nATOM    129  CB  GLU A  18      34.123  42.089  55.071  1.00  0.00          C   \nATOM    130  CG  GLU A  18      35.138  43.120  55.528  1.00  0.00          C   \nATOM    131  CD  GLU A  18      34.861  43.593  56.945  1.00  0.00          C   \nATOM    132  OE1 GLU A  18      33.861  44.316  57.158  1.00  0.00          O   \nATOM    133  OE2 GLU A  18      35.637  43.228  57.849  1.00  0.00          O   \nATOM    134  N   SER A  19      33.649  39.198  53.428  1.00  0.00          N   \nATOM    135  CA  SER A  19      32.681  38.120  53.237  1.00  0.00          C   \nATOM    136  C   SER A  19      32.448  37.374  54.548  1.00  0.00          C   \nATOM    137  O   SER A  19      31.349  36.871  54.800  1.00  0.00          O   \nATOM    138  CB  SER A  19      33.160  37.125  52.180  1.00  0.00          C   \nATOM    139  OG  SER A  19      34.303  36.419  52.624  1.00  0.00          O   \nATOM    140  N   LYS A  20      33.480  37.291  55.385  1.00  0.00          N   \nATOM    141  CA  LYS A  20      33.339  36.587  56.652  1.00  0.00          C   \nATOM    142  C   LYS A  20      32.833  37.538  57.718  1.00  0.00          C   \nATOM    143  O   LYS A  20      33.151  38.721  57.702  1.00  0.00          O   \nATOM    144  CB  LYS A  20      34.675  35.977  57.098  1.00  0.00          C   \nATOM    145  CG  LYS A  20      35.166  34.829  56.220  1.00  0.00          C   \nATOM    146  CD  LYS A  20      35.703  35.336  54.877  1.00  0.00          C   \nATOM    147  CE  LYS A  20      36.005  34.194  53.890  1.00  0.00          C   \nATOM    148  NZ  LYS A  20      34.768  33.491  53.409  1.00  0.00          N   \nATOM    149  N   LEU A  21      32.041  37.015  58.643  1.00  0.00          N   \nATOM    150  CA  LEU A  21      31.493  37.824  59.723  1.00  0.00          C   \nATOM    151  C   LEU A  21      32.568  38.443  60.616  1.00  0.00          C   \nATOM    152  O   LEU A  21      33.398  37.735  61.196  1.00  0.00          O   \nATOM    153  CB  LEU A  21      30.571  36.975  60.601  1.00  0.00          C   \nATOM    154  CG  LEU A  21      29.990  37.699  61.820  1.00  0.00          C   \nATOM    155  CD1 LEU A  21      28.959  38.727  61.348  1.00  0.00          C   \nATOM    156  CD2 LEU A  21      29.353  36.703  62.778  1.00  0.00          C   \nATOM    157  N   PRO A  22      32.573  39.779  60.735  1.00  0.00          N   \nATOM    158  CA  PRO A  22      33.573  40.422  61.590  1.00  0.00          C   \nATOM    159  C   PRO A  22      33.191  40.135  63.044  1.00  0.00          C   \nATOM    160  O   PRO A  22      32.012  40.135  63.393  1.00  0.00          O   \nATOM    161  CB  PRO A  22      33.426  41.906  61.244  1.00  0.00          C   \nATOM    162  CG  PRO A  22      32.859  41.894  59.853  1.00  0.00          C   \nATOM    163  CD  PRO A  22      31.842  40.784  59.946  1.00  0.00          C   \nATOM    164  N   ILE A  23      34.183  39.889  63.885  1.00  0.00          N   \nATOM    165  CA  ILE A  23      33.942  39.586  65.283  1.00  0.00          C   \nATOM    166  C   ILE A  23      34.525  40.667  66.192  1.00  0.00          C   \nATOM    167  O   ILE A  23      35.661  41.097  65.990  1.00  0.00          O   \nATOM    168  CB  ILE A  23      34.619  38.237  65.663  1.00  0.00          C   \nATOM    169  CG1 ILE A  23      34.179  37.138  64.695  1.00  0.00          C   \nATOM    170  CG2 ILE A  23      34.272  37.846  67.097  1.00  0.00          C   \nATOM    171  CD1 ILE A  23      32.677  36.944  64.628  1.00  0.00          C   \nATOM    172  N   ASN A  24      33.749  41.129  67.169  1.00  0.00          N   \nATOM    173  CA  ASN A  24      34.266  42.102  68.126  1.00  0.00          C   \nATOM    174  C   ASN A  24      33.946  41.566  69.526  1.00  0.00          C   \nATOM    175  O   ASN A  24      33.588  40.394  69.669  1.00  0.00          O   \nATOM    176  CB  ASN A  24      33.704  43.524  67.869  1.00  0.00          C   \nATOM    177  CG  ASN A  24      32.217  43.679  68.204  1.00  0.00          C   \nATOM    178  OD1 ASN A  24      31.516  42.720  68.542  1.00  0.00          O   \nATOM    179  ND2 ASN A  24      31.730  44.913  68.094  1.00  0.00          N   \nATOM    180  N   ALA A  25      34.094  42.382  70.559  1.00  0.00          N   \nATOM    181  CA  ALA A  25      33.835  41.904  71.910  1.00  0.00          C   \nATOM    182  C   ALA A  25      32.367  41.594  72.194  1.00  0.00          C   \nATOM    183  O   ALA A  25      32.053  40.940  73.183  1.00  0.00          O   \nATOM    184  CB  ALA A  25      34.346  42.913  72.920  1.00  0.00          C   \nATOM    185  N   LEU A  26      31.470  42.048  71.327  1.00  0.00          N   \nATOM    186  CA  LEU A  26      30.039  41.829  71.534  1.00  0.00          C   \nATOM    187  C   LEU A  26      29.457  40.652  70.764  1.00  0.00          C   \nATOM    188  O   LEU A  26      28.394  40.133  71.107  1.00  0.00          O   \nATOM    189  CB  LEU A  26      29.269  43.088  71.133  1.00  0.00          C   \nATOM    190  CG  LEU A  26      29.663  44.381  71.842  1.00  0.00          C   \nATOM    191  CD1 LEU A  26      28.939  45.564  71.206  1.00  0.00          C   \nATOM    192  CD2 LEU A  26      29.306  44.255  73.322  1.00  0.00          C   \nATOM    193  N   SER A  27      30.152  40.228  69.724  1.00  0.00          N   \nATOM    194  CA  SER A  27      29.649  39.157  68.882  1.00  0.00          C   \nATOM    195  C   SER A  27      29.162  37.913  69.597  1.00  0.00          C   \nATOM    196  O   SER A  27      28.041  37.456  69.352  1.00  0.00          O   \nATOM    197  CB  SER A  27      30.711  38.768  67.860  1.00  0.00          C   \nATOM    198  OG  SER A  27      31.119  39.906  67.139  1.00  0.00          O   \nATOM    199  N   ASN A  28      29.995  37.365  70.476  1.00  0.00          N   \nATOM    200  CA  ASN A  28      29.634  36.138  71.165  1.00  0.00          C   \nATOM    201  C   ASN A  28      28.393  36.261  72.020  1.00  0.00          C   \nATOM    202  O   ASN A  28      27.688  35.280  72.199  1.00  0.00          O   \nATOM    203  CB  ASN A  28      30.814  35.598  71.990  1.00  0.00          C   \nATOM    204  CG  ASN A  28      31.883  34.947  71.114  1.00  0.00          C   \nATOM    205  OD1 ASN A  28      31.585  34.067  70.302  1.00  0.00          O   \nATOM    206  ND2 ASN A  28      33.134  35.383  71.271  1.00  0.00          N   \nATOM    207  N   SER A  29      28.092  37.453  72.529  1.00  0.00          N   \nATOM    208  CA  SER A  29      26.884  37.584  73.339  1.00  0.00          C   \nATOM    209  C   SER A  29      25.633  37.299  72.496  1.00  0.00          C   \nATOM    210  O   SER A  29      24.622  36.838  73.026  1.00  0.00          O   \nATOM    211  CB  SER A  29      26.793  38.973  73.979  1.00  0.00          C   \nATOM    212  OG  SER A  29      26.852  39.988  73.004  1.00  0.00          O   \nATOM    213  N   LEU A  30      25.703  37.558  71.191  1.00  0.00          N   \nATOM    214  CA  LEU A  30      24.569  37.289  70.301  1.00  0.00          C   \nATOM    215  C   LEU A  30      24.556  35.845  69.756  1.00  0.00          C   \nATOM    216  O   LEU A  30      23.569  35.131  69.916  1.00  0.00          O   \nATOM    217  CB  LEU A  30      24.543  38.270  69.118  1.00  0.00          C   \nATOM    218  CG  LEU A  30      23.432  38.019  68.083  1.00  0.00          C   \nATOM    219  CD1 LEU A  30      22.055  38.035  68.776  1.00  0.00          C   \nATOM    220  CD2 LEU A  30      23.514  39.069  66.976  1.00  0.00          C   \nATOM    221  N   LEU A  31      25.638  35.416  69.110  1.00  0.00          N   \nATOM    222  CA  LEU A  31      25.689  34.056  68.569  1.00  0.00          C   \nATOM    223  C   LEU A  31      27.121  33.531  68.572  1.00  0.00          C   \nATOM    224  O   LEU A  31      28.077  34.315  68.532  1.00  0.00          O   \nATOM    225  CB  LEU A  31      25.119  34.018  67.140  1.00  0.00          C   \nATOM    226  CG  LEU A  31      25.858  34.679  65.962  1.00  0.00          C   \nATOM    227  CD1 LEU A  31      27.064  33.858  65.547  1.00  0.00          C   \nATOM    228  CD2 LEU A  31      24.904  34.773  64.770  1.00  0.00          C   \nATOM    229  N   ARG A  32      27.265  32.206  68.607  1.00  0.00          N   \nATOM    230  CA  ARG A  32      28.582  31.580  68.636  1.00  0.00          C   \nATOM    231  C   ARG A  32      29.076  30.943  67.337  1.00  0.00          C   \nATOM    232  O   ARG A  32      30.280  30.884  67.109  1.00  0.00          O   \nATOM    233  CB  ARG A  32      28.634  30.552  69.769  1.00  0.00          C   \nATOM    234  CG  ARG A  32      28.662  31.176  71.154  1.00  0.00          C   \nATOM    235  CD  ARG A  32      28.596  30.101  72.220  1.00  0.00          C   \nATOM    236  NE  ARG A  32      28.578  30.700  73.557  1.00  0.00          N   \nATOM    237  CZ  ARG A  32      29.622  31.291  74.146  1.00  0.00          C   \nATOM    238  NH1 ARG A  32      30.802  31.362  73.522  1.00  0.00          N   \nATOM    239  NH2 ARG A  32      29.467  31.838  75.359  1.00  0.00          N   \nATOM    240  N   HIS A  33      28.164  30.487  66.481  1.00  0.00          N   \nATOM    241  CA  HIS A  33      28.550  29.853  65.222  1.00  0.00          C   \nATOM    242  C   HIS A  33      28.765  30.904  64.134  1.00  0.00          C   \nATOM    243  O   HIS A  33      27.991  31.021  63.176  1.00  0.00          O   \nATOM    244  CB  HIS A  33      27.479  28.846  64.795  1.00  0.00          C   \nATOM    245  CG  HIS A  33      27.174  27.808  65.832  1.00  0.00          C   \nATOM    246  ND1 HIS A  33      26.039  27.026  65.794  1.00  0.00          N   \nATOM    247  CD2 HIS A  33      27.854  27.429  66.941  1.00  0.00          C   \nATOM    248  CE1 HIS A  33      26.031  26.212  66.832  1.00  0.00          C   \nATOM    249  NE2 HIS A  33      27.121  26.434  67.545  1.00  0.00          N   \nATOM    250  N   HIS A  34      29.846  31.654  64.295  1.00  0.00          N   \nATOM    251  CA  HIS A  34      30.233  32.734  63.392  1.00  0.00          C   \nATOM    252  C   HIS A  34      30.383  32.332  61.923  1.00  0.00          C   \nATOM    253  O   HIS A  34      30.072  33.121  61.027  1.00  0.00          O   \nATOM    254  CB  HIS A  34      31.545  33.341  63.890  1.00  0.00          C   \nATOM    255  CG  HIS A  34      31.510  33.732  65.334  1.00  0.00          C   \nATOM    256  ND1 HIS A  34      32.647  33.831  66.105  1.00  0.00          N   \nATOM    257  CD2 HIS A  34      30.474  34.035  66.153  1.00  0.00          C   \nATOM    258  CE1 HIS A  34      32.313  34.174  67.338  1.00  0.00          C   \nATOM    259  NE2 HIS A  34      31.001  34.304  67.392  1.00  0.00          N   \nATOM    260  N   ASN A  35      30.863  31.115  61.676  1.00  0.00          N   \nATOM    261  CA  ASN A  35      31.070  30.644  60.306  1.00  0.00          C   \nATOM    262  C   ASN A  35      29.790  30.360  59.536  1.00  0.00          C   \nATOM    263  O   ASN A  35      29.827  29.988  58.369  1.00  0.00          O   \nATOM    264  CB  ASN A  35      31.968  29.409  60.308  1.00  0.00          C   \nATOM    265  CG  ASN A  35      33.346  29.701  60.879  1.00  0.00          C   \nATOM    266  OD1 ASN A  35      33.978  30.704  60.529  1.00  0.00          O   \nATOM    267  ND2 ASN A  35      33.821  28.828  61.757  1.00  0.00          N   \nATOM    268  N   MET A  36      28.656  30.548  60.194  1.00  0.00          N   \nATOM    269  CA  MET A  36      27.366  30.331  59.562  1.00  0.00          C   \nATOM    270  C   MET A  36      26.974  31.604  58.809  1.00  0.00          C   \nATOM    271  O   MET A  36      26.179  31.573  57.870  1.00  0.00          O   \nATOM    272  CB  MET A  36      26.297  30.056  60.623  1.00  0.00          C   \nATOM    273  CG  MET A  36      26.490  28.802  61.448  1.00  0.00          C   \nATOM    274  SD  MET A  36      26.183  27.344  60.478  1.00  0.00          S   \nATOM    275  CE  MET A  36      24.396  27.451  60.266  1.00  0.00          C   \nATOM    276  N   VAL A  37      27.542  32.726  59.232  1.00  0.00          N   \nATOM    277  CA  VAL A  37      27.201  34.008  58.631  1.00  0.00          C   \nATOM    278  C   VAL A  37      28.212  34.509  57.612  1.00  0.00          C   \nATOM    279  O   VAL A  37      29.406  34.375  57.805  1.00  0.00          O   \nATOM    280  CB  VAL A  37      27.009  35.058  59.732  1.00  0.00          C   \nATOM    281  CG1 VAL A  37      26.601  36.397  59.127  1.00  0.00          C   \nATOM    282  CG2 VAL A  37      25.946  34.563  60.714  1.00  0.00          C   \nATOM    283  N   TYR A  38      27.714  35.088  56.523  1.00  0.00          N   \nATOM    284  CA  TYR A  38      28.576  35.611  55.470  1.00  0.00          C   \nATOM    285  C   TYR A  38      27.879  36.742  54.728  1.00  0.00          C   \nATOM    286  O   TYR A  38      26.653  36.897  54.802  1.00  0.00          O   \nATOM    287  CB  TYR A  38      28.921  34.509  54.462  1.00  0.00          C   \nATOM    288  CG  TYR A  38      27.723  34.058  53.650  1.00  0.00          C   \nATOM    289  CD1 TYR A  38      26.708  33.286  54.226  1.00  0.00          C   \nATOM    290  CD2 TYR A  38      27.579  34.448  52.316  1.00  0.00          C   \nATOM    291  CE1 TYR A  38      25.582  32.918  53.491  1.00  0.00          C   \nATOM    292  CE2 TYR A  38      26.456  34.090  51.573  1.00  0.00          C   \nATOM    293  CZ  TYR A  38      25.469  33.328  52.163  1.00  0.00          C   \nATOM    294  OH  TYR A  38      24.374  32.965  51.421  1.00  0.00          O   \nATOM    295  N   ALA A  39      28.678  37.544  54.030  1.00  0.00          N   \nATOM    296  CA  ALA A  39      28.151  38.629  53.217  1.00  0.00          C   \nATOM    297  C   ALA A  39      28.515  38.261  51.784  1.00  0.00          C   \nATOM    298  O   ALA A  39      29.570  37.671  51.548  1.00  0.00          O   \nATOM    299  CB  ALA A  39      28.795  39.939  53.590  1.00  0.00          C   \nATOM    300  N   THR A  40      27.641  38.573  50.831  1.00  0.00          N   \nATOM    301  CA  THR A  40      27.951  38.285  49.439  1.00  0.00          C   \nATOM    302  C   THR A  40      28.877  39.400  48.959  1.00  0.00          C   \nATOM    303  O   THR A  40      28.820  40.518  49.477  1.00  0.00          O   \nATOM    304  CB  THR A  40      26.682  38.269  48.541  1.00  0.00          C   \nATOM    305  OG1 THR A  40      25.978  39.514  48.659  1.00  0.00          O   \nATOM    306  CG2 THR A  40      25.755  37.119  48.942  1.00  0.00          C   \nATOM    307  N   THR A  41      29.745  39.090  47.998  1.00  0.00          N   \nATOM    308  CA  THR A  41      30.650  40.092  47.425  1.00  0.00          C   \nATOM    309  C   THR A  41      30.731  39.881  45.916  1.00  0.00          C   \nATOM    310  O   THR A  41      30.230  38.881  45.394  1.00  0.00          O   \nATOM    311  CB  THR A  41      32.087  39.983  47.970  1.00  0.00          C   \nATOM    312  OG1 THR A  41      32.680  38.762  47.511  1.00  0.00          O   \nATOM    313  CG2 THR A  41      32.089  40.004  49.474  1.00  0.00          C   \nATOM    314  N   SER A  42      31.384  40.813  45.227  1.00  0.00          N   \nATOM    315  CA  SER A  42      31.527  40.721  43.778  1.00  0.00          C   \nATOM    316  C   SER A  42      32.219  39.440  43.321  1.00  0.00          C   \nATOM    317  O   SER A  42      32.096  39.049  42.160  1.00  0.00          O   \nATOM    318  CB  SER A  42      32.278  41.938  43.229  1.00  0.00          C   \nATOM    319  OG  SER A  42      33.540  42.102  43.854  1.00  0.00          O   \nATOM    320  N   ARG A  43      32.939  38.788  44.227  1.00  0.00          N   \nATOM    321  CA  ARG A  43      33.617  37.534  43.873  1.00  0.00          C   \nATOM    322  C   ARG A  43      32.650  36.495  43.316  1.00  0.00          C   \nATOM    323  O   ARG A  43      33.059  35.622  42.558  1.00  0.00          O   \nATOM    324  CB  ARG A  43      34.269  36.873  45.082  1.00  0.00          C   \nATOM    325  CG  ARG A  43      35.536  37.562  45.564  1.00  0.00          C   \nATOM    326  CD  ARG A  43      36.527  36.574  46.189  1.00  0.00          C   \nATOM    327  NE  ARG A  43      36.209  36.199  47.568  1.00  0.00          N   \nATOM    328  CZ  ARG A  43      36.850  35.246  48.242  1.00  0.00          C   \nATOM    329  NH1 ARG A  43      36.510  34.961  49.495  1.00  0.00          N   \nATOM    330  NH2 ARG A  43      37.829  34.567  47.653  1.00  0.00          N   \nATOM    331  N   SER A  44      31.378  36.577  43.704  1.00  0.00          N   \nATOM    332  CA  SER A  44      30.375  35.618  43.233  1.00  0.00          C   \nATOM    333  C   SER A  44      29.480  36.194  42.140  1.00  0.00          C   \nATOM    334  O   SER A  44      28.525  35.538  41.709  1.00  0.00          O   \nATOM    335  CB  SER A  44      29.513  35.145  44.405  1.00  0.00          C   \nATOM    336  OG  SER A  44      28.819  36.220  44.998  1.00  0.00          O   \nATOM    337  N   ALA A  45      29.798  37.404  41.679  1.00  0.00          N   \nATOM    338  CA  ALA A  45      29.009  38.057  40.628  1.00  0.00          C   \nATOM    339  C   ALA A  45      28.884  37.223  39.353  1.00  0.00          C   \nATOM    340  O   ALA A  45      27.852  37.252  38.686  1.00  0.00          O   \nATOM    341  CB  ALA A  45      29.606  39.423  40.289  1.00  0.00          C   \nATOM    342  N   GLY A  46      29.945  36.496  39.014  1.00  0.00          N   \nATOM    343  CA  GLY A  46      29.928  35.665  37.830  1.00  0.00          C   \nATOM    344  C   GLY A  46      28.845  34.608  37.907  1.00  0.00          C   \nATOM    345  O   GLY A  46      28.137  34.344  36.927  1.00  0.00          O   \nATOM    346  N   LEU A  47      28.717  33.998  39.081  1.00  0.00          N   \nATOM    347  CA  LEU A  47      27.717  32.961  39.303  1.00  0.00          C   \nATOM    348  C   LEU A  47      26.308  33.524  39.130  1.00  0.00          C   \nATOM    349  O   LEU A  47      25.410  32.844  38.615  1.00  0.00          O   \nATOM    350  CB  LEU A  47      27.874  32.378  40.708  1.00  0.00          C   \nATOM    351  CG  LEU A  47      29.172  31.598  40.957  1.00  0.00          C   \nATOM    352  CD1 LEU A  47      29.246  31.151  42.420  1.00  0.00          C   \nATOM    353  CD2 LEU A  47      29.214  30.381  40.033  1.00  0.00          C   \nATOM    354  N   ARG A  48      26.116  34.766  39.569  1.00  0.00          N   \nATOM    355  CA  ARG A  48      24.811  35.408  39.455  1.00  0.00          C   \nATOM    356  C   ARG A  48      24.516  35.685  37.990  1.00  0.00          C   \nATOM    357  O   ARG A  48      23.416  35.411  37.514  1.00  0.00          O   \nATOM    358  CB  ARG A  48      24.787  36.716  40.251  1.00  0.00          C   \nATOM    359  CG  ARG A  48      23.522  37.555  40.062  1.00  0.00          C   \nATOM    360  CD  ARG A  48      22.277  36.800  40.482  1.00  0.00          C   \nATOM    361  NE  ARG A  48      22.310  36.411  41.885  1.00  0.00          N   \nATOM    362  CZ  ARG A  48      21.360  35.687  42.474  1.00  0.00          C   \nATOM    363  NH1 ARG A  48      20.305  35.279  41.771  1.00  0.00          N   \nATOM    364  NH2 ARG A  48      21.467  35.364  43.761  1.00  0.00          N   \nATOM    365  N   GLN A  49      25.504  36.219  37.276  1.00  0.00          N   \nATOM    366  CA  GLN A  49      25.338  36.526  35.857  1.00  0.00          C   \nATOM    367  C   GLN A  49      24.852  35.296  35.115  1.00  0.00          C   \nATOM    368  O   GLN A  49      23.965  35.376  34.268  1.00  0.00          O   \nATOM    369  CB  GLN A  49      26.655  37.011  35.250  1.00  0.00          C   \nATOM    370  CG  GLN A  49      27.165  38.296  35.879  1.00  0.00          C   \nATOM    371  CD  GLN A  49      28.546  38.677  35.382  1.00  0.00          C   \nATOM    372  OE1 GLN A  49      28.699  39.247  34.301  1.00  0.00          O   \nATOM    373  NE2 GLN A  49      29.563  38.347  36.165  1.00  0.00          N   \nATOM    374  N   LYS A  50      25.434  34.152  35.445  1.00  0.00          N   \nATOM    375  CA  LYS A  50      25.042  32.907  34.809  1.00  0.00          C   \nATOM    376  C   LYS A  50      23.590  32.567  35.106  1.00  0.00          C   \nATOM    377  O   LYS A  50      22.861  32.135  34.221  1.00  0.00          O   \nATOM    378  CB  LYS A  50      25.958  31.770  35.269  1.00  0.00          C   \nATOM    379  CG  LYS A  50      27.304  31.793  34.564  1.00  0.00          C   \nATOM    380  CD  LYS A  50      28.246  30.721  35.077  1.00  0.00          C   \nATOM    381  CE  LYS A  50      29.504  30.654  34.221  1.00  0.00          C   \nATOM    382  NZ  LYS A  50      30.148  31.993  34.063  1.00  0.00          N   \nATOM    383  N   LYS A  51      23.162  32.768  36.345  1.00  0.00          N   \nATOM    384  CA  LYS A  51      21.778  32.464  36.704  1.00  0.00          C   \nATOM    385  C   LYS A  51      20.774  33.403  36.035  1.00  0.00          C   \nATOM    386  O   LYS A  51      19.739  32.945  35.562  1.00  0.00          O   \nATOM    387  CB  LYS A  51      21.565  32.545  38.221  1.00  0.00          C   \nATOM    388  CG  LYS A  51      22.409  31.586  39.046  1.00  0.00          C   \nATOM    389  CD  LYS A  51      22.182  31.804  40.542  1.00  0.00          C   \nATOM    390  CE  LYS A  51      23.269  31.127  41.389  1.00  0.00          C   \nATOM    391  NZ  LYS A  51      23.156  29.642  41.486  1.00  0.00          N   \nATOM    392  N   VAL A  52      21.081  34.705  35.997  1.00  0.00          N   \nATOM    393  CA  VAL A  52      20.159  35.702  35.435  1.00  0.00          C   \nATOM    394  C   VAL A  52      20.151  35.881  33.922  1.00  0.00          C   \nATOM    395  O   VAL A  52      19.344  36.645  33.400  1.00  0.00          O   \nATOM    396  CB  VAL A  52      20.393  37.108  36.064  1.00  0.00          C   \nATOM    397  CG1 VAL A  52      20.196  37.038  37.586  1.00  0.00          C   \nATOM    398  CG2 VAL A  52      21.796  37.618  35.714  1.00  0.00          C   \nATOM    399  N   THR A  53      21.034  35.170  33.227  1.00  0.00          N   \nATOM    400  CA  THR A  53      21.134  35.283  31.776  1.00  0.00          C   \nATOM    401  C   THR A  53      20.429  34.147  31.032  1.00  0.00          C   \nATOM    402  O   THR A  53      20.871  33.003  31.078  1.00  0.00          O   \nATOM    403  CB  THR A  53      22.616  35.315  31.345  1.00  0.00          C   \nATOM    404  OG1 THR A  53      23.305  36.321  32.097  1.00  0.00          O   \nATOM    405  CG2 THR A  53      22.736  35.632  29.870  1.00  0.00          C   \nATOM    406  N   PHE A  54      19.328  34.475  30.359  1.00  0.00          N   \nATOM    407  CA  PHE A  54      18.555  33.505  29.579  1.00  0.00          C   \nATOM    408  C   PHE A  54      17.618  34.183  28.580  1.00  0.00          C   \nATOM    409  O   PHE A  54      17.361  35.393  28.658  1.00  0.00          O   \nATOM    410  CB  PHE A  54      17.756  32.565  30.498  1.00  0.00          C   \nATOM    411  CG  PHE A  54      16.970  33.268  31.574  1.00  0.00          C   \nATOM    412  CD1 PHE A  54      15.760  33.892  31.281  1.00  0.00          C   \nATOM    413  CD2 PHE A  54      17.448  33.306  32.883  1.00  0.00          C   \nATOM    414  CE1 PHE A  54      15.030  34.543  32.276  1.00  0.00          C   \nATOM    415  CE2 PHE A  54      16.726  33.961  33.897  1.00  0.00          C   \nATOM    416  CZ  PHE A  54      15.510  34.581  33.590  1.00  0.00          C   \nATOM    417  N   ASP A  55      17.120  33.402  27.629  1.00  0.00          N   \nATOM    418  CA  ASP A  55      16.209  33.931  26.630  1.00  0.00          C   \nATOM    419  C   ASP A  55      14.769  33.964  27.171  1.00  0.00          C   \nATOM    420  O   ASP A  55      14.358  33.094  27.936  1.00  0.00          O   \nATOM    421  CB  ASP A  55      16.288  33.068  25.371  1.00  0.00          C   \nATOM    422  CG  ASP A  55      16.009  33.854  24.102  1.00  0.00          C   \nATOM    423  OD1 ASP A  55      16.205  33.285  23.012  1.00  0.00          O   \nATOM    424  OD2 ASP A  55      15.594  35.035  24.190  1.00  0.00          O   \nATOM    425  N   ARG A  56      14.014  34.987  26.796  1.00  0.00          N   \nATOM    426  CA  ARG A  56      12.631  35.077  27.239  1.00  0.00          C   \nATOM    427  C   ARG A  56      11.712  34.927  26.037  1.00  0.00          C   \nATOM    428  O   ARG A  56      11.988  35.436  24.949  1.00  0.00          O   \nATOM    429  CB  ARG A  56      12.340  36.417  27.934  1.00  0.00          C   \nATOM    430  CG  ARG A  56      12.997  36.609  29.343  1.00  0.00          C   \nATOM    431  CD  ARG A  56      14.487  36.866  29.235  1.00  0.00          C   \nATOM    432  NE  ARG A  56      14.737  37.994  28.347  1.00  0.00          N   \nATOM    433  CZ  ARG A  56      14.470  39.269  28.637  1.00  0.00          C   \nATOM    434  NH1 ARG A  56      13.950  39.620  29.820  1.00  0.00          N   \nATOM    435  NH2 ARG A  56      14.675  40.194  27.713  1.00  0.00          N   \nATOM    436  N   LEU A  57      10.623  34.204  26.234  1.00  0.00          N   \nATOM    437  CA  LEU A  57       9.638  34.016  25.182  1.00  0.00          C   \nATOM    438  C   LEU A  57       8.300  34.342  25.833  1.00  0.00          C   \nATOM    439  O   LEU A  57       8.130  34.132  27.040  1.00  0.00          O   \nATOM    440  CB  LEU A  57       9.674  32.565  24.697  1.00  0.00          C   \nATOM    441  CG  LEU A  57       9.865  32.317  23.199  1.00  0.00          C   \nATOM    442  CD1 LEU A  57      10.906  33.261  22.608  1.00  0.00          C   \nATOM    443  CD2 LEU A  57      10.278  30.860  22.994  1.00  0.00          C   \nATOM    444  N   GLN A  58       7.375  34.893  25.056  1.00  0.00          N   \nATOM    445  CA  GLN A  58       6.053  35.223  25.567  1.00  0.00          C   \nATOM    446  C   GLN A  58       4.938  34.923  24.578  1.00  0.00          C   \nATOM    447  O   GLN A  58       5.000  35.288  23.407  1.00  0.00          O   \nATOM    448  CB  GLN A  58       5.960  36.700  25.968  1.00  0.00          C   \nATOM    449  CG  GLN A  58       6.566  37.036  27.309  1.00  0.00          C   \nATOM    450  CD  GLN A  58       6.329  38.487  27.680  1.00  0.00          C   \nATOM    451  OE1 GLN A  58       6.722  39.382  26.946  1.00  0.00          O   \nATOM    452  NE2 GLN A  58       5.684  38.724  28.825  1.00  0.00          N   \nATOM    453  N   VAL A  59       3.913  34.261  25.088  1.00  0.00          N   \nATOM    454  CA  VAL A  59       2.732  33.928  24.321  1.00  0.00          C   \nATOM    455  C   VAL A  59       1.597  34.588  25.107  1.00  0.00          C   \nATOM    456  O   VAL A  59       1.221  34.126  26.196  1.00  0.00          O   \nATOM    457  CB  VAL A  59       2.541  32.404  24.250  1.00  0.00          C   \nATOM    458  CG1 VAL A  59       1.217  32.095  23.626  1.00  0.00          C   \nATOM    459  CG2 VAL A  59       3.667  31.775  23.428  1.00  0.00          C   \nATOM    460  N   LEU A  60       1.071  35.681  24.553  1.00  0.00          N   \nATOM    461  CA  LEU A  60       0.019  36.458  25.207  1.00  0.00          C   \nATOM    462  C   LEU A  60      -1.381  36.186  24.658  1.00  0.00          C   \nATOM    463  O   LEU A  60      -1.676  36.469  23.494  1.00  0.00          O   \nATOM    464  CB  LEU A  60       0.354  37.948  25.101  1.00  0.00          C   \nATOM    465  CG  LEU A  60       1.745  38.367  25.606  1.00  0.00          C   \nATOM    466  CD1 LEU A  60       1.918  39.876  25.429  1.00  0.00          C   \nATOM    467  CD2 LEU A  60       1.919  37.976  27.070  1.00  0.00          C   \nATOM    468  N   ASP A  61      -2.235  35.657  25.535  1.00  0.00          N   \nATOM    469  CA  ASP A  61      -3.609  35.283  25.205  1.00  0.00          C   \nATOM    470  C   ASP A  61      -4.642  36.348  25.571  1.00  0.00          C   \nATOM    471  O   ASP A  61      -4.301  37.441  26.037  1.00  0.00          O   \nATOM    472  CB  ASP A  61      -3.947  33.961  25.895  1.00  0.00          C   \nATOM    473  CG  ASP A  61      -4.051  34.100  27.402  1.00  0.00          C   \nATOM    474  OD1 ASP A  61      -3.549  35.103  27.956  1.00  0.00          O   \nATOM    475  OD2 ASP A  61      -4.640  33.208  28.036  1.00  0.00          O   \nATOM    476  N   ASP A  62      -5.915  36.032  25.364  1.00  0.00          N   \nATOM    477  CA  ASP A  62      -6.956  37.013  25.643  1.00  0.00          C   \nATOM    478  C   ASP A  62      -7.148  37.348  27.130  1.00  0.00          C   \nATOM    479  O   ASP A  62      -7.532  38.474  27.466  1.00  0.00          O   \nATOM    480  CB  ASP A  62      -8.271  36.570  24.980  1.00  0.00          C   \nATOM    481  CG  ASP A  62      -8.165  36.555  23.449  1.00  0.00          C   \nATOM    482  OD1 ASP A  62      -7.920  37.624  22.853  1.00  0.00          O   \nATOM    483  OD2 ASP A  62      -8.301  35.476  22.837  1.00  0.00          O   \nATOM    484  N   HIS A  63      -6.881  36.394  28.019  1.00  0.00          N   \nATOM    485  CA  HIS A  63      -7.007  36.682  29.454  1.00  0.00          C   \nATOM    486  C   HIS A  63      -5.986  37.764  29.815  1.00  0.00          C   \nATOM    487  O   HIS A  63      -6.264  38.671  30.615  1.00  0.00          O   \nATOM    488  CB  HIS A  63      -6.744  35.435  30.304  1.00  0.00          C   \nATOM    489  CG  HIS A  63      -7.843  34.416  30.250  1.00  0.00          C   \nATOM    490  ND1 HIS A  63      -9.171  34.741  30.441  1.00  0.00          N   \nATOM    491  CD2 HIS A  63      -7.807  33.074  30.075  1.00  0.00          C   \nATOM    492  CE1 HIS A  63      -9.904  33.642  30.387  1.00  0.00          C   \nATOM    493  NE2 HIS A  63      -9.099  32.616  30.167  1.00  0.00          N   \nATOM    494  N   TYR A  64      -4.807  37.672  29.205  1.00  0.00          N   \nATOM    495  CA  TYR A  64      -3.757  38.660  29.447  1.00  0.00          C   \nATOM    496  C   TYR A  64      -4.253  40.046  29.011  1.00  0.00          C   \nATOM    497  O   TYR A  64      -4.162  41.005  29.769  1.00  0.00          O   \nATOM    498  CB  TYR A  64      -2.490  38.302  28.668  1.00  0.00          C   \nATOM    499  CG  TYR A  64      -1.363  39.321  28.803  1.00  0.00          C   \nATOM    500  CD1 TYR A  64      -0.439  39.249  29.856  1.00  0.00          C   \nATOM    501  CD2 TYR A  64      -1.227  40.357  27.877  1.00  0.00          C   \nATOM    502  CE1 TYR A  64       0.592  40.184  29.967  1.00  0.00          C   \nATOM    503  CE2 TYR A  64      -0.207  41.295  27.980  1.00  0.00          C   \nATOM    504  CZ  TYR A  64       0.697  41.201  29.022  1.00  0.00          C   \nATOM    505  OH  TYR A  64       1.717  42.117  29.092  1.00  0.00          O   \nATOM    506  N   ARG A  65      -4.786  40.152  27.796  1.00  0.00          N   \nATOM    507  CA  ARG A  65      -5.272  41.449  27.319  1.00  0.00          C   \nATOM    508  C   ARG A  65      -6.530  41.949  28.041  1.00  0.00          C   \nATOM    509  O   ARG A  65      -6.692  43.158  28.224  1.00  0.00          O   \nATOM    510  CB  ARG A  65      -5.474  41.427  25.793  1.00  0.00          C   \nATOM    511  CG  ARG A  65      -4.148  41.427  25.000  1.00  0.00          C   \nATOM    512  CD  ARG A  65      -4.374  41.373  23.485  1.00  0.00          C   \nATOM    513  NE  ARG A  65      -4.963  40.098  23.089  1.00  0.00          N   \nATOM    514  CZ  ARG A  65      -4.277  38.965  22.967  1.00  0.00          C   \nATOM    515  NH1 ARG A  65      -2.973  38.946  23.182  1.00  0.00          N   \nATOM    516  NH2 ARG A  65      -4.912  37.832  22.713  1.00  0.00          N   \nATOM    517  N   ASP A  66      -7.407  41.036  28.465  1.00  0.00          N   \nATOM    518  CA  ASP A  66      -8.613  41.420  29.198  1.00  0.00          C   \nATOM    519  C   ASP A  66      -8.212  42.085  30.526  1.00  0.00          C   \nATOM    520  O   ASP A  66      -8.741  43.137  30.905  1.00  0.00          O   \nATOM    521  CB  ASP A  66      -9.501  40.200  29.526  1.00  0.00          C   \nATOM    522  CG  ASP A  66     -10.254  39.648  28.313  1.00  0.00          C   \nATOM    523  OD1 ASP A  66     -10.344  40.329  27.271  1.00  0.00          O   \nATOM    524  OD2 ASP A  66     -10.778  38.517  28.416  1.00  0.00          O   \nATOM    525  N   VAL A  67      -7.281  41.462  31.240  1.00  0.00          N   \nATOM    526  CA  VAL A  67      -6.846  42.007  32.521  1.00  0.00          C   \nATOM    527  C   VAL A  67      -6.146  43.342  32.286  1.00  0.00          C   \nATOM    528  O   VAL A  67      -6.376  44.317  33.012  1.00  0.00          O   \nATOM    529  CB  VAL A  67      -5.893  41.016  33.267  1.00  0.00          C   \nATOM    530  CG1 VAL A  67      -5.251  41.715  34.464  1.00  0.00          C   \nATOM    531  CG2 VAL A  67      -6.684  39.753  33.746  1.00  0.00          C   \nATOM    532  N   LEU A  68      -5.316  43.403  31.252  1.00  0.00          N   \nATOM    533  CA  LEU A  68      -4.610  44.649  30.975  1.00  0.00          C   \nATOM    534  C   LEU A  68      -5.606  45.779  30.728  1.00  0.00          C   \nATOM    535  O   LEU A  68      -5.451  46.894  31.249  1.00  0.00          O   \nATOM    536  CB  LEU A  68      -3.695  44.478  29.758  1.00  0.00          C   \nATOM    537  CG  LEU A  68      -2.917  45.710  29.292  1.00  0.00          C   \nATOM    538  CD1 LEU A  68      -2.181  46.326  30.479  1.00  0.00          C   \nATOM    539  CD2 LEU A  68      -1.933  45.306  28.166  1.00  0.00          C   \nATOM    540  N   LYS A  69      -6.639  45.496  29.942  1.00  0.00          N   \nATOM    541  CA  LYS A  69      -7.639  46.520  29.647  1.00  0.00          C   \nATOM    542  C   LYS A  69      -8.281  47.029  30.945  1.00  0.00          C   \nATOM    543  O   LYS A  69      -8.503  48.224  31.100  1.00  0.00          O   \nATOM    544  CB  LYS A  69      -8.709  45.976  28.688  1.00  0.00          C   \nATOM    545  CG  LYS A  69      -9.781  46.992  28.302  1.00  0.00          C   \nATOM    546  CD  LYS A  69     -10.542  46.551  27.046  1.00  0.00          C   \nATOM    547  CE  LYS A  69     -11.387  47.685  26.491  1.00  0.00          C   \nATOM    548  NZ  LYS A  69     -12.202  47.275  25.310  1.00  0.00          N   \nATOM    549  N   GLU A  70      -8.565  46.120  31.874  1.00  0.00          N   \nATOM    550  CA  GLU A  70      -9.164  46.489  33.169  1.00  0.00          C   \nATOM    551  C   GLU A  70      -8.218  47.374  33.963  1.00  0.00          C   \nATOM    552  O   GLU A  70      -8.634  48.366  34.559  1.00  0.00          O   \nATOM    553  CB  GLU A  70      -9.484  45.239  33.995  1.00  0.00          C   \nATOM    554  CG  GLU A  70     -10.682  44.439  33.485  1.00  0.00          C   \nATOM    555  CD  GLU A  70     -10.774  43.044  34.100  1.00  0.00          C   \nATOM    556  OE1 GLU A  70     -10.562  42.898  35.324  1.00  0.00          O   \nATOM    557  OE2 GLU A  70     -11.068  42.089  33.353  1.00  0.00          O   \nATOM    558  N   MET A  71      -6.943  47.004  33.980  1.00  0.00          N   \nATOM    559  CA  MET A  71      -5.944  47.786  34.697  1.00  0.00          C   \nATOM    560  C   MET A  71      -5.846  49.206  34.135  1.00  0.00          C   \nATOM    561  O   MET A  71      -5.763  50.173  34.897  1.00  0.00          O   \nATOM    562  CB  MET A  71      -4.588  47.089  34.619  1.00  0.00          C   \nATOM    563  CG  MET A  71      -4.522  45.867  35.521  1.00  0.00          C   \nATOM    564  SD  MET A  71      -3.080  44.867  35.252  1.00  0.00          S   \nATOM    565  CE  MET A  71      -1.805  45.804  36.133  1.00  0.00          C   \nATOM    566  N   LYS A  72      -5.854  49.325  32.805  1.00  0.00          N   \nATOM    567  CA  LYS A  72      -5.785  50.629  32.151  1.00  0.00          C   \nATOM    568  C   LYS A  72      -7.019  51.488  32.460  1.00  0.00          C   \nATOM    569  O   LYS A  72      -6.913  52.707  32.626  1.00  0.00          O   \nATOM    570  CB  LYS A  72      -5.656  50.451  30.639  1.00  0.00          C   \nATOM    571  CG  LYS A  72      -4.289  49.981  30.164  1.00  0.00          C   \nATOM    572  CD  LYS A  72      -4.344  49.815  28.655  1.00  0.00          C   \nATOM    573  CE  LYS A  72      -2.982  49.809  28.016  1.00  0.00          C   \nATOM    574  NZ  LYS A  72      -3.126  49.727  26.532  1.00  0.00          N   \nATOM    575  N   ALA A  73      -8.192  50.866  32.514  1.00  0.00          N   \nATOM    576  CA  ALA A  73      -9.400  51.630  32.838  1.00  0.00          C   \nATOM    577  C   ALA A  73      -9.231  52.233  34.239  1.00  0.00          C   \nATOM    578  O   ALA A  73      -9.566  53.391  34.473  1.00  0.00          O   \nATOM    579  CB  ALA A  73     -10.632  50.730  32.794  1.00  0.00          C   \nATOM    580  N   LYS A  74      -8.697  51.455  35.173  1.00  0.00          N   \nATOM    581  CA  LYS A  74      -8.497  51.991  36.511  1.00  0.00          C   \nATOM    582  C   LYS A  74      -7.410  53.059  36.485  1.00  0.00          C   \nATOM    583  O   LYS A  74      -7.551  54.108  37.119  1.00  0.00          O   \nATOM    584  CB  LYS A  74      -8.139  50.878  37.498  1.00  0.00          C   \nATOM    585  CG  LYS A  74      -9.308  49.926  37.782  1.00  0.00          C   \nATOM    586  CD  LYS A  74      -8.850  48.700  38.558  1.00  0.00          C   \nATOM    587  CE  LYS A  74     -10.019  47.807  38.969  1.00  0.00          C   \nATOM    588  NZ  LYS A  74     -10.653  47.108  37.803  1.00  0.00          N   \nATOM    589  N   ALA A  75      -6.342  52.814  35.730  1.00  0.00          N   \nATOM    590  CA  ALA A  75      -5.241  53.776  35.631  1.00  0.00          C   \nATOM    591  C   ALA A  75      -5.707  55.122  35.072  1.00  0.00          C   \nATOM    592  O   ALA A  75      -5.155  56.164  35.416  1.00  0.00          O   \nATOM    593  CB  ALA A  75      -4.108  53.213  34.742  1.00  0.00          C   \nATOM    594  N   SER A  76      -6.707  55.096  34.198  1.00  0.00          N   \nATOM    595  CA  SER A  76      -7.211  56.324  33.604  1.00  0.00          C   \nATOM    596  C   SER A  76      -7.885  57.249  34.606  1.00  0.00          C   \nATOM    597  O   SER A  76      -8.124  58.411  34.290  1.00  0.00          O   \nATOM    598  CB  SER A  76      -8.211  56.010  32.488  1.00  0.00          C   \nATOM    599  OG  SER A  76      -7.597  55.211  31.495  1.00  0.00          O   \nATOM    600  N   THR A  77      -8.215  56.741  35.792  1.00  0.00          N   \nATOM    601  CA  THR A  77      -8.861  57.580  36.805  1.00  0.00          C   \nATOM    602  C   THR A  77      -7.818  58.331  37.628  1.00  0.00          C   \nATOM    603  O   THR A  77      -8.140  59.264  38.369  1.00  0.00          O   \nATOM    604  CB  THR A  77      -9.734  56.742  37.758  1.00  0.00          C   \nATOM    605  OG1 THR A  77      -8.900  55.945  38.614  1.00  0.00          O   \nATOM    606  CG2 THR A  77     -10.640  55.838  36.954  1.00  0.00          C   \nATOM    607  N   VAL A  78      -6.560  57.930  37.469  1.00  0.00          N   \nATOM    608  CA  VAL A  78      -5.452  58.538  38.199  1.00  0.00          C   \nATOM    609  C   VAL A  78      -4.932  59.849  37.615  1.00  0.00          C   \nATOM    610  O   VAL A  78      -4.766  59.983  36.400  1.00  0.00          O   \nATOM    611  CB  VAL A  78      -4.250  57.559  38.284  1.00  0.00          C   \nATOM    612  CG1 VAL A  78      -3.033  58.266  38.880  1.00  0.00          C   \nATOM    613  CG2 VAL A  78      -4.624  56.351  39.130  1.00  0.00          C   \nATOM    614  N   LYS A  79      -4.687  60.820  38.489  1.00  0.00          N   \nATOM    615  CA  LYS A  79      -4.118  62.100  38.078  1.00  0.00          C   \nATOM    616  C   LYS A  79      -2.846  62.233  38.904  1.00  0.00          C   \nATOM    617  O   LYS A  79      -2.886  62.174  40.134  1.00  0.00          O   \nATOM    618  CB  LYS A  79      -5.068  63.265  38.371  1.00  0.00          C   \nATOM    619  CG  LYS A  79      -4.471  64.614  37.994  1.00  0.00          C   \nATOM    620  CD  LYS A  79      -5.492  65.743  38.042  1.00  0.00          C   \nATOM    621  CE  LYS A  79      -4.816  67.079  37.746  1.00  0.00          C   \nATOM    622  NZ  LYS A  79      -5.773  68.212  37.748  1.00  0.00          N   \nATOM    623  N   ALA A  80      -1.719  62.381  38.220  1.00  0.00          N   \nATOM    624  CA  ALA A  80      -0.419  62.482  38.875  1.00  0.00          C   \nATOM    625  C   ALA A  80       0.243  63.810  38.586  1.00  0.00          C   \nATOM    626  O   ALA A  80       0.106  64.358  37.498  1.00  0.00          O   \nATOM    627  CB  ALA A  80       0.496  61.338  38.407  1.00  0.00          C   \nATOM    628  N   LYS A  81       0.979  64.317  39.562  1.00  0.00          N   \nATOM    629  CA  LYS A  81       1.651  65.580  39.382  1.00  0.00          C   \nATOM    630  C   LYS A  81       3.146  65.427  39.182  1.00  0.00          C   \nATOM    631  O   LYS A  81       3.785  64.499  39.689  1.00  0.00          O   \nATOM    632  CB  LYS A  81       1.389  66.509  40.575  1.00  0.00          C   \nATOM    633  CG  LYS A  81       1.991  66.061  41.907  1.00  0.00          C   \nATOM    634  CD  LYS A  81       2.147  67.269  42.851  1.00  0.00          C   \nATOM    635  CE  LYS A  81       2.608  66.887  44.258  1.00  0.00          C   \nATOM    636  NZ  LYS A  81       1.526  66.265  45.090  1.00  0.00          N   \nATOM    637  N   LEU A  82       3.688  66.372  38.434  1.00  0.00          N   \nATOM    638  CA  LEU A  82       5.100  66.436  38.139  1.00  0.00          C   \nATOM    639  C   LEU A  82       5.752  67.077  39.360  1.00  0.00          C   \nATOM    640  O   LEU A  82       5.335  68.143  39.799  1.00  0.00          O   \nATOM    641  CB  LEU A  82       5.289  67.315  36.908  1.00  0.00          C   \nATOM    642  CG  LEU A  82       6.583  67.376  36.112  1.00  0.00          C   \nATOM    643  CD1 LEU A  82       6.966  65.993  35.615  1.00  0.00          C   \nATOM    644  CD2 LEU A  82       6.373  68.332  34.940  1.00  0.00          C   \nATOM    645  N   LEU A  83       6.754  66.421  39.927  1.00  0.00          N   \nATOM    646  CA  LEU A  83       7.439  66.967  41.088  1.00  0.00          C   \nATOM    647  C   LEU A  83       8.415  68.053  40.638  1.00  0.00          C   \nATOM    648  O   LEU A  83       8.919  68.022  39.513  1.00  0.00          O   \nATOM    649  CB  LEU A  83       8.211  65.860  41.816  1.00  0.00          C   \nATOM    650  CG  LEU A  83       7.838  65.479  43.251  1.00  0.00          C   \nATOM    651  CD1 LEU A  83       6.437  64.863  43.302  1.00  0.00          C   \nATOM    652  CD2 LEU A  83       8.871  64.492  43.780  1.00  0.00          C   \nATOM    653  N   SER A  84       8.676  69.020  41.514  1.00  0.00          N   \nATOM    654  CA  SER A  84       9.619  70.089  41.189  1.00  0.00          C   \nATOM    655  C   SER A  84      11.017  69.557  41.466  1.00  0.00          C   \nATOM    656  O   SER A  84      11.176  68.570  42.182  1.00  0.00          O   \nATOM    657  CB  SER A  84       9.370  71.319  42.071  1.00  0.00          C   \nATOM    658  OG  SER A  84       9.661  71.034  43.433  1.00  0.00          O   \nATOM    659  N   VAL A  85      12.030  70.205  40.907  1.00  0.00          N   \nATOM    660  CA  VAL A  85      13.400  69.773  41.143  1.00  0.00          C   \nATOM    661  C   VAL A  85      13.680  69.689  42.639  1.00  0.00          C   \nATOM    662  O   VAL A  85      14.179  68.675  43.133  1.00  0.00          O   \nATOM    663  CB  VAL A  85      14.412  70.741  40.505  1.00  0.00          C   \nATOM    664  CG1 VAL A  85      15.833  70.347  40.896  1.00  0.00          C   \nATOM    665  CG2 VAL A  85      14.254  70.722  38.987  1.00  0.00          C   \nATOM    666  N   GLU A  86      13.344  70.751  43.365  1.00  0.00          N   \nATOM    667  CA  GLU A  86      13.588  70.787  44.801  1.00  0.00          C   \nATOM    668  C   GLU A  86      12.900  69.643  45.533  1.00  0.00          C   \nATOM    669  O   GLU A  86      13.509  68.996  46.388  1.00  0.00          O   \nATOM    670  CB  GLU A  86      13.144  72.132  45.384  1.00  0.00          C   \nATOM    671  CG  GLU A  86      11.805  72.621  44.871  1.00  0.00          C   \nATOM    672  CD  GLU A  86      11.945  73.708  43.818  1.00  0.00          C   \nATOM    673  OE1 GLU A  86      12.721  73.521  42.849  1.00  0.00          O   \nATOM    674  OE2 GLU A  86      11.268  74.753  43.965  1.00  0.00          O   \nATOM    675  N   GLU A  87      11.635  69.391  45.200  1.00  0.00          N   \nATOM    676  CA  GLU A  87      10.895  68.307  45.838  1.00  0.00          C   \nATOM    677  C   GLU A  87      11.608  66.986  45.583  1.00  0.00          C   \nATOM    678  O   GLU A  87      11.815  66.188  46.500  1.00  0.00          O   \nATOM    679  CB  GLU A  87       9.467  68.231  45.287  1.00  0.00          C   \nATOM    680  CG  GLU A  87       8.586  69.408  45.670  1.00  0.00          C   \nATOM    681  CD  GLU A  87       7.224  69.360  45.001  1.00  0.00          C   \nATOM    682  OE1 GLU A  87       7.160  69.433  43.756  1.00  0.00          O   \nATOM    683  OE2 GLU A  87       6.216  69.247  45.724  1.00  0.00          O   \nATOM    684  N   ALA A  88      11.984  66.768  44.327  1.00  0.00          N   \nATOM    685  CA  ALA A  88      12.671  65.546  43.942  1.00  0.00          C   \nATOM    686  C   ALA A  88      13.969  65.443  44.718  1.00  0.00          C   \nATOM    687  O   ALA A  88      14.313  64.373  45.221  1.00  0.00          O   \nATOM    688  CB  ALA A  88      12.947  65.543  42.445  1.00  0.00          C   \nATOM    689  N   CYS A  89      14.685  66.560  44.826  1.00  0.00          N   \nATOM    690  CA  CYS A  89      15.946  66.572  45.554  1.00  0.00          C   \nATOM    691  C   CYS A  89      15.781  66.134  47.007  1.00  0.00          C   \nATOM    692  O   CYS A  89      16.594  65.376  47.517  1.00  0.00          O   \nATOM    693  CB  CYS A  89      16.580  67.964  45.516  1.00  0.00          C   \nATOM    694  SG  CYS A  89      17.293  68.419  43.931  1.00  0.00          S   \nATOM    695  N   LYS A  90      14.731  66.610  47.670  1.00  0.00          N   \nATOM    696  CA  LYS A  90      14.480  66.258  49.071  1.00  0.00          C   \nATOM    697  C   LYS A  90      14.223  64.754  49.297  1.00  0.00          C   \nATOM    698  O   LYS A  90      14.393  64.246  50.411  1.00  0.00          O   \nATOM    699  CB  LYS A  90      13.285  67.053  49.607  1.00  0.00          C   \nATOM    700  CG  LYS A  90      13.450  68.570  49.609  1.00  0.00          C   \nATOM    701  CD  LYS A  90      12.109  69.237  49.943  1.00  0.00          C   \nATOM    702  CE  LYS A  90      12.142  70.760  49.778  1.00  0.00          C   \nATOM    703  NZ  LYS A  90      10.763  71.347  49.866  1.00  0.00          N   \nATOM    704  N   LEU A  91      13.811  64.043  48.251  1.00  0.00          N   \nATOM    705  CA  LEU A  91      13.544  62.608  48.377  1.00  0.00          C   \nATOM    706  C   LEU A  91      14.802  61.753  48.246  1.00  0.00          C   \nATOM    707  O   LEU A  91      14.752  60.533  48.407  1.00  0.00          O   \nATOM    708  CB  LEU A  91      12.525  62.163  47.330  1.00  0.00          C   \nATOM    709  CG  LEU A  91      11.081  62.617  47.551  1.00  0.00          C   \nATOM    710  CD1 LEU A  91      10.261  62.285  46.321  1.00  0.00          C   \nATOM    711  CD2 LEU A  91      10.503  61.935  48.792  1.00  0.00          C   \nATOM    712  N   THR A  92      15.929  62.401  47.966  1.00  0.00          N   \nATOM    713  CA  THR A  92      17.204  61.710  47.800  1.00  0.00          C   \nATOM    714  C   THR A  92      17.913  61.348  49.111  1.00  0.00          C   \nATOM    715  O   THR A  92      18.076  62.193  49.993  1.00  0.00          O   \nATOM    716  CB  THR A  92      18.164  62.563  46.961  1.00  0.00          C   \nATOM    717  OG1 THR A  92      17.583  62.803  45.674  1.00  0.00          O   \nATOM    718  CG2 THR A  92      19.496  61.860  46.795  1.00  0.00          C   \nATOM    719  N   PRO A  93      18.338  60.078  49.252  1.00  0.00          N   \nATOM    720  CA  PRO A  93      19.036  59.604  50.454  1.00  0.00          C   \nATOM    721  C   PRO A  93      20.332  60.362  50.686  1.00  0.00          C   \nATOM    722  O   PRO A  93      21.134  60.532  49.768  1.00  0.00          O   \nATOM    723  CB  PRO A  93      19.280  58.134  50.147  1.00  0.00          C   \nATOM    724  CG  PRO A  93      18.044  57.769  49.379  1.00  0.00          C   \nATOM    725  CD  PRO A  93      17.928  58.937  48.414  1.00  0.00          C   \nATOM    726  N   PRO A  94      20.556  60.828  51.924  1.00  0.00          N   \nATOM    727  CA  PRO A  94      21.771  61.576  52.262  1.00  0.00          C   \nATOM    728  C   PRO A  94      23.055  60.889  51.811  1.00  0.00          C   \nATOM    729  O   PRO A  94      24.063  61.551  51.569  1.00  0.00          O   \nATOM    730  CB  PRO A  94      21.671  61.715  53.777  1.00  0.00          C   \nATOM    731  CG  PRO A  94      20.189  61.865  53.971  1.00  0.00          C   \nATOM    732  CD  PRO A  94      19.640  60.773  53.077  1.00  0.00          C   \nATOM    733  N   HIS A  95      23.018  59.568  51.684  1.00  0.00          N   \nATOM    734  CA  HIS A  95      24.203  58.835  51.254  1.00  0.00          C   \nATOM    735  C   HIS A  95      24.024  58.116  49.921  1.00  0.00          C   \nATOM    736  O   HIS A  95      24.719  57.148  49.631  1.00  0.00          O   \nATOM    737  CB  HIS A  95      24.634  57.844  52.341  1.00  0.00          C   \nATOM    738  CG  HIS A  95      25.070  58.506  53.611  1.00  0.00          C   \nATOM    739  ND1 HIS A  95      24.190  59.163  54.450  1.00  0.00          N   \nATOM    740  CD2 HIS A  95      26.295  58.657  54.167  1.00  0.00          C   \nATOM    741  CE1 HIS A  95      24.855  59.687  55.462  1.00  0.00          C   \nATOM    742  NE2 HIS A  95      26.137  59.395  55.314  1.00  0.00          N   \nATOM    743  N   SER A  96      23.095  58.600  49.108  1.00  0.00          N   \nATOM    744  CA  SER A  96      22.856  58.000  47.805  1.00  0.00          C   \nATOM    745  C   SER A  96      24.140  58.163  46.988  1.00  0.00          C   \nATOM    746  O   SER A  96      24.882  59.126  47.187  1.00  0.00          O   \nATOM    747  CB  SER A  96      21.693  58.705  47.111  1.00  0.00          C   \nATOM    748  OG  SER A  96      21.333  58.037  45.920  1.00  0.00          O   \nATOM    749  N   ALA A  97      24.404  57.219  46.087  1.00  0.00          N   \nATOM    750  CA  ALA A  97      25.606  57.260  45.253  1.00  0.00          C   \nATOM    751  C   ALA A  97      25.783  58.618  44.585  1.00  0.00          C   \nATOM    752  O   ALA A  97      24.832  59.189  44.039  1.00  0.00          O   \nATOM    753  CB  ALA A  97      25.549  56.164  44.189  1.00  0.00          C   \nATOM    754  N   LYS A  98      27.008  59.129  44.625  1.00  0.00          N   \nATOM    755  CA  LYS A  98      27.310  60.423  44.027  1.00  0.00          C   \nATOM    756  C   LYS A  98      27.234  60.359  42.499  1.00  0.00          C   \nATOM    757  O   LYS A  98      27.459  59.306  41.888  1.00  0.00          O   \nATOM    758  CB  LYS A  98      28.712  60.876  44.445  1.00  0.00          C   \nATOM    759  CG  LYS A  98      29.829  59.941  43.962  1.00  0.00          C   \nATOM    760  CD  LYS A  98      31.219  60.517  44.226  1.00  0.00          C   \nATOM    761  CE  LYS A  98      32.291  59.695  43.516  1.00  0.00          C   \nATOM    762  NZ  LYS A  98      33.645  60.338  43.545  1.00  0.00          N   \nATOM    763  N   SER A  99      26.914  61.495  41.890  1.00  0.00          N   \nATOM    764  CA  SER A  99      26.830  61.596  40.444  1.00  0.00          C   \nATOM    765  C   SER A  99      28.228  61.517  39.839  1.00  0.00          C   \nATOM    766  O   SER A  99      29.215  61.802  40.515  1.00  0.00          O   \nATOM    767  CB  SER A  99      26.207  62.930  40.055  1.00  0.00          C   \nATOM    768  OG  SER A  99      26.318  63.150  38.662  1.00  0.00          O   \nATOM    769  N   LYS A 100      28.306  61.133  38.568  1.00  0.00          N   \nATOM    770  CA  LYS A 100      29.585  61.060  37.872  1.00  0.00          C   \nATOM    771  C   LYS A 100      29.897  62.446  37.319  1.00  0.00          C   \nATOM    772  O   LYS A 100      30.972  62.677  36.758  1.00  0.00          O   \nATOM    773  CB  LYS A 100      29.517  60.062  36.709  1.00  0.00          C   \nATOM    774  CG  LYS A 100      29.410  58.598  37.132  1.00  0.00          C   \nATOM    775  CD  LYS A 100      29.344  57.654  35.915  1.00  0.00          C   \nATOM    776  CE  LYS A 100      29.276  56.188  36.344  1.00  0.00          C   \nATOM    777  NZ  LYS A 100      29.152  55.238  35.193  1.00  0.00          N   \nATOM    778  N   PHE A 101      28.965  63.377  37.499  1.00  0.00          N   \nATOM    779  CA  PHE A 101      29.143  64.716  36.962  1.00  0.00          C   \nATOM    780  C   PHE A 101      29.531  65.827  37.938  1.00  0.00          C   \nATOM    781  O   PHE A 101      29.184  66.992  37.739  1.00  0.00          O   \nATOM    782  CB  PHE A 101      27.889  65.081  36.161  1.00  0.00          C   \nATOM    783  CG  PHE A 101      27.614  64.123  35.033  1.00  0.00          C   \nATOM    784  CD1 PHE A 101      28.395  64.149  33.875  1.00  0.00          C   \nATOM    785  CD2 PHE A 101      26.639  63.133  35.158  1.00  0.00          C   \nATOM    786  CE1 PHE A 101      28.210  63.199  32.861  1.00  0.00          C   \nATOM    787  CE2 PHE A 101      26.447  62.181  34.154  1.00  0.00          C   \nATOM    788  CZ  PHE A 101      27.235  62.211  33.004  1.00  0.00          C   \nATOM    789  N   GLY A 102      30.250  65.464  38.996  1.00  0.00          N   \nATOM    790  CA  GLY A 102      30.720  66.465  39.943  1.00  0.00          C   \nATOM    791  C   GLY A 102      29.936  66.855  41.188  1.00  0.00          C   \nATOM    792  O   GLY A 102      30.180  67.930  41.742  1.00  0.00          O   \nATOM    793  N   TYR A 103      29.003  66.024  41.641  1.00  0.00          N   \nATOM    794  CA  TYR A 103      28.257  66.360  42.850  1.00  0.00          C   \nATOM    795  C   TYR A 103      27.675  65.120  43.505  1.00  0.00          C   \nATOM    796  O   TYR A 103      27.585  64.060  42.881  1.00  0.00          O   \nATOM    797  CB  TYR A 103      27.152  67.375  42.539  1.00  0.00          C   \nATOM    798  CG  TYR A 103      26.114  66.882  41.563  1.00  0.00          C   \nATOM    799  CD1 TYR A 103      24.955  66.238  42.008  1.00  0.00          C   \nATOM    800  CD2 TYR A 103      26.291  67.049  40.191  1.00  0.00          C   \nATOM    801  CE1 TYR A 103      24.002  65.779  41.104  1.00  0.00          C   \nATOM    802  CE2 TYR A 103      25.344  66.587  39.283  1.00  0.00          C   \nATOM    803  CZ  TYR A 103      24.206  65.955  39.747  1.00  0.00          C   \nATOM    804  OH  TYR A 103      23.281  65.487  38.851  1.00  0.00          O   \nATOM    805  N   GLY A 104      27.282  65.258  44.766  1.00  0.00          N   \nATOM    806  CA  GLY A 104      26.739  64.127  45.491  1.00  0.00          C   \nATOM    807  C   GLY A 104      25.333  64.323  46.011  1.00  0.00          C   \nATOM    808  O   GLY A 104      24.679  65.323  45.717  1.00  0.00          O   \nATOM    809  N   ALA A 105      24.872  63.344  46.782  1.00  0.00          N   \nATOM    810  CA  ALA A 105      23.540  63.378  47.360  1.00  0.00          C   \nATOM    811  C   ALA A 105      23.376  64.594  48.259  1.00  0.00          C   \nATOM    812  O   ALA A 105      22.302  65.190  48.330  1.00  0.00          O   \nATOM    813  CB  ALA A 105      23.288  62.107  48.151  1.00  0.00          C   \nATOM    814  N   LYS A 106      24.451  64.968  48.942  1.00  0.00          N   \nATOM    815  CA  LYS A 106      24.397  66.112  49.838  1.00  0.00          C   \nATOM    816  C   LYS A 106      24.158  67.395  49.058  1.00  0.00          C   \nATOM    817  O   LYS A 106      23.351  68.228  49.464  1.00  0.00          O   \nATOM    818  CB  LYS A 106      25.696  66.227  50.633  1.00  0.00          C   \nATOM    819  CG  LYS A 106      25.483  66.612  52.087  1.00  0.00          C   \nATOM    820  CD  LYS A 106      25.603  65.398  53.011  1.00  0.00          C   \nATOM    821  CE  LYS A 106      24.697  64.259  52.575  1.00  0.00          C   \nATOM    822  NZ  LYS A 106      23.281  64.702  52.501  1.00  0.00          N   \nATOM    823  N   ASP A 107      24.864  67.551  47.942  1.00  0.00          N   \nATOM    824  CA  ASP A 107      24.720  68.735  47.102  1.00  0.00          C   \nATOM    825  C   ASP A 107      23.299  68.838  46.568  1.00  0.00          C   \nATOM    826  O   ASP A 107      22.774  69.932  46.371  1.00  0.00          O   \nATOM    827  CB  ASP A 107      25.699  68.679  45.926  1.00  0.00          C   \nATOM    828  CG  ASP A 107      27.143  68.682  46.374  1.00  0.00          C   \nATOM    829  OD1 ASP A 107      27.518  69.577  47.162  1.00  0.00          O   \nATOM    830  OD2 ASP A 107      27.902  67.797  45.936  1.00  0.00          O   \nATOM    831  N   VAL A 108      22.688  67.683  46.331  1.00  0.00          N   \nATOM    832  CA  VAL A 108      21.329  67.610  45.815  1.00  0.00          C   \nATOM    833  C   VAL A 108      20.329  68.027  46.892  1.00  0.00          C   \nATOM    834  O   VAL A 108      19.439  68.847  46.652  1.00  0.00          O   \nATOM    835  CB  VAL A 108      21.020  66.170  45.326  1.00  0.00          C   \nATOM    836  CG1 VAL A 108      19.533  66.005  45.031  1.00  0.00          C   \nATOM    837  CG2 VAL A 108      21.832  65.881  44.067  1.00  0.00          C   \nATOM    838  N   ARG A 109      20.488  67.465  48.083  1.00  0.00          N   \nATOM    839  CA  ARG A 109      19.596  67.781  49.184  1.00  0.00          C   \nATOM    840  C   ARG A 109      19.695  69.255  49.548  1.00  0.00          C   \nATOM    841  O   ARG A 109      18.732  69.835  50.041  1.00  0.00          O   \nATOM    842  CB  ARG A 109      19.923  66.912  50.399  1.00  0.00          C   \nATOM    843  CG  ARG A 109      19.654  65.438  50.179  1.00  0.00          C   \nATOM    844  CD  ARG A 109      19.957  64.636  51.421  1.00  0.00          C   \nATOM    845  NE  ARG A 109      19.191  65.107  52.571  1.00  0.00          N   \nATOM    846  CZ  ARG A 109      17.873  64.997  52.698  1.00  0.00          C   \nATOM    847  NH1 ARG A 109      17.150  64.422  51.744  1.00  0.00          N   \nATOM    848  NH2 ARG A 109      17.274  65.475  53.780  1.00  0.00          N   \nATOM    849  N   ASN A 110      20.853  69.859  49.290  1.00  0.00          N   \nATOM    850  CA  ASN A 110      21.072  71.273  49.590  1.00  0.00          C   \nATOM    851  C   ASN A 110      20.656  72.178  48.436  1.00  0.00          C   \nATOM    852  O   ASN A 110      20.773  73.405  48.519  1.00  0.00          O   \nATOM    853  CB  ASN A 110      22.549  71.539  49.903  1.00  0.00          C   \nATOM    854  CG  ASN A 110      23.014  70.850  51.168  1.00  0.00          C   \nATOM    855  OD1 ASN A 110      22.251  70.705  52.127  1.00  0.00          O   \nATOM    856  ND2 ASN A 110      24.281  70.436  51.188  1.00  0.00          N   \nATOM    857  N   LEU A 111      20.175  71.572  47.360  1.00  0.00          N   \nATOM    858  CA  LEU A 111      19.767  72.327  46.188  1.00  0.00          C   \nATOM    859  C   LEU A 111      20.949  73.136  45.673  1.00  0.00          C   \nATOM    860  O   LEU A 111      20.777  74.261  45.205  1.00  0.00          O   \nATOM    861  CB  LEU A 111      18.604  73.266  46.528  1.00  0.00          C   \nATOM    862  CG  LEU A 111      17.363  72.659  47.193  1.00  0.00          C   \nATOM    863  CD1 LEU A 111      16.304  73.735  47.344  1.00  0.00          C   \nATOM    864  CD2 LEU A 111      16.817  71.506  46.360  1.00  0.00          C   \nATOM    865  N   SER A 112      22.151  72.567  45.761  1.00  0.00          N   \nATOM    866  CA  SER A 112      23.346  73.262  45.290  1.00  0.00          C   \nATOM    867  C   SER A 112      23.146  73.613  43.822  1.00  0.00          C   \nATOM    868  O   SER A 112      22.587  72.825  43.060  1.00  0.00          O   \nATOM    869  CB  SER A 112      24.588  72.380  45.447  1.00  0.00          C   \nATOM    870  OG  SER A 112      24.777  71.542  44.319  1.00  0.00          O   \nATOM    871  N   SER A 113      23.611  74.793  43.424  1.00  0.00          N   \nATOM    872  CA  SER A 113      23.450  75.247  42.047  1.00  0.00          C   \nATOM    873  C   SER A 113      24.017  74.262  41.036  1.00  0.00          C   \nATOM    874  O   SER A 113      23.429  74.041  39.985  1.00  0.00          O   \nATOM    875  CB  SER A 113      24.108  76.620  41.850  1.00  0.00          C   \nATOM    876  OG  SER A 113      25.519  76.511  41.793  1.00  0.00          O   \nATOM    877  N   LYS A 114      25.158  73.669  41.358  1.00  0.00          N   \nATOM    878  CA  LYS A 114      25.784  72.712  40.457  1.00  0.00          C   \nATOM    879  C   LYS A 114      24.855  71.517  40.236  1.00  0.00          C   \nATOM    880  O   LYS A 114      24.596  71.129  39.101  1.00  0.00          O   \nATOM    881  CB  LYS A 114      27.114  72.236  41.036  1.00  0.00          C   \nATOM    882  CG  LYS A 114      27.945  71.387  40.080  1.00  0.00          C   \nATOM    883  CD  LYS A 114      29.228  70.899  40.756  1.00  0.00          C   \nATOM    884  CE  LYS A 114      30.096  70.104  39.795  1.00  0.00          C   \nATOM    885  NZ  LYS A 114      30.396  70.906  38.580  1.00  0.00          N   \nATOM    886  N   ALA A 115      24.356  70.942  41.325  1.00  0.00          N   \nATOM    887  CA  ALA A 115      23.453  69.796  41.241  1.00  0.00          C   \nATOM    888  C   ALA A 115      22.179  70.158  40.482  1.00  0.00          C   \nATOM    889  O   ALA A 115      21.812  69.491  39.516  1.00  0.00          O   \nATOM    890  CB  ALA A 115      23.107  69.294  42.647  1.00  0.00          C   \nATOM    891  N   VAL A 116      21.510  71.220  40.923  1.00  0.00          N   \nATOM    892  CA  VAL A 116      20.278  71.678  40.294  1.00  0.00          C   \nATOM    893  C   VAL A 116      20.458  71.979  38.812  1.00  0.00          C   \nATOM    894  O   VAL A 116      19.571  71.698  38.000  1.00  0.00          O   \nATOM    895  CB  VAL A 116      19.741  72.941  40.991  1.00  0.00          C   \nATOM    896  CG1 VAL A 116      18.520  73.471  40.253  1.00  0.00          C   \nATOM    897  CG2 VAL A 116      19.396  72.622  42.432  1.00  0.00          C   \nATOM    898  N   ASN A 117      21.594  72.572  38.460  1.00  0.00          N   \nATOM    899  CA  ASN A 117      21.875  72.892  37.068  1.00  0.00          C   \nATOM    900  C   ASN A 117      22.017  71.621  36.231  1.00  0.00          C   \nATOM    901  O   ASN A 117      21.465  71.530  35.138  1.00  0.00          O   \nATOM    902  CB  ASN A 117      23.146  73.737  36.961  1.00  0.00          C   \nATOM    903  CG  ASN A 117      22.895  75.199  37.289  1.00  0.00          C   \nATOM    904  OD1 ASN A 117      22.010  75.823  36.707  1.00  0.00          O   \nATOM    905  ND2 ASN A 117      23.671  75.752  38.219  1.00  0.00          N   \nATOM    906  N   HIS A 118      22.753  70.638  36.742  1.00  0.00          N   \nATOM    907  CA  HIS A 118      22.922  69.393  36.001  1.00  0.00          C   \nATOM    908  C   HIS A 118      21.594  68.654  35.844  1.00  0.00          C   \nATOM    909  O   HIS A 118      21.272  68.150  34.767  1.00  0.00          O   \nATOM    910  CB  HIS A 118      23.930  68.486  36.694  1.00  0.00          C   \nATOM    911  CG  HIS A 118      24.155  67.191  35.977  1.00  0.00          C   \nATOM    912  ND1 HIS A 118      23.668  65.988  36.440  1.00  0.00          N   \nATOM    913  CD2 HIS A 118      24.781  66.920  34.805  1.00  0.00          C   \nATOM    914  CE1 HIS A 118      23.985  65.029  35.586  1.00  0.00          C   \nATOM    915  NE2 HIS A 118      24.658  65.567  34.587  1.00  0.00          N   \nATOM    916  N   ILE A 119      20.827  68.591  36.925  1.00  0.00          N   \nATOM    917  CA  ILE A 119      19.529  67.926  36.900  1.00  0.00          C   \nATOM    918  C   ILE A 119      18.646  68.543  35.812  1.00  0.00          C   \nATOM    919  O   ILE A 119      17.949  67.832  35.073  1.00  0.00          O   \nATOM    920  CB  ILE A 119      18.846  68.038  38.287  1.00  0.00          C   \nATOM    921  CG1 ILE A 119      19.628  67.196  39.302  1.00  0.00          C   \nATOM    922  CG2 ILE A 119      17.395  67.588  38.209  1.00  0.00          C   \nATOM    923  CD1 ILE A 119      19.240  67.424  40.741  1.00  0.00          C   \nATOM    924  N   HIS A 120      18.688  69.870  35.705  1.00  0.00          N   \nATOM    925  CA  HIS A 120      17.894  70.574  34.703  1.00  0.00          C   \nATOM    926  C   HIS A 120      18.327  70.238  33.286  1.00  0.00          C   \nATOM    927  O   HIS A 120      17.493  70.194  32.375  1.00  0.00          O   \nATOM    928  CB  HIS A 120      17.973  72.091  34.926  1.00  0.00          C   \nATOM    929  CG  HIS A 120      16.943  72.616  35.879  1.00  0.00          C   \nATOM    930  ND1 HIS A 120      17.187  73.669  36.736  1.00  0.00          N   \nATOM    931  CD2 HIS A 120      15.653  72.257  36.086  1.00  0.00          C   \nATOM    932  CE1 HIS A 120      16.094  73.935  37.429  1.00  0.00          C   \nATOM    933  NE2 HIS A 120      15.148  73.092  37.053  1.00  0.00          N   \nATOM    934  N   SER A 121      19.623  70.005  33.087  1.00  0.00          N   \nATOM    935  CA  SER A 121      20.113  69.670  31.754  1.00  0.00          C   \nATOM    936  C   SER A 121      19.740  68.221  31.427  1.00  0.00          C   \nATOM    937  O   SER A 121      19.426  67.890  30.285  1.00  0.00          O   \nATOM    938  CB  SER A 121      21.630  69.869  31.664  1.00  0.00          C   \nATOM    939  OG  SER A 121      22.302  69.012  32.565  1.00  0.00          O   \nATOM    940  N   VAL A 122      19.761  67.358  32.434  1.00  0.00          N   \nATOM    941  CA  VAL A 122      19.382  65.965  32.211  1.00  0.00          C   \nATOM    942  C   VAL A 122      17.902  65.925  31.791  1.00  0.00          C   \nATOM    943  O   VAL A 122      17.535  65.239  30.834  1.00  0.00          O   \nATOM    944  CB  VAL A 122      19.574  65.135  33.486  1.00  0.00          C   \nATOM    945  CG1 VAL A 122      19.056  63.719  33.274  1.00  0.00          C   \nATOM    946  CG2 VAL A 122      21.038  65.111  33.859  1.00  0.00          C   \nATOM    947  N   TRP A 123      17.064  66.676  32.504  1.00  0.00          N   \nATOM    948  CA  TRP A 123      15.629  66.740  32.210  1.00  0.00          C   \nATOM    949  C   TRP A 123      15.389  67.226  30.779  1.00  0.00          C   \nATOM    950  O   TRP A 123      14.597  66.644  30.023  1.00  0.00          O   \nATOM    951  CB  TRP A 123      14.920  67.668  33.215  1.00  0.00          C   \nATOM    952  CG  TRP A 123      13.423  67.719  33.036  1.00  0.00          C   \nATOM    953  CD1 TRP A 123      12.711  68.657  32.346  1.00  0.00          C   \nATOM    954  CD2 TRP A 123      12.466  66.756  33.508  1.00  0.00          C   \nATOM    955  NE1 TRP A 123      11.375  68.338  32.355  1.00  0.00          N   \nATOM    956  CE2 TRP A 123      11.196  67.180  33.061  1.00  0.00          C   \nATOM    957  CE3 TRP A 123      12.562  65.578  34.264  1.00  0.00          C   \nATOM    958  CZ2 TRP A 123      10.022  66.463  33.342  1.00  0.00          C   \nATOM    959  CZ3 TRP A 123      11.399  64.866  34.543  1.00  0.00          C   \nATOM    960  CH2 TRP A 123      10.143  65.314  34.083  1.00  0.00          C   \nATOM    961  N   LYS A 124      16.084  68.293  30.403  1.00  0.00          N   \nATOM    962  CA  LYS A 124      15.947  68.837  29.061  1.00  0.00          C   \nATOM    963  C   LYS A 124      16.341  67.771  28.033  1.00  0.00          C   \nATOM    964  O   LYS A 124      15.660  67.596  27.024  1.00  0.00          O   \nATOM    965  CB  LYS A 124      16.826  70.079  28.915  1.00  0.00          C   \nATOM    966  CG  LYS A 124      16.670  70.791  27.593  1.00  0.00          C   \nATOM    967  CD  LYS A 124      17.566  72.015  27.534  1.00  0.00          C   \nATOM    968  CE  LYS A 124      17.425  72.737  26.205  1.00  0.00          C   \nATOM    969  NZ  LYS A 124      18.214  74.000  26.160  1.00  0.00          N   \nATOM    970  N   ASP A 125      17.435  67.052  28.285  1.00  0.00          N   \nATOM    971  CA  ASP A 125      17.856  66.000  27.359  1.00  0.00          C   \nATOM    972  C   ASP A 125      16.785  64.912  27.259  1.00  0.00          C   \nATOM    973  O   ASP A 125      16.574  64.342  26.194  1.00  0.00          O   \nATOM    974  CB  ASP A 125      19.167  65.359  27.801  1.00  0.00          C   \nATOM    975  CG  ASP A 125      19.679  64.341  26.795  1.00  0.00          C   \nATOM    976  OD1 ASP A 125      20.084  64.753  25.693  1.00  0.00          O   \nATOM    977  OD2 ASP A 125      19.666  63.126  27.095  1.00  0.00          O   \nATOM    978  N   LEU A 126      16.127  64.602  28.374  1.00  0.00          N   \nATOM    979  CA  LEU A 126      15.080  63.590  28.340  1.00  0.00          C   \nATOM    980  C   LEU A 126      13.952  64.025  27.417  1.00  0.00          C   \nATOM    981  O   LEU A 126      13.436  63.229  26.631  1.00  0.00          O   \nATOM    982  CB  LEU A 126      14.524  63.341  29.741  1.00  0.00          C   \nATOM    983  CG  LEU A 126      15.387  62.456  30.633  1.00  0.00          C   \nATOM    984  CD1 LEU A 126      14.875  62.507  32.062  1.00  0.00          C   \nATOM    985  CD2 LEU A 126      15.378  61.031  30.069  1.00  0.00          C   \nATOM    986  N   LEU A 127      13.574  65.297  27.490  1.00  0.00          N   \nATOM    987  CA  LEU A 127      12.483  65.777  26.649  1.00  0.00          C   \nATOM    988  C   LEU A 127      12.867  65.864  25.172  1.00  0.00          C   \nATOM    989  O   LEU A 127      12.034  65.641  24.300  1.00  0.00          O   \nATOM    990  CB  LEU A 127      11.988  67.147  27.137  1.00  0.00          C   \nATOM    991  CG  LEU A 127      11.459  67.257  28.577  1.00  0.00          C   \nATOM    992  CD1 LEU A 127      11.206  68.727  28.920  1.00  0.00          C   \nATOM    993  CD2 LEU A 127      10.180  66.429  28.738  1.00  0.00          C   \nATOM    994  N   GLU A 128      14.131  66.152  24.889  1.00  0.00          N   \nATOM    995  CA  GLU A 128      14.591  66.302  23.508  1.00  0.00          C   \nATOM    996  C   GLU A 128      15.065  65.025  22.821  1.00  0.00          C   \nATOM    997  O   GLU A 128      15.045  64.932  21.597  1.00  0.00          O   \nATOM    998  CB  GLU A 128      15.713  67.344  23.465  1.00  0.00          C   \nATOM    999  CG  GLU A 128      15.300  68.703  24.037  1.00  0.00          C   \nATOM   1000  CD  GLU A 128      16.471  69.660  24.224  1.00  0.00          C   \nATOM   1001  OE1 GLU A 128      16.217  70.811  24.641  1.00  0.00          O   \nATOM   1002  OE2 GLU A 128      17.635  69.264  23.961  1.00  0.00          O   \nATOM   1003  N   ASP A 129      15.477  64.040  23.612  1.00  0.00          N   \nATOM   1004  CA  ASP A 129      15.991  62.777  23.082  1.00  0.00          C   \nATOM   1005  C   ASP A 129      15.193  61.607  23.679  1.00  0.00          C   \nATOM   1006  O   ASP A 129      15.195  61.408  24.883  1.00  0.00          O   \nATOM   1007  CB  ASP A 129      17.480  62.673  23.452  1.00  0.00          C   \nATOM   1008  CG  ASP A 129      18.132  61.386  22.965  1.00  0.00          C   \nATOM   1009  OD1 ASP A 129      17.474  60.329  22.994  1.00  0.00          O   \nATOM   1010  OD2 ASP A 129      19.320  61.433  22.575  1.00  0.00          O   \nATOM   1011  N   THR A 130      14.514  60.835  22.837  1.00  0.00          N   \nATOM   1012  CA  THR A 130      13.730  59.708  23.336  1.00  0.00          C   \nATOM   1013  C   THR A 130      14.242  58.366  22.819  1.00  0.00          C   \nATOM   1014  O   THR A 130      13.514  57.378  22.850  1.00  0.00          O   \nATOM   1015  CB  THR A 130      12.227  59.853  22.954  1.00  0.00          C   \nATOM   1016  OG1 THR A 130      12.100  59.923  21.533  1.00  0.00          O   \nATOM   1017  CG2 THR A 130      11.637  61.127  23.558  1.00  0.00          C   \nATOM   1018  N   VAL A 131      15.496  58.321  22.367  1.00  0.00          N   \nATOM   1019  CA  VAL A 131      16.061  57.077  21.823  1.00  0.00          C   \nATOM   1020  C   VAL A 131      17.425  56.642  22.353  1.00  0.00          C   \nATOM   1021  O   VAL A 131      17.716  55.450  22.398  1.00  0.00          O   \nATOM   1022  CB  VAL A 131      16.203  57.132  20.266  1.00  0.00          C   \nATOM   1023  CG1 VAL A 131      14.837  57.303  19.610  1.00  0.00          C   \nATOM   1024  CG2 VAL A 131      17.145  58.263  19.864  1.00  0.00          C   \nATOM   1025  N   THR A 132      18.266  57.590  22.742  1.00  0.00          N   \nATOM   1026  CA  THR A 132      19.600  57.242  23.208  1.00  0.00          C   \nATOM   1027  C   THR A 132      19.655  56.496  24.528  1.00  0.00          C   \nATOM   1028  O   THR A 132      19.257  57.016  25.562  1.00  0.00          O   \nATOM   1029  CB  THR A 132      20.468  58.495  23.306  1.00  0.00          C   \nATOM   1030  OG1 THR A 132      20.387  59.207  22.067  1.00  0.00          O   \nATOM   1031  CG2 THR A 132      21.937  58.117  23.563  1.00  0.00          C   \nATOM   1032  N   PRO A 133      20.157  55.252  24.508  1.00  0.00          N   \nATOM   1033  CA  PRO A 133      20.233  54.501  25.759  1.00  0.00          C   \nATOM   1034  C   PRO A 133      20.987  55.296  26.814  1.00  0.00          C   \nATOM   1035  O   PRO A 133      21.981  55.974  26.519  1.00  0.00          O   \nATOM   1036  CB  PRO A 133      20.978  53.229  25.356  1.00  0.00          C   \nATOM   1037  CG  PRO A 133      20.506  53.017  23.946  1.00  0.00          C   \nATOM   1038  CD  PRO A 133      20.609  54.424  23.374  1.00  0.00          C   \nATOM   1039  N   ILE A 134      20.493  55.217  28.043  1.00  0.00          N   \nATOM   1040  CA  ILE A 134      21.105  55.900  29.164  1.00  0.00          C   \nATOM   1041  C   ILE A 134      22.032  54.939  29.884  1.00  0.00          C   \nATOM   1042  O   ILE A 134      21.699  53.769  30.085  1.00  0.00          O   \nATOM   1043  CB  ILE A 134      20.013  56.441  30.110  1.00  0.00          C   \nATOM   1044  CG1 ILE A 134      19.388  57.692  29.469  1.00  0.00          C   \nATOM   1045  CG2 ILE A 134      20.585  56.705  31.502  1.00  0.00          C   \nATOM   1046  CD1 ILE A 134      18.234  58.292  30.248  1.00  0.00          C   \nATOM   1047  N   ASP A 135      23.211  55.424  30.256  1.00  0.00          N   \nATOM   1048  CA  ASP A 135      24.165  54.566  30.929  1.00  0.00          C   \nATOM   1049  C   ASP A 135      23.725  54.135  32.326  1.00  0.00          C   \nATOM   1050  O   ASP A 135      22.947  54.828  33.012  1.00  0.00          O   \nATOM   1051  CB  ASP A 135      25.534  55.249  31.006  1.00  0.00          C   \nATOM   1052  CG  ASP A 135      26.604  54.328  31.552  1.00  0.00          C   \nATOM   1053  OD1 ASP A 135      26.815  53.239  30.966  1.00  0.00          O   \nATOM   1054  OD2 ASP A 135      27.224  54.690  32.574  1.00  0.00          O   \nATOM   1055  N   THR A 136      24.204  52.962  32.733  1.00  0.00          N   \nATOM   1056  CA  THR A 136      23.894  52.454  34.057  1.00  0.00          C   \nATOM   1057  C   THR A 136      25.134  51.809  34.632  1.00  0.00          C   \nATOM   1058  O   THR A 136      26.022  51.362  33.903  1.00  0.00          O   \nATOM   1059  CB  THR A 136      22.794  51.376  34.046  1.00  0.00          C   \nATOM   1060  OG1 THR A 136      23.236  50.257  33.265  1.00  0.00          O   \nATOM   1061  CG2 THR A 136      21.492  51.936  33.481  1.00  0.00          C   \nATOM   1062  N   THR A 137      25.182  51.769  35.953  1.00  0.00          N   \nATOM   1063  CA  THR A 137      26.282  51.144  36.644  1.00  0.00          C   \nATOM   1064  C   THR A 137      25.750  49.810  37.156  1.00  0.00          C   \nATOM   1065  O   THR A 137      24.611  49.726  37.616  1.00  0.00          O   \nATOM   1066  CB  THR A 137      26.720  51.975  37.848  1.00  0.00          C   \nATOM   1067  OG1 THR A 137      27.100  53.288  37.406  1.00  0.00          O   \nATOM   1068  CG2 THR A 137      27.889  51.285  38.570  1.00  0.00          C   \nATOM   1069  N   ILE A 138      26.555  48.761  37.036  1.00  0.00          N   \nATOM   1070  CA  ILE A 138      26.162  47.470  37.549  1.00  0.00          C   \nATOM   1071  C   ILE A 138      27.131  47.149  38.692  1.00  0.00          C   \nATOM   1072  O   ILE A 138      28.337  47.278  38.540  1.00  0.00          O   \nATOM   1073  CB  ILE A 138      26.214  46.366  36.461  1.00  0.00          C   \nATOM   1074  CG1 ILE A 138      25.774  45.028  37.079  1.00  0.00          C   \nATOM   1075  CG2 ILE A 138      27.631  46.275  35.852  1.00  0.00          C   \nATOM   1076  CD1 ILE A 138      25.410  43.952  36.074  1.00  0.00          C   \nATOM   1077  N   MET A 139      26.586  46.771  39.845  1.00  0.00          N   \nATOM   1078  CA  MET A 139      27.389  46.426  41.016  1.00  0.00          C   \nATOM   1079  C   MET A 139      26.816  45.159  41.634  1.00  0.00          C   \nATOM   1080  O   MET A 139      25.640  44.828  41.433  1.00  0.00          O   \nATOM   1081  CB  MET A 139      27.302  47.522  42.076  1.00  0.00          C   \nATOM   1082  CG  MET A 139      27.791  48.892  41.661  1.00  0.00          C   \nATOM   1083  SD  MET A 139      29.573  49.053  41.777  1.00  0.00          S   \nATOM   1084  CE  MET A 139      29.810  49.242  43.552  1.00  0.00          C   \nATOM   1085  N   ALA A 140      27.650  44.461  42.396  1.00  0.00          N   \nATOM   1086  CA  ALA A 140      27.235  43.261  43.098  1.00  0.00          C   \nATOM   1087  C   ALA A 140      26.877  43.755  44.497  1.00  0.00          C   \nATOM   1088  O   ALA A 140      27.677  44.446  45.124  1.00  0.00          O   \nATOM   1089  CB  ALA A 140      28.387  42.273  43.170  1.00  0.00          C   \nATOM   1090  N   LYS A 141      25.683  43.423  44.983  1.00  0.00          N   \nATOM   1091  CA  LYS A 141      25.259  43.850  46.323  1.00  0.00          C   \nATOM   1092  C   LYS A 141      25.989  43.100  47.428  1.00  0.00          C   \nATOM   1093  O   LYS A 141      26.299  41.920  47.289  1.00  0.00          O   \nATOM   1094  CB  LYS A 141      23.755  43.629  46.526  1.00  0.00          C   \nATOM   1095  CG  LYS A 141      22.849  44.435  45.612  1.00  0.00          C   \nATOM   1096  CD  LYS A 141      21.404  44.381  46.096  1.00  0.00          C   \nATOM   1097  CE  LYS A 141      20.512  45.280  45.255  1.00  0.00          C   \nATOM   1098  NZ  LYS A 141      19.141  45.411  45.827  1.00  0.00          N   \nATOM   1099  N   ASN A 142      26.266  43.788  48.526  1.00  0.00          N   \nATOM   1100  CA  ASN A 142      26.928  43.154  49.662  1.00  0.00          C   \nATOM   1101  C   ASN A 142      25.801  42.940  50.658  1.00  0.00          C   \nATOM   1102  O   ASN A 142      25.366  43.884  51.302  1.00  0.00          O   \nATOM   1103  CB  ASN A 142      27.979  44.079  50.281  1.00  0.00          C   \nATOM   1104  CG  ASN A 142      29.037  44.516  49.287  1.00  0.00          C   \nATOM   1105  OD1 ASN A 142      29.281  45.716  49.119  1.00  0.00          O   \nATOM   1106  ND2 ASN A 142      29.674  43.552  48.625  1.00  0.00          N   \nATOM   1107  N   GLU A 143      25.319  41.705  50.766  1.00  0.00          N   \nATOM   1108  CA  GLU A 143      24.223  41.395  51.675  1.00  0.00          C   \nATOM   1109  C   GLU A 143      24.574  40.224  52.564  1.00  0.00          C   \nATOM   1110  O   GLU A 143      25.272  39.297  52.140  1.00  0.00          O   \nATOM   1111  CB  GLU A 143      22.956  41.082  50.869  1.00  0.00          C   \nATOM   1112  CG  GLU A 143      22.493  42.256  50.031  1.00  0.00          C   \nATOM   1113  CD  GLU A 143      21.208  41.976  49.283  1.00  0.00          C   \nATOM   1114  OE1 GLU A 143      21.217  41.113  48.376  1.00  0.00          O   \nATOM   1115  OE2 GLU A 143      20.189  42.625  49.608  1.00  0.00          O   \nATOM   1116  N   VAL A 144      24.062  40.259  53.790  1.00  0.00          N   \nATOM   1117  CA  VAL A 144      24.339  39.220  54.782  1.00  0.00          C   \nATOM   1118  C   VAL A 144      23.262  38.120  54.894  1.00  0.00          C   \nATOM   1119  O   VAL A 144      22.057  38.400  54.935  1.00  0.00          O   \nATOM   1120  CB  VAL A 144      24.577  39.893  56.168  1.00  0.00          C   \nATOM   1121  CG1 VAL A 144      24.795  38.844  57.263  1.00  0.00          C   \nATOM   1122  CG2 VAL A 144      25.793  40.808  56.075  1.00  0.00          C   \nATOM   1123  N   PHE A 145      23.719  36.868  54.947  1.00  0.00          N   \nATOM   1124  CA  PHE A 145      22.837  35.710  55.066  1.00  0.00          C   \nATOM   1125  C   PHE A 145      23.466  34.655  55.969  1.00  0.00          C   \nATOM   1126  O   PHE A 145      24.610  34.787  56.401  1.00  0.00          O   \nATOM   1127  CB  PHE A 145      22.602  35.070  53.693  1.00  0.00          C   \nATOM   1128  CG  PHE A 145      21.961  35.989  52.693  1.00  0.00          C   \nATOM   1129  CD1 PHE A 145      20.588  36.211  52.716  1.00  0.00          C   \nATOM   1130  CD2 PHE A 145      22.738  36.638  51.731  1.00  0.00          C   \nATOM   1131  CE1 PHE A 145      19.980  37.073  51.789  1.00  0.00          C   \nATOM   1132  CE2 PHE A 145      22.152  37.501  50.796  1.00  0.00          C   \nATOM   1133  CZ  PHE A 145      20.765  37.720  50.825  1.00  0.00          C   \nATOM   1134  N   CYS A 146      22.703  33.605  56.249  1.00  0.00          N   \nATOM   1135  CA  CYS A 146      23.201  32.488  57.034  1.00  0.00          C   \nATOM   1136  C   CYS A 146      23.319  31.375  55.993  1.00  0.00          C   \nATOM   1137  O   CYS A 146      22.471  31.272  55.107  1.00  0.00          O   \nATOM   1138  CB  CYS A 146      22.210  32.087  58.129  1.00  0.00          C   \nATOM   1139  SG  CYS A 146      22.676  30.525  58.933  1.00  0.00          S   \nATOM   1140  N   VAL A 147      24.357  30.550  56.087  1.00  0.00          N   \nATOM   1141  CA  VAL A 147      24.567  29.493  55.100  1.00  0.00          C   \nATOM   1142  C   VAL A 147      23.385  28.567  54.874  1.00  0.00          C   \nATOM   1143  O   VAL A 147      22.555  28.364  55.765  1.00  0.00          O   \nATOM   1144  CB  VAL A 147      25.809  28.626  55.437  1.00  0.00          C   \nATOM   1145  CG1 VAL A 147      27.073  29.488  55.395  1.00  0.00          C   \nATOM   1146  CG2 VAL A 147      25.641  27.980  56.803  1.00  0.00          C   \nATOM   1147  N   GLN A 148      23.337  28.027  53.656  1.00  0.00          N   \nATOM   1148  CA  GLN A 148      22.310  27.100  53.179  1.00  0.00          C   \nATOM   1149  C   GLN A 148      21.378  26.547  54.248  1.00  0.00          C   \nATOM   1150  O   GLN A 148      20.148  26.699  54.090  1.00  0.00          O   \nATOM   1151  CB  GLN A 148      22.979  25.932  52.449  1.00  0.00          C   \nATOM   1152  CG  GLN A 148      22.044  24.776  52.162  1.00  0.00          C   \nATOM   1153  CD  GLN A 148      22.741  23.620  51.474  1.00  0.00          C   \nATOM   1154  OE1 GLN A 148      23.712  23.062  51.995  1.00  0.00          O   \nATOM   1155  NE2 GLN A 148      22.246  23.251  50.296  1.00  0.00          N   \nATOM   1156  N   ARG A 154      25.589  28.807  50.280  1.00  0.00          N   \nATOM   1157  CA  ARG A 154      25.887  30.246  50.010  1.00  0.00          C   \nATOM   1158  C   ARG A 154      25.306  30.714  48.685  1.00  0.00          C   \nATOM   1159  O   ARG A 154      25.536  30.099  47.644  1.00  0.00          O   \nATOM   1160  CB  ARG A 154      27.398  30.490  50.000  1.00  0.00          C   \nATOM   1161  CG  ARG A 154      28.050  30.337  51.359  1.00  0.00          C   \nATOM   1162  CD  ARG A 154      29.499  30.801  51.343  1.00  0.00          C   \nATOM   1163  NE  ARG A 154      29.633  32.167  50.831  1.00  0.00          N   \nATOM   1164  CZ  ARG A 154      30.696  32.942  51.034  1.00  0.00          C   \nATOM   1165  NH1 ARG A 154      31.721  32.486  51.743  1.00  0.00          N   \nATOM   1166  NH2 ARG A 154      30.736  34.175  50.531  1.00  0.00          N   \nATOM   1167  N   LYS A 155      24.550  31.805  48.716  1.00  0.00          N   \nATOM   1168  CA  LYS A 155      23.979  32.326  47.490  1.00  0.00          C   \nATOM   1169  C   LYS A 155      24.888  33.415  46.927  1.00  0.00          C   \nATOM   1170  O   LYS A 155      25.577  34.111  47.676  1.00  0.00          O   \nATOM   1171  CB  LYS A 155      22.574  32.876  47.750  1.00  0.00          C   \nATOM   1172  CG  LYS A 155      22.482  33.952  48.814  1.00  0.00          C   \nATOM   1173  CD  LYS A 155      21.027  34.238  49.179  1.00  0.00          C   \nATOM   1174  CE  LYS A 155      20.345  32.998  49.728  1.00  0.00          C   \nATOM   1175  NZ  LYS A 155      18.899  33.233  50.007  1.00  0.00          N   \nATOM   1176  N   PRO A 156      24.926  33.557  45.595  1.00  0.00          N   \nATOM   1177  CA  PRO A 156      25.770  34.580  44.974  1.00  0.00          C   \nATOM   1178  C   PRO A 156      25.151  35.959  45.177  1.00  0.00          C   \nATOM   1179  O   PRO A 156      23.963  36.066  45.427  1.00  0.00          O   \nATOM   1180  CB  PRO A 156      25.777  34.171  43.508  1.00  0.00          C   \nATOM   1181  CG  PRO A 156      24.402  33.632  43.323  1.00  0.00          C   \nATOM   1182  CD  PRO A 156      24.224  32.767  44.569  1.00  0.00          C   \nATOM   1183  N   ALA A 157      25.959  37.006  45.061  1.00  0.00          N   \nATOM   1184  CA  ALA A 157      25.461  38.362  45.234  1.00  0.00          C   \nATOM   1185  C   ALA A 157      24.401  38.718  44.201  1.00  0.00          C   \nATOM   1186  O   ALA A 157      24.465  38.276  43.059  1.00  0.00          O   \nATOM   1187  CB  ALA A 157      26.605  39.340  45.112  1.00  0.00          C   \nATOM   1188  N   ARG A 158      23.426  39.527  44.600  1.00  0.00          N   \nATOM   1189  CA  ARG A 158      22.427  39.980  43.645  1.00  0.00          C   \nATOM   1190  C   ARG A 158      23.086  41.142  42.913  1.00  0.00          C   \nATOM   1191  O   ARG A 158      24.070  41.710  43.399  1.00  0.00          O   \nATOM   1192  CB  ARG A 158      21.157  40.460  44.352  1.00  0.00          C   \nATOM   1193  CG  ARG A 158      20.307  39.332  44.940  1.00  0.00          C   \nATOM   1194  CD  ARG A 158      18.952  39.856  45.388  1.00  0.00          C   \nATOM   1195  NE  ARG A 158      19.090  41.001  46.286  1.00  0.00          N   \nATOM   1196  CZ  ARG A 158      18.066  41.666  46.809  1.00  0.00          C   \nATOM   1197  NH1 ARG A 158      16.822  41.299  46.525  1.00  0.00          N   \nATOM   1198  NH2 ARG A 158      18.285  42.700  47.613  1.00  0.00          N   \nATOM   1199  N   LEU A 159      22.551  41.479  41.748  1.00  0.00          N   \nATOM   1200  CA  LEU A 159      23.066  42.559  40.919  1.00  0.00          C   \nATOM   1201  C   LEU A 159      22.151  43.789  40.953  1.00  0.00          C   \nATOM   1202  O   LEU A 159      20.946  43.682  40.706  1.00  0.00          O   \nATOM   1203  CB  LEU A 159      23.179  42.079  39.475  1.00  0.00          C   \nATOM   1204  CG  LEU A 159      24.408  41.316  38.984  1.00  0.00          C   \nATOM   1205  CD1 LEU A 159      25.048  40.491  40.079  1.00  0.00          C   \nATOM   1206  CD2 LEU A 159      23.972  40.460  37.809  1.00  0.00          C   \nATOM   1207  N   ILE A 160      22.723  44.951  41.251  1.00  0.00          N   \nATOM   1208  CA  ILE A 160      21.954  46.187  41.289  1.00  0.00          C   \nATOM   1209  C   ILE A 160      22.387  46.971  40.060  1.00  0.00          C   \nATOM   1210  O   ILE A 160      23.580  47.074  39.792  1.00  0.00          O   \nATOM   1211  CB  ILE A 160      22.249  47.002  42.583  1.00  0.00          C   \nATOM   1212  CG1 ILE A 160      21.459  48.310  42.587  1.00  0.00          C   \nATOM   1213  CG2 ILE A 160      23.712  47.295  42.700  1.00  0.00          C   \nATOM   1214  CD1 ILE A 160      19.975  48.135  42.931  1.00  0.00          C   \nATOM   1215  N   VAL A 161      21.421  47.500  39.312  1.00  0.00          N   \nATOM   1216  CA  VAL A 161      21.694  48.263  38.086  1.00  0.00          C   \nATOM   1217  C   VAL A 161      21.002  49.615  38.251  1.00  0.00          C   \nATOM   1218  O   VAL A 161      19.788  49.679  38.477  1.00  0.00          O   \nATOM   1219  CB  VAL A 161      21.143  47.503  36.846  1.00  0.00          C   \nATOM   1220  CG1 VAL A 161      21.311  48.352  35.574  1.00  0.00          C   \nATOM   1221  CG2 VAL A 161      21.881  46.152  36.695  1.00  0.00          C   \nATOM   1222  N   PHE A 162      21.767  50.699  38.152  1.00  0.00          N   \nATOM   1223  CA  PHE A 162      21.196  52.020  38.371  1.00  0.00          C   \nATOM   1224  C   PHE A 162      21.804  53.128  37.521  1.00  0.00          C   \nATOM   1225  O   PHE A 162      22.976  53.073  37.165  1.00  0.00          O   \nATOM   1226  CB  PHE A 162      21.342  52.396  39.856  1.00  0.00          C   \nATOM   1227  CG  PHE A 162      22.787  52.491  40.333  1.00  0.00          C   \nATOM   1228  CD1 PHE A 162      23.488  51.351  40.742  1.00  0.00          C   \nATOM   1229  CD2 PHE A 162      23.438  53.723  40.383  1.00  0.00          C   \nATOM   1230  CE1 PHE A 162      24.827  51.447  41.199  1.00  0.00          C   \nATOM   1231  CE2 PHE A 162      24.765  53.829  40.833  1.00  0.00          C   \nATOM   1232  CZ  PHE A 162      25.459  52.689  41.243  1.00  0.00          C   \nATOM   1233  N   PRO A 163      20.999  54.150  37.184  1.00  0.00          N   \nATOM   1234  CA  PRO A 163      21.456  55.281  36.378  1.00  0.00          C   \nATOM   1235  C   PRO A 163      22.086  56.319  37.295  1.00  0.00          C   \nATOM   1236  O   PRO A 163      22.016  56.203  38.521  1.00  0.00          O   \nATOM   1237  CB  PRO A 163      20.167  55.787  35.735  1.00  0.00          C   \nATOM   1238  CG  PRO A 163      19.172  55.581  36.832  1.00  0.00          C   \nATOM   1239  CD  PRO A 163      19.541  54.217  37.397  1.00  0.00          C   \nATOM   1240  N   ASP A 164      22.682  57.341  36.698  1.00  0.00          N   \nATOM   1241  CA  ASP A 164      23.334  58.400  37.461  1.00  0.00          C   \nATOM   1242  C   ASP A 164      22.355  59.169  38.337  1.00  0.00          C   \nATOM   1243  O   ASP A 164      21.180  59.276  38.004  1.00  0.00          O   \nATOM   1244  CB  ASP A 164      24.016  59.389  36.518  1.00  0.00          C   \nATOM   1245  CG  ASP A 164      24.865  60.388  37.262  1.00  0.00          C   \nATOM   1246  OD1 ASP A 164      25.943  59.982  37.751  1.00  0.00          O   \nATOM   1247  OD2 ASP A 164      24.441  61.560  37.381  1.00  0.00          O   \nATOM   1248  N   LEU A 165      22.857  59.701  39.452  1.00  0.00          N   \nATOM   1249  CA  LEU A 165      22.063  60.486  40.406  1.00  0.00          C   \nATOM   1250  C   LEU A 165      21.172  61.537  39.739  1.00  0.00          C   \nATOM   1251  O   LEU A 165      20.034  61.731  40.148  1.00  0.00          O   \nATOM   1252  CB  LEU A 165      23.001  61.171  41.410  1.00  0.00          C   \nATOM   1253  CG  LEU A 165      22.474  62.220  42.398  1.00  0.00          C   \nATOM   1254  CD1 LEU A 165      21.342  61.661  43.266  1.00  0.00          C   \nATOM   1255  CD2 LEU A 165      23.647  62.662  43.272  1.00  0.00          C   \nATOM   1256  N   GLY A 166      21.695  62.218  38.719  1.00  0.00          N   \nATOM   1257  CA  GLY A 166      20.910  63.225  38.022  1.00  0.00          C   \nATOM   1258  C   GLY A 166      19.693  62.605  37.355  1.00  0.00          C   \nATOM   1259  O   GLY A 166      18.597  63.178  37.356  1.00  0.00          O   \nATOM   1260  N   VAL A 167      19.877  61.430  36.766  1.00  0.00          N   \nATOM   1261  CA  VAL A 167      18.750  60.751  36.122  1.00  0.00          C   \nATOM   1262  C   VAL A 167      17.749  60.319  37.199  1.00  0.00          C   \nATOM   1263  O   VAL A 167      16.540  60.399  36.995  1.00  0.00          O   \nATOM   1264  CB  VAL A 167      19.206  59.499  35.335  1.00  0.00          C   \nATOM   1265  CG1 VAL A 167      17.996  58.726  34.842  1.00  0.00          C   \nATOM   1266  CG2 VAL A 167      20.062  59.916  34.145  1.00  0.00          C   \nATOM   1267  N   ARG A 168      18.254  59.856  38.342  1.00  0.00          N   \nATOM   1268  CA  ARG A 168      17.372  59.429  39.431  1.00  0.00          C   \nATOM   1269  C   ARG A 168      16.493  60.599  39.918  1.00  0.00          C   \nATOM   1270  O   ARG A 168      15.305  60.435  40.173  1.00  0.00          O   \nATOM   1271  CB  ARG A 168      18.195  58.850  40.585  1.00  0.00          C   \nATOM   1272  CG  ARG A 168      19.022  57.625  40.187  1.00  0.00          C   \nATOM   1273  CD  ARG A 168      19.376  56.722  41.382  1.00  0.00          C   \nATOM   1274  NE  ARG A 168      20.215  57.359  42.402  1.00  0.00          N   \nATOM   1275  CZ  ARG A 168      21.527  57.550  42.303  1.00  0.00          C   \nATOM   1276  NH1 ARG A 168      22.187  57.161  41.219  1.00  0.00          N   \nATOM   1277  NH2 ARG A 168      22.186  58.113  43.309  1.00  0.00          N   \nATOM   1278  N   VAL A 169      17.067  61.785  40.047  1.00  0.00          N   \nATOM   1279  CA  VAL A 169      16.262  62.920  40.480  1.00  0.00          C   \nATOM   1280  C   VAL A 169      15.172  63.152  39.422  1.00  0.00          C   \nATOM   1281  O   VAL A 169      14.001  63.378  39.745  1.00  0.00          O   \nATOM   1282  CB  VAL A 169      17.155  64.174  40.679  1.00  0.00          C   \nATOM   1283  CG1 VAL A 169      16.297  65.403  40.966  1.00  0.00          C   \nATOM   1284  CG2 VAL A 169      18.115  63.925  41.850  1.00  0.00          C   \nATOM   1285  N   CYS A 170      15.550  63.059  38.153  1.00  0.00          N   \nATOM   1286  CA  CYS A 170      14.597  63.227  37.065  1.00  0.00          C   \nATOM   1287  C   CYS A 170      13.472  62.177  37.111  1.00  0.00          C   \nATOM   1288  O   CYS A 170      12.318  62.502  36.843  1.00  0.00          O   \nATOM   1289  CB  CYS A 170      15.329  63.174  35.725  1.00  0.00          C   \nATOM   1290  SG  CYS A 170      16.162  64.734  35.352  1.00  0.00          S   \nATOM   1291  N   GLU A 171      13.804  60.928  37.454  1.00  0.00          N   \nATOM   1292  CA  GLU A 171      12.795  59.877  37.560  1.00  0.00          C   \nATOM   1293  C   GLU A 171      11.724  60.258  38.598  1.00  0.00          C   \nATOM   1294  O   GLU A 171      10.534  60.030  38.385  1.00  0.00          O   \nATOM   1295  CB  GLU A 171      13.418  58.548  38.011  1.00  0.00          C   \nATOM   1296  CG  GLU A 171      14.314  57.843  37.018  1.00  0.00          C   \nATOM   1297  CD  GLU A 171      14.729  56.471  37.538  1.00  0.00          C   \nATOM   1298  OE1 GLU A 171      13.882  55.547  37.521  1.00  0.00          O   \nATOM   1299  OE2 GLU A 171      15.889  56.323  37.986  1.00  0.00          O   \nATOM   1300  N   LYS A 172      12.157  60.798  39.734  1.00  0.00          N   \nATOM   1301  CA  LYS A 172      11.230  61.206  40.798  1.00  0.00          C   \nATOM   1302  C   LYS A 172      10.304  62.308  40.301  1.00  0.00          C   \nATOM   1303  O   LYS A 172       9.086  62.251  40.508  1.00  0.00          O   \nATOM   1304  CB  LYS A 172      11.990  61.728  42.020  1.00  0.00          C   \nATOM   1305  CG  LYS A 172      12.903  60.713  42.694  1.00  0.00          C   \nATOM   1306  CD  LYS A 172      13.737  61.385  43.772  1.00  0.00          C   \nATOM   1307  CE  LYS A 172      14.358  60.377  44.738  1.00  0.00          C   \nATOM   1308  NZ  LYS A 172      15.351  59.460  44.101  1.00  0.00          N   \nATOM   1309  N   MET A 173      10.873  63.311  39.639  1.00  0.00          N   \nATOM   1310  CA  MET A 173      10.058  64.408  39.145  1.00  0.00          C   \nATOM   1311  C   MET A 173       8.944  63.911  38.234  1.00  0.00          C   \nATOM   1312  O   MET A 173       7.783  64.286  38.387  1.00  0.00          O   \nATOM   1313  CB  MET A 173      10.920  65.435  38.407  1.00  0.00          C   \nATOM   1314  CG  MET A 173      11.911  66.162  39.309  1.00  0.00          C   \nATOM   1315  SD  MET A 173      12.639  67.620  38.525  1.00  0.00          S   \nATOM   1316  CE  MET A 173      13.823  66.865  37.501  1.00  0.00          C   \nATOM   1317  N   ALA A 174       9.300  63.046  37.300  1.00  0.00          N   \nATOM   1318  CA  ALA A 174       8.329  62.520  36.360  1.00  0.00          C   \nATOM   1319  C   ALA A 174       7.464  61.384  36.881  1.00  0.00          C   \nATOM   1320  O   ALA A 174       6.323  61.222  36.446  1.00  0.00          O   \nATOM   1321  CB  ALA A 174       9.041  62.052  35.104  1.00  0.00          C   \nATOM   1322  N   LEU A 175       7.977  60.612  37.826  1.00  0.00          N   \nATOM   1323  CA  LEU A 175       7.214  59.446  38.259  1.00  0.00          C   \nATOM   1324  C   LEU A 175       6.988  59.171  39.734  1.00  0.00          C   \nATOM   1325  O   LEU A 175       6.352  58.167  40.067  1.00  0.00          O   \nATOM   1326  CB  LEU A 175       7.848  58.192  37.620  1.00  0.00          C   \nATOM   1327  CG  LEU A 175       7.675  58.050  36.103  1.00  0.00          C   \nATOM   1328  CD1 LEU A 175       8.796  57.222  35.499  1.00  0.00          C   \nATOM   1329  CD2 LEU A 175       6.324  57.400  35.816  1.00  0.00          C   \nATOM   1330  N   TYR A 176       7.508  60.009  40.625  1.00  0.00          N   \nATOM   1331  CA  TYR A 176       7.320  59.732  42.046  1.00  0.00          C   \nATOM   1332  C   TYR A 176       5.845  59.678  42.429  1.00  0.00          C   \nATOM   1333  O   TYR A 176       5.406  58.786  43.160  1.00  0.00          O   \nATOM   1334  CB  TYR A 176       8.014  60.784  42.915  1.00  0.00          C   \nATOM   1335  CG  TYR A 176       7.867  60.490  44.388  1.00  0.00          C   \nATOM   1336  CD1 TYR A 176       8.565  59.433  44.978  1.00  0.00          C   \nATOM   1337  CD2 TYR A 176       6.997  61.234  45.183  1.00  0.00          C   \nATOM   1338  CE1 TYR A 176       8.399  59.123  46.324  1.00  0.00          C   \nATOM   1339  CE2 TYR A 176       6.822  60.931  46.524  1.00  0.00          C   \nATOM   1340  CZ  TYR A 176       7.523  59.875  47.089  1.00  0.00          C   \nATOM   1341  OH  TYR A 176       7.336  59.567  48.418  1.00  0.00          O   \nATOM   1342  N   ASP A 177       5.081  60.637  41.927  1.00  0.00          N   \nATOM   1343  CA  ASP A 177       3.669  60.708  42.254  1.00  0.00          C   \nATOM   1344  C   ASP A 177       2.932  59.519  41.649  1.00  0.00          C   \nATOM   1345  O   ASP A 177       2.053  58.926  42.283  1.00  0.00          O   \nATOM   1346  CB  ASP A 177       3.088  62.034  41.760  1.00  0.00          C   \nATOM   1347  CG  ASP A 177       1.786  62.386  42.444  1.00  0.00          C   \nATOM   1348  OD1 ASP A 177       1.584  61.957  43.599  1.00  0.00          O   \nATOM   1349  OD2 ASP A 177       0.966  63.101  41.834  1.00  0.00          O   \nATOM   1350  N   VAL A 178       3.331  59.145  40.438  1.00  0.00          N   \nATOM   1351  CA  VAL A 178       2.723  58.016  39.754  1.00  0.00          C   \nATOM   1352  C   VAL A 178       2.939  56.693  40.494  1.00  0.00          C   \nATOM   1353  O   VAL A 178       1.977  55.984  40.791  1.00  0.00          O   \nATOM   1354  CB  VAL A 178       3.275  57.883  38.302  1.00  0.00          C   \nATOM   1355  CG1 VAL A 178       2.895  56.526  37.706  1.00  0.00          C   \nATOM   1356  CG2 VAL A 178       2.709  58.986  37.443  1.00  0.00          C   \nATOM   1357  N   VAL A 179       4.190  56.356  40.801  1.00  0.00          N   \nATOM   1358  CA  VAL A 179       4.454  55.095  41.476  1.00  0.00          C   \nATOM   1359  C   VAL A 179       3.946  55.099  42.909  1.00  0.00          C   \nATOM   1360  O   VAL A 179       3.758  54.046  43.515  1.00  0.00          O   \nATOM   1361  CB  VAL A 179       5.976  54.729  41.466  1.00  0.00          C   \nATOM   1362  CG1 VAL A 179       6.472  54.641  40.029  1.00  0.00          C   \nATOM   1363  CG2 VAL A 179       6.779  55.741  42.259  1.00  0.00          C   \nATOM   1364  N   SER A 180       3.715  56.286  43.453  1.00  0.00          N   \nATOM   1365  CA  SER A 180       3.227  56.397  44.824  1.00  0.00          C   \nATOM   1366  C   SER A 180       1.714  56.270  44.890  1.00  0.00          C   \nATOM   1367  O   SER A 180       1.167  55.933  45.933  1.00  0.00          O   \nATOM   1368  CB  SER A 180       3.630  57.741  45.434  1.00  0.00          C   \nATOM   1369  OG  SER A 180       5.034  57.839  45.577  1.00  0.00          O   \nATOM   1370  N   THR A 181       1.026  56.533  43.785  1.00  0.00          N   \nATOM   1371  CA  THR A 181      -0.420  56.469  43.816  1.00  0.00          C   \nATOM   1372  C   THR A 181      -1.104  55.463  42.893  1.00  0.00          C   \nATOM   1373  O   THR A 181      -2.115  54.879  43.269  1.00  0.00          O   \nATOM   1374  CB  THR A 181      -1.015  57.874  43.580  1.00  0.00          C   \nATOM   1375  OG1 THR A 181      -1.050  58.163  42.175  1.00  0.00          O   \nATOM   1376  CG2 THR A 181      -0.165  58.929  44.273  1.00  0.00          C   \nATOM   1377  N   LEU A 182      -0.556  55.270  41.697  1.00  0.00          N   \nATOM   1378  CA  LEU A 182      -1.152  54.368  40.698  1.00  0.00          C   \nATOM   1379  C   LEU A 182      -1.441  52.921  41.120  1.00  0.00          C   \nATOM   1380  O   LEU A 182      -2.535  52.407  40.882  1.00  0.00          O   \nATOM   1381  CB  LEU A 182      -0.287  54.360  39.416  1.00  0.00          C   \nATOM   1382  CG  LEU A 182      -0.713  53.521  38.191  1.00  0.00          C   \nATOM   1383  CD1 LEU A 182       0.169  53.868  37.001  1.00  0.00          C   \nATOM   1384  CD2 LEU A 182      -0.582  52.049  38.491  1.00  0.00          C   \nATOM   1385  N   PRO A 183      -0.463  52.245  41.744  1.00  0.00          N   \nATOM   1386  CA  PRO A 183      -0.615  50.851  42.183  1.00  0.00          C   \nATOM   1387  C   PRO A 183      -1.872  50.533  42.988  1.00  0.00          C   \nATOM   1388  O   PRO A 183      -2.572  49.561  42.700  1.00  0.00          O   \nATOM   1389  CB  PRO A 183       0.656  50.599  42.996  1.00  0.00          C   \nATOM   1390  CG  PRO A 183       1.646  51.532  42.373  1.00  0.00          C   \nATOM   1391  CD  PRO A 183       0.830  52.792  42.195  1.00  0.00          C   \nATOM   1392  N   GLN A 184      -2.153  51.342  44.008  1.00  0.00          N   \nATOM   1393  CA  GLN A 184      -3.332  51.097  44.831  1.00  0.00          C   \nATOM   1394  C   GLN A 184      -4.622  51.248  44.032  1.00  0.00          C   \nATOM   1395  O   GLN A 184      -5.575  50.484  44.212  1.00  0.00          O   \nATOM   1396  CB  GLN A 184      -3.357  52.032  46.036  1.00  0.00          C   \nATOM   1397  CG  GLN A 184      -4.446  51.674  47.058  1.00  0.00          C   \nATOM   1398  CD  GLN A 184      -5.803  52.280  46.723  1.00  0.00          C   \nATOM   1399  OE1 GLN A 184      -5.893  53.453  46.353  1.00  0.00          O   \nATOM   1400  NE2 GLN A 184      -6.866  51.490  46.869  1.00  0.00          N   \nATOM   1401  N   VAL A 185      -4.658  52.228  43.142  1.00  0.00          N   \nATOM   1402  CA  VAL A 185      -5.850  52.426  42.341  1.00  0.00          C   \nATOM   1403  C   VAL A 185      -6.075  51.275  41.366  1.00  0.00          C   \nATOM   1404  O   VAL A 185      -7.199  50.793  41.206  1.00  0.00          O   \nATOM   1405  CB  VAL A 185      -5.776  53.747  41.563  1.00  0.00          C   \nATOM   1406  CG1 VAL A 185      -6.926  53.842  40.556  1.00  0.00          C   \nATOM   1407  CG2 VAL A 185      -5.849  54.901  42.534  1.00  0.00          C   \nATOM   1408  N   VAL A 186      -5.009  50.816  40.722  1.00  0.00          N   \nATOM   1409  CA  VAL A 186      -5.153  49.735  39.756  1.00  0.00          C   \nATOM   1410  C   VAL A 186      -5.440  48.371  40.389  1.00  0.00          C   \nATOM   1411  O   VAL A 186      -6.249  47.592  39.871  1.00  0.00          O   \nATOM   1412  CB  VAL A 186      -3.886  49.643  38.863  1.00  0.00          C   \nATOM   1413  CG1 VAL A 186      -3.925  48.386  37.990  1.00  0.00          C   \nATOM   1414  CG2 VAL A 186      -3.785  50.893  38.003  1.00  0.00          C   \nATOM   1415  N   MET A 187      -4.806  48.096  41.523  1.00  0.00          N   \nATOM   1416  CA  MET A 187      -4.946  46.793  42.167  1.00  0.00          C   \nATOM   1417  C   MET A 187      -5.790  46.739  43.440  1.00  0.00          C   \nATOM   1418  O   MET A 187      -6.017  45.667  43.987  1.00  0.00          O   \nATOM   1419  CB  MET A 187      -3.542  46.227  42.418  1.00  0.00          C   \nATOM   1420  CG  MET A 187      -2.767  45.984  41.108  1.00  0.00          C   \nATOM   1421  SD  MET A 187      -0.943  45.829  41.254  1.00  0.00          S   \nATOM   1422  CE  MET A 187      -0.886  44.778  42.738  1.00  0.00          C   \nATOM   1423  N   GLY A 188      -6.268  47.890  43.898  1.00  0.00          N   \nATOM   1424  CA  GLY A 188      -7.076  47.912  45.106  1.00  0.00          C   \nATOM   1425  C   GLY A 188      -6.438  47.191  46.282  1.00  0.00          C   \nATOM   1426  O   GLY A 188      -5.225  47.268  46.498  1.00  0.00          O   \nATOM   1427  N   SER A 189      -7.263  46.470  47.034  1.00  0.00          N   \nATOM   1428  CA  SER A 189      -6.809  45.751  48.216  1.00  0.00          C   \nATOM   1429  C   SER A 189      -5.669  44.776  47.957  1.00  0.00          C   \nATOM   1430  O   SER A 189      -4.980  44.370  48.903  1.00  0.00          O   \nATOM   1431  CB  SER A 189      -7.988  45.008  48.853  1.00  0.00          C   \nATOM   1432  OG  SER A 189      -8.540  44.070  47.941  1.00  0.00          O   \nATOM   1433  N   SER A 190      -5.456  44.407  46.693  1.00  0.00          N   \nATOM   1434  CA  SER A 190      -4.386  43.466  46.344  1.00  0.00          C   \nATOM   1435  C   SER A 190      -2.977  44.061  46.384  1.00  0.00          C   \nATOM   1436  O   SER A 190      -1.984  43.326  46.417  1.00  0.00          O   \nATOM   1437  CB  SER A 190      -4.641  42.849  44.966  1.00  0.00          C   \nATOM   1438  OG  SER A 190      -5.781  42.003  44.994  1.00  0.00          O   \nATOM   1439  N   TYR A 191      -2.876  45.382  46.390  1.00  0.00          N   \nATOM   1440  CA  TYR A 191      -1.559  46.010  46.438  1.00  0.00          C   \nATOM   1441  C   TYR A 191      -0.993  45.893  47.854  1.00  0.00          C   \nATOM   1442  O   TYR A 191      -1.499  46.525  48.776  1.00  0.00          O   \nATOM   1443  CB  TYR A 191      -1.664  47.481  46.031  1.00  0.00          C   \nATOM   1444  CG  TYR A 191      -0.330  48.187  46.017  1.00  0.00          C   \nATOM   1445  CD1 TYR A 191       0.758  47.643  45.336  1.00  0.00          C   \nATOM   1446  CD2 TYR A 191      -0.166  49.414  46.659  1.00  0.00          C   \nATOM   1447  CE1 TYR A 191       1.984  48.312  45.288  1.00  0.00          C   \nATOM   1448  CE2 TYR A 191       1.049  50.094  46.618  1.00  0.00          C   \nATOM   1449  CZ  TYR A 191       2.118  49.542  45.931  1.00  0.00          C   \nATOM   1450  OH  TYR A 191       3.310  50.228  45.878  1.00  0.00          O   \nATOM   1451  N   GLY A 192       0.064  45.106  48.030  1.00  0.00          N   \nATOM   1452  CA  GLY A 192       0.603  44.919  49.368  1.00  0.00          C   \nATOM   1453  C   GLY A 192       1.292  46.063  50.107  1.00  0.00          C   \nATOM   1454  O   GLY A 192       1.207  46.146  51.333  1.00  0.00          O   \nATOM   1455  N   PHE A 193       1.969  46.948  49.390  1.00  0.00          N   \nATOM   1456  CA  PHE A 193       2.703  48.022  50.049  1.00  0.00          C   \nATOM   1457  C   PHE A 193       1.874  49.103  50.735  1.00  0.00          C   \nATOM   1458  O   PHE A 193       2.437  50.053  51.299  1.00  0.00          O   \nATOM   1459  CB  PHE A 193       3.677  48.665  49.061  1.00  0.00          C   \nATOM   1460  CG  PHE A 193       4.782  47.745  48.619  1.00  0.00          C   \nATOM   1461  CD1 PHE A 193       5.614  47.129  49.561  1.00  0.00          C   \nATOM   1462  CD2 PHE A 193       4.981  47.477  47.264  1.00  0.00          C   \nATOM   1463  CE1 PHE A 193       6.636  46.245  49.154  1.00  0.00          C   \nATOM   1464  CE2 PHE A 193       5.996  46.603  46.843  1.00  0.00          C   \nATOM   1465  CZ  PHE A 193       6.826  45.982  47.791  1.00  0.00          C   \nATOM   1466  N   GLN A 194       0.550  48.970  50.694  1.00  0.00          N   \nATOM   1467  CA  GLN A 194      -0.324  49.953  51.334  1.00  0.00          C   \nATOM   1468  C   GLN A 194      -0.562  49.542  52.780  1.00  0.00          C   \nATOM   1469  O   GLN A 194      -1.060  50.330  53.585  1.00  0.00          O   \nATOM   1470  CB  GLN A 194      -1.679  50.038  50.612  1.00  0.00          C   \nATOM   1471  CG  GLN A 194      -2.554  48.791  50.807  1.00  0.00          C   \nATOM   1472  CD  GLN A 194      -3.815  48.805  49.950  1.00  0.00          C   \nATOM   1473  OE1 GLN A 194      -4.710  49.630  50.147  1.00  0.00          O   \nATOM   1474  NE2 GLN A 194      -3.884  47.893  48.991  1.00  0.00          N   \nATOM   1475  N   TYR A 195      -0.195  48.306  53.105  1.00  0.00          N   \nATOM   1476  CA  TYR A 195      -0.405  47.782  54.448  1.00  0.00          C   \nATOM   1477  C   TYR A 195       0.793  47.798  55.387  1.00  0.00          C   \nATOM   1478  O   TYR A 195       1.942  47.659  54.969  1.00  0.00          O   \nATOM   1479  CB  TYR A 195      -0.903  46.323  54.381  1.00  0.00          C   \nATOM   1480  CG  TYR A 195      -2.124  46.091  53.512  1.00  0.00          C   \nATOM   1481  CD1 TYR A 195      -3.330  46.738  53.782  1.00  0.00          C   \nATOM   1482  CD2 TYR A 195      -2.077  45.211  52.433  1.00  0.00          C   \nATOM   1483  CE1 TYR A 195      -4.462  46.514  53.001  1.00  0.00          C   \nATOM   1484  CE2 TYR A 195      -3.212  44.971  51.640  1.00  0.00          C   \nATOM   1485  CZ  TYR A 195      -4.401  45.626  51.931  1.00  0.00          C   \nATOM   1486  OH  TYR A 195      -5.526  45.391  51.161  1.00  0.00          O   \nATOM   1487  N   SER A 196       0.497  47.974  56.671  1.00  0.00          N   \nATOM   1488  CA  SER A 196       1.501  47.892  57.714  1.00  0.00          C   \nATOM   1489  C   SER A 196       1.462  46.381  57.988  1.00  0.00          C   \nATOM   1490  O   SER A 196       0.553  45.698  57.512  1.00  0.00          O   \nATOM   1491  CB  SER A 196       1.036  48.657  58.956  1.00  0.00          C   \nATOM   1492  OG  SER A 196      -0.208  48.161  59.411  1.00  0.00          O   \nATOM   1493  N   PRO A 197       2.433  45.834  58.743  1.00  0.00          N   \nATOM   1494  CA  PRO A 197       2.397  44.389  59.007  1.00  0.00          C   \nATOM   1495  C   PRO A 197       1.046  43.930  59.554  1.00  0.00          C   \nATOM   1496  O   PRO A 197       0.533  42.881  59.162  1.00  0.00          O   \nATOM   1497  CB  PRO A 197       3.509  44.190  60.036  1.00  0.00          C   \nATOM   1498  CG  PRO A 197       4.491  45.261  59.672  1.00  0.00          C   \nATOM   1499  CD  PRO A 197       3.598  46.460  59.393  1.00  0.00          C   \nATOM   1500  N   GLY A 198       0.477  44.710  60.468  1.00  0.00          N   \nATOM   1501  CA  GLY A 198      -0.802  44.344  61.055  1.00  0.00          C   \nATOM   1502  C   GLY A 198      -1.931  44.320  60.043  1.00  0.00          C   \nATOM   1503  O   GLY A 198      -2.760  43.403  60.035  1.00  0.00          O   \nATOM   1504  N   GLN A 199      -1.974  45.324  59.180  1.00  0.00          N   \nATOM   1505  CA  GLN A 199      -3.022  45.368  58.170  1.00  0.00          C   \nATOM   1506  C   GLN A 199      -2.855  44.239  57.160  1.00  0.00          C   \nATOM   1507  O   GLN A 199      -3.837  43.755  56.591  1.00  0.00          O   \nATOM   1508  CB  GLN A 199      -3.007  46.713  57.464  1.00  0.00          C   \nATOM   1509  CG  GLN A 199      -3.193  47.854  58.428  1.00  0.00          C   \nATOM   1510  CD  GLN A 199      -2.951  49.193  57.784  1.00  0.00          C   \nATOM   1511  OE1 GLN A 199      -1.967  49.379  57.063  1.00  0.00          O   \nATOM   1512  NE2 GLN A 199      -3.841  50.143  58.047  1.00  0.00          N   \nATOM   1513  N   ARG A 200      -1.612  43.817  56.936  1.00  0.00          N   \nATOM   1514  CA  ARG A 200      -1.353  42.734  55.991  1.00  0.00          C   \nATOM   1515  C   ARG A 200      -1.987  41.470  56.564  1.00  0.00          C   \nATOM   1516  O   ARG A 200      -2.731  40.753  55.884  1.00  0.00          O   \nATOM   1517  CB  ARG A 200       0.155  42.525  55.807  1.00  0.00          C   \nATOM   1518  CG  ARG A 200       0.500  41.570  54.680  1.00  0.00          C   \nATOM   1519  CD  ARG A 200       2.013  41.385  54.566  1.00  0.00          C   \nATOM   1520  NE  ARG A 200       2.340  40.253  53.702  1.00  0.00          N   \nATOM   1521  CZ  ARG A 200       2.384  40.304  52.373  1.00  0.00          C   \nATOM   1522  NH1 ARG A 200       2.130  41.443  51.744  1.00  0.00          N   \nATOM   1523  NH2 ARG A 200       2.668  39.207  51.675  1.00  0.00          N   \nATOM   1524  N   VAL A 201      -1.689  41.204  57.829  1.00  0.00          N   \nATOM   1525  CA  VAL A 201      -2.230  40.035  58.514  1.00  0.00          C   \nATOM   1526  C   VAL A 201      -3.754  40.076  58.434  1.00  0.00          C   \nATOM   1527  O   VAL A 201      -4.413  39.077  58.107  1.00  0.00          O   \nATOM   1528  CB  VAL A 201      -1.806  40.030  59.998  1.00  0.00          C   \nATOM   1529  CG1 VAL A 201      -2.555  38.940  60.741  1.00  0.00          C   \nATOM   1530  CG2 VAL A 201      -0.296  39.827  60.097  1.00  0.00          C   \nATOM   1531  N   GLU A 202      -4.293  41.252  58.727  1.00  0.00          N   \nATOM   1532  CA  GLU A 202      -5.727  41.488  58.696  1.00  0.00          C   \nATOM   1533  C   GLU A 202      -6.312  41.160  57.322  1.00  0.00          C   \nATOM   1534  O   GLU A 202      -7.333  40.466  57.223  1.00  0.00          O   \nATOM   1535  CB  GLU A 202      -6.020  42.946  59.050  1.00  0.00          C   \nATOM   1536  CG  GLU A 202      -7.498  43.292  59.037  1.00  0.00          C   \nATOM   1537  CD  GLU A 202      -7.771  44.669  59.593  1.00  0.00          C   \nATOM   1538  OE1 GLU A 202      -7.329  45.669  58.982  1.00  0.00          O   \nATOM   1539  OE2 GLU A 202      -8.425  44.750  60.654  1.00  0.00          O   \nATOM   1540  N   PHE A 203      -5.669  41.651  56.263  1.00  0.00          N   \nATOM   1541  CA  PHE A 203      -6.159  41.376  54.920  1.00  0.00          C   \nATOM   1542  C   PHE A 203      -6.095  39.890  54.631  1.00  0.00          C   \nATOM   1543  O   PHE A 203      -7.030  39.316  54.078  1.00  0.00          O   \nATOM   1544  CB  PHE A 203      -5.341  42.114  53.861  1.00  0.00          C   \nATOM   1545  CG  PHE A 203      -5.815  41.855  52.455  1.00  0.00          C   \nATOM   1546  CD1 PHE A 203      -7.063  42.309  52.036  1.00  0.00          C   \nATOM   1547  CD2 PHE A 203      -5.026  41.144  51.558  1.00  0.00          C   \nATOM   1548  CE1 PHE A 203      -7.519  42.052  50.752  1.00  0.00          C   \nATOM   1549  CE2 PHE A 203      -5.475  40.882  50.270  1.00  0.00          C   \nATOM   1550  CZ  PHE A 203      -6.722  41.340  49.866  1.00  0.00          C   \nATOM   1551  N   LEU A 204      -4.985  39.265  55.007  1.00  0.00          N   \nATOM   1552  CA  LEU A 204      -4.813  37.839  54.763  1.00  0.00          C   \nATOM   1553  C   LEU A 204      -5.843  36.997  55.506  1.00  0.00          C   \nATOM   1554  O   LEU A 204      -6.424  36.067  54.937  1.00  0.00          O   \nATOM   1555  CB  LEU A 204      -3.392  37.403  55.151  1.00  0.00          C   \nATOM   1556  CG  LEU A 204      -2.286  37.814  54.165  1.00  0.00          C   \nATOM   1557  CD1 LEU A 204      -0.925  37.640  54.818  1.00  0.00          C   \nATOM   1558  CD2 LEU A 204      -2.384  36.976  52.886  1.00  0.00          C   \nATOM   1559  N   VAL A 205      -6.064  37.323  56.776  1.00  0.00          N   \nATOM   1560  CA  VAL A 205      -7.021  36.577  57.588  1.00  0.00          C   \nATOM   1561  C   VAL A 205      -8.447  36.753  57.073  1.00  0.00          C   \nATOM   1562  O   VAL A 205      -9.177  35.780  56.938  1.00  0.00          O   \nATOM   1563  CB  VAL A 205      -6.964  37.008  59.077  1.00  0.00          C   \nATOM   1564  CG1 VAL A 205      -8.028  36.249  59.884  1.00  0.00          C   \nATOM   1565  CG2 VAL A 205      -5.586  36.726  59.637  1.00  0.00          C   \nATOM   1566  N   ASN A 206      -8.838  37.988  56.782  1.00  0.00          N   \nATOM   1567  CA  ASN A 206     -10.176  38.234  56.269  1.00  0.00          C   \nATOM   1568  C   ASN A 206     -10.378  37.558  54.923  1.00  0.00          C   \nATOM   1569  O   ASN A 206     -11.444  37.001  54.652  1.00  0.00          O   \nATOM   1570  CB  ASN A 206     -10.445  39.736  56.138  1.00  0.00          C   \nATOM   1571  CG  ASN A 206     -10.642  40.399  57.479  1.00  0.00          C   \nATOM   1572  OD1 ASN A 206     -11.040  39.745  58.433  1.00  0.00          O   \nATOM   1573  ND2 ASN A 206     -10.382  41.703  57.558  1.00  0.00          N   \nATOM   1574  N   THR A 207      -9.358  37.604  54.073  1.00  0.00          N   \nATOM   1575  CA  THR A 207      -9.465  36.978  52.763  1.00  0.00          C   \nATOM   1576  C   THR A 207      -9.595  35.478  52.950  1.00  0.00          C   \nATOM   1577  O   THR A 207     -10.419  34.822  52.313  1.00  0.00          O   \nATOM   1578  CB  THR A 207      -8.224  37.256  51.895  1.00  0.00          C   \nATOM   1579  OG1 THR A 207      -8.146  38.659  51.609  1.00  0.00          O   \nATOM   1580  CG2 THR A 207      -8.296  36.468  50.592  1.00  0.00          C   \nATOM   1581  N   TRP A 208      -8.770  34.935  53.829  1.00  0.00          N   \nATOM   1582  CA  TRP A 208      -8.800  33.514  54.093  1.00  0.00          C   \nATOM   1583  C   TRP A 208     -10.172  33.086  54.614  1.00  0.00          C   \nATOM   1584  O   TRP A 208     -10.676  32.026  54.240  1.00  0.00          O   \nATOM   1585  CB  TRP A 208      -7.704  33.171  55.097  1.00  0.00          C   \nATOM   1586  CG  TRP A 208      -7.522  31.721  55.318  1.00  0.00          C   \nATOM   1587  CD1 TRP A 208      -8.064  30.973  56.316  1.00  0.00          C   \nATOM   1588  CD2 TRP A 208      -6.732  30.832  54.525  1.00  0.00          C   \nATOM   1589  NE1 TRP A 208      -7.657  29.669  56.202  1.00  0.00          N   \nATOM   1590  CE2 TRP A 208      -6.840  29.552  55.113  1.00  0.00          C   \nATOM   1591  CE3 TRP A 208      -5.946  30.992  53.376  1.00  0.00          C   \nATOM   1592  CZ2 TRP A 208      -6.184  28.431  54.587  1.00  0.00          C   \nATOM   1593  CZ3 TRP A 208      -5.291  29.870  52.851  1.00  0.00          C   \nATOM   1594  CH2 TRP A 208      -5.417  28.608  53.461  1.00  0.00          C   \nATOM   1595  N   LYS A 209     -10.768  33.926  55.458  1.00  0.00          N   \nATOM   1596  CA  LYS A 209     -12.085  33.656  56.041  1.00  0.00          C   \nATOM   1597  C   LYS A 209     -13.240  33.881  55.061  1.00  0.00          C   \nATOM   1598  O   LYS A 209     -14.354  33.418  55.288  1.00  0.00          O   \nATOM   1599  CB  LYS A 209     -12.330  34.548  57.261  1.00  0.00          C   \nATOM   1600  CG  LYS A 209     -11.497  34.239  58.494  1.00  0.00          C   \nATOM   1601  CD  LYS A 209     -11.903  35.200  59.595  1.00  0.00          C   \nATOM   1602  CE  LYS A 209     -11.113  34.996  60.866  1.00  0.00          C   \nATOM   1603  NZ  LYS A 209     -11.504  36.028  61.872  1.00  0.00          N   \nATOM   1604  N   SER A 210     -12.977  34.595  53.976  1.00  0.00          N   \nATOM   1605  CA  SER A 210     -14.024  34.873  53.003  1.00  0.00          C   \nATOM   1606  C   SER A 210     -14.289  33.712  52.055  1.00  0.00          C   \nATOM   1607  O   SER A 210     -15.218  33.776  51.250  1.00  0.00          O   \nATOM   1608  CB  SER A 210     -13.667  36.114  52.188  1.00  0.00          C   \nATOM   1609  OG  SER A 210     -12.593  35.828  51.308  1.00  0.00          O   \nATOM   1610  N   LYS A 211     -13.480  32.658  52.134  1.00  0.00          N   \nATOM   1611  CA  LYS A 211     -13.669  31.501  51.260  1.00  0.00          C   \nATOM   1612  C   LYS A 211     -14.363  30.392  52.047  1.00  0.00          C   \nATOM   1613  O   LYS A 211     -14.181  30.286  53.259  1.00  0.00          O   \nATOM   1614  CB  LYS A 211     -12.324  30.968  50.739  1.00  0.00          C   \nATOM   1615  CG  LYS A 211     -11.316  32.027  50.293  1.00  0.00          C   \nATOM   1616  CD  LYS A 211     -11.853  32.930  49.198  1.00  0.00          C   \nATOM   1617  CE  LYS A 211     -10.811  33.980  48.807  1.00  0.00          C   \nATOM   1618  NZ  LYS A 211     -11.339  34.971  47.821  1.00  0.00          N   \nATOM   1619  N   LYS A 212     -15.147  29.565  51.358  1.00  0.00          N   \nATOM   1620  CA  LYS A 212     -15.846  28.465  52.016  1.00  0.00          C   \nATOM   1621  C   LYS A 212     -14.817  27.411  52.407  1.00  0.00          C   \nATOM   1622  O   LYS A 212     -14.832  26.896  53.524  1.00  0.00          O   \nATOM   1623  CB  LYS A 212     -16.899  27.865  51.077  1.00  0.00          C   \nATOM   1624  CG  LYS A 212     -17.945  28.870  50.610  1.00  0.00          C   \nATOM   1625  CD  LYS A 212     -19.083  28.193  49.856  1.00  0.00          C   \nATOM   1626  CE  LYS A 212     -20.092  29.206  49.337  1.00  0.00          C   \nATOM   1627  NZ  LYS A 212     -19.538  30.027  48.222  1.00  0.00          N   \nATOM   1628  N   ASN A 213     -13.931  27.090  51.472  1.00  0.00          N   \nATOM   1629  CA  ASN A 213     -12.859  26.135  51.707  1.00  0.00          C   \nATOM   1630  C   ASN A 213     -11.588  26.790  51.164  1.00  0.00          C   \nATOM   1631  O   ASN A 213     -11.194  26.567  50.016  1.00  0.00          O   \nATOM   1632  CB  ASN A 213     -13.125  24.810  50.984  1.00  0.00          C   \nATOM   1633  CG  ASN A 213     -14.464  24.201  51.357  1.00  0.00          C   \nATOM   1634  OD1 ASN A 213     -14.843  24.181  52.529  1.00  0.00          O   \nATOM   1635  ND2 ASN A 213     -15.185  23.690  50.359  1.00  0.00          N   \nATOM   1636  N   PRO A 214     -10.927  27.607  51.995  1.00  0.00          N   \nATOM   1637  CA  PRO A 214      -9.704  28.301  51.588  1.00  0.00          C   \nATOM   1638  C   PRO A 214      -8.520  27.431  51.216  1.00  0.00          C   \nATOM   1639  O   PRO A 214      -8.278  26.372  51.797  1.00  0.00          O   \nATOM   1640  CB  PRO A 214      -9.403  29.205  52.785  1.00  0.00          C   \nATOM   1641  CG  PRO A 214      -9.896  28.394  53.938  1.00  0.00          C   \nATOM   1642  CD  PRO A 214     -11.230  27.874  53.414  1.00  0.00          C   \nATOM   1643  N   MET A 215      -7.792  27.904  50.213  1.00  0.00          N   \nATOM   1644  CA  MET A 215      -6.585  27.266  49.718  1.00  0.00          C   \nATOM   1645  C   MET A 215      -5.774  28.460  49.256  1.00  0.00          C   \nATOM   1646  O   MET A 215      -6.328  29.429  48.736  1.00  0.00          O   \nATOM   1647  CB  MET A 215      -6.879  26.336  48.536  1.00  0.00          C   \nATOM   1648  CG  MET A 215      -5.648  25.626  47.951  1.00  0.00          C   \nATOM   1649  SD  MET A 215      -4.547  26.679  46.902  1.00  0.00          S   \nATOM   1650  CE  MET A 215      -5.199  26.317  45.251  1.00  0.00          C   \nATOM   1651  N   GLY A 216      -4.470  28.402  49.472  1.00  0.00          N   \nATOM   1652  CA  GLY A 216      -3.623  29.499  49.070  1.00  0.00          C   \nATOM   1653  C   GLY A 216      -2.234  29.012  48.782  1.00  0.00          C   \nATOM   1654  O   GLY A 216      -1.849  27.910  49.186  1.00  0.00          O   \nATOM   1655  N   PHE A 217      -1.483  29.835  48.060  1.00  0.00          N   \nATOM   1656  CA  PHE A 217      -0.119  29.506  47.732  1.00  0.00          C   \nATOM   1657  C   PHE A 217       0.651  30.777  47.429  1.00  0.00          C   \nATOM   1658  O   PHE A 217       0.074  31.826  47.115  1.00  0.00          O   \nATOM   1659  CB  PHE A 217      -0.055  28.568  46.519  1.00  0.00          C   \nATOM   1660  CG  PHE A 217      -0.691  29.126  45.279  1.00  0.00          C   \nATOM   1661  CD1 PHE A 217      -2.062  28.979  45.057  1.00  0.00          C   \nATOM   1662  CD2 PHE A 217       0.075  29.808  44.334  1.00  0.00          C   \nATOM   1663  CE1 PHE A 217      -2.668  29.506  43.910  1.00  0.00          C   \nATOM   1664  CE2 PHE A 217      -0.514  30.342  43.179  1.00  0.00          C   \nATOM   1665  CZ  PHE A 217      -1.890  30.192  42.966  1.00  0.00          C   \nATOM   1666  N   SER A 218       1.961  30.689  47.564  1.00  0.00          N   \nATOM   1667  CA  SER A 218       2.818  31.810  47.237  1.00  0.00          C   \nATOM   1668  C   SER A 218       3.412  31.329  45.927  1.00  0.00          C   \nATOM   1669  O   SER A 218       3.569  30.124  45.737  1.00  0.00          O   \nATOM   1670  CB  SER A 218       3.919  31.982  48.291  1.00  0.00          C   \nATOM   1671  OG  SER A 218       4.680  30.797  48.430  1.00  0.00          O   \nATOM   1672  N   TYR A 219       3.690  32.243  45.009  1.00  0.00          N   \nATOM   1673  CA  TYR A 219       4.285  31.860  43.729  1.00  0.00          C   \nATOM   1674  C   TYR A 219       5.623  32.575  43.572  1.00  0.00          C   \nATOM   1675  O   TYR A 219       5.673  33.791  43.441  1.00  0.00          O   \nATOM   1676  CB  TYR A 219       3.370  32.240  42.562  1.00  0.00          C   \nATOM   1677  CG  TYR A 219       3.891  31.771  41.222  1.00  0.00          C   \nATOM   1678  CD1 TYR A 219       3.570  30.501  40.733  1.00  0.00          C   \nATOM   1679  CD2 TYR A 219       4.741  32.575  40.460  1.00  0.00          C   \nATOM   1680  CE1 TYR A 219       4.079  30.040  39.516  1.00  0.00          C   \nATOM   1681  CE2 TYR A 219       5.271  32.119  39.237  1.00  0.00          C   \nATOM   1682  CZ  TYR A 219       4.935  30.851  38.771  1.00  0.00          C   \nATOM   1683  OH  TYR A 219       5.462  30.386  37.574  1.00  0.00          O   \nATOM   1684  N   ASP A 220       6.704  31.812  43.604  1.00  0.00          N   \nATOM   1685  CA  ASP A 220       8.028  32.385  43.460  1.00  0.00          C   \nATOM   1686  C   ASP A 220       8.486  32.338  42.009  1.00  0.00          C   \nATOM   1687  O   ASP A 220       8.741  31.266  41.462  1.00  0.00          O   \nATOM   1688  CB  ASP A 220       9.019  31.629  44.347  1.00  0.00          C   \nATOM   1689  CG  ASP A 220      10.456  32.018  44.075  1.00  0.00          C   \nATOM   1690  OD1 ASP A 220      10.752  33.236  44.041  1.00  0.00          O   \nATOM   1691  OD2 ASP A 220      11.286  31.101  43.900  1.00  0.00          O   \nATOM   1692  N   THR A 221       8.572  33.502  41.378  1.00  0.00          N   \nATOM   1693  CA  THR A 221       9.026  33.556  39.994  1.00  0.00          C   \nATOM   1694  C   THR A 221      10.536  33.465  40.095  1.00  0.00          C   \nATOM   1695  O   THR A 221      11.131  34.048  40.996  1.00  0.00          O   \nATOM   1696  CB  THR A 221       8.659  34.897  39.300  1.00  0.00          C   \nATOM   1697  OG1 THR A 221       7.235  35.040  39.212  1.00  0.00          O   \nATOM   1698  CG2 THR A 221       9.254  34.946  37.908  1.00  0.00          C   \nATOM   1699  N   ARG A 222      11.157  32.722  39.191  1.00  0.00          N   \nATOM   1700  CA  ARG A 222      12.610  32.592  39.221  1.00  0.00          C   \nATOM   1701  C   ARG A 222      13.265  33.840  38.610  1.00  0.00          C   \nATOM   1702  O   ARG A 222      12.978  34.197  37.465  1.00  0.00          O   \nATOM   1703  CB  ARG A 222      13.026  31.332  38.463  1.00  0.00          C   \nATOM   1704  CG  ARG A 222      14.509  31.136  38.368  1.00  0.00          C   \nATOM   1705  CD  ARG A 222      14.897  31.022  36.916  1.00  0.00          C   \nATOM   1706  NE  ARG A 222      14.294  29.853  36.284  1.00  0.00          N   \nATOM   1707  CZ  ARG A 222      14.078  29.738  34.976  1.00  0.00          C   \nATOM   1708  NH1 ARG A 222      14.410  30.728  34.153  1.00  0.00          N   \nATOM   1709  NH2 ARG A 222      13.540  28.628  34.486  1.00  0.00          N   \nATOM   1710  N   CYS A 223      14.128  34.505  39.383  1.00  0.00          N   \nATOM   1711  CA  CYS A 223      14.813  35.719  38.914  1.00  0.00          C   \nATOM   1712  C   CYS A 223      13.808  36.605  38.171  1.00  0.00          C   \nATOM   1713  O   CYS A 223      13.943  36.851  36.973  1.00  0.00          O   \nATOM   1714  CB  CYS A 223      15.966  35.333  37.979  1.00  0.00          C   \nATOM   1715  SG  CYS A 223      17.228  34.308  38.800  1.00  0.00          S   \nATOM   1716  N   PHE A 224      12.800  37.082  38.887  1.00  0.00          N   \nATOM   1717  CA  PHE A 224      11.754  37.897  38.281  1.00  0.00          C   \nATOM   1718  C   PHE A 224      12.254  39.037  37.396  1.00  0.00          C   \nATOM   1719  O   PHE A 224      11.780  39.193  36.272  1.00  0.00          O   \nATOM   1720  CB  PHE A 224      10.812  38.447  39.362  1.00  0.00          C   \nATOM   1721  CG  PHE A 224       9.549  39.088  38.810  1.00  0.00          C   \nATOM   1722  CD1 PHE A 224       9.563  40.401  38.339  1.00  0.00          C   \nATOM   1723  CD2 PHE A 224       8.340  38.379  38.783  1.00  0.00          C   \nATOM   1724  CE1 PHE A 224       8.395  41.005  37.855  1.00  0.00          C   \nATOM   1725  CE2 PHE A 224       7.165  38.970  38.302  1.00  0.00          C   \nATOM   1726  CZ  PHE A 224       7.192  40.290  37.836  1.00  0.00          C   \nATOM   1727  N   ASP A 225      13.205  39.830  37.887  1.00  0.00          N   \nATOM   1728  CA  ASP A 225      13.700  40.947  37.088  1.00  0.00          C   \nATOM   1729  C   ASP A 225      14.152  40.523  35.702  1.00  0.00          C   \nATOM   1730  O   ASP A 225      13.911  41.226  34.723  1.00  0.00          O   \nATOM   1731  CB  ASP A 225      14.858  41.650  37.793  1.00  0.00          C   \nATOM   1732  CG  ASP A 225      14.404  42.470  38.982  1.00  0.00          C   \nATOM   1733  OD1 ASP A 225      13.178  42.589  39.222  1.00  0.00          O   \nATOM   1734  OD2 ASP A 225      15.288  43.007  39.672  1.00  0.00          O   \nATOM   1735  N   SER A 226      14.813  39.371  35.626  1.00  0.00          N   \nATOM   1736  CA  SER A 226      15.308  38.843  34.356  1.00  0.00          C   \nATOM   1737  C   SER A 226      14.207  38.332  33.444  1.00  0.00          C   \nATOM   1738  O   SER A 226      14.410  38.231  32.232  1.00  0.00          O   \nATOM   1739  CB  SER A 226      16.310  37.711  34.600  1.00  0.00          C   \nATOM   1740  OG  SER A 226      17.533  38.225  35.095  1.00  0.00          O   \nATOM   1741  N   THR A 227      13.049  38.014  34.013  1.00  0.00          N   \nATOM   1742  CA  THR A 227      11.933  37.509  33.209  1.00  0.00          C   \nATOM   1743  C   THR A 227      11.132  38.640  32.576  1.00  0.00          C   \nATOM   1744  O   THR A 227      10.353  38.415  31.643  1.00  0.00          O   \nATOM   1745  CB  THR A 227      10.962  36.630  34.053  1.00  0.00          C   \nATOM   1746  OG1 THR A 227      10.222  37.455  34.969  1.00  0.00          O   \nATOM   1747  CG2 THR A 227      11.742  35.556  34.841  1.00  0.00          C   \nATOM   1748  N   VAL A 228      11.328  39.862  33.072  1.00  0.00          N   \nATOM   1749  CA  VAL A 228      10.605  41.023  32.536  1.00  0.00          C   \nATOM   1750  C   VAL A 228      11.164  41.350  31.161  1.00  0.00          C   \nATOM   1751  O   VAL A 228      12.346  41.660  31.016  1.00  0.00          O   \nATOM   1752  CB  VAL A 228      10.739  42.249  33.481  1.00  0.00          C   \nATOM   1753  CG1 VAL A 228      10.017  43.490  32.894  1.00  0.00          C   \nATOM   1754  CG2 VAL A 228      10.150  41.893  34.850  1.00  0.00          C   \nATOM   1755  N   THR A 229      10.286  41.298  30.162  1.00  0.00          N   \nATOM   1756  CA  THR A 229      10.629  41.526  28.758  1.00  0.00          C   \nATOM   1757  C   THR A 229      10.390  42.962  28.284  1.00  0.00          C   \nATOM   1758  O   THR A 229       9.770  43.755  28.985  1.00  0.00          O   \nATOM   1759  CB  THR A 229       9.769  40.643  27.867  1.00  0.00          C   \nATOM   1760  OG1 THR A 229       8.403  41.076  27.983  1.00  0.00          O   \nATOM   1761  CG2 THR A 229       9.881  39.145  28.292  1.00  0.00          C   \nATOM   1762  N   GLU A 230      10.848  43.275  27.068  1.00  0.00          N   \nATOM   1763  CA  GLU A 230      10.628  44.613  26.508  1.00  0.00          C   \nATOM   1764  C   GLU A 230       9.116  44.857  26.431  1.00  0.00          C   \nATOM   1765  O   GLU A 230       8.645  45.948  26.708  1.00  0.00          O   \nATOM   1766  CB  GLU A 230      11.224  44.748  25.094  1.00  0.00          C   \nATOM   1767  CG  GLU A 230      11.022  46.158  24.500  1.00  0.00          C   \nATOM   1768  CD  GLU A 230      11.557  46.307  23.083  1.00  0.00          C   \nATOM   1769  OE1 GLU A 230      12.623  45.725  22.788  1.00  0.00          O   \nATOM   1770  OE2 GLU A 230      10.923  47.025  22.269  1.00  0.00          O   \nATOM   1771  N   ASN A 231       8.360  43.836  26.036  1.00  0.00          N   \nATOM   1772  CA  ASN A 231       6.907  43.953  25.957  1.00  0.00          C   \nATOM   1773  C   ASN A 231       6.342  44.371  27.309  1.00  0.00          C   \nATOM   1774  O   ASN A 231       5.530  45.300  27.404  1.00  0.00          O   \nATOM   1775  CB  ASN A 231       6.261  42.614  25.584  1.00  0.00          C   \nATOM   1776  CG  ASN A 231       4.759  42.609  25.859  1.00  0.00          C   \nATOM   1777  OD1 ASN A 231       3.964  43.061  25.026  1.00  0.00          O   \nATOM   1778  ND2 ASN A 231       4.364  42.133  27.059  1.00  0.00          N   \nATOM   1779  N   ASP A 232       6.762  43.660  28.352  1.00  0.00          N   \nATOM   1780  CA  ASP A 232       6.308  43.952  29.704  1.00  0.00          C   \nATOM   1781  C   ASP A 232       6.556  45.427  30.065  1.00  0.00          C   \nATOM   1782  O   ASP A 232       5.688  46.105  30.627  1.00  0.00          O   \nATOM   1783  CB  ASP A 232       7.042  43.068  30.722  1.00  0.00          C   \nATOM   1784  CG  ASP A 232       6.685  41.600  30.600  1.00  0.00          C   \nATOM   1785  OD1 ASP A 232       5.658  41.285  29.958  1.00  0.00          O   \nATOM   1786  OD2 ASP A 232       7.431  40.762  31.165  1.00  0.00          O   \nATOM   1787  N   ILE A 233       7.748  45.910  29.743  1.00  0.00          N   \nATOM   1788  CA  ILE A 233       8.110  47.278  30.083  1.00  0.00          C   \nATOM   1789  C   ILE A 233       7.348  48.321  29.267  1.00  0.00          C   \nATOM   1790  O   ILE A 233       7.082  49.411  29.761  1.00  0.00          O   \nATOM   1791  CB  ILE A 233       9.643  47.448  30.002  1.00  0.00          C   \nATOM   1792  CG1 ILE A 233      10.296  46.525  31.053  1.00  0.00          C   \nATOM   1793  CG2 ILE A 233      10.050  48.887  30.281  1.00  0.00          C   \nATOM   1794  CD1 ILE A 233      11.793  46.234  30.798  1.00  0.00          C   \nATOM   1795  N   ARG A 234       6.972  47.985  28.035  1.00  0.00          N   \nATOM   1796  CA  ARG A 234       6.184  48.914  27.228  1.00  0.00          C   \nATOM   1797  C   ARG A 234       4.733  48.849  27.702  1.00  0.00          C   \nATOM   1798  O   ARG A 234       4.020  49.844  27.671  1.00  0.00          O   \nATOM   1799  CB  ARG A 234       6.272  48.574  25.734  1.00  0.00          C   \nATOM   1800  CG  ARG A 234       7.656  48.817  25.145  1.00  0.00          C   \nATOM   1801  CD  ARG A 234       7.679  48.537  23.645  1.00  0.00          C   \nATOM   1802  NE  ARG A 234       8.999  48.768  23.069  1.00  0.00          N   \nATOM   1803  CZ  ARG A 234       9.517  49.965  22.830  1.00  0.00          C   \nATOM   1804  NH1 ARG A 234       8.824  51.061  23.116  1.00  0.00          N   \nATOM   1805  NH2 ARG A 234      10.733  50.062  22.301  1.00  0.00          N   \nATOM   1806  N   VAL A 235       4.285  47.687  28.149  1.00  0.00          N   \nATOM   1807  CA  VAL A 235       2.914  47.598  28.649  1.00  0.00          C   \nATOM   1808  C   VAL A 235       2.824  48.414  29.952  1.00  0.00          C   \nATOM   1809  O   VAL A 235       1.822  49.066  30.214  1.00  0.00          O   \nATOM   1810  CB  VAL A 235       2.498  46.134  28.902  1.00  0.00          C   \nATOM   1811  CG1 VAL A 235       1.252  46.088  29.774  1.00  0.00          C   \nATOM   1812  CG2 VAL A 235       2.239  45.439  27.577  1.00  0.00          C   \nATOM   1813  N   GLU A 236       3.872  48.369  30.769  1.00  0.00          N   \nATOM   1814  CA  GLU A 236       3.891  49.161  31.987  1.00  0.00          C   \nATOM   1815  C   GLU A 236       3.765  50.652  31.602  1.00  0.00          C   \nATOM   1816  O   GLU A 236       3.001  51.397  32.207  1.00  0.00          O   \nATOM   1817  CB  GLU A 236       5.202  48.944  32.737  1.00  0.00          C   \nATOM   1818  CG  GLU A 236       5.283  47.645  33.519  1.00  0.00          C   \nATOM   1819  CD  GLU A 236       6.714  47.218  33.798  1.00  0.00          C   \nATOM   1820  OE1 GLU A 236       7.621  48.080  33.772  1.00  0.00          O   \nATOM   1821  OE2 GLU A 236       6.927  46.009  34.052  1.00  0.00          O   \nATOM   1822  N   GLU A 237       4.513  51.089  30.595  1.00  0.00          N   \nATOM   1823  CA  GLU A 237       4.421  52.486  30.188  1.00  0.00          C   \nATOM   1824  C   GLU A 237       3.012  52.819  29.680  1.00  0.00          C   \nATOM   1825  O   GLU A 237       2.487  53.908  29.941  1.00  0.00          O   \nATOM   1826  CB  GLU A 237       5.427  52.803  29.081  1.00  0.00          C   \nATOM   1827  CG  GLU A 237       5.697  54.312  28.956  1.00  0.00          C   \nATOM   1828  CD  GLU A 237       5.727  54.795  27.526  1.00  0.00          C   \nATOM   1829  OE1 GLU A 237       6.257  54.058  26.671  1.00  0.00          O   \nATOM   1830  OE2 GLU A 237       5.241  55.916  27.253  1.00  0.00          O   \nATOM   1831  N   SER A 238       2.395  51.888  28.954  1.00  0.00          N   \nATOM   1832  CA  SER A 238       1.059  52.155  28.437  1.00  0.00          C   \nATOM   1833  C   SER A 238       0.112  52.379  29.619  1.00  0.00          C   \nATOM   1834  O   SER A 238      -0.862  53.136  29.512  1.00  0.00          O   \nATOM   1835  CB  SER A 238       0.557  51.008  27.532  1.00  0.00          C   \nATOM   1836  OG  SER A 238       0.308  49.818  28.263  1.00  0.00          O   \nATOM   1837  N   ILE A 239       0.401  51.734  30.748  1.00  0.00          N   \nATOM   1838  CA  ILE A 239      -0.430  51.914  31.937  1.00  0.00          C   \nATOM   1839  C   ILE A 239      -0.155  53.305  32.516  1.00  0.00          C   \nATOM   1840  O   ILE A 239      -1.090  54.080  32.736  1.00  0.00          O   \nATOM   1841  CB  ILE A 239      -0.151  50.828  33.002  1.00  0.00          C   \nATOM   1842  CG1 ILE A 239      -0.538  49.457  32.439  1.00  0.00          C   \nATOM   1843  CG2 ILE A 239      -0.963  51.111  34.276  1.00  0.00          C   \nATOM   1844  CD1 ILE A 239      -0.162  48.301  33.325  1.00  0.00          C   \nATOM   1845  N   TYR A 240       1.117  53.632  32.749  1.00  0.00          N   \nATOM   1846  CA  TYR A 240       1.451  54.959  33.285  1.00  0.00          C   \nATOM   1847  C   TYR A 240       0.820  56.064  32.434  1.00  0.00          C   \nATOM   1848  O   TYR A 240       0.257  57.019  32.968  1.00  0.00          O   \nATOM   1849  CB  TYR A 240       2.960  55.214  33.315  1.00  0.00          C   \nATOM   1850  CG  TYR A 240       3.801  54.179  34.024  1.00  0.00          C   \nATOM   1851  CD1 TYR A 240       3.360  53.548  35.190  1.00  0.00          C   \nATOM   1852  CD2 TYR A 240       5.068  53.861  33.542  1.00  0.00          C   \nATOM   1853  CE1 TYR A 240       4.177  52.618  35.851  1.00  0.00          C   \nATOM   1854  CE2 TYR A 240       5.884  52.947  34.188  1.00  0.00          C   \nATOM   1855  CZ  TYR A 240       5.437  52.325  35.335  1.00  0.00          C   \nATOM   1856  OH  TYR A 240       6.244  51.375  35.921  1.00  0.00          O   \nATOM   1857  N   GLN A 241       0.920  55.935  31.114  1.00  0.00          N   \nATOM   1858  CA  GLN A 241       0.368  56.943  30.210  1.00  0.00          C   \nATOM   1859  C   GLN A 241      -1.156  57.072  30.279  1.00  0.00          C   \nATOM   1860  O   GLN A 241      -1.715  58.026  29.743  1.00  0.00          O   \nATOM   1861  CB  GLN A 241       0.817  56.671  28.761  1.00  0.00          C   \nATOM   1862  CG  GLN A 241       2.339  56.763  28.537  1.00  0.00          C   \nATOM   1863  CD  GLN A 241       2.882  58.199  28.576  1.00  0.00          C   \nATOM   1864  OE1 GLN A 241       2.140  59.165  28.807  1.00  0.00          O   \nATOM   1865  NE2 GLN A 241       4.183  58.339  28.353  1.00  0.00          N   \nATOM   1866  N   CYS A 242      -1.837  56.129  30.935  1.00  0.00          N   \nATOM   1867  CA  CYS A 242      -3.289  56.241  31.059  1.00  0.00          C   \nATOM   1868  C   CYS A 242      -3.640  57.349  32.036  1.00  0.00          C   \nATOM   1869  O   CYS A 242      -4.723  57.926  31.958  1.00  0.00          O   \nATOM   1870  CB  CYS A 242      -3.914  54.933  31.548  1.00  0.00          C   \nATOM   1871  SG  CYS A 242      -4.017  53.676  30.266  1.00  0.00          S   \nATOM   1872  N   CYS A 243      -2.728  57.641  32.960  1.00  0.00          N   \nATOM   1873  CA  CYS A 243      -2.969  58.678  33.959  1.00  0.00          C   \nATOM   1874  C   CYS A 243      -3.146  60.065  33.339  1.00  0.00          C   \nATOM   1875  O   CYS A 243      -2.740  60.307  32.200  1.00  0.00          O   \nATOM   1876  CB  CYS A 243      -1.806  58.745  34.966  1.00  0.00          C   \nATOM   1877  SG  CYS A 243      -1.464  57.199  35.864  1.00  0.00          S   \nATOM   1878  N   ASP A 244      -3.769  60.961  34.097  1.00  0.00          N   \nATOM   1879  CA  ASP A 244      -3.956  62.354  33.684  1.00  0.00          C   \nATOM   1880  C   ASP A 244      -2.576  62.908  34.047  1.00  0.00          C   \nATOM   1881  O   ASP A 244      -2.191  62.910  35.218  1.00  0.00          O   \nATOM   1882  CB  ASP A 244      -5.041  62.999  34.556  1.00  0.00          C   \nATOM   1883  CG  ASP A 244      -5.233  64.490  34.285  1.00  0.00          C   \nATOM   1884  OD1 ASP A 244      -4.268  65.194  33.921  1.00  0.00          O   \nATOM   1885  OD2 ASP A 244      -6.372  64.969  34.473  1.00  0.00          O   \nATOM   1886  N   LEU A 245      -1.823  63.363  33.060  1.00  0.00          N   \nATOM   1887  CA  LEU A 245      -0.471  63.845  33.323  1.00  0.00          C   \nATOM   1888  C   LEU A 245      -0.126  65.113  32.562  1.00  0.00          C   \nATOM   1889  O   LEU A 245      -0.685  65.386  31.510  1.00  0.00          O   \nATOM   1890  CB  LEU A 245       0.539  62.779  32.898  1.00  0.00          C   \nATOM   1891  CG  LEU A 245       0.450  61.352  33.448  1.00  0.00          C   \nATOM   1892  CD1 LEU A 245       1.206  60.403  32.531  1.00  0.00          C   \nATOM   1893  CD2 LEU A 245       1.023  61.303  34.831  1.00  0.00          C   \nATOM   1894  N   ALA A 246       0.836  65.859  33.090  1.00  0.00          N   \nATOM   1895  CA  ALA A 246       1.318  67.076  32.446  1.00  0.00          C   \nATOM   1896  C   ALA A 246       2.030  66.624  31.174  1.00  0.00          C   \nATOM   1897  O   ALA A 246       2.636  65.551  31.144  1.00  0.00          O   \nATOM   1898  CB  ALA A 246       2.301  67.793  33.362  1.00  0.00          C   \nATOM   1899  N   PRO A 247       1.961  67.426  30.103  1.00  0.00          N   \nATOM   1900  CA  PRO A 247       2.624  67.049  28.850  1.00  0.00          C   \nATOM   1901  C   PRO A 247       4.111  66.711  29.011  1.00  0.00          C   \nATOM   1902  O   PRO A 247       4.623  65.803  28.371  1.00  0.00          O   \nATOM   1903  CB  PRO A 247       2.405  68.274  27.966  1.00  0.00          C   \nATOM   1904  CG  PRO A 247       1.066  68.763  28.412  1.00  0.00          C   \nATOM   1905  CD  PRO A 247       1.155  68.649  29.927  1.00  0.00          C   \nATOM   1906  N   GLU A 248       4.794  67.445  29.875  1.00  0.00          N   \nATOM   1907  CA  GLU A 248       6.217  67.241  30.099  1.00  0.00          C   \nATOM   1908  C   GLU A 248       6.447  65.905  30.806  1.00  0.00          C   \nATOM   1909  O   GLU A 248       7.462  65.241  30.596  1.00  0.00          O   \nATOM   1910  CB  GLU A 248       6.761  68.407  30.929  1.00  0.00          C   \nATOM   1911  CG  GLU A 248       8.227  68.729  30.719  1.00  0.00          C   \nATOM   1912  CD  GLU A 248       8.615  70.109  31.248  1.00  0.00          C   \nATOM   1913  OE1 GLU A 248       9.810  70.329  31.531  1.00  0.00          O   \nATOM   1914  OE2 GLU A 248       7.731  70.982  31.369  1.00  0.00          O   \nATOM   1915  N   ALA A 249       5.490  65.509  31.639  1.00  0.00          N   \nATOM   1916  CA  ALA A 249       5.596  64.249  32.360  1.00  0.00          C   \nATOM   1917  C   ALA A 249       5.471  63.076  31.393  1.00  0.00          C   \nATOM   1918  O   ALA A 249       6.205  62.093  31.509  1.00  0.00          O   \nATOM   1919  CB  ALA A 249       4.519  64.163  33.440  1.00  0.00          C   \nATOM   1920  N   ARG A 250       4.547  63.183  30.440  1.00  0.00          N   \nATOM   1921  CA  ARG A 250       4.345  62.126  29.449  1.00  0.00          C   \nATOM   1922  C   ARG A 250       5.600  61.905  28.625  1.00  0.00          C   \nATOM   1923  O   ARG A 250       6.019  60.765  28.394  1.00  0.00          O   \nATOM   1924  CB  ARG A 250       3.198  62.473  28.493  1.00  0.00          C   \nATOM   1925  CG  ARG A 250       1.831  62.529  29.149  1.00  0.00          C   \nATOM   1926  CD  ARG A 250       0.732  62.803  28.130  1.00  0.00          C   \nATOM   1927  NE  ARG A 250      -0.580  62.815  28.774  1.00  0.00          N   \nATOM   1928  CZ  ARG A 250      -1.168  61.743  29.302  1.00  0.00          C   \nATOM   1929  NH1 ARG A 250      -0.564  60.559  29.261  1.00  0.00          N   \nATOM   1930  NH2 ARG A 250      -2.357  61.858  29.881  1.00  0.00          N   \nATOM   1931  N   GLN A 251       6.189  63.005  28.169  1.00  0.00          N   \nATOM   1932  CA  GLN A 251       7.392  62.937  27.360  1.00  0.00          C   \nATOM   1933  C   GLN A 251       8.522  62.336  28.178  1.00  0.00          C   \nATOM   1934  O   GLN A 251       9.238  61.453  27.702  1.00  0.00          O   \nATOM   1935  CB  GLN A 251       7.792  64.336  26.872  1.00  0.00          C   \nATOM   1936  CG  GLN A 251       9.028  64.353  25.993  1.00  0.00          C   \nATOM   1937  CD  GLN A 251       8.765  63.767  24.622  1.00  0.00          C   \nATOM   1938  OE1 GLN A 251       7.702  63.205  24.370  1.00  0.00          O   \nATOM   1939  NE2 GLN A 251       9.731  63.895  23.728  1.00  0.00          N   \nATOM   1940  N   ALA A 252       8.688  62.810  29.409  1.00  0.00          N   \nATOM   1941  CA  ALA A 252       9.753  62.287  30.265  1.00  0.00          C   \nATOM   1942  C   ALA A 252       9.598  60.782  30.498  1.00  0.00          C   \nATOM   1943  O   ALA A 252      10.578  60.039  30.464  1.00  0.00          O   \nATOM   1944  CB  ALA A 252       9.766  63.011  31.596  1.00  0.00          C   \nATOM   1945  N   ILE A 253       8.364  60.337  30.722  1.00  0.00          N   \nATOM   1946  CA  ILE A 253       8.093  58.923  30.964  1.00  0.00          C   \nATOM   1947  C   ILE A 253       8.324  58.076  29.719  1.00  0.00          C   \nATOM   1948  O   ILE A 253       8.814  56.950  29.796  1.00  0.00          O   \nATOM   1949  CB  ILE A 253       6.658  58.740  31.475  1.00  0.00          C   \nATOM   1950  CG1 ILE A 253       6.566  59.274  32.908  1.00  0.00          C   \nATOM   1951  CG2 ILE A 253       6.257  57.273  31.419  1.00  0.00          C   \nATOM   1952  CD1 ILE A 253       5.151  59.488  33.373  1.00  0.00          C   \nATOM   1953  N   LYS A 254       7.960  58.612  28.567  1.00  0.00          N   \nATOM   1954  CA  LYS A 254       8.187  57.910  27.319  1.00  0.00          C   \nATOM   1955  C   LYS A 254       9.695  57.772  27.108  1.00  0.00          C   \nATOM   1956  O   LYS A 254      10.200  56.702  26.749  1.00  0.00          O   \nATOM   1957  CB  LYS A 254       7.587  58.712  26.162  1.00  0.00          C   \nATOM   1958  CG  LYS A 254       8.011  58.240  24.775  1.00  0.00          C   \nATOM   1959  CD  LYS A 254       7.259  59.018  23.705  1.00  0.00          C   \nATOM   1960  CE  LYS A 254       7.584  58.523  22.302  1.00  0.00          C   \nATOM   1961  NZ  LYS A 254       8.993  58.806  21.893  1.00  0.00          N   \nATOM   1962  N   SER A 255      10.409  58.870  27.335  1.00  0.00          N   \nATOM   1963  CA  SER A 255      11.855  58.895  27.145  1.00  0.00          C   \nATOM   1964  C   SER A 255      12.581  57.963  28.118  1.00  0.00          C   \nATOM   1965  O   SER A 255      13.455  57.201  27.706  1.00  0.00          O   \nATOM   1966  CB  SER A 255      12.374  60.334  27.285  1.00  0.00          C   \nATOM   1967  OG  SER A 255      13.747  60.420  26.952  1.00  0.00          O   \nATOM   1968  N   LEU A 256      12.208  58.006  29.396  1.00  0.00          N   \nATOM   1969  CA  LEU A 256      12.838  57.141  30.381  1.00  0.00          C   \nATOM   1970  C   LEU A 256      12.557  55.682  30.030  1.00  0.00          C   \nATOM   1971  O   LEU A 256      13.401  54.818  30.227  1.00  0.00          O   \nATOM   1972  CB  LEU A 256      12.325  57.459  31.790  1.00  0.00          C   \nATOM   1973  CG  LEU A 256      12.887  58.743  32.420  1.00  0.00          C   \nATOM   1974  CD1 LEU A 256      12.031  59.182  33.589  1.00  0.00          C   \nATOM   1975  CD2 LEU A 256      14.326  58.500  32.858  1.00  0.00          C   \nATOM   1976  N   THR A 257      11.373  55.403  29.501  1.00  0.00          N   \nATOM   1977  CA  THR A 257      11.056  54.023  29.134  1.00  0.00          C   \nATOM   1978  C   THR A 257      11.935  53.513  27.979  1.00  0.00          C   \nATOM   1979  O   THR A 257      12.536  52.447  28.081  1.00  0.00          O   \nATOM   1980  CB  THR A 257       9.568  53.864  28.737  1.00  0.00          C   \nATOM   1981  OG1 THR A 257       8.744  54.127  29.880  1.00  0.00          O   \nATOM   1982  CG2 THR A 257       9.288  52.424  28.246  1.00  0.00          C   \nATOM   1983  N   GLU A 258      12.012  54.273  26.890  1.00  0.00          N   \nATOM   1984  CA  GLU A 258      12.811  53.858  25.733  1.00  0.00          C   \nATOM   1985  C   GLU A 258      14.302  53.818  26.016  1.00  0.00          C   \nATOM   1986  O   GLU A 258      15.007  52.913  25.571  1.00  0.00          O   \nATOM   1987  CB  GLU A 258      12.624  54.817  24.549  1.00  0.00          C   \nATOM   1988  CG  GLU A 258      11.243  54.884  23.916  1.00  0.00          C   \nATOM   1989  CD  GLU A 258      10.809  53.576  23.263  1.00  0.00          C   \nATOM   1990  OE1 GLU A 258      11.657  52.820  22.735  1.00  0.00          O   \nATOM   1991  OE2 GLU A 258       9.592  53.319  23.267  1.00  0.00          O   \nATOM   1992  N   ARG A 259      14.775  54.812  26.753  1.00  0.00          N   \nATOM   1993  CA  ARG A 259      16.194  54.973  27.038  1.00  0.00          C   \nATOM   1994  C   ARG A 259      16.749  54.259  28.256  1.00  0.00          C   \nATOM   1995  O   ARG A 259      17.928  53.924  28.295  1.00  0.00          O   \nATOM   1996  CB  ARG A 259      16.507  56.472  27.159  1.00  0.00          C   \nATOM   1997  CG  ARG A 259      16.092  57.270  25.923  1.00  0.00          C   \nATOM   1998  CD  ARG A 259      16.373  58.741  26.097  1.00  0.00          C   \nATOM   1999  NE  ARG A 259      17.801  59.003  26.250  1.00  0.00          N   \nATOM   2000  CZ  ARG A 259      18.307  60.211  26.481  1.00  0.00          C   \nATOM   2001  NH1 ARG A 259      17.499  61.259  26.583  1.00  0.00          N   \nATOM   2002  NH2 ARG A 259      19.616  60.373  26.612  1.00  0.00          N   \nATOM   2003  N   LEU A 260      15.914  54.032  29.258  1.00  0.00          N   \nATOM   2004  CA  LEU A 260      16.407  53.395  30.470  1.00  0.00          C   \nATOM   2005  C   LEU A 260      15.678  52.128  30.926  1.00  0.00          C   \nATOM   2006  O   LEU A 260      16.298  51.090  31.133  1.00  0.00          O   \nATOM   2007  CB  LEU A 260      16.384  54.413  31.605  1.00  0.00          C   \nATOM   2008  CG  LEU A 260      16.689  53.907  33.016  1.00  0.00          C   \nATOM   2009  CD1 LEU A 260      18.139  53.479  33.093  1.00  0.00          C   \nATOM   2010  CD2 LEU A 260      16.401  55.004  34.038  1.00  0.00          C   \nATOM   2011  N   TYR A 261      14.366  52.223  31.099  1.00  0.00          N   \nATOM   2012  CA  TYR A 261      13.601  51.083  31.586  1.00  0.00          C   \nATOM   2013  C   TYR A 261      13.642  49.815  30.737  1.00  0.00          C   \nATOM   2014  O   TYR A 261      13.775  48.723  31.275  1.00  0.00          O   \nATOM   2015  CB  TYR A 261      12.159  51.515  31.858  1.00  0.00          C   \nATOM   2016  CG  TYR A 261      12.086  52.672  32.855  1.00  0.00          C   \nATOM   2017  CD1 TYR A 261      13.089  52.839  33.828  1.00  0.00          C   \nATOM   2018  CD2 TYR A 261      11.006  53.560  32.863  1.00  0.00          C   \nATOM   2019  CE1 TYR A 261      13.024  53.850  34.778  1.00  0.00          C   \nATOM   2020  CE2 TYR A 261      10.926  54.584  33.820  1.00  0.00          C   \nATOM   2021  CZ  TYR A 261      11.941  54.719  34.771  1.00  0.00          C   \nATOM   2022  OH  TYR A 261      11.889  55.724  35.704  1.00  0.00          O   \nATOM   2023  N   ILE A 262      13.554  49.957  29.421  1.00  0.00          N   \nATOM   2024  CA  ILE A 262      13.573  48.808  28.527  1.00  0.00          C   \nATOM   2025  C   ILE A 262      14.936  48.115  28.465  1.00  0.00          C   \nATOM   2026  O   ILE A 262      15.030  46.906  28.246  1.00  0.00          O   \nATOM   2027  CB  ILE A 262      13.142  49.244  27.104  1.00  0.00          C   \nATOM   2028  CG1 ILE A 262      11.657  49.632  27.113  1.00  0.00          C   \nATOM   2029  CG2 ILE A 262      13.420  48.138  26.097  1.00  0.00          C   \nATOM   2030  CD1 ILE A 262      11.122  49.965  25.733  1.00  0.00          C   \nATOM   2031  N   GLY A 263      16.001  48.874  28.659  1.00  0.00          N   \nATOM   2032  CA  GLY A 263      17.310  48.262  28.602  1.00  0.00          C   \nATOM   2033  C   GLY A 263      18.393  49.298  28.452  1.00  0.00          C   \nATOM   2034  O   GLY A 263      18.108  50.490  28.348  1.00  0.00          O   \nATOM   2035  N   GLY A 264      19.643  48.856  28.455  1.00  0.00          N   \nATOM   2036  CA  GLY A 264      20.721  49.807  28.306  1.00  0.00          C   \nATOM   2037  C   GLY A 264      22.078  49.212  28.590  1.00  0.00          C   \nATOM   2038  O   GLY A 264      22.170  48.100  29.108  1.00  0.00          O   \nATOM   2039  N   PRO A 265      23.154  49.934  28.251  1.00  0.00          N   \nATOM   2040  CA  PRO A 265      24.513  49.443  28.487  1.00  0.00          C   \nATOM   2041  C   PRO A 265      24.799  49.396  29.993  1.00  0.00          C   \nATOM   2042  O   PRO A 265      24.237  50.172  30.773  1.00  0.00          O   \nATOM   2043  CB  PRO A 265      25.379  50.444  27.712  1.00  0.00          C   \nATOM   2044  CG  PRO A 265      24.594  51.737  27.833  1.00  0.00          C   \nATOM   2045  CD  PRO A 265      23.160  51.283  27.645  1.00  0.00          C   \nATOM   2046  N   LEU A 266      25.653  48.459  30.385  1.00  0.00          N   \nATOM   2047  CA  LEU A 266      26.031  48.242  31.776  1.00  0.00          C   \nATOM   2048  C   LEU A 266      27.509  48.526  31.953  1.00  0.00          C   \nATOM   2049  O   LEU A 266      28.347  47.897  31.301  1.00  0.00          O   \nATOM   2050  CB  LEU A 266      25.789  46.780  32.154  1.00  0.00          C   \nATOM   2051  CG  LEU A 266      24.367  46.231  32.021  1.00  0.00          C   \nATOM   2052  CD1 LEU A 266      24.390  44.714  32.005  1.00  0.00          C   \nATOM   2053  CD2 LEU A 266      23.525  46.757  33.174  1.00  0.00          C   \nATOM   2054  N   THR A 267      27.825  49.443  32.862  1.00  0.00          N   \nATOM   2055  CA  THR A 267      29.205  49.815  33.141  1.00  0.00          C   \nATOM   2056  C   THR A 267      29.581  49.420  34.565  1.00  0.00          C   \nATOM   2057  O   THR A 267      28.780  49.597  35.491  1.00  0.00          O   \nATOM   2058  CB  THR A 267      29.373  51.327  32.986  1.00  0.00          C   \nATOM   2059  OG1 THR A 267      28.826  51.716  31.725  1.00  0.00          O   \nATOM   2060  CG2 THR A 267      30.848  51.727  33.036  1.00  0.00          C   \nATOM   2061  N   ASN A 268      30.784  48.871  34.747  1.00  0.00          N   \nATOM   2062  CA  ASN A 268      31.215  48.483  36.088  1.00  0.00          C   \nATOM   2063  C   ASN A 268      31.847  49.679  36.797  1.00  0.00          C   \nATOM   2064  O   ASN A 268      32.030  50.746  36.192  1.00  0.00          O   \nATOM   2065  CB  ASN A 268      32.193  47.287  36.047  1.00  0.00          C   \nATOM   2066  CG  ASN A 268      33.534  47.622  35.396  1.00  0.00          C   \nATOM   2067  OD1 ASN A 268      33.913  48.790  35.254  1.00  0.00          O   \nATOM   2068  ND2 ASN A 268      34.268  46.583  35.014  1.00  0.00          N   \nATOM   2069  N   SER A 269      32.192  49.503  38.070  1.00  0.00          N   \nATOM   2070  CA  SER A 269      32.765  50.581  38.862  1.00  0.00          C   \nATOM   2071  C   SER A 269      34.082  51.134  38.315  1.00  0.00          C   \nATOM   2072  O   SER A 269      34.514  52.213  38.711  1.00  0.00          O   \nATOM   2073  CB  SER A 269      32.981  50.110  40.302  1.00  0.00          C   \nATOM   2074  OG  SER A 269      34.017  49.147  40.353  1.00  0.00          O   \nATOM   2075  N   LYS A 270      34.726  50.393  37.420  1.00  0.00          N   \nATOM   2076  CA  LYS A 270      35.987  50.844  36.850  1.00  0.00          C   \nATOM   2077  C   LYS A 270      35.792  51.619  35.554  1.00  0.00          C   \nATOM   2078  O   LYS A 270      36.737  52.202  35.037  1.00  0.00          O   \nATOM   2079  CB  LYS A 270      36.917  49.656  36.599  1.00  0.00          C   \nATOM   2080  CG  LYS A 270      37.322  48.903  37.869  1.00  0.00          C   \nATOM   2081  CD  LYS A 270      38.590  48.075  37.672  1.00  0.00          C   \nATOM   2082  CE  LYS A 270      38.496  47.173  36.452  1.00  0.00          C   \nATOM   2083  NZ  LYS A 270      39.725  46.342  36.322  1.00  0.00          N   \nATOM   2084  N   GLY A 271      34.570  51.628  35.031  1.00  0.00          N   \nATOM   2085  CA  GLY A 271      34.317  52.350  33.798  1.00  0.00          C   \nATOM   2086  C   GLY A 271      34.340  51.490  32.545  1.00  0.00          C   \nATOM   2087  O   GLY A 271      34.297  52.011  31.438  1.00  0.00          O   \nATOM   2088  N   GLN A 272      34.418  50.171  32.706  1.00  0.00          N   \nATOM   2089  CA  GLN A 272      34.428  49.270  31.552  1.00  0.00          C   \nATOM   2090  C   GLN A 272      33.003  48.868  31.175  1.00  0.00          C   \nATOM   2091  O   GLN A 272      32.116  48.795  32.034  1.00  0.00          O   \nATOM   2092  CB  GLN A 272      35.220  47.995  31.868  1.00  0.00          C   \nATOM   2093  CG  GLN A 272      36.674  48.213  32.218  1.00  0.00          C   \nATOM   2094  CD  GLN A 272      37.351  46.934  32.662  1.00  0.00          C   \nATOM   2095  OE1 GLN A 272      36.943  46.310  33.647  1.00  0.00          O   \nATOM   2096  NE2 GLN A 272      38.395  46.535  31.940  1.00  0.00          N   \nATOM   2097  N   ASN A 273      32.786  48.603  29.890  1.00  0.00          N   \nATOM   2098  CA  ASN A 273      31.474  48.171  29.432  1.00  0.00          C   \nATOM   2099  C   ASN A 273      31.372  46.690  29.758  1.00  0.00          C   \nATOM   2100  O   ASN A 273      32.155  45.890  29.249  1.00  0.00          O   \nATOM   2101  CB  ASN A 273      31.325  48.346  27.922  1.00  0.00          C   \nATOM   2102  CG  ASN A 273      30.018  47.764  27.411  1.00  0.00          C   \nATOM   2103  OD1 ASN A 273      29.972  47.126  26.361  1.00  0.00          O   \nATOM   2104  ND2 ASN A 273      28.953  47.975  28.162  1.00  0.00          N   \nATOM   2105  N   CYS A 274      30.410  46.332  30.608  1.00  0.00          N   \nATOM   2106  CA  CYS A 274      30.205  44.947  31.016  1.00  0.00          C   \nATOM   2107  C   CYS A 274      29.265  44.202  30.102  1.00  0.00          C   \nATOM   2108  O   CYS A 274      29.295  42.976  30.040  1.00  0.00          O   \nATOM   2109  CB  CYS A 274      29.623  44.892  32.426  1.00  0.00          C   \nATOM   2110  SG  CYS A 274      30.833  45.132  33.685  1.00  0.00          S   \nATOM   2111  N   GLY A 275      28.407  44.947  29.415  1.00  0.00          N   \nATOM   2112  CA  GLY A 275      27.444  44.325  28.534  1.00  0.00          C   \nATOM   2113  C   GLY A 275      26.177  45.140  28.349  1.00  0.00          C   \nATOM   2114  O   GLY A 275      26.193  46.370  28.468  1.00  0.00          O   \nATOM   2115  N   TYR A 276      25.073  44.441  28.101  1.00  0.00          N   \nATOM   2116  CA  TYR A 276      23.796  45.080  27.833  1.00  0.00          C   \nATOM   2117  C   TYR A 276      22.635  44.388  28.525  1.00  0.00          C   \nATOM   2118  O   TYR A 276      22.579  43.153  28.586  1.00  0.00          O   \nATOM   2119  CB  TYR A 276      23.524  45.073  26.318  1.00  0.00          C   \nATOM   2120  CG  TYR A 276      22.616  46.196  25.871  1.00  0.00          C   \nATOM   2121  CD1 TYR A 276      23.129  47.475  25.639  1.00  0.00          C   \nATOM   2122  CD2 TYR A 276      21.242  45.999  25.718  1.00  0.00          C   \nATOM   2123  CE1 TYR A 276      22.298  48.532  25.266  1.00  0.00          C   \nATOM   2124  CE2 TYR A 276      20.395  47.061  25.341  1.00  0.00          C   \nATOM   2125  CZ  TYR A 276      20.942  48.321  25.120  1.00  0.00          C   \nATOM   2126  OH  TYR A 276      20.138  49.374  24.766  1.00  0.00          O   \nATOM   2127  N   ARG A 277      21.701  45.192  29.029  1.00  0.00          N   \nATOM   2128  CA  ARG A 277      20.505  44.694  29.723  1.00  0.00          C   \nATOM   2129  C   ARG A 277      19.265  44.867  28.855  1.00  0.00          C   \nATOM   2130  O   ARG A 277      19.100  45.904  28.205  1.00  0.00          O   \nATOM   2131  CB  ARG A 277      20.305  45.469  31.040  1.00  0.00          C   \nATOM   2132  CG  ARG A 277      18.942  45.300  31.734  1.00  0.00          C   \nATOM   2133  CD  ARG A 277      18.820  46.210  32.976  1.00  0.00          C   \nATOM   2134  NE  ARG A 277      19.036  47.629  32.652  1.00  0.00          N   \nATOM   2135  CZ  ARG A 277      18.101  48.451  32.183  1.00  0.00          C   \nATOM   2136  NH1 ARG A 277      16.857  48.027  31.989  1.00  0.00          N   \nATOM   2137  NH2 ARG A 277      18.422  49.693  31.858  1.00  0.00          N   \nATOM   2138  N   ARG A 278      18.407  43.846  28.824  1.00  0.00          N   \nATOM   2139  CA  ARG A 278      17.154  43.929  28.075  1.00  0.00          C   \nATOM   2140  C   ARG A 278      16.025  43.448  28.982  1.00  0.00          C   \nATOM   2141  O   ARG A 278      15.003  42.941  28.501  1.00  0.00          O   \nATOM   2142  CB  ARG A 278      17.189  43.060  26.808  1.00  0.00          C   \nATOM   2143  CG  ARG A 278      18.302  43.433  25.836  1.00  0.00          C   \nATOM   2144  CD  ARG A 278      18.177  42.670  24.535  1.00  0.00          C   \nATOM   2145  NE  ARG A 278      19.444  42.678  23.810  1.00  0.00          N   \nATOM   2146  CZ  ARG A 278      20.551  42.053  24.210  1.00  0.00          C   \nATOM   2147  NH1 ARG A 278      21.651  42.141  23.473  1.00  0.00          N   \nATOM   2148  NH2 ARG A 278      20.563  41.329  25.328  1.00  0.00          N   \nATOM   2149  N   CYS A 279      16.214  43.601  30.289  1.00  0.00          N   \nATOM   2150  CA  CYS A 279      15.206  43.180  31.264  1.00  0.00          C   \nATOM   2151  C   CYS A 279      15.062  44.279  32.298  1.00  0.00          C   \nATOM   2152  O   CYS A 279      15.603  45.366  32.105  1.00  0.00          O   \nATOM   2153  CB  CYS A 279      15.623  41.852  31.934  1.00  0.00          C   \nATOM   2154  SG  CYS A 279      17.173  41.896  32.874  1.00  0.00          S   \nATOM   2155  N   ARG A 280      14.325  44.025  33.383  1.00  0.00          N   \nATOM   2156  CA  ARG A 280      14.145  45.032  34.444  1.00  0.00          C   \nATOM   2157  C   ARG A 280      15.455  45.463  35.120  1.00  0.00          C   \nATOM   2158  O   ARG A 280      16.286  44.619  35.472  1.00  0.00          O   \nATOM   2159  CB  ARG A 280      13.207  44.501  35.551  1.00  0.00          C   \nATOM   2160  CG  ARG A 280      13.090  45.423  36.791  1.00  0.00          C   \nATOM   2161  CD  ARG A 280      12.097  46.564  36.561  1.00  0.00          C   \nATOM   2162  NE  ARG A 280      10.755  45.988  36.520  1.00  0.00          N   \nATOM   2163  CZ  ARG A 280       9.771  46.381  35.725  1.00  0.00          C   \nATOM   2164  NH1 ARG A 280       9.933  47.396  34.873  1.00  0.00          N   \nATOM   2165  NH2 ARG A 280       8.637  45.702  35.741  1.00  0.00          N   \nATOM   2166  N   ALA A 281      15.641  46.777  35.285  1.00  0.00          N   \nATOM   2167  CA  ALA A 281      16.803  47.305  35.997  1.00  0.00          C   \nATOM   2168  C   ALA A 281      16.369  47.260  37.462  1.00  0.00          C   \nATOM   2169  O   ALA A 281      15.276  47.721  37.813  1.00  0.00          O   \nATOM   2170  CB  ALA A 281      17.097  48.756  35.586  1.00  0.00          C   \nATOM   2171  N   SER A 282      17.210  46.711  38.320  1.00  0.00          N   \nATOM   2172  CA  SER A 282      16.854  46.591  39.728  1.00  0.00          C   \nATOM   2173  C   SER A 282      16.936  47.877  40.526  1.00  0.00          C   \nATOM   2174  O   SER A 282      16.354  47.976  41.602  1.00  0.00          O   \nATOM   2175  CB  SER A 282      17.731  45.539  40.402  1.00  0.00          C   \nATOM   2176  OG  SER A 282      19.098  45.914  40.357  1.00  0.00          O   \nATOM   2177  N   GLY A 283      17.643  48.863  39.994  1.00  0.00          N   \nATOM   2178  CA  GLY A 283      17.807  50.103  40.723  1.00  0.00          C   \nATOM   2179  C   GLY A 283      17.234  51.364  40.120  1.00  0.00          C   \nATOM   2180  O   GLY A 283      17.930  52.378  40.059  1.00  0.00          O   \nATOM   2181  N   VAL A 284      15.990  51.306  39.656  1.00  0.00          N   \nATOM   2182  CA  VAL A 284      15.332  52.489  39.109  1.00  0.00          C   \nATOM   2183  C   VAL A 284      14.081  52.767  39.951  1.00  0.00          C   \nATOM   2184  O   VAL A 284      13.653  51.923  40.741  1.00  0.00          O   \nATOM   2185  CB  VAL A 284      14.967  52.335  37.587  1.00  0.00          C   \nATOM   2186  CG1 VAL A 284      16.248  52.399  36.759  1.00  0.00          C   \nATOM   2187  CG2 VAL A 284      14.196  51.013  37.320  1.00  0.00          C   \nATOM   2188  N   LEU A 285      13.494  53.943  39.791  1.00  0.00          N   \nATOM   2189  CA  LEU A 285      12.330  54.308  40.594  1.00  0.00          C   \nATOM   2190  C   LEU A 285      11.112  53.403  40.400  1.00  0.00          C   \nATOM   2191  O   LEU A 285      10.378  53.124  41.351  1.00  0.00          O   \nATOM   2192  CB  LEU A 285      11.927  55.757  40.319  1.00  0.00          C   \nATOM   2193  CG  LEU A 285      10.714  56.232  41.131  1.00  0.00          C   \nATOM   2194  CD1 LEU A 285      10.928  56.005  42.620  1.00  0.00          C   \nATOM   2195  CD2 LEU A 285      10.490  57.706  40.844  1.00  0.00          C   \nATOM   2196  N   THR A 286      10.909  52.961  39.163  1.00  0.00          N   \nATOM   2197  CA  THR A 286       9.784  52.103  38.797  1.00  0.00          C   \nATOM   2198  C   THR A 286      10.013  50.605  39.010  1.00  0.00          C   \nATOM   2199  O   THR A 286       9.185  49.800  38.584  1.00  0.00          O   \nATOM   2200  CB  THR A 286       9.435  52.283  37.312  1.00  0.00          C   \nATOM   2201  OG1 THR A 286      10.640  52.140  36.548  1.00  0.00          O   \nATOM   2202  CG2 THR A 286       8.791  53.655  37.047  1.00  0.00          C   \nATOM   2203  N   THR A 287      11.119  50.208  39.634  1.00  0.00          N   \nATOM   2204  CA  THR A 287      11.330  48.769  39.832  1.00  0.00          C   \nATOM   2205  C   THR A 287      10.209  48.103  40.647  1.00  0.00          C   \nATOM   2206  O   THR A 287       9.683  47.070  40.258  1.00  0.00          O   \nATOM   2207  CB  THR A 287      12.667  48.477  40.525  1.00  0.00          C   \nATOM   2208  OG1 THR A 287      13.734  49.028  39.751  1.00  0.00          O   \nATOM   2209  CG2 THR A 287      12.885  46.948  40.641  1.00  0.00          C   \nATOM   2210  N   SER A 288       9.845  48.687  41.781  1.00  0.00          N   \nATOM   2211  CA  SER A 288       8.794  48.113  42.610  1.00  0.00          C   \nATOM   2212  C   SER A 288       7.401  48.205  41.964  1.00  0.00          C   \nATOM   2213  O   SER A 288       6.672  47.205  41.875  1.00  0.00          O   \nATOM   2214  CB  SER A 288       8.796  48.796  43.976  1.00  0.00          C   \nATOM   2215  OG  SER A 288       7.762  48.258  44.764  1.00  0.00          O   \nATOM   2216  N   CYS A 289       7.033  49.398  41.505  1.00  0.00          N   \nATOM   2217  CA  CYS A 289       5.744  49.589  40.860  1.00  0.00          C   \nATOM   2218  C   CYS A 289       5.645  48.738  39.594  1.00  0.00          C   \nATOM   2219  O   CYS A 289       4.652  48.046  39.386  1.00  0.00          O   \nATOM   2220  CB  CYS A 289       5.540  51.070  40.514  1.00  0.00          C   \nATOM   2221  SG  CYS A 289       3.929  51.407  39.761  1.00  0.00          S   \nATOM   2222  N   GLY A 290       6.668  48.782  38.743  1.00  0.00          N   \nATOM   2223  CA  GLY A 290       6.636  47.982  37.531  1.00  0.00          C   \nATOM   2224  C   GLY A 290       6.514  46.484  37.834  1.00  0.00          C   \nATOM   2225  O   GLY A 290       5.695  45.795  37.237  1.00  0.00          O   \nATOM   2226  N   ASN A 291       7.327  45.970  38.749  1.00  0.00          N   \nATOM   2227  CA  ASN A 291       7.234  44.555  39.092  1.00  0.00          C   \nATOM   2228  C   ASN A 291       5.862  44.178  39.670  1.00  0.00          C   \nATOM   2229  O   ASN A 291       5.303  43.121  39.349  1.00  0.00          O   \nATOM   2230  CB  ASN A 291       8.331  44.176  40.082  1.00  0.00          C   \nATOM   2231  CG  ASN A 291       9.671  44.013  39.419  1.00  0.00          C   \nATOM   2232  OD1 ASN A 291       9.771  44.008  38.187  1.00  0.00          O   \nATOM   2233  ND2 ASN A 291      10.722  43.867  40.230  1.00  0.00          N   \nATOM   2234  N   THR A 292       5.318  45.035  40.524  1.00  0.00          N   \nATOM   2235  CA  THR A 292       4.018  44.764  41.120  1.00  0.00          C   \nATOM   2236  C   THR A 292       2.941  44.723  40.029  1.00  0.00          C   \nATOM   2237  O   THR A 292       2.139  43.783  39.976  1.00  0.00          O   \nATOM   2238  CB  THR A 292       3.633  45.835  42.173  1.00  0.00          C   \nATOM   2239  OG1 THR A 292       4.635  45.900  43.203  1.00  0.00          O   \nATOM   2240  CG2 THR A 292       2.300  45.483  42.814  1.00  0.00          C   \nATOM   2241  N   LEU A 293       2.903  45.731  39.166  1.00  0.00          N   \nATOM   2242  CA  LEU A 293       1.901  45.746  38.093  1.00  0.00          C   \nATOM   2243  C   LEU A 293       2.020  44.510  37.193  1.00  0.00          C   \nATOM   2244  O   LEU A 293       1.030  43.837  36.892  1.00  0.00          O   \nATOM   2245  CB  LEU A 293       2.049  47.013  37.237  1.00  0.00          C   \nATOM   2246  CG  LEU A 293       1.630  48.338  37.886  1.00  0.00          C   \nATOM   2247  CD1 LEU A 293       2.019  49.527  36.976  1.00  0.00          C   \nATOM   2248  CD2 LEU A 293       0.121  48.313  38.131  1.00  0.00          C   \nATOM   2249  N   THR A 294       3.242  44.218  36.769  1.00  0.00          N   \nATOM   2250  CA  THR A 294       3.502  43.086  35.896  1.00  0.00          C   \nATOM   2251  C   THR A 294       3.191  41.743  36.567  1.00  0.00          C   \nATOM   2252  O   THR A 294       2.595  40.858  35.960  1.00  0.00          O   \nATOM   2253  CB  THR A 294       4.962  43.133  35.420  1.00  0.00          C   \nATOM   2254  OG1 THR A 294       5.153  44.333  34.651  1.00  0.00          O   \nATOM   2255  CG2 THR A 294       5.316  41.889  34.564  1.00  0.00          C   \nATOM   2256  N   CYS A 295       3.570  41.594  37.825  1.00  0.00          N   \nATOM   2257  CA  CYS A 295       3.305  40.349  38.529  1.00  0.00          C   \nATOM   2258  C   CYS A 295       1.799  40.167  38.687  1.00  0.00          C   \nATOM   2259  O   CYS A 295       1.266  39.062  38.512  1.00  0.00          O   \nATOM   2260  CB  CYS A 295       3.972  40.367  39.904  1.00  0.00          C   \nATOM   2261  SG  CYS A 295       3.818  38.802  40.800  1.00  0.00          S   \nATOM   2262  N   TYR A 296       1.123  41.262  39.015  1.00  0.00          N   \nATOM   2263  CA  TYR A 296      -0.324  41.259  39.197  1.00  0.00          C   \nATOM   2264  C   TYR A 296      -1.043  40.971  37.874  1.00  0.00          C   \nATOM   2265  O   TYR A 296      -2.015  40.208  37.835  1.00  0.00          O   \nATOM   2266  CB  TYR A 296      -0.777  42.612  39.749  1.00  0.00          C   \nATOM   2267  CG  TYR A 296      -2.276  42.772  39.831  1.00  0.00          C   \nATOM   2268  CD1 TYR A 296      -3.016  43.224  38.737  1.00  0.00          C   \nATOM   2269  CD2 TYR A 296      -2.953  42.477  41.006  1.00  0.00          C   \nATOM   2270  CE1 TYR A 296      -4.398  43.382  38.821  1.00  0.00          C   \nATOM   2271  CE2 TYR A 296      -4.334  42.629  41.102  1.00  0.00          C   \nATOM   2272  CZ  TYR A 296      -5.053  43.081  40.012  1.00  0.00          C   \nATOM   2273  OH  TYR A 296      -6.418  43.229  40.132  1.00  0.00          O   \nATOM   2274  N   LEU A 297      -0.568  41.586  36.795  1.00  0.00          N   \nATOM   2275  CA  LEU A 297      -1.168  41.371  35.489  1.00  0.00          C   \nATOM   2276  C   LEU A 297      -1.057  39.892  35.087  1.00  0.00          C   \nATOM   2277  O   LEU A 297      -2.057  39.248  34.752  1.00  0.00          O   \nATOM   2278  CB  LEU A 297      -0.481  42.255  34.443  1.00  0.00          C   \nATOM   2279  CG  LEU A 297      -0.724  41.945  32.954  1.00  0.00          C   \nATOM   2280  CD1 LEU A 297      -2.217  41.944  32.641  1.00  0.00          C   \nATOM   2281  CD2 LEU A 297      -0.012  42.976  32.099  1.00  0.00          C   \nATOM   2282  N   LYS A 298       0.155  39.353  35.119  1.00  0.00          N   \nATOM   2283  CA  LYS A 298       0.359  37.950  34.739  1.00  0.00          C   \nATOM   2284  C   LYS A 298      -0.398  36.966  35.628  1.00  0.00          C   \nATOM   2285  O   LYS A 298      -1.029  36.033  35.134  1.00  0.00          O   \nATOM   2286  CB  LYS A 298       1.855  37.607  34.737  1.00  0.00          C   \nATOM   2287  CG  LYS A 298       2.613  38.360  33.652  1.00  0.00          C   \nATOM   2288  CD  LYS A 298       4.126  38.119  33.683  1.00  0.00          C   \nATOM   2289  CE  LYS A 298       4.813  38.926  32.561  1.00  0.00          C   \nATOM   2290  NZ  LYS A 298       6.279  38.693  32.496  1.00  0.00          N   \nATOM   2291  N   ALA A 299      -0.333  37.177  36.939  1.00  0.00          N   \nATOM   2292  CA  ALA A 299      -1.021  36.298  37.883  1.00  0.00          C   \nATOM   2293  C   ALA A 299      -2.534  36.367  37.713  1.00  0.00          C   \nATOM   2294  O   ALA A 299      -3.202  35.334  37.724  1.00  0.00          O   \nATOM   2295  CB  ALA A 299      -0.633  36.651  39.318  1.00  0.00          C   \nATOM   2296  N   SER A 300      -3.081  37.575  37.555  1.00  0.00          N   \nATOM   2297  CA  SER A 300      -4.525  37.725  37.369  1.00  0.00          C   \nATOM   2298  C   SER A 300      -4.970  36.957  36.130  1.00  0.00          C   \nATOM   2299  O   SER A 300      -5.954  36.216  36.169  1.00  0.00          O   \nATOM   2300  CB  SER A 300      -4.917  39.200  37.216  1.00  0.00          C   \nATOM   2301  OG  SER A 300      -4.704  39.911  38.421  1.00  0.00          O   \nATOM   2302  N   ALA A 301      -4.253  37.142  35.027  1.00  0.00          N   \nATOM   2303  CA  ALA A 301      -4.574  36.436  33.794  1.00  0.00          C   \nATOM   2304  C   ALA A 301      -4.377  34.923  33.986  1.00  0.00          C   \nATOM   2305  O   ALA A 301      -5.193  34.120  33.521  1.00  0.00          O   \nATOM   2306  CB  ALA A 301      -3.689  36.947  32.654  1.00  0.00          C   \nATOM   2307  N   ALA A 302      -3.305  34.529  34.672  1.00  0.00          N   \nATOM   2308  CA  ALA A 302      -3.054  33.104  34.899  1.00  0.00          C   \nATOM   2309  C   ALA A 302      -4.171  32.447  35.730  1.00  0.00          C   \nATOM   2310  O   ALA A 302      -4.499  31.270  35.527  1.00  0.00          O   \nATOM   2311  CB  ALA A 302      -1.705  32.912  35.597  1.00  0.00          C   \nATOM   2312  N   CYS A 303      -4.736  33.199  36.676  1.00  0.00          N   \nATOM   2313  CA  CYS A 303      -5.811  32.678  37.522  1.00  0.00          C   \nATOM   2314  C   CYS A 303      -7.019  32.317  36.655  1.00  0.00          C   \nATOM   2315  O   CYS A 303      -7.678  31.299  36.884  1.00  0.00          O   \nATOM   2316  CB  CYS A 303      -6.220  33.712  38.577  1.00  0.00          C   \nATOM   2317  SG  CYS A 303      -7.962  33.578  39.122  1.00  0.00          S   \nATOM   2318  N   ARG A 304      -7.302  33.162  35.665  1.00  0.00          N   \nATOM   2319  CA  ARG A 304      -8.411  32.932  34.741  1.00  0.00          C   \nATOM   2320  C   ARG A 304      -8.088  31.733  33.862  1.00  0.00          C   \nATOM   2321  O   ARG A 304      -8.917  30.848  33.671  1.00  0.00          O   \nATOM   2322  CB  ARG A 304      -8.637  34.166  33.867  1.00  0.00          C   \nATOM   2323  CG  ARG A 304      -8.862  35.421  34.679  1.00  0.00          C   \nATOM   2324  CD  ARG A 304      -9.287  36.579  33.818  1.00  0.00          C   \nATOM   2325  NE  ARG A 304      -9.653  37.735  34.640  1.00  0.00          N   \nATOM   2326  CZ  ARG A 304     -10.221  38.838  34.161  1.00  0.00          C   \nATOM   2327  NH1 ARG A 304     -10.490  38.939  32.870  1.00  0.00          N   \nATOM   2328  NH2 ARG A 304     -10.522  39.841  34.971  1.00  0.00          N   \nATOM   2329  N   ALA A 305      -6.876  31.706  33.321  1.00  0.00          N   \nATOM   2330  CA  ALA A 305      -6.464  30.588  32.488  1.00  0.00          C   \nATOM   2331  C   ALA A 305      -6.591  29.259  33.251  1.00  0.00          C   \nATOM   2332  O   ALA A 305      -7.065  28.265  32.696  1.00  0.00          O   \nATOM   2333  CB  ALA A 305      -5.024  30.786  32.025  1.00  0.00          C   \nATOM   2334  N   ALA A 306      -6.177  29.240  34.520  1.00  0.00          N   \nATOM   2335  CA  ALA A 306      -6.229  28.016  35.327  1.00  0.00          C   \nATOM   2336  C   ALA A 306      -7.596  27.717  35.938  1.00  0.00          C   \nATOM   2337  O   ALA A 306      -7.778  26.701  36.611  1.00  0.00          O   \nATOM   2338  CB  ALA A 306      -5.163  28.064  36.434  1.00  0.00          C   \nATOM   2339  N   LYS A 307      -8.552  28.607  35.720  1.00  0.00          N   \nATOM   2340  CA  LYS A 307      -9.897  28.411  36.236  1.00  0.00          C   \nATOM   2341  C   LYS A 307      -9.954  28.350  37.759  1.00  0.00          C   \nATOM   2342  O   LYS A 307     -10.750  27.601  38.336  1.00  0.00          O   \nATOM   2343  CB  LYS A 307     -10.494  27.134  35.647  1.00  0.00          C   \nATOM   2344  CG  LYS A 307     -10.636  27.168  34.140  1.00  0.00          C   \nATOM   2345  CD  LYS A 307     -10.672  25.758  33.576  1.00  0.00          C   \nATOM   2346  CE  LYS A 307      -9.334  25.040  33.804  1.00  0.00          C   \nATOM   2347  NZ  LYS A 307      -9.405  23.585  33.476  1.00  0.00          N   \nATOM   2348  N   LEU A 308      -9.103  29.135  38.409  1.00  0.00          N   \nATOM   2349  CA  LEU A 308      -9.098  29.186  39.859  1.00  0.00          C   \nATOM   2350  C   LEU A 308     -10.385  29.860  40.305  1.00  0.00          C   \nATOM   2351  O   LEU A 308     -10.784  30.883  39.749  1.00  0.00          O   \nATOM   2352  CB  LEU A 308      -7.887  29.983  40.362  1.00  0.00          C   \nATOM   2353  CG  LEU A 308      -6.626  29.199  40.739  1.00  0.00          C   \nATOM   2354  CD1 LEU A 308      -6.308  28.143  39.694  1.00  0.00          C   \nATOM   2355  CD2 LEU A 308      -5.467  30.176  40.906  1.00  0.00          C   \nATOM   2356  N   GLN A 309     -11.032  29.285  41.310  1.00  0.00          N   \nATOM   2357  CA  GLN A 309     -12.274  29.835  41.840  1.00  0.00          C   \nATOM   2358  C   GLN A 309     -12.016  30.999  42.803  1.00  0.00          C   \nATOM   2359  O   GLN A 309     -11.299  30.856  43.791  1.00  0.00          O   \nATOM   2360  CB  GLN A 309     -13.049  28.727  42.560  1.00  0.00          C   \nATOM   2361  CG  GLN A 309     -14.235  28.155  41.794  1.00  0.00          C   \nATOM   2362  CD  GLN A 309     -15.542  28.855  42.142  1.00  0.00          C   \nATOM   2363  OE1 GLN A 309     -16.161  29.511  41.297  1.00  0.00          O   \nATOM   2364  NE2 GLN A 309     -15.968  28.718  43.397  1.00  0.00          N   \nATOM   2365  N   ASP A 310     -12.608  32.147  42.501  1.00  0.00          N   \nATOM   2366  CA  ASP A 310     -12.478  33.348  43.319  1.00  0.00          C   \nATOM   2367  C   ASP A 310     -11.062  33.612  43.851  1.00  0.00          C   \nATOM   2368  O   ASP A 310     -10.855  33.777  45.056  1.00  0.00          O   \nATOM   2369  CB  ASP A 310     -13.478  33.297  44.482  1.00  0.00          C   \nATOM   2370  CG  ASP A 310     -13.470  34.571  45.314  1.00  0.00          C   \nATOM   2371  OD1 ASP A 310     -13.340  35.666  44.724  1.00  0.00          O   \nATOM   2372  OD2 ASP A 310     -13.605  34.484  46.556  1.00  0.00          O   \nATOM   2373  N   CYS A 311     -10.091  33.668  42.943  1.00  0.00          N   \nATOM   2374  CA  CYS A 311      -8.714  33.923  43.330  1.00  0.00          C   \nATOM   2375  C   CYS A 311      -8.536  35.356  43.819  1.00  0.00          C   \nATOM   2376  O   CYS A 311      -8.888  36.314  43.122  1.00  0.00          O   \nATOM   2377  CB  CYS A 311      -7.776  33.689  42.143  1.00  0.00          C   \nATOM   2378  SG  CYS A 311      -8.092  34.806  40.734  1.00  0.00          S   \nATOM   2379  N   THR A 312      -8.024  35.511  45.033  1.00  0.00          N   \nATOM   2380  CA  THR A 312      -7.756  36.843  45.563  1.00  0.00          C   \nATOM   2381  C   THR A 312      -6.241  36.897  45.649  1.00  0.00          C   \nATOM   2382  O   THR A 312      -5.600  35.925  46.054  1.00  0.00          O   \nATOM   2383  CB  THR A 312      -8.325  37.055  46.969  1.00  0.00          C   \nATOM   2384  OG1 THR A 312      -9.753  36.978  46.927  1.00  0.00          O   \nATOM   2385  CG2 THR A 312      -7.906  38.430  47.508  1.00  0.00          C   \nATOM   2386  N   MET A 313      -5.653  38.019  45.267  1.00  0.00          N   \nATOM   2387  CA  MET A 313      -4.207  38.094  45.319  1.00  0.00          C   \nATOM   2388  C   MET A 313      -3.626  39.184  46.181  1.00  0.00          C   \nATOM   2389  O   MET A 313      -4.273  40.197  46.458  1.00  0.00          O   \nATOM   2390  CB  MET A 313      -3.655  38.197  43.904  1.00  0.00          C   \nATOM   2391  CG  MET A 313      -3.955  36.940  43.124  1.00  0.00          C   \nATOM   2392  SD  MET A 313      -3.504  37.039  41.439  1.00  0.00          S   \nATOM   2393  CE  MET A 313      -4.300  38.628  41.015  1.00  0.00          C   \nATOM   2394  N   LEU A 314      -2.399  38.940  46.629  1.00  0.00          N   \nATOM   2395  CA  LEU A 314      -1.661  39.902  47.439  1.00  0.00          C   \nATOM   2396  C   LEU A 314      -0.304  39.958  46.756  1.00  0.00          C   \nATOM   2397  O   LEU A 314       0.424  38.969  46.713  1.00  0.00          O   \nATOM   2398  CB  LEU A 314      -1.542  39.437  48.895  1.00  0.00          C   \nATOM   2399  CG  LEU A 314      -0.852  40.454  49.816  1.00  0.00          C   \nATOM   2400  CD1 LEU A 314      -1.546  41.820  49.711  1.00  0.00          C   \nATOM   2401  CD2 LEU A 314      -0.885  39.944  51.256  1.00  0.00          C   \nATOM   2402  N   VAL A 315       0.006  41.125  46.206  1.00  0.00          N   \nATOM   2403  CA  VAL A 315       1.224  41.330  45.448  1.00  0.00          C   \nATOM   2404  C   VAL A 315       2.079  42.479  45.979  1.00  0.00          C   \nATOM   2405  O   VAL A 315       1.578  43.571  46.238  1.00  0.00          O   \nATOM   2406  CB  VAL A 315       0.872  41.630  43.958  1.00  0.00          C   \nATOM   2407  CG1 VAL A 315       2.131  41.624  43.100  1.00  0.00          C   \nATOM   2408  CG2 VAL A 315      -0.135  40.606  43.433  1.00  0.00          C   \nATOM   2409  N   ASN A 316       3.368  42.206  46.134  1.00  0.00          N   \nATOM   2410  CA  ASN A 316       4.360  43.180  46.607  1.00  0.00          C   \nATOM   2411  C   ASN A 316       5.552  42.999  45.663  1.00  0.00          C   \nATOM   2412  O   ASN A 316       6.350  42.078  45.836  1.00  0.00          O   \nATOM   2413  CB  ASN A 316       4.808  42.857  48.045  1.00  0.00          C   \nATOM   2414  CG  ASN A 316       3.717  43.097  49.079  1.00  0.00          C   \nATOM   2415  OD1 ASN A 316       2.702  42.401  49.110  1.00  0.00          O   \nATOM   2416  ND2 ASN A 316       3.931  44.091  49.938  1.00  0.00          N   \nATOM   2417  N   GLY A 317       5.680  43.857  44.662  1.00  0.00          N   \nATOM   2418  CA  GLY A 317       6.777  43.677  43.726  1.00  0.00          C   \nATOM   2419  C   GLY A 317       6.679  42.293  43.086  1.00  0.00          C   \nATOM   2420  O   GLY A 317       5.627  41.905  42.583  1.00  0.00          O   \nATOM   2421  N   ASP A 318       7.758  41.527  43.114  1.00  0.00          N   \nATOM   2422  CA  ASP A 318       7.725  40.194  42.514  1.00  0.00          C   \nATOM   2423  C   ASP A 318       7.158  39.157  43.483  1.00  0.00          C   \nATOM   2424  O   ASP A 318       7.018  37.985  43.131  1.00  0.00          O   \nATOM   2425  CB  ASP A 318       9.135  39.778  42.073  1.00  0.00          C   \nATOM   2426  CG  ASP A 318      10.057  39.471  43.247  1.00  0.00          C   \nATOM   2427  OD1 ASP A 318       9.969  40.154  44.289  1.00  0.00          O   \nATOM   2428  OD2 ASP A 318      10.887  38.550  43.121  1.00  0.00          O   \nATOM   2429  N   ASP A 319       6.828  39.586  44.697  1.00  0.00          N   \nATOM   2430  CA  ASP A 319       6.305  38.654  45.686  1.00  0.00          C   \nATOM   2431  C   ASP A 319       4.810  38.479  45.461  1.00  0.00          C   \nATOM   2432  O   ASP A 319       4.055  39.454  45.433  1.00  0.00          O   \nATOM   2433  CB  ASP A 319       6.606  39.165  47.096  1.00  0.00          C   \nATOM   2434  CG  ASP A 319       6.631  38.052  48.118  1.00  0.00          C   \nATOM   2435  OD1 ASP A 319       5.548  37.603  48.560  1.00  0.00          O   \nATOM   2436  OD2 ASP A 319       7.743  37.612  48.467  1.00  0.00          O   \nATOM   2437  N   LEU A 320       4.389  37.229  45.303  1.00  0.00          N   \nATOM   2438  CA  LEU A 320       2.995  36.931  45.018  1.00  0.00          C   \nATOM   2439  C   LEU A 320       2.403  35.821  45.866  1.00  0.00          C   \nATOM   2440  O   LEU A 320       3.023  34.777  46.073  1.00  0.00          O   \nATOM   2441  CB  LEU A 320       2.855  36.541  43.542  1.00  0.00          C   \nATOM   2442  CG  LEU A 320       1.549  35.869  43.107  1.00  0.00          C   \nATOM   2443  CD1 LEU A 320       0.393  36.846  43.260  1.00  0.00          C   \nATOM   2444  CD2 LEU A 320       1.676  35.392  41.657  1.00  0.00          C   \nATOM   2445  N   VAL A 321       1.194  36.054  46.350  1.00  0.00          N   \nATOM   2446  CA  VAL A 321       0.500  35.057  47.134  1.00  0.00          C   \nATOM   2447  C   VAL A 321      -0.953  35.069  46.670  1.00  0.00          C   \nATOM   2448  O   VAL A 321      -1.555  36.130  46.453  1.00  0.00          O   \nATOM   2449  CB  VAL A 321       0.623  35.337  48.655  1.00  0.00          C   \nATOM   2450  CG1 VAL A 321      -0.084  36.610  49.008  1.00  0.00          C   \nATOM   2451  CG2 VAL A 321       0.072  34.176  49.451  1.00  0.00          C   \nATOM   2452  N   VAL A 322      -1.494  33.872  46.474  1.00  0.00          N   \nATOM   2453  CA  VAL A 322      -2.861  33.716  46.016  1.00  0.00          C   \nATOM   2454  C   VAL A 322      -3.696  32.888  46.990  1.00  0.00          C   \nATOM   2455  O   VAL A 322      -3.258  31.834  47.460  1.00  0.00          O   \nATOM   2456  CB  VAL A 322      -2.902  33.036  44.628  1.00  0.00          C   \nATOM   2457  CG1 VAL A 322      -4.357  32.797  44.205  1.00  0.00          C   \nATOM   2458  CG2 VAL A 322      -2.182  33.909  43.595  1.00  0.00          C   \nATOM   2459  N   ILE A 323      -4.888  33.391  47.298  1.00  0.00          N   \nATOM   2460  CA  ILE A 323      -5.823  32.696  48.171  1.00  0.00          C   \nATOM   2461  C   ILE A 323      -7.137  32.515  47.384  1.00  0.00          C   \nATOM   2462  O   ILE A 323      -7.744  33.490  46.926  1.00  0.00          O   \nATOM   2463  CB  ILE A 323      -6.081  33.499  49.467  1.00  0.00          C   \nATOM   2464  CG1 ILE A 323      -4.762  33.680  50.230  1.00  0.00          C   \nATOM   2465  CG2 ILE A 323      -7.088  32.753  50.365  1.00  0.00          C   \nATOM   2466  CD1 ILE A 323      -4.901  34.476  51.505  1.00  0.00          C   \nATOM   2467  N   CYS A 324      -7.571  31.272  47.214  1.00  0.00          N   \nATOM   2468  CA  CYS A 324      -8.795  31.024  46.454  1.00  0.00          C   \nATOM   2469  C   CYS A 324      -9.748  30.019  47.098  1.00  0.00          C   \nATOM   2470  O   CYS A 324      -9.568  29.620  48.252  1.00  0.00          O   \nATOM   2471  CB  CYS A 324      -8.438  30.552  45.038  1.00  0.00          C   \nATOM   2472  SG  CYS A 324      -7.420  29.040  44.959  1.00  0.00          S   \nATOM   2473  N   GLU A 325     -10.761  29.617  46.333  1.00  0.00          N   \nATOM   2474  CA  GLU A 325     -11.757  28.649  46.789  1.00  0.00          C   \nATOM   2475  C   GLU A 325     -11.232  27.276  46.366  1.00  0.00          C   \nATOM   2476  O   GLU A 325     -10.953  27.052  45.191  1.00  0.00          O   \nATOM   2477  CB  GLU A 325     -13.107  28.954  46.122  1.00  0.00          C   \nATOM   2478  CG  GLU A 325     -14.303  28.319  46.781  1.00  0.00          C   \nATOM   2479  CD  GLU A 325     -14.487  28.753  48.225  1.00  0.00          C   \nATOM   2480  OE1 GLU A 325     -14.976  29.878  48.489  1.00  0.00          O   \nATOM   2481  OE2 GLU A 325     -14.127  27.955  49.104  1.00  0.00          O   \nATOM   2482  N   SER A 326     -11.075  26.364  47.320  1.00  0.00          N   \nATOM   2483  CA  SER A 326     -10.550  25.036  47.000  1.00  0.00          C   \nATOM   2484  C   SER A 326     -11.553  24.168  46.247  1.00  0.00          C   \nATOM   2485  O   SER A 326     -12.759  24.396  46.316  1.00  0.00          O   \nATOM   2486  CB  SER A 326     -10.126  24.302  48.271  1.00  0.00          C   \nATOM   2487  OG  SER A 326      -9.801  22.952  47.973  1.00  0.00          O   \nATOM   2488  N   ALA A 327     -11.041  23.170  45.535  1.00  0.00          N   \nATOM   2489  CA  ALA A 327     -11.887  22.254  44.778  1.00  0.00          C   \nATOM   2490  C   ALA A 327     -11.428  20.833  45.053  1.00  0.00          C   \nATOM   2491  O   ALA A 327     -11.683  19.921  44.272  1.00  0.00          O   \nATOM   2492  CB  ALA A 327     -11.786  22.554  43.293  1.00  0.00          C   \nATOM   2493  N   GLY A 328     -10.751  20.653  46.179  1.00  0.00          N   \nATOM   2494  CA  GLY A 328     -10.240  19.346  46.525  1.00  0.00          C   \nATOM   2495  C   GLY A 328      -8.746  19.401  46.308  1.00  0.00          C   \nATOM   2496  O   GLY A 328      -8.273  20.130  45.448  1.00  0.00          O   \nATOM   2497  N   THR A 329      -7.994  18.640  47.087  1.00  0.00          N   \nATOM   2498  CA  THR A 329      -6.539  18.643  46.971  1.00  0.00          C   \nATOM   2499  C   THR A 329      -6.059  18.269  45.556  1.00  0.00          C   \nATOM   2500  O   THR A 329      -5.091  18.847  45.037  1.00  0.00          O   \nATOM   2501  CB  THR A 329      -5.930  17.676  48.014  1.00  0.00          C   \nATOM   2502  OG1 THR A 329      -4.496  17.702  47.932  1.00  0.00          O   \nATOM   2503  CG2 THR A 329      -6.424  16.263  47.755  1.00  0.00          C   \nATOM   2504  N   GLN A 330      -6.737  17.307  44.934  1.00  0.00          N   \nATOM   2505  CA  GLN A 330      -6.376  16.865  43.590  1.00  0.00          C   \nATOM   2506  C   GLN A 330      -6.600  17.940  42.540  1.00  0.00          C   \nATOM   2507  O   GLN A 330      -5.731  18.206  41.710  1.00  0.00          O   \nATOM   2508  CB  GLN A 330      -7.178  15.616  43.211  1.00  0.00          C   \nATOM   2509  CG  GLN A 330      -6.614  14.318  43.748  1.00  0.00          C   \nATOM   2510  CD  GLN A 330      -5.242  14.013  43.178  1.00  0.00          C   \nATOM   2511  OE1 GLN A 330      -4.293  14.779  43.368  1.00  0.00          O   \nATOM   2512  NE2 GLN A 330      -5.130  12.890  42.472  1.00  0.00          N   \nATOM   2513  N   GLU A 331      -7.782  18.543  42.576  1.00  0.00          N   \nATOM   2514  CA  GLU A 331      -8.132  19.590  41.633  1.00  0.00          C   \nATOM   2515  C   GLU A 331      -7.224  20.800  41.842  1.00  0.00          C   \nATOM   2516  O   GLU A 331      -6.895  21.531  40.902  1.00  0.00          O   \nATOM   2517  CB  GLU A 331      -9.595  19.992  41.827  1.00  0.00          C   \nATOM   2518  CG  GLU A 331     -10.081  21.039  40.846  1.00  0.00          C   \nATOM   2519  CD  GLU A 331      -9.867  20.615  39.408  1.00  0.00          C   \nATOM   2520  OE1 GLU A 331     -10.411  19.558  39.018  1.00  0.00          O   \nATOM   2521  OE2 GLU A 331      -9.156  21.337  38.674  1.00  0.00          O   \nATOM   2522  N   ASP A 332      -6.816  21.000  43.088  1.00  0.00          N   \nATOM   2523  CA  ASP A 332      -5.949  22.111  43.428  1.00  0.00          C   \nATOM   2524  C   ASP A 332      -4.583  21.940  42.792  1.00  0.00          C   \nATOM   2525  O   ASP A 332      -4.046  22.884  42.213  1.00  0.00          O   \nATOM   2526  CB  ASP A 332      -5.827  22.242  44.949  1.00  0.00          C   \nATOM   2527  CG  ASP A 332      -7.103  22.756  45.587  1.00  0.00          C   \nATOM   2528  OD1 ASP A 332      -7.898  23.420  44.884  1.00  0.00          O   \nATOM   2529  OD2 ASP A 332      -7.308  22.513  46.794  1.00  0.00          O   \nATOM   2530  N   ALA A 333      -4.032  20.731  42.883  1.00  0.00          N   \nATOM   2531  CA  ALA A 333      -2.721  20.445  42.313  1.00  0.00          C   \nATOM   2532  C   ALA A 333      -2.737  20.654  40.807  1.00  0.00          C   \nATOM   2533  O   ALA A 333      -1.776  21.167  40.225  1.00  0.00          O   \nATOM   2534  CB  ALA A 333      -2.303  19.014  42.636  1.00  0.00          C   \nATOM   2535  N   ALA A 334      -3.837  20.256  40.181  1.00  0.00          N   \nATOM   2536  CA  ALA A 334      -3.986  20.396  38.740  1.00  0.00          C   \nATOM   2537  C   ALA A 334      -4.046  21.864  38.328  1.00  0.00          C   \nATOM   2538  O   ALA A 334      -3.340  22.286  37.421  1.00  0.00          O   \nATOM   2539  CB  ALA A 334      -5.248  19.675  38.274  1.00  0.00          C   \nATOM   2540  N   SER A 335      -4.898  22.632  38.993  1.00  0.00          N   \nATOM   2541  CA  SER A 335      -5.056  24.051  38.682  1.00  0.00          C   \nATOM   2542  C   SER A 335      -3.730  24.793  38.793  1.00  0.00          C   \nATOM   2543  O   SER A 335      -3.279  25.449  37.848  1.00  0.00          O   \nATOM   2544  CB  SER A 335      -6.069  24.673  39.637  1.00  0.00          C   \nATOM   2545  OG  SER A 335      -7.266  23.917  39.645  1.00  0.00          O   \nATOM   2546  N   LEU A 336      -3.107  24.665  39.959  1.00  0.00          N   \nATOM   2547  CA  LEU A 336      -1.838  25.311  40.233  1.00  0.00          C   \nATOM   2548  C   LEU A 336      -0.818  24.966  39.158  1.00  0.00          C   \nATOM   2549  O   LEU A 336       0.022  25.783  38.797  1.00  0.00          O   \nATOM   2550  CB  LEU A 336      -1.354  24.893  41.624  1.00  0.00          C   \nATOM   2551  CG  LEU A 336       0.050  25.251  42.090  1.00  0.00          C   \nATOM   2552  CD1 LEU A 336       0.105  25.230  43.614  1.00  0.00          C   \nATOM   2553  CD2 LEU A 336       1.045  24.258  41.499  1.00  0.00          C   \nATOM   2554  N   ARG A 337      -0.907  23.746  38.642  1.00  0.00          N   \nATOM   2555  CA  ARG A 337      -0.007  23.297  37.591  1.00  0.00          C   \nATOM   2556  C   ARG A 337      -0.308  24.054  36.303  1.00  0.00          C   \nATOM   2557  O   ARG A 337       0.591  24.414  35.564  1.00  0.00          O   \nATOM   2558  CB  ARG A 337      -0.174  21.792  37.378  1.00  0.00          C   \nATOM   2559  CG  ARG A 337       0.815  21.172  36.406  1.00  0.00          C   \nATOM   2560  CD  ARG A 337       0.716  19.651  36.458  1.00  0.00          C   \nATOM   2561  NE  ARG A 337       1.514  19.003  35.419  1.00  0.00          N   \nATOM   2562  CZ  ARG A 337       1.260  19.092  34.115  1.00  0.00          C   \nATOM   2563  NH1 ARG A 337       0.225  19.805  33.689  1.00  0.00          N   \nATOM   2564  NH2 ARG A 337       2.036  18.465  33.239  1.00  0.00          N   \nATOM   2565  N   VAL A 338      -1.582  24.288  36.036  1.00  0.00          N   \nATOM   2566  CA  VAL A 338      -1.980  25.018  34.846  1.00  0.00          C   \nATOM   2567  C   VAL A 338      -1.640  26.504  35.060  1.00  0.00          C   \nATOM   2568  O   VAL A 338      -1.196  27.195  34.142  1.00  0.00          O   \nATOM   2569  CB  VAL A 338      -3.506  24.836  34.585  1.00  0.00          C   \nATOM   2570  CG1 VAL A 338      -3.937  25.680  33.397  1.00  0.00          C   \nATOM   2571  CG2 VAL A 338      -3.826  23.350  34.295  1.00  0.00          C   \nATOM   2572  N   PHE A 339      -1.860  26.976  36.283  1.00  0.00          N   \nATOM   2573  CA  PHE A 339      -1.566  28.356  36.652  1.00  0.00          C   \nATOM   2574  C   PHE A 339      -0.088  28.620  36.370  1.00  0.00          C   \nATOM   2575  O   PHE A 339       0.276  29.654  35.830  1.00  0.00          O   \nATOM   2576  CB  PHE A 339      -1.836  28.578  38.148  1.00  0.00          C   \nATOM   2577  CG  PHE A 339      -1.503  29.964  38.622  1.00  0.00          C   \nATOM   2578  CD1 PHE A 339      -2.448  30.974  38.569  1.00  0.00          C   \nATOM   2579  CD2 PHE A 339      -0.239  30.254  39.123  1.00  0.00          C   \nATOM   2580  CE1 PHE A 339      -2.148  32.263  39.007  1.00  0.00          C   \nATOM   2581  CE2 PHE A 339       0.078  31.545  39.564  1.00  0.00          C   \nATOM   2582  CZ  PHE A 339      -0.878  32.546  39.509  1.00  0.00          C   \nATOM   2583  N   THR A 340       0.752  27.665  36.756  1.00  0.00          N   \nATOM   2584  CA  THR A 340       2.192  27.758  36.558  1.00  0.00          C   \nATOM   2585  C   THR A 340       2.552  27.722  35.076  1.00  0.00          C   \nATOM   2586  O   THR A 340       3.450  28.444  34.623  1.00  0.00          O   \nATOM   2587  CB  THR A 340       2.898  26.608  37.297  1.00  0.00          C   \nATOM   2588  OG1 THR A 340       2.637  26.732  38.698  1.00  0.00          O   \nATOM   2589  CG2 THR A 340       4.396  26.632  37.064  1.00  0.00          C   \nATOM   2590  N   GLU A 341       1.848  26.885  34.316  1.00  0.00          N   \nATOM   2591  CA  GLU A 341       2.101  26.778  32.881  1.00  0.00          C   \nATOM   2592  C   GLU A 341       1.784  28.117  32.208  1.00  0.00          C   \nATOM   2593  O   GLU A 341       2.496  28.538  31.304  1.00  0.00          O   \nATOM   2594  CB  GLU A 341       1.244  25.653  32.254  1.00  0.00          C   \nATOM   2595  CG  GLU A 341       1.516  24.272  32.861  1.00  0.00          C   \nATOM   2596  CD  GLU A 341       0.711  23.143  32.218  1.00  0.00          C   \nATOM   2597  OE1 GLU A 341      -0.467  23.360  31.855  1.00  0.00          O   \nATOM   2598  OE2 GLU A 341       1.257  22.024  32.093  1.00  0.00          O   \nATOM   2599  N   ALA A 342       0.715  28.768  32.658  1.00  0.00          N   \nATOM   2600  CA  ALA A 342       0.297  30.063  32.118  1.00  0.00          C   \nATOM   2601  C   ALA A 342       1.339  31.142  32.448  1.00  0.00          C   \nATOM   2602  O   ALA A 342       1.780  31.890  31.562  1.00  0.00          O   \nATOM   2603  CB  ALA A 342      -1.063  30.452  32.700  1.00  0.00          C   \nATOM   2604  N   MET A 343       1.735  31.217  33.722  1.00  0.00          N   \nATOM   2605  CA  MET A 343       2.742  32.191  34.143  1.00  0.00          C   \nATOM   2606  C   MET A 343       4.004  31.992  33.315  1.00  0.00          C   \nATOM   2607  O   MET A 343       4.685  32.958  32.958  1.00  0.00          O   \nATOM   2608  CB  MET A 343       3.104  32.004  35.625  1.00  0.00          C   \nATOM   2609  CG  MET A 343       2.030  32.397  36.597  1.00  0.00          C   \nATOM   2610  SD  MET A 343       1.671  34.184  36.518  1.00  0.00          S   \nATOM   2611  CE  MET A 343       2.912  34.833  37.714  1.00  0.00          C   \nATOM   2612  N   THR A 344       4.334  30.729  33.028  1.00  0.00          N   \nATOM   2613  CA  THR A 344       5.529  30.425  32.253  1.00  0.00          C   \nATOM   2614  C   THR A 344       5.373  30.970  30.834  1.00  0.00          C   \nATOM   2615  O   THR A 344       6.297  31.582  30.296  1.00  0.00          O   \nATOM   2616  CB  THR A 344       5.817  28.897  32.233  1.00  0.00          C   \nATOM   2617  OG1 THR A 344       6.127  28.457  33.565  1.00  0.00          O   \nATOM   2618  CG2 THR A 344       7.002  28.579  31.322  1.00  0.00          C   \nATOM   2619  N   ARG A 345       4.196  30.779  30.242  1.00  0.00          N   \nATOM   2620  CA  ARG A 345       3.952  31.296  28.902  1.00  0.00          C   \nATOM   2621  C   ARG A 345       4.039  32.824  28.908  1.00  0.00          C   \nATOM   2622  O   ARG A 345       4.362  33.436  27.895  1.00  0.00          O   \nATOM   2623  CB  ARG A 345       2.576  30.857  28.384  1.00  0.00          C   \nATOM   2624  CG  ARG A 345       2.491  29.375  28.031  1.00  0.00          C   \nATOM   2625  CD  ARG A 345       1.239  29.058  27.228  1.00  0.00          C   \nATOM   2626  NE  ARG A 345       0.010  29.300  27.977  1.00  0.00          N   \nATOM   2627  CZ  ARG A 345      -0.589  28.407  28.764  1.00  0.00          C   \nATOM   2628  NH1 ARG A 345      -0.073  27.193  28.918  1.00  0.00          N   \nATOM   2629  NH2 ARG A 345      -1.720  28.724  29.391  1.00  0.00          N   \nATOM   2630  N   TYR A 346       3.761  33.430  30.056  1.00  0.00          N   \nATOM   2631  CA  TYR A 346       3.811  34.887  30.182  1.00  0.00          C   \nATOM   2632  C   TYR A 346       5.217  35.355  30.533  1.00  0.00          C   \nATOM   2633  O   TYR A 346       5.447  36.546  30.758  1.00  0.00          O   \nATOM   2634  CB  TYR A 346       2.865  35.369  31.282  1.00  0.00          C   \nATOM   2635  CG  TYR A 346       1.400  35.103  31.038  1.00  0.00          C   \nATOM   2636  CD1 TYR A 346       0.870  35.082  29.736  1.00  0.00          C   \nATOM   2637  CD2 TYR A 346       0.522  34.965  32.106  1.00  0.00          C   \nATOM   2638  CE1 TYR A 346      -0.501  34.936  29.524  1.00  0.00          C   \nATOM   2639  CE2 TYR A 346      -0.828  34.824  31.908  1.00  0.00          C   \nATOM   2640  CZ  TYR A 346      -1.342  34.814  30.619  1.00  0.00          C   \nATOM   2641  OH  TYR A 346      -2.714  34.726  30.442  1.00  0.00          O   \nATOM   2642  N   SER A 347       6.150  34.413  30.569  1.00  0.00          N   \nATOM   2643  CA  SER A 347       7.527  34.704  30.935  1.00  0.00          C   \nATOM   2644  C   SER A 347       7.642  35.030  32.416  1.00  0.00          C   \nATOM   2645  O   SER A 347       8.206  36.065  32.791  1.00  0.00          O   \nATOM   2646  CB  SER A 347       8.094  35.876  30.145  1.00  0.00          C   \nATOM   2647  OG  SER A 347       9.431  36.106  30.543  1.00  0.00          O   \nATOM   2648  N   ALA A 348       7.065  34.167  33.244  1.00  0.00          N   \nATOM   2649  CA  ALA A 348       7.154  34.297  34.696  1.00  0.00          C   \nATOM   2650  C   ALA A 348       7.276  32.877  35.251  1.00  0.00          C   \nATOM   2651  O   ALA A 348       6.489  32.453  36.101  1.00  0.00          O   \nATOM   2652  CB  ALA A 348       5.929  34.990  35.267  1.00  0.00          C   \nATOM   2653  N   PRO A 349       8.264  32.113  34.751  1.00  0.00          N   \nATOM   2654  CA  PRO A 349       8.504  30.737  35.187  1.00  0.00          C   \nATOM   2655  C   PRO A 349       8.834  30.689  36.683  1.00  0.00          C   \nATOM   2656  O   PRO A 349       9.436  31.613  37.227  1.00  0.00          O   \nATOM   2657  CB  PRO A 349       9.682  30.311  34.325  1.00  0.00          C   \nATOM   2658  CG  PRO A 349      10.446  31.561  34.211  1.00  0.00          C   \nATOM   2659  CD  PRO A 349       9.368  32.570  33.892  1.00  0.00          C   \nATOM   2660  N   PRO A 350       8.450  29.602  37.359  1.00  0.00          N   \nATOM   2661  CA  PRO A 350       8.698  29.432  38.793  1.00  0.00          C   \nATOM   2662  C   PRO A 350      10.101  28.987  39.168  1.00  0.00          C   \nATOM   2663  O   PRO A 350      10.795  28.340  38.380  1.00  0.00          O   \nATOM   2664  CB  PRO A 350       7.672  28.382  39.185  1.00  0.00          C   \nATOM   2665  CG  PRO A 350       7.697  27.482  37.984  1.00  0.00          C   \nATOM   2666  CD  PRO A 350       7.659  28.474  36.832  1.00  0.00          C   \nATOM   2667  N   GLY A 351      10.511  29.351  40.382  1.00  0.00          N   \nATOM   2668  CA  GLY A 351      11.798  28.926  40.887  1.00  0.00          C   \nATOM   2669  C   GLY A 351      11.410  27.630  41.572  1.00  0.00          C   \nATOM   2670  O   GLY A 351      11.607  26.544  41.034  1.00  0.00          O   \nATOM   2671  N   ASP A 352      10.828  27.758  42.759  1.00  0.00          N   \nATOM   2672  CA  ASP A 352      10.340  26.610  43.508  1.00  0.00          C   \nATOM   2673  C   ASP A 352       8.909  26.397  43.027  1.00  0.00          C   \nATOM   2674  O   ASP A 352       8.111  27.329  43.031  1.00  0.00          O   \nATOM   2675  CB  ASP A 352      10.317  26.913  45.007  1.00  0.00          C   \nATOM   2676  CG  ASP A 352      11.699  27.114  45.576  1.00  0.00          C   \nATOM   2677  OD1 ASP A 352      12.482  26.140  45.559  1.00  0.00          O   \nATOM   2678  OD2 ASP A 352      12.003  28.240  46.034  1.00  0.00          O   \nATOM   2679  N   PRO A 353       8.570  25.175  42.591  1.00  0.00          N   \nATOM   2680  CA  PRO A 353       7.203  24.926  42.124  1.00  0.00          C   \nATOM   2681  C   PRO A 353       6.215  25.303  43.235  1.00  0.00          C   \nATOM   2682  O   PRO A 353       6.471  25.062  44.413  1.00  0.00          O   \nATOM   2683  CB  PRO A 353       7.204  23.423  41.836  1.00  0.00          C   \nATOM   2684  CG  PRO A 353       8.637  23.142  41.484  1.00  0.00          C   \nATOM   2685  CD  PRO A 353       9.392  23.955  42.505  1.00  0.00          C   \nATOM   2686  N   PRO A 354       5.083  25.917  42.875  1.00  0.00          N   \nATOM   2687  CA  PRO A 354       4.096  26.305  43.887  1.00  0.00          C   \nATOM   2688  C   PRO A 354       3.342  25.110  44.463  1.00  0.00          C   \nATOM   2689  O   PRO A 354       3.077  24.139  43.752  1.00  0.00          O   \nATOM   2690  CB  PRO A 354       3.178  27.245  43.120  1.00  0.00          C   \nATOM   2691  CG  PRO A 354       3.206  26.658  41.719  1.00  0.00          C   \nATOM   2692  CD  PRO A 354       4.667  26.344  41.525  1.00  0.00          C   \nATOM   2693  N   GLN A 355       3.011  25.171  45.748  1.00  0.00          N   \nATOM   2694  CA  GLN A 355       2.265  24.082  46.371  1.00  0.00          C   \nATOM   2695  C   GLN A 355       1.036  24.623  47.069  1.00  0.00          C   \nATOM   2696  O   GLN A 355       1.060  25.695  47.672  1.00  0.00          O   \nATOM   2697  CB  GLN A 355       3.124  23.297  47.376  1.00  0.00          C   \nATOM   2698  CG  GLN A 355       3.430  24.016  48.694  1.00  0.00          C   \nATOM   2699  CD  GLN A 355       3.968  23.069  49.786  1.00  0.00          C   \nATOM   2700  OE1 GLN A 355       3.256  22.171  50.259  1.00  0.00          O   \nATOM   2701  NE2 GLN A 355       5.225  23.273  50.185  1.00  0.00          N   \nATOM   2702  N   PRO A 356      -0.074  23.890  46.983  1.00  0.00          N   \nATOM   2703  CA  PRO A 356      -1.268  24.396  47.653  1.00  0.00          C   \nATOM   2704  C   PRO A 356      -1.152  24.215  49.169  1.00  0.00          C   \nATOM   2705  O   PRO A 356      -0.599  23.222  49.641  1.00  0.00          O   \nATOM   2706  CB  PRO A 356      -2.391  23.570  47.015  1.00  0.00          C   \nATOM   2707  CG  PRO A 356      -1.741  22.274  46.728  1.00  0.00          C   \nATOM   2708  CD  PRO A 356      -0.352  22.653  46.233  1.00  0.00          C   \nATOM   2709  N   GLU A 357      -1.638  25.198  49.917  1.00  0.00          N   \nATOM   2710  CA  GLU A 357      -1.619  25.150  51.377  1.00  0.00          C   \nATOM   2711  C   GLU A 357      -3.039  25.299  51.906  1.00  0.00          C   \nATOM   2712  O   GLU A 357      -3.843  26.034  51.347  1.00  0.00          O   \nATOM   2713  CB  GLU A 357      -0.736  26.264  51.949  1.00  0.00          C   \nATOM   2714  CG  GLU A 357       0.742  26.067  51.707  1.00  0.00          C   \nATOM   2715  CD  GLU A 357       1.293  24.834  52.405  1.00  0.00          C   \nATOM   2716  OE1 GLU A 357       0.813  24.497  53.514  1.00  0.00          O   \nATOM   2717  OE2 GLU A 357       2.223  24.212  51.851  1.00  0.00          O   \nATOM   2718  N   TYR A 358      -3.341  24.592  52.988  1.00  0.00          N   \nATOM   2719  CA  TYR A 358      -4.668  24.628  53.577  1.00  0.00          C   \nATOM   2720  C   TYR A 358      -4.634  25.230  54.980  1.00  0.00          C   \nATOM   2721  O   TYR A 358      -5.659  25.317  55.663  1.00  0.00          O   \nATOM   2722  CB  TYR A 358      -5.235  23.202  53.583  1.00  0.00          C   \nATOM   2723  CG  TYR A 358      -5.372  22.651  52.178  1.00  0.00          C   \nATOM   2724  CD1 TYR A 358      -6.435  23.035  51.365  1.00  0.00          C   \nATOM   2725  CD2 TYR A 358      -4.379  21.857  51.619  1.00  0.00          C   \nATOM   2726  CE1 TYR A 358      -6.498  22.652  50.023  1.00  0.00          C   \nATOM   2727  CE2 TYR A 358      -4.433  21.469  50.277  1.00  0.00          C   \nATOM   2728  CZ  TYR A 358      -5.492  21.876  49.488  1.00  0.00          C   \nATOM   2729  OH  TYR A 358      -5.533  21.538  48.155  1.00  0.00          O   \nATOM   2730  N   ASP A 359      -3.442  25.662  55.382  1.00  0.00          N   \nATOM   2731  CA  ASP A 359      -3.196  26.276  56.687  1.00  0.00          C   \nATOM   2732  C   ASP A 359      -2.577  27.650  56.403  1.00  0.00          C   \nATOM   2733  O   ASP A 359      -1.451  27.729  55.933  1.00  0.00          O   \nATOM   2734  CB  ASP A 359      -2.197  25.416  57.470  1.00  0.00          C   \nATOM   2735  CG  ASP A 359      -2.017  25.876  58.908  1.00  0.00          C   \nATOM   2736  OD1 ASP A 359      -2.270  27.070  59.202  1.00  0.00          O   \nATOM   2737  OD2 ASP A 359      -1.603  25.038  59.742  1.00  0.00          O   \nATOM   2738  N   LEU A 360      -3.309  28.725  56.673  1.00  0.00          N   \nATOM   2739  CA  LEU A 360      -2.794  30.069  56.415  1.00  0.00          C   \nATOM   2740  C   LEU A 360      -1.407  30.288  57.029  1.00  0.00          C   \nATOM   2741  O   LEU A 360      -0.561  30.955  56.438  1.00  0.00          O   \nATOM   2742  CB  LEU A 360      -3.774  31.124  56.946  1.00  0.00          C   \nATOM   2743  CG  LEU A 360      -3.350  32.605  56.883  1.00  0.00          C   \nATOM   2744  CD1 LEU A 360      -3.134  33.037  55.435  1.00  0.00          C   \nATOM   2745  CD2 LEU A 360      -4.415  33.472  57.531  1.00  0.00          C   \nATOM   2746  N   GLU A 361      -1.174  29.705  58.202  1.00  0.00          N   \nATOM   2747  CA  GLU A 361       0.096  29.851  58.909  1.00  0.00          C   \nATOM   2748  C   GLU A 361       1.273  29.247  58.153  1.00  0.00          C   \nATOM   2749  O   GLU A 361       2.435  29.586  58.409  1.00  0.00          O   \nATOM   2750  CB  GLU A 361      -0.014  29.220  60.306  1.00  0.00          C   \nATOM   2751  CG  GLU A 361      -0.941  29.987  61.270  1.00  0.00          C   \nATOM   2752  CD  GLU A 361      -1.029  29.360  62.668  1.00  0.00          C   \nATOM   2753  OE1 GLU A 361       0.029  29.049  63.261  1.00  0.00          O   \nATOM   2754  OE2 GLU A 361      -2.161  29.191  63.177  1.00  0.00          O   \nATOM   2755  N   LEU A 362       0.973  28.359  57.211  1.00  0.00          N   \nATOM   2756  CA  LEU A 362       2.020  27.706  56.439  1.00  0.00          C   \nATOM   2757  C   LEU A 362       2.340  28.410  55.134  1.00  0.00          C   \nATOM   2758  O   LEU A 362       3.141  27.920  54.342  1.00  0.00          O   \nATOM   2759  CB  LEU A 362       1.635  26.254  56.151  1.00  0.00          C   \nATOM   2760  CG  LEU A 362       1.399  25.375  57.386  1.00  0.00          C   \nATOM   2761  CD1 LEU A 362       1.304  23.906  56.950  1.00  0.00          C   \nATOM   2762  CD2 LEU A 362       2.535  25.569  58.387  1.00  0.00          C   \nATOM   2763  N   ILE A 363       1.718  29.557  54.902  1.00  0.00          N   \nATOM   2764  CA  ILE A 363       1.975  30.282  53.669  1.00  0.00          C   \nATOM   2765  C   ILE A 363       2.939  31.428  53.908  1.00  0.00          C   \nATOM   2766  O   ILE A 363       2.668  32.320  54.722  1.00  0.00          O   \nATOM   2767  CB  ILE A 363       0.690  30.856  53.068  1.00  0.00          C   \nATOM   2768  CG1 ILE A 363      -0.274  29.710  52.705  1.00  0.00          C   \nATOM   2769  CG2 ILE A 363       1.044  31.708  51.864  1.00  0.00          C   \nATOM   2770  CD1 ILE A 363      -1.584  30.169  52.118  1.00  0.00          C   \nATOM   2771  N   THR A 364       4.056  31.389  53.186  1.00  0.00          N   \nATOM   2772  CA  THR A 364       5.089  32.402  53.289  1.00  0.00          C   \nATOM   2773  C   THR A 364       5.058  33.335  52.078  1.00  0.00          C   \nATOM   2774  O   THR A 364       5.206  32.890  50.939  1.00  0.00          O   \nATOM   2775  CB  THR A 364       6.483  31.752  53.384  1.00  0.00          C   \nATOM   2776  OG1 THR A 364       6.601  31.025  54.619  1.00  0.00          O   \nATOM   2777  CG2 THR A 364       7.569  32.825  53.315  1.00  0.00          C   \nATOM   2778  N   SER A 365       4.850  34.624  52.339  1.00  0.00          N   \nATOM   2779  CA  SER A 365       4.810  35.659  51.306  1.00  0.00          C   \nATOM   2780  C   SER A 365       5.554  36.868  51.903  1.00  0.00          C   \nATOM   2781  O   SER A 365       5.359  37.208  53.072  1.00  0.00          O   \nATOM   2782  CB  SER A 365       3.364  36.020  50.972  1.00  0.00          C   \nATOM   2783  OG  SER A 365       2.681  36.510  52.116  1.00  0.00          O   \nATOM   2784  N   CYS A 366       6.397  37.509  51.098  1.00  0.00          N   \nATOM   2785  CA  CYS A 366       7.233  38.610  51.577  1.00  0.00          C   \nATOM   2786  C   CYS A 366       8.081  38.063  52.716  1.00  0.00          C   \nATOM   2787  O   CYS A 366       8.258  38.711  53.745  1.00  0.00          O   \nATOM   2788  CB  CYS A 366       6.399  39.790  52.072  1.00  0.00          C   \nATOM   2789  SG  CYS A 366       5.988  40.955  50.770  1.00  0.00          S   \nATOM   2790  N   SER A 367       8.571  36.839  52.527  1.00  0.00          N   \nATOM   2791  CA  SER A 367       9.411  36.171  53.512  1.00  0.00          C   \nATOM   2792  C   SER A 367       8.759  36.109  54.890  1.00  0.00          C   \nATOM   2793  O   SER A 367       9.443  35.871  55.885  1.00  0.00          O   \nATOM   2794  CB  SER A 367      10.756  36.900  53.636  1.00  0.00          C   \nATOM   2795  OG  SER A 367      11.435  36.932  52.393  1.00  0.00          O   \nATOM   2796  N   SER A 368       7.444  36.301  54.949  1.00  0.00          N   \nATOM   2797  CA  SER A 368       6.744  36.305  56.230  1.00  0.00          C   \nATOM   2798  C   SER A 368       5.520  35.412  56.224  1.00  0.00          C   \nATOM   2799  O   SER A 368       5.091  34.932  55.177  1.00  0.00          O   \nATOM   2800  CB  SER A 368       6.292  37.726  56.568  1.00  0.00          C   \nATOM   2801  OG  SER A 368       7.335  38.666  56.374  1.00  0.00          O   \nATOM   2802  N   ASN A 369       4.944  35.214  57.403  1.00  0.00          N   \nATOM   2803  CA  ASN A 369       3.741  34.400  57.522  1.00  0.00          C   \nATOM   2804  C   ASN A 369       2.946  34.827  58.743  1.00  0.00          C   \nATOM   2805  O   ASN A 369       3.489  35.407  59.694  1.00  0.00          O   \nATOM   2806  CB  ASN A 369       4.086  32.908  57.650  1.00  0.00          C   \nATOM   2807  CG  ASN A 369       4.762  32.571  58.977  1.00  0.00          C   \nATOM   2808  OD1 ASN A 369       5.972  32.740  59.132  1.00  0.00          O   \nATOM   2809  ND2 ASN A 369       3.975  32.092  59.942  1.00  0.00          N   \nATOM   2810  N   VAL A 370       1.653  34.526  58.709  1.00  0.00          N   \nATOM   2811  CA  VAL A 370       0.759  34.850  59.804  1.00  0.00          C   \nATOM   2812  C   VAL A 370       0.884  33.779  60.874  1.00  0.00          C   \nATOM   2813  O   VAL A 370       1.017  32.593  60.576  1.00  0.00          O   \nATOM   2814  CB  VAL A 370      -0.726  34.886  59.347  1.00  0.00          C   \nATOM   2815  CG1 VAL A 370      -1.624  35.194  60.541  1.00  0.00          C   \nATOM   2816  CG2 VAL A 370      -0.934  35.934  58.264  1.00  0.00          C   \nATOM   2817  N   SER A 371       0.872  34.203  62.128  1.00  0.00          N   \nATOM   2818  CA  SER A 371       0.918  33.259  63.224  1.00  0.00          C   \nATOM   2819  C   SER A 371      -0.013  33.814  64.283  1.00  0.00          C   \nATOM   2820  O   SER A 371      -0.529  34.925  64.144  1.00  0.00          O   \nATOM   2821  CB  SER A 371       2.328  33.115  63.789  1.00  0.00          C   \nATOM   2822  OG  SER A 371       2.428  31.899  64.519  1.00  0.00          O   \nATOM   2823  N   VAL A 372      -0.229  33.046  65.343  1.00  0.00          N   \nATOM   2824  CA  VAL A 372      -1.124  33.484  66.398  1.00  0.00          C   \nATOM   2825  C   VAL A 372      -0.499  33.353  67.778  1.00  0.00          C   \nATOM   2826  O   VAL A 372       0.301  32.453  68.034  1.00  0.00          O   \nATOM   2827  CB  VAL A 372      -2.457  32.681  66.358  1.00  0.00          C   \nATOM   2828  CG1 VAL A 372      -2.178  31.193  66.498  1.00  0.00          C   \nATOM   2829  CG2 VAL A 372      -3.387  33.153  67.465  1.00  0.00          C   \nATOM   2830  N   ALA A 373      -0.862  34.290  68.645  1.00  0.00          N   \nATOM   2831  CA  ALA A 373      -0.400  34.323  70.030  1.00  0.00          C   \nATOM   2832  C   ALA A 373      -1.586  34.799  70.867  1.00  0.00          C   \nATOM   2833  O   ALA A 373      -2.710  34.882  70.368  1.00  0.00          O   \nATOM   2834  CB  ALA A 373       0.776  35.291  70.178  1.00  0.00          C   \nATOM   2835  N   HIS A 374      -1.343  35.114  72.130  1.00  0.00          N   \nATOM   2836  CA  HIS A 374      -2.414  35.576  73.007  1.00  0.00          C   \nATOM   2837  C   HIS A 374      -2.014  36.862  73.699  1.00  0.00          C   \nATOM   2838  O   HIS A 374      -0.864  37.006  74.115  1.00  0.00          O   \nATOM   2839  CB  HIS A 374      -2.713  34.519  74.057  1.00  0.00          C   \nATOM   2840  CG  HIS A 374      -3.179  33.223  73.486  1.00  0.00          C   \nATOM   2841  ND1 HIS A 374      -4.476  33.020  73.065  1.00  0.00          N   \nATOM   2842  CD2 HIS A 374      -2.516  32.072  73.223  1.00  0.00          C   \nATOM   2843  CE1 HIS A 374      -4.590  31.803  72.569  1.00  0.00          C   \nATOM   2844  NE2 HIS A 374      -3.415  31.206  72.652  1.00  0.00          N   \nATOM   2845  N   ASP A 375      -2.956  37.799  73.820  1.00  0.00          N   \nATOM   2846  CA  ASP A 375      -2.665  39.061  74.489  1.00  0.00          C   \nATOM   2847  C   ASP A 375      -2.833  38.911  75.998  1.00  0.00          C   \nATOM   2848  O   ASP A 375      -3.026  37.805  76.510  1.00  0.00          O   \nATOM   2849  CB  ASP A 375      -3.564  40.195  73.979  1.00  0.00          C   \nATOM   2850  CG  ASP A 375      -5.053  39.883  74.107  1.00  0.00          C   \nATOM   2851  OD1 ASP A 375      -5.457  39.139  75.025  1.00  0.00          O   \nATOM   2852  OD2 ASP A 375      -5.830  40.401  73.282  1.00  0.00          O   \nATOM   2853  N   ALA A 376      -2.758  40.028  76.706  1.00  0.00          N   \nATOM   2854  CA  ALA A 376      -2.877  40.014  78.156  1.00  0.00          C   \nATOM   2855  C   ALA A 376      -4.232  39.515  78.660  1.00  0.00          C   \nATOM   2856  O   ALA A 376      -4.383  39.246  79.847  1.00  0.00          O   \nATOM   2857  CB  ALA A 376      -2.593  41.402  78.702  1.00  0.00          C   \nATOM   2858  N   SER A 377      -5.206  39.387  77.762  1.00  0.00          N   \nATOM   2859  CA  SER A 377      -6.542  38.923  78.129  1.00  0.00          C   \nATOM   2860  C   SER A 377      -6.725  37.443  77.847  1.00  0.00          C   \nATOM   2861  O   SER A 377      -7.706  36.837  78.282  1.00  0.00          O   \nATOM   2862  CB  SER A 377      -7.603  39.703  77.356  1.00  0.00          C   \nATOM   2863  OG  SER A 377      -7.451  41.098  77.556  1.00  0.00          O   \nATOM   2864  N   GLY A 378      -5.788  36.861  77.110  1.00  0.00          N   \nATOM   2865  CA  GLY A 378      -5.887  35.449  76.792  1.00  0.00          C   \nATOM   2866  C   GLY A 378      -6.501  35.261  75.419  1.00  0.00          C   \nATOM   2867  O   GLY A 378      -6.635  34.139  74.933  1.00  0.00          O   \nATOM   2868  N   LYS A 379      -6.874  36.376  74.798  1.00  0.00          N   \nATOM   2869  CA  LYS A 379      -7.477  36.371  73.466  1.00  0.00          C   \nATOM   2870  C   LYS A 379      -6.468  36.036  72.368  1.00  0.00          C   \nATOM   2871  O   LYS A 379      -5.316  36.461  72.413  1.00  0.00          O   \nATOM   2872  CB  LYS A 379      -8.097  37.745  73.167  1.00  0.00          C   \nATOM   2873  CG  LYS A 379      -8.594  37.905  71.731  1.00  0.00          C   \nATOM   2874  CD  LYS A 379      -9.312  39.234  71.502  1.00  0.00          C   \nATOM   2875  CE  LYS A 379      -8.380  40.417  71.708  1.00  0.00          C   \nATOM   2876  NZ  LYS A 379      -9.032  41.728  71.413  1.00  0.00          N   \nATOM   2877  N   ARG A 380      -6.909  35.273  71.378  1.00  0.00          N   \nATOM   2878  CA  ARG A 380      -6.046  34.929  70.262  1.00  0.00          C   \nATOM   2879  C   ARG A 380      -5.836  36.188  69.430  1.00  0.00          C   \nATOM   2880  O   ARG A 380      -6.787  36.921  69.156  1.00  0.00          O   \nATOM   2881  CB  ARG A 380      -6.692  33.848  69.401  1.00  0.00          C   \nATOM   2882  CG  ARG A 380      -6.717  32.474  70.041  1.00  0.00          C   \nATOM   2883  CD  ARG A 380      -7.741  31.592  69.363  1.00  0.00          C   \nATOM   2884  NE  ARG A 380      -7.440  31.358  67.956  1.00  0.00          N   \nATOM   2885  CZ  ARG A 380      -6.449  30.588  67.518  1.00  0.00          C   \nATOM   2886  NH1 ARG A 380      -5.645  29.971  68.379  1.00  0.00          N   \nATOM   2887  NH2 ARG A 380      -6.277  30.414  66.215  1.00  0.00          N   \nATOM   2888  N   VAL A 381      -4.584  36.446  69.058  1.00  0.00          N   \nATOM   2889  CA  VAL A 381      -4.230  37.600  68.240  1.00  0.00          C   \nATOM   2890  C   VAL A 381      -3.292  37.149  67.118  1.00  0.00          C   \nATOM   2891  O   VAL A 381      -2.335  36.404  67.354  1.00  0.00          O   \nATOM   2892  CB  VAL A 381      -3.531  38.703  69.085  1.00  0.00          C   \nATOM   2893  CG1 VAL A 381      -2.569  38.062  70.065  1.00  0.00          C   \nATOM   2894  CG2 VAL A 381      -2.772  39.675  68.177  1.00  0.00          C   \nATOM   2895  N   TYR A 382      -3.583  37.592  65.899  1.00  0.00          N   \nATOM   2896  CA  TYR A 382      -2.765  37.251  64.738  1.00  0.00          C   \nATOM   2897  C   TYR A 382      -1.732  38.338  64.538  1.00  0.00          C   \nATOM   2898  O   TYR A 382      -2.004  39.516  64.794  1.00  0.00          O   \nATOM   2899  CB  TYR A 382      -3.633  37.152  63.482  1.00  0.00          C   \nATOM   2900  CG  TYR A 382      -4.575  35.981  63.498  1.00  0.00          C   \nATOM   2901  CD1 TYR A 382      -4.103  34.685  63.323  1.00  0.00          C   \nATOM   2902  CD2 TYR A 382      -5.934  36.165  63.727  1.00  0.00          C   \nATOM   2903  CE1 TYR A 382      -4.966  33.592  63.377  1.00  0.00          C   \nATOM   2904  CE2 TYR A 382      -6.808  35.085  63.787  1.00  0.00          C   \nATOM   2905  CZ  TYR A 382      -6.317  33.799  63.612  1.00  0.00          C   \nATOM   2906  OH  TYR A 382      -7.174  32.722  63.683  1.00  0.00          O   \nATOM   2907  N   TYR A 383      -0.555  37.949  64.056  1.00  0.00          N   \nATOM   2908  CA  TYR A 383       0.517  38.913  63.834  1.00  0.00          C   \nATOM   2909  C   TYR A 383       1.488  38.371  62.794  1.00  0.00          C   \nATOM   2910  O   TYR A 383       1.517  37.175  62.523  1.00  0.00          O   \nATOM   2911  CB  TYR A 383       1.265  39.181  65.146  1.00  0.00          C   \nATOM   2912  CG  TYR A 383       2.045  37.983  65.625  1.00  0.00          C   \nATOM   2913  CD1 TYR A 383       1.395  36.868  66.175  1.00  0.00          C   \nATOM   2914  CD2 TYR A 383       3.430  37.923  65.457  1.00  0.00          C   \nATOM   2915  CE1 TYR A 383       2.115  35.726  66.536  1.00  0.00          C   \nATOM   2916  CE2 TYR A 383       4.151  36.786  65.813  1.00  0.00          C   \nATOM   2917  CZ  TYR A 383       3.496  35.698  66.346  1.00  0.00          C   \nATOM   2918  OH  TYR A 383       4.222  34.578  66.674  1.00  0.00          O   \nATOM   2919  N   LEU A 384       2.294  39.253  62.221  1.00  0.00          N   \nATOM   2920  CA  LEU A 384       3.243  38.833  61.206  1.00  0.00          C   \nATOM   2921  C   LEU A 384       4.594  38.466  61.822  1.00  0.00          C   \nATOM   2922  O   LEU A 384       5.116  39.185  62.675  1.00  0.00          O   \nATOM   2923  CB  LEU A 384       3.434  39.946  60.180  1.00  0.00          C   \nATOM   2924  CG  LEU A 384       4.017  39.510  58.837  1.00  0.00          C   \nATOM   2925  CD1 LEU A 384       2.956  38.722  58.091  1.00  0.00          C   \nATOM   2926  CD2 LEU A 384       4.424  40.722  58.012  1.00  0.00          C   \nATOM   2927  N   THR A 385       5.146  37.339  61.387  1.00  0.00          N   \nATOM   2928  CA  THR A 385       6.438  36.883  61.868  1.00  0.00          C   \nATOM   2929  C   THR A 385       7.188  36.314  60.671  1.00  0.00          C   \nATOM   2930  O   THR A 385       6.697  36.356  59.534  1.00  0.00          O   \nATOM   2931  CB  THR A 385       6.305  35.777  62.930  1.00  0.00          C   \nATOM   2932  OG1 THR A 385       7.612  35.385  63.388  1.00  0.00          O   \nATOM   2933  CG2 THR A 385       5.607  34.561  62.337  1.00  0.00          C   \nATOM   2934  N   ARG A 386       8.382  35.799  60.926  1.00  0.00          N   \nATOM   2935  CA  ARG A 386       9.181  35.207  59.875  1.00  0.00          C   \nATOM   2936  C   ARG A 386      10.297  34.387  60.481  1.00  0.00          C   \nATOM   2937  O   ARG A 386      10.559  34.466  61.691  1.00  0.00          O   \nATOM   2938  CB  ARG A 386       9.788  36.287  58.977  1.00  0.00          C   \nATOM   2939  CG  ARG A 386      10.731  37.271  59.687  1.00  0.00          C   \nATOM   2940  CD  ARG A 386      11.627  37.948  58.663  1.00  0.00          C   \nATOM   2941  NE  ARG A 386      12.553  36.983  58.070  1.00  0.00          N   \nATOM   2942  CZ  ARG A 386      13.163  37.139  56.898  1.00  0.00          C   \nATOM   2943  NH1 ARG A 386      12.953  38.231  56.168  1.00  0.00          N   \nATOM   2944  NH2 ARG A 386      13.996  36.206  56.456  1.00  0.00          N   \nATOM   2945  N   ASP A 387      10.936  33.577  59.645  1.00  0.00          N   \nATOM   2946  CA  ASP A 387      12.064  32.788  60.111  1.00  0.00          C   \nATOM   2947  C   ASP A 387      13.053  33.883  60.537  1.00  0.00          C   \nATOM   2948  O   ASP A 387      13.295  34.827  59.784  1.00  0.00          O   \nATOM   2949  CB  ASP A 387      12.642  31.968  58.966  1.00  0.00          C   \nATOM   2950  CG  ASP A 387      13.804  31.121  59.405  1.00  0.00          C   \nATOM   2951  OD1 ASP A 387      14.837  31.694  59.795  1.00  0.00          O   \nATOM   2952  OD2 ASP A 387      13.679  29.880  59.371  1.00  0.00          O   \nATOM   2953  N   PRO A 388      13.636  33.777  61.742  1.00  0.00          N   \nATOM   2954  CA  PRO A 388      14.577  34.813  62.198  1.00  0.00          C   \nATOM   2955  C   PRO A 388      16.022  34.669  61.758  1.00  0.00          C   \nATOM   2956  O   PRO A 388      16.865  35.501  62.120  1.00  0.00          O   \nATOM   2957  CB  PRO A 388      14.439  34.748  63.710  1.00  0.00          C   \nATOM   2958  CG  PRO A 388      14.250  33.256  63.955  1.00  0.00          C   \nATOM   2959  CD  PRO A 388      13.395  32.762  62.787  1.00  0.00          C   \nATOM   2960  N   THR A 389      16.305  33.643  60.961  1.00  0.00          N   \nATOM   2961  CA  THR A 389      17.672  33.381  60.537  1.00  0.00          C   \nATOM   2962  C   THR A 389      18.392  34.563  59.910  1.00  0.00          C   \nATOM   2963  O   THR A 389      19.460  34.945  60.380  1.00  0.00          O   \nATOM   2964  CB  THR A 389      17.753  32.195  59.558  1.00  0.00          C   \nATOM   2965  OG1 THR A 389      17.241  31.010  60.184  1.00  0.00          O   \nATOM   2966  CG2 THR A 389      19.176  31.951  59.164  1.00  0.00          C   \nATOM   2967  N   THR A 390      17.813  35.137  58.859  1.00  0.00          N   \nATOM   2968  CA  THR A 390      18.449  36.250  58.190  1.00  0.00          C   \nATOM   2969  C   THR A 390      18.489  37.477  59.085  1.00  0.00          C   \nATOM   2970  O   THR A 390      19.497  38.165  59.129  1.00  0.00          O   \nATOM   2971  CB  THR A 390      17.768  36.566  56.849  1.00  0.00          C   \nATOM   2972  OG1 THR A 390      17.881  35.425  55.991  1.00  0.00          O   \nATOM   2973  CG2 THR A 390      18.449  37.748  56.170  1.00  0.00          C   \nATOM   2974  N   PRO A 391      17.384  37.781  59.790  1.00  0.00          N   \nATOM   2975  CA  PRO A 391      17.399  38.948  60.677  1.00  0.00          C   \nATOM   2976  C   PRO A 391      18.515  38.807  61.736  1.00  0.00          C   \nATOM   2977  O   PRO A 391      19.190  39.787  62.079  1.00  0.00          O   \nATOM   2978  CB  PRO A 391      16.001  38.917  61.303  1.00  0.00          C   \nATOM   2979  CG  PRO A 391      15.152  38.441  60.167  1.00  0.00          C   \nATOM   2980  CD  PRO A 391      15.999  37.333  59.534  1.00  0.00          C   \nATOM   2981  N   LEU A 392      18.727  37.591  62.244  1.00  0.00          N   \nATOM   2982  CA  LEU A 392      19.762  37.391  63.262  1.00  0.00          C   \nATOM   2983  C   LEU A 392      21.173  37.416  62.669  1.00  0.00          C   \nATOM   2984  O   LEU A 392      22.108  37.906  63.297  1.00  0.00          O   \nATOM   2985  CB  LEU A 392      19.515  36.091  64.047  1.00  0.00          C   \nATOM   2986  CG  LEU A 392      18.241  36.139  64.904  1.00  0.00          C   \nATOM   2987  CD1 LEU A 392      18.057  34.833  65.604  1.00  0.00          C   \nATOM   2988  CD2 LEU A 392      18.308  37.292  65.908  1.00  0.00          C   \nATOM   2989  N   ALA A 393      21.325  36.891  61.461  1.00  0.00          N   \nATOM   2990  CA  ALA A 393      22.619  36.911  60.793  1.00  0.00          C   \nATOM   2991  C   ALA A 393      23.012  38.371  60.562  1.00  0.00          C   \nATOM   2992  O   ALA A 393      24.167  38.767  60.751  1.00  0.00          O   \nATOM   2993  CB  ALA A 393      22.523  36.197  59.457  1.00  0.00          C   \nATOM   2994  N   ARG A 394      22.044  39.175  60.134  1.00  0.00          N   \nATOM   2995  CA  ARG A 394      22.322  40.582  59.872  1.00  0.00          C   \nATOM   2996  C   ARG A 394      22.599  41.346  61.155  1.00  0.00          C   \nATOM   2997  O   ARG A 394      23.455  42.237  61.188  1.00  0.00          O   \nATOM   2998  CB  ARG A 394      21.164  41.187  59.081  1.00  0.00          C   \nATOM   2999  CG  ARG A 394      21.101  40.520  57.725  1.00  0.00          C   \nATOM   3000  CD  ARG A 394      20.060  41.047  56.773  1.00  0.00          C   \nATOM   3001  NE  ARG A 394      20.303  40.441  55.466  1.00  0.00          N   \nATOM   3002  CZ  ARG A 394      19.600  40.687  54.367  1.00  0.00          C   \nATOM   3003  NH1 ARG A 394      18.582  41.538  54.396  1.00  0.00          N   \nATOM   3004  NH2 ARG A 394      19.930  40.088  53.235  1.00  0.00          N   \nATOM   3005  N   ALA A 395      21.902  40.986  62.227  1.00  0.00          N   \nATOM   3006  CA  ALA A 395      22.138  41.665  63.497  1.00  0.00          C   \nATOM   3007  C   ALA A 395      23.558  41.326  63.970  1.00  0.00          C   \nATOM   3008  O   ALA A 395      24.240  42.165  64.571  1.00  0.00          O   \nATOM   3009  CB  ALA A 395      21.113  41.227  64.535  1.00  0.00          C   \nATOM   3010  N   ALA A 396      24.003  40.097  63.707  1.00  0.00          N   \nATOM   3011  CA  ALA A 396      25.355  39.713  64.113  1.00  0.00          C   \nATOM   3012  C   ALA A 396      26.339  40.642  63.416  1.00  0.00          C   \nATOM   3013  O   ALA A 396      27.254  41.172  64.039  1.00  0.00          O   \nATOM   3014  CB  ALA A 396      25.641  38.252  63.739  1.00  0.00          C   \nATOM   3015  N   TRP A 397      26.140  40.849  62.115  1.00  0.00          N   \nATOM   3016  CA  TRP A 397      27.014  41.724  61.336  1.00  0.00          C   \nATOM   3017  C   TRP A 397      26.966  43.153  61.873  1.00  0.00          C   \nATOM   3018  O   TRP A 397      27.993  43.820  62.015  1.00  0.00          O   \nATOM   3019  CB  TRP A 397      26.588  41.709  59.859  1.00  0.00          C   \nATOM   3020  CG  TRP A 397      27.631  42.261  58.942  1.00  0.00          C   \nATOM   3021  CD1 TRP A 397      27.951  43.583  58.750  1.00  0.00          C   \nATOM   3022  CD2 TRP A 397      28.537  41.504  58.132  1.00  0.00          C   \nATOM   3023  NE1 TRP A 397      29.005  43.685  57.871  1.00  0.00          N   \nATOM   3024  CE2 TRP A 397      29.384  42.427  57.478  1.00  0.00          C   \nATOM   3025  CE3 TRP A 397      28.717  40.135  57.896  1.00  0.00          C   \nATOM   3026  CZ2 TRP A 397      30.402  42.021  56.601  1.00  0.00          C   \nATOM   3027  CZ3 TRP A 397      29.728  39.732  57.025  1.00  0.00          C   \nATOM   3028  CH2 TRP A 397      30.557  40.677  56.389  1.00  0.00          C   \nATOM   3029  N   GLU A 398      25.763  43.626  62.168  1.00  0.00          N   \nATOM   3030  CA  GLU A 398      25.592  44.976  62.686  1.00  0.00          C   \nATOM   3031  C   GLU A 398      26.118  45.135  64.113  1.00  0.00          C   \nATOM   3032  O   GLU A 398      26.254  46.251  64.612  1.00  0.00          O   \nATOM   3033  CB  GLU A 398      24.116  45.382  62.578  1.00  0.00          C   \nATOM   3034  CG  GLU A 398      23.722  45.704  61.116  1.00  0.00          C   \nATOM   3035  CD  GLU A 398      22.222  45.640  60.824  1.00  0.00          C   \nATOM   3036  OE1 GLU A 398      21.413  45.507  61.760  1.00  0.00          O   \nATOM   3037  OE2 GLU A 398      21.848  45.725  59.635  1.00  0.00          O   \nATOM   3038  N   THR A 399      26.437  44.019  64.760  1.00  0.00          N   \nATOM   3039  CA  THR A 399      26.972  44.060  66.118  1.00  0.00          C   \nATOM   3040  C   THR A 399      28.447  44.484  66.087  1.00  0.00          C   \nATOM   3041  O   THR A 399      28.948  45.097  67.027  1.00  0.00          O   \nATOM   3042  CB  THR A 399      26.850  42.672  66.798  1.00  0.00          C   \nATOM   3043  OG1 THR A 399      25.461  42.347  66.967  1.00  0.00          O   \nATOM   3044  CG2 THR A 399      27.552  42.660  68.164  1.00  0.00          C   \nATOM   3045  N   ALA A 400      29.137  44.162  64.996  1.00  0.00          N   \nATOM   3046  CA  ALA A 400      30.556  44.497  64.857  1.00  0.00          C   \nATOM   3047  C   ALA A 400      30.821  45.602  63.837  1.00  0.00          C   \nATOM   3048  O   ALA A 400      31.914  46.167  63.807  1.00  0.00          O   \nATOM   3049  CB  ALA A 400      31.354  43.245  64.452  1.00  0.00          C   \nATOM   3050  N   ARG A 401      29.827  45.901  63.001  1.00  0.00          N   \nATOM   3051  CA  ARG A 401      29.981  46.902  61.954  1.00  0.00          C   \nATOM   3052  C   ARG A 401      28.787  47.826  61.784  1.00  0.00          C   \nATOM   3053  O   ARG A 401      27.640  47.385  61.794  1.00  0.00          O   \nATOM   3054  CB  ARG A 401      30.232  46.199  60.622  1.00  0.00          C   \nATOM   3055  CG  ARG A 401      31.552  45.507  60.515  1.00  0.00          C   \nATOM   3056  CD  ARG A 401      32.585  46.404  59.849  1.00  0.00          C   \nATOM   3057  NE  ARG A 401      33.789  45.648  59.526  1.00  0.00          N   \nATOM   3058  CZ  ARG A 401      34.691  45.277  60.422  1.00  0.00          C   \nATOM   3059  NH1 ARG A 401      34.528  45.602  61.699  1.00  0.00          N   \nATOM   3060  NH2 ARG A 401      35.744  44.569  60.043  1.00  0.00          N   \nATOM   3061  N   HIS A 402      29.061  49.113  61.620  1.00  0.00          N   \nATOM   3062  CA  HIS A 402      28.001  50.079  61.390  1.00  0.00          C   \nATOM   3063  C   HIS A 402      27.759  50.037  59.889  1.00  0.00          C   \nATOM   3064  O   HIS A 402      28.700  50.094  59.104  1.00  0.00          O   \nATOM   3065  CB  HIS A 402      28.439  51.480  61.825  1.00  0.00          C   \nATOM   3066  CG  HIS A 402      28.630  51.610  63.304  1.00  0.00          C   \nATOM   3067  ND1 HIS A 402      29.822  52.002  63.873  1.00  0.00          N   \nATOM   3068  CD2 HIS A 402      27.783  51.362  64.333  1.00  0.00          C   \nATOM   3069  CE1 HIS A 402      29.704  51.988  65.189  1.00  0.00          C   \nATOM   3070  NE2 HIS A 402      28.478  51.603  65.494  1.00  0.00          N   \nATOM   3071  N   THR A 403      26.500  49.913  59.491  1.00  0.00          N   \nATOM   3072  CA  THR A 403      26.156  49.836  58.080  1.00  0.00          C   \nATOM   3073  C   THR A 403      25.167  50.940  57.727  1.00  0.00          C   \nATOM   3074  O   THR A 403      24.585  51.552  58.611  1.00  0.00          O   \nATOM   3075  CB  THR A 403      25.534  48.455  57.747  1.00  0.00          C   \nATOM   3076  OG1 THR A 403      24.293  48.302  58.443  1.00  0.00          O   \nATOM   3077  CG2 THR A 403      26.469  47.339  58.182  1.00  0.00          C   \nATOM   3078  N   PRO A 404      24.972  51.211  56.426  1.00  0.00          N   \nATOM   3079  CA  PRO A 404      24.048  52.248  55.950  1.00  0.00          C   \nATOM   3080  C   PRO A 404      22.612  52.004  56.408  1.00  0.00          C   \nATOM   3081  O   PRO A 404      21.915  52.925  56.833  1.00  0.00          O   \nATOM   3082  CB  PRO A 404      24.174  52.150  54.431  1.00  0.00          C   \nATOM   3083  CG  PRO A 404      25.564  51.639  54.230  1.00  0.00          C   \nATOM   3084  CD  PRO A 404      25.652  50.568  55.288  1.00  0.00          C   \nATOM   3085  N   VAL A 405      22.184  50.752  56.316  1.00  0.00          N   \nATOM   3086  CA  VAL A 405      20.837  50.346  56.693  1.00  0.00          C   \nATOM   3087  C   VAL A 405      20.929  49.462  57.927  1.00  0.00          C   \nATOM   3088  O   VAL A 405      21.551  48.401  57.898  1.00  0.00          O   \nATOM   3089  CB  VAL A 405      20.170  49.534  55.558  1.00  0.00          C   \nATOM   3090  CG1 VAL A 405      18.749  49.154  55.936  1.00  0.00          C   \nATOM   3091  CG2 VAL A 405      20.194  50.336  54.266  1.00  0.00          C   \nATOM   3092  N   ASN A 406      20.307  49.906  59.008  1.00  0.00          N   \nATOM   3093  CA  ASN A 406      20.315  49.166  60.251  1.00  0.00          C   \nATOM   3094  C   ASN A 406      19.153  48.179  60.228  1.00  0.00          C   \nATOM   3095  O   ASN A 406      18.056  48.500  60.670  1.00  0.00          O   \nATOM   3096  CB  ASN A 406      20.174  50.152  61.400  1.00  0.00          C   \nATOM   3097  CG  ASN A 406      21.428  50.987  61.597  1.00  0.00          C   \nATOM   3098  OD1 ASN A 406      22.416  50.509  62.162  1.00  0.00          O   \nATOM   3099  ND2 ASN A 406      21.405  52.231  61.112  1.00  0.00          N   \nATOM   3100  N   SER A 407      19.393  46.977  59.716  1.00  0.00          N   \nATOM   3101  CA  SER A 407      18.313  45.996  59.637  1.00  0.00          C   \nATOM   3102  C   SER A 407      17.734  45.640  61.004  1.00  0.00          C   \nATOM   3103  O   SER A 407      16.540  45.372  61.118  1.00  0.00          O   \nATOM   3104  CB  SER A 407      18.790  44.714  58.937  1.00  0.00          C   \nATOM   3105  OG  SER A 407      19.664  43.996  59.786  1.00  0.00          O   \nATOM   3106  N   TRP A 408      18.550  45.641  62.054  1.00  0.00          N   \nATOM   3107  CA  TRP A 408      17.998  45.288  63.358  1.00  0.00          C   \nATOM   3108  C   TRP A 408      16.840  46.204  63.762  1.00  0.00          C   \nATOM   3109  O   TRP A 408      15.855  45.747  64.340  1.00  0.00          O   \nATOM   3110  CB  TRP A 408      19.075  45.315  64.463  1.00  0.00          C   \nATOM   3111  CG  TRP A 408      19.666  46.683  64.783  1.00  0.00          C   \nATOM   3112  CD1 TRP A 408      20.795  47.246  64.242  1.00  0.00          C   \nATOM   3113  CD2 TRP A 408      19.151  47.643  65.721  1.00  0.00          C   \nATOM   3114  NE1 TRP A 408      21.013  48.496  64.793  1.00  0.00          N   \nATOM   3115  CE2 TRP A 408      20.021  48.758  65.698  1.00  0.00          C   \nATOM   3116  CE3 TRP A 408      18.046  47.664  66.580  1.00  0.00          C   \nATOM   3117  CZ2 TRP A 408      19.808  49.884  66.500  1.00  0.00          C   \nATOM   3118  CZ3 TRP A 408      17.841  48.786  67.376  1.00  0.00          C   \nATOM   3119  CH2 TRP A 408      18.721  49.877  67.328  1.00  0.00          C   \nATOM   3120  N   LEU A 409      16.971  47.496  63.461  1.00  0.00          N   \nATOM   3121  CA  LEU A 409      15.953  48.478  63.829  1.00  0.00          C   \nATOM   3122  C   LEU A 409      14.706  48.315  62.981  1.00  0.00          C   \nATOM   3123  O   LEU A 409      13.584  48.392  63.489  1.00  0.00          O   \nATOM   3124  CB  LEU A 409      16.499  49.898  63.670  1.00  0.00          C   \nATOM   3125  CG  LEU A 409      15.520  51.029  63.982  1.00  0.00          C   \nATOM   3126  CD1 LEU A 409      14.854  50.800  65.332  1.00  0.00          C   \nATOM   3127  CD2 LEU A 409      16.268  52.340  63.960  1.00  0.00          C   \nATOM   3128  N   GLY A 410      14.916  48.097  61.685  1.00  0.00          N   \nATOM   3129  CA  GLY A 410      13.802  47.889  60.787  1.00  0.00          C   \nATOM   3130  C   GLY A 410      13.063  46.626  61.189  1.00  0.00          C   \nATOM   3131  O   GLY A 410      11.832  46.607  61.164  1.00  0.00          O   \nATOM   3132  N   ASN A 411      13.793  45.566  61.556  1.00  0.00          N   \nATOM   3133  CA  ASN A 411      13.142  44.317  61.961  1.00  0.00          C   \nATOM   3134  C   ASN A 411      12.384  44.434  63.272  1.00  0.00          C   \nATOM   3135  O   ASN A 411      11.350  43.776  63.470  1.00  0.00          O   \nATOM   3136  CB  ASN A 411      14.151  43.160  62.026  1.00  0.00          C   \nATOM   3137  CG  ASN A 411      14.465  42.619  60.656  1.00  0.00          C   \nATOM   3138  OD1 ASN A 411      13.556  42.444  59.854  1.00  0.00          O   \nATOM   3139  ND2 ASN A 411      15.736  42.361  60.371  1.00  0.00          N   \nATOM   3140  N   ILE A 412      12.880  45.272  64.174  1.00  0.00          N   \nATOM   3141  CA  ILE A 412      12.174  45.470  65.436  1.00  0.00          C   \nATOM   3142  C   ILE A 412      10.850  46.180  65.133  1.00  0.00          C   \nATOM   3143  O   ILE A 412       9.799  45.847  65.685  1.00  0.00          O   \nATOM   3144  CB  ILE A 412      13.010  46.317  66.421  1.00  0.00          C   \nATOM   3145  CG1 ILE A 412      14.084  45.420  67.059  1.00  0.00          C   \nATOM   3146  CG2 ILE A 412      12.095  46.965  67.463  1.00  0.00          C   \nATOM   3147  CD1 ILE A 412      14.962  46.103  68.072  1.00  0.00          C   \nATOM   3148  N   ILE A 413      10.907  47.154  64.235  1.00  0.00          N   \nATOM   3149  CA  ILE A 413       9.721  47.898  63.873  1.00  0.00          C   \nATOM   3150  C   ILE A 413       8.685  47.017  63.192  1.00  0.00          C   \nATOM   3151  O   ILE A 413       7.522  47.020  63.568  1.00  0.00          O   \nATOM   3152  CB  ILE A 413      10.080  49.071  62.940  1.00  0.00          C   \nATOM   3153  CG1 ILE A 413      10.815  50.145  63.742  1.00  0.00          C   \nATOM   3154  CG2 ILE A 413       8.825  49.659  62.316  1.00  0.00          C   \nATOM   3155  CD1 ILE A 413      11.418  51.242  62.914  1.00  0.00          C   \nATOM   3156  N   MET A 414       9.118  46.247  62.202  1.00  0.00          N   \nATOM   3157  CA  MET A 414       8.203  45.411  61.445  1.00  0.00          C   \nATOM   3158  C   MET A 414       7.781  44.121  62.133  1.00  0.00          C   \nATOM   3159  O   MET A 414       6.678  43.625  61.905  1.00  0.00          O   \nATOM   3160  CB  MET A 414       8.818  45.112  60.074  1.00  0.00          C   \nATOM   3161  CG  MET A 414       9.108  46.373  59.242  1.00  0.00          C   \nATOM   3162  SD  MET A 414       7.595  47.269  58.668  1.00  0.00          S   \nATOM   3163  CE  MET A 414       7.138  46.220  57.285  1.00  0.00          C   \nATOM   3164  N   TYR A 415       8.637  43.586  62.990  1.00  0.00          N   \nATOM   3165  CA  TYR A 415       8.321  42.341  63.689  1.00  0.00          C   \nATOM   3166  C   TYR A 415       8.266  42.497  65.199  1.00  0.00          C   \nATOM   3167  O   TYR A 415       8.572  41.567  65.939  1.00  0.00          O   \nATOM   3168  CB  TYR A 415       9.347  41.276  63.288  1.00  0.00          C   \nATOM   3169  CG  TYR A 415       9.263  40.944  61.806  1.00  0.00          C   \nATOM   3170  CD1 TYR A 415       8.204  40.185  61.307  1.00  0.00          C   \nATOM   3171  CD2 TYR A 415      10.211  41.436  60.901  1.00  0.00          C   \nATOM   3172  CE1 TYR A 415       8.081  39.923  59.945  1.00  0.00          C   \nATOM   3173  CE2 TYR A 415      10.104  41.173  59.524  1.00  0.00          C   \nATOM   3174  CZ  TYR A 415       9.030  40.415  59.058  1.00  0.00          C   \nATOM   3175  OH  TYR A 415       8.899  40.151  57.708  1.00  0.00          O   \nATOM   3176  N   ALA A 416       7.853  43.677  65.651  1.00  0.00          N   \nATOM   3177  CA  ALA A 416       7.763  43.980  67.081  1.00  0.00          C   \nATOM   3178  C   ALA A 416       7.042  42.965  67.965  1.00  0.00          C   \nATOM   3179  O   ALA A 416       7.486  42.708  69.072  1.00  0.00          O   \nATOM   3180  CB  ALA A 416       7.134  45.358  67.274  1.00  0.00          C   \nATOM   3181  N   PRO A 417       5.916  42.387  67.505  1.00  0.00          N   \nATOM   3182  CA  PRO A 417       5.166  41.407  68.303  1.00  0.00          C   \nATOM   3183  C   PRO A 417       5.810  40.013  68.426  1.00  0.00          C   \nATOM   3184  O   PRO A 417       5.413  39.213  69.275  1.00  0.00          O   \nATOM   3185  CB  PRO A 417       3.815  41.308  67.570  1.00  0.00          C   \nATOM   3186  CG  PRO A 417       3.745  42.530  66.733  1.00  0.00          C   \nATOM   3187  CD  PRO A 417       5.174  42.716  66.277  1.00  0.00          C   \nATOM   3188  N   THR A 418       6.801  39.727  67.589  1.00  0.00          N   \nATOM   3189  CA  THR A 418       7.418  38.399  67.587  1.00  0.00          C   \nATOM   3190  C   THR A 418       8.257  38.005  68.781  1.00  0.00          C   \nATOM   3191  O   THR A 418       8.879  38.848  69.439  1.00  0.00          O   \nATOM   3192  CB  THR A 418       8.277  38.188  66.319  1.00  0.00          C   \nATOM   3193  OG1 THR A 418       9.495  38.943  66.428  1.00  0.00          O   \nATOM   3194  CG2 THR A 418       7.497  38.663  65.078  1.00  0.00          C   \nATOM   3195  N   LEU A 419       8.282  36.703  69.048  1.00  0.00          N   \nATOM   3196  CA  LEU A 419       9.062  36.177  70.161  1.00  0.00          C   \nATOM   3197  C   LEU A 419      10.541  36.535  69.993  1.00  0.00          C   \nATOM   3198  O   LEU A 419      11.182  37.027  70.929  1.00  0.00          O   \nATOM   3199  CB  LEU A 419       8.887  34.652  70.239  1.00  0.00          C   \nATOM   3200  CG  LEU A 419       9.813  33.836  71.140  1.00  0.00          C   \nATOM   3201  CD1 LEU A 419       9.612  34.213  72.603  1.00  0.00          C   \nATOM   3202  CD2 LEU A 419       9.522  32.340  70.912  1.00  0.00          C   \nATOM   3203  N   TRP A 420      11.073  36.306  68.789  1.00  0.00          N   \nATOM   3204  CA  TRP A 420      12.482  36.569  68.508  1.00  0.00          C   \nATOM   3205  C   TRP A 420      12.891  38.044  68.474  1.00  0.00          C   \nATOM   3206  O   TRP A 420      13.981  38.396  68.934  1.00  0.00          O   \nATOM   3207  CB  TRP A 420      12.910  35.876  67.201  1.00  0.00          C   \nATOM   3208  CG  TRP A 420      12.051  36.192  66.025  1.00  0.00          C   \nATOM   3209  CD1 TRP A 420      10.864  35.594  65.684  1.00  0.00          C   \nATOM   3210  CD2 TRP A 420      12.282  37.221  65.050  1.00  0.00          C   \nATOM   3211  NE1 TRP A 420      10.336  36.203  64.560  1.00  0.00          N   \nATOM   3212  CE2 TRP A 420      11.175  37.203  64.162  1.00  0.00          C   \nATOM   3213  CE3 TRP A 420      13.302  38.170  64.862  1.00  0.00          C   \nATOM   3214  CZ2 TRP A 420      11.083  38.089  63.067  1.00  0.00          C   \nATOM   3215  CZ3 TRP A 420      13.209  39.048  63.778  1.00  0.00          C   \nATOM   3216  CH2 TRP A 420      12.094  39.005  62.896  1.00  0.00          C   \nATOM   3217  N   ALA A 421      12.046  38.914  67.927  1.00  0.00          N   \nATOM   3218  CA  ALA A 421      12.421  40.327  67.891  1.00  0.00          C   \nATOM   3219  C   ALA A 421      12.472  40.899  69.312  1.00  0.00          C   \nATOM   3220  O   ALA A 421      13.362  41.683  69.652  1.00  0.00          O   \nATOM   3221  CB  ALA A 421      11.424  41.140  67.023  1.00  0.00          C   \nATOM   3222  N   ARG A 422      11.518  40.497  70.144  1.00  0.00          N   \nATOM   3223  CA  ARG A 422      11.452  40.999  71.520  1.00  0.00          C   \nATOM   3224  C   ARG A 422      12.514  40.413  72.441  1.00  0.00          C   \nATOM   3225  O   ARG A 422      13.200  41.140  73.158  1.00  0.00          O   \nATOM   3226  CB  ARG A 422      10.075  40.710  72.107  1.00  0.00          C   \nATOM   3227  CG  ARG A 422       8.936  41.273  71.292  1.00  0.00          C   \nATOM   3228  CD  ARG A 422       7.597  40.690  71.756  1.00  0.00          C   \nATOM   3229  NE  ARG A 422       7.207  41.167  73.083  1.00  0.00          N   \nATOM   3230  CZ  ARG A 422       6.253  40.611  73.825  1.00  0.00          C   \nATOM   3231  NH1 ARG A 422       5.593  39.545  73.379  1.00  0.00          N   \nATOM   3232  NH2 ARG A 422       5.928  41.146  75.000  1.00  0.00          N   \nATOM   3233  N   MET A 423      12.643  39.091  72.428  1.00  0.00          N   \nATOM   3234  CA  MET A 423      13.623  38.440  73.283  1.00  0.00          C   \nATOM   3235  C   MET A 423      15.054  38.804  72.942  1.00  0.00          C   \nATOM   3236  O   MET A 423      15.853  39.068  73.833  1.00  0.00          O   \nATOM   3237  CB  MET A 423      13.490  36.908  73.194  1.00  0.00          C   \nATOM   3238  CG  MET A 423      14.502  36.126  74.030  1.00  0.00          C   \nATOM   3239  SD  MET A 423      14.122  34.328  74.025  1.00  0.00          S   \nATOM   3240  CE  MET A 423      14.592  33.920  72.360  1.00  0.00          C   \nATOM   3241  N   ILE A 424      15.363  38.830  71.650  1.00  0.00          N   \nATOM   3242  CA  ILE A 424      16.729  39.048  71.200  1.00  0.00          C   \nATOM   3243  C   ILE A 424      17.153  40.406  70.660  1.00  0.00          C   \nATOM   3244  O   ILE A 424      18.060  41.032  71.216  1.00  0.00          O   \nATOM   3245  CB  ILE A 424      17.096  37.960  70.147  1.00  0.00          C   \nATOM   3246  CG1 ILE A 424      16.845  36.571  70.745  1.00  0.00          C   \nATOM   3247  CG2 ILE A 424      18.557  38.094  69.722  1.00  0.00          C   \nATOM   3248  CD1 ILE A 424      16.915  35.436  69.746  1.00  0.00          C   \nATOM   3249  N   LEU A 425      16.528  40.861  69.578  1.00  0.00          N   \nATOM   3250  CA  LEU A 425      16.937  42.133  68.989  1.00  0.00          C   \nATOM   3251  C   LEU A 425      16.788  43.315  69.924  1.00  0.00          C   \nATOM   3252  O   LEU A 425      17.686  44.149  70.021  1.00  0.00          O   \nATOM   3253  CB  LEU A 425      16.161  42.398  67.701  1.00  0.00          C   \nATOM   3254  CG  LEU A 425      16.376  41.380  66.576  1.00  0.00          C   \nATOM   3255  CD1 LEU A 425      15.516  41.778  65.361  1.00  0.00          C   \nATOM   3256  CD2 LEU A 425      17.856  41.333  66.207  1.00  0.00          C   \nATOM   3257  N   MET A 426      15.642  43.408  70.589  1.00  0.00          N   \nATOM   3258  CA  MET A 426      15.403  44.507  71.511  1.00  0.00          C   \nATOM   3259  C   MET A 426      16.419  44.472  72.657  1.00  0.00          C   \nATOM   3260  O   MET A 426      17.079  45.466  72.940  1.00  0.00          O   \nATOM   3261  CB  MET A 426      13.981  44.434  72.068  1.00  0.00          C   \nATOM   3262  CG  MET A 426      12.912  44.992  71.115  1.00  0.00          C   \nATOM   3263  SD  MET A 426      11.285  44.915  71.905  1.00  0.00          S   \nATOM   3264  CE  MET A 426      10.201  45.166  70.449  1.00  0.00          C   \nATOM   3265  N   THR A 427      16.565  43.316  73.290  1.00  0.00          N   \nATOM   3266  CA  THR A 427      17.488  43.184  74.407  1.00  0.00          C   \nATOM   3267  C   THR A 427      18.925  43.490  73.998  1.00  0.00          C   \nATOM   3268  O   THR A 427      19.603  44.319  74.611  1.00  0.00          O   \nATOM   3269  CB  THR A 427      17.421  41.779  74.979  1.00  0.00          C   \nATOM   3270  OG1 THR A 427      16.050  41.445  75.201  1.00  0.00          O   \nATOM   3271  CG2 THR A 427      18.198  41.684  76.305  1.00  0.00          C   \nATOM   3272  N   HIS A 428      19.366  42.836  72.934  1.00  0.00          N   \nATOM   3273  CA  HIS A 428      20.722  42.992  72.441  1.00  0.00          C   \nATOM   3274  C   HIS A 428      21.100  44.421  72.042  1.00  0.00          C   \nATOM   3275  O   HIS A 428      22.109  44.966  72.494  1.00  0.00          O   \nATOM   3276  CB  HIS A 428      20.923  42.057  71.261  1.00  0.00          C   \nATOM   3277  CG  HIS A 428      22.317  42.043  70.731  1.00  0.00          C   \nATOM   3278  ND1 HIS A 428      23.378  41.521  71.442  1.00  0.00          N   \nATOM   3279  CD2 HIS A 428      22.823  42.449  69.542  1.00  0.00          C   \nATOM   3280  CE1 HIS A 428      24.474  41.601  70.708  1.00  0.00          C   \nATOM   3281  NE2 HIS A 428      24.164  42.160  69.550  1.00  0.00          N   \nATOM   3282  N   PHE A 429      20.297  45.040  71.200  1.00  0.00          N   \nATOM   3283  CA  PHE A 429      20.639  46.379  70.762  1.00  0.00          C   \nATOM   3284  C   PHE A 429      20.407  47.487  71.786  1.00  0.00          C   \nATOM   3285  O   PHE A 429      21.173  48.460  71.836  1.00  0.00          O   \nATOM   3286  CB  PHE A 429      19.957  46.652  69.416  1.00  0.00          C   \nATOM   3287  CG  PHE A 429      20.636  45.947  68.274  1.00  0.00          C   \nATOM   3288  CD1 PHE A 429      21.850  46.415  67.780  1.00  0.00          C   \nATOM   3289  CD2 PHE A 429      20.149  44.728  67.801  1.00  0.00          C   \nATOM   3290  CE1 PHE A 429      22.576  45.677  66.830  1.00  0.00          C   \nATOM   3291  CE2 PHE A 429      20.870  43.982  66.858  1.00  0.00          C   \nATOM   3292  CZ  PHE A 429      22.081  44.454  66.378  1.00  0.00          C   \nATOM   3293  N   PHE A 430      19.378  47.361  72.620  1.00  0.00          N   \nATOM   3294  CA  PHE A 430      19.190  48.395  73.621  1.00  0.00          C   \nATOM   3295  C   PHE A 430      20.332  48.319  74.624  1.00  0.00          C   \nATOM   3296  O   PHE A 430      20.784  49.338  75.130  1.00  0.00          O   \nATOM   3297  CB  PHE A 430      17.835  48.281  74.314  1.00  0.00          C   \nATOM   3298  CG  PHE A 430      16.774  49.104  73.647  1.00  0.00          C   \nATOM   3299  CD1 PHE A 430      16.074  48.609  72.557  1.00  0.00          C   \nATOM   3300  CD2 PHE A 430      16.539  50.415  74.054  1.00  0.00          C   \nATOM   3301  CE1 PHE A 430      15.159  49.408  71.873  1.00  0.00          C   \nATOM   3302  CE2 PHE A 430      15.628  51.221  73.381  1.00  0.00          C   \nATOM   3303  CZ  PHE A 430      14.934  50.713  72.287  1.00  0.00          C   \nATOM   3304  N   SER A 431      20.813  47.105  74.878  1.00  0.00          N   \nATOM   3305  CA  SER A 431      21.921  46.918  75.792  1.00  0.00          C   \nATOM   3306  C   SER A 431      23.129  47.663  75.229  1.00  0.00          C   \nATOM   3307  O   SER A 431      23.803  48.403  75.941  1.00  0.00          O   \nATOM   3308  CB  SER A 431      22.227  45.433  75.930  1.00  0.00          C   \nATOM   3309  OG  SER A 431      23.401  45.232  76.680  1.00  0.00          O   \nATOM   3310  N   ILE A 432      23.389  47.492  73.936  1.00  0.00          N   \nATOM   3311  CA  ILE A 432      24.509  48.179  73.308  1.00  0.00          C   \nATOM   3312  C   ILE A 432      24.287  49.704  73.316  1.00  0.00          C   \nATOM   3313  O   ILE A 432      25.188  50.457  73.672  1.00  0.00          O   \nATOM   3314  CB  ILE A 432      24.715  47.704  71.854  1.00  0.00          C   \nATOM   3315  CG1 ILE A 432      25.133  46.231  71.840  1.00  0.00          C   \nATOM   3316  CG2 ILE A 432      25.796  48.545  71.175  1.00  0.00          C   \nATOM   3317  CD1 ILE A 432      25.090  45.614  70.441  1.00  0.00          C   \nATOM   3318  N   LEU A 433      23.099  50.158  72.921  1.00  0.00          N   \nATOM   3319  CA  LEU A 433      22.814  51.593  72.915  1.00  0.00          C   \nATOM   3320  C   LEU A 433      23.056  52.225  74.287  1.00  0.00          C   \nATOM   3321  O   LEU A 433      23.614  53.316  74.379  1.00  0.00          O   \nATOM   3322  CB  LEU A 433      21.374  51.861  72.480  1.00  0.00          C   \nATOM   3323  CG  LEU A 433      21.008  51.545  71.033  1.00  0.00          C   \nATOM   3324  CD1 LEU A 433      19.536  51.903  70.815  1.00  0.00          C   \nATOM   3325  CD2 LEU A 433      21.908  52.309  70.075  1.00  0.00          C   \nATOM   3326  N   LEU A 434      22.629  51.541  75.343  1.00  0.00          N   \nATOM   3327  CA  LEU A 434      22.830  52.032  76.700  1.00  0.00          C   \nATOM   3328  C   LEU A 434      24.324  52.189  76.979  1.00  0.00          C   \nATOM   3329  O   LEU A 434      24.793  53.268  77.345  1.00  0.00          O   \nATOM   3330  CB  LEU A 434      22.239  51.049  77.726  1.00  0.00          C   \nATOM   3331  CG  LEU A 434      20.767  51.180  78.140  1.00  0.00          C   \nATOM   3332  CD1 LEU A 434      20.315  49.910  78.850  1.00  0.00          C   \nATOM   3333  CD2 LEU A 434      20.586  52.384  79.047  1.00  0.00          C   \nATOM   3334  N   ALA A 435      25.062  51.099  76.793  1.00  0.00          N   \nATOM   3335  CA  ALA A 435      26.498  51.081  77.037  1.00  0.00          C   \nATOM   3336  C   ALA A 435      27.257  52.171  76.284  1.00  0.00          C   \nATOM   3337  O   ALA A 435      28.231  52.727  76.791  1.00  0.00          O   \nATOM   3338  CB  ALA A 435      27.062  49.703  76.682  1.00  0.00          C   \nATOM   3339  N   GLN A 436      26.816  52.472  75.068  1.00  0.00          N   \nATOM   3340  CA  GLN A 436      27.468  53.491  74.256  1.00  0.00          C   \nATOM   3341  C   GLN A 436      26.827  54.869  74.431  1.00  0.00          C   \nATOM   3342  O   GLN A 436      27.272  55.848  73.834  1.00  0.00          O   \nATOM   3343  CB  GLN A 436      27.402  53.089  72.779  1.00  0.00          C   \nATOM   3344  CG  GLN A 436      28.086  51.772  72.439  1.00  0.00          C   \nATOM   3345  CD  GLN A 436      29.563  51.778  72.786  1.00  0.00          C   \nATOM   3346  OE1 GLN A 436      30.291  52.699  72.424  1.00  0.00          O   \nATOM   3347  NE2 GLN A 436      30.011  50.746  73.489  1.00  0.00          N   \nATOM   3348  N   GLU A 437      25.790  54.949  75.255  1.00  0.00          N   \nATOM   3349  CA  GLU A 437      25.082  56.207  75.454  1.00  0.00          C   \nATOM   3350  C   GLU A 437      24.706  56.808  74.103  1.00  0.00          C   \nATOM   3351  O   GLU A 437      25.069  57.943  73.788  1.00  0.00          O   \nATOM   3352  CB  GLU A 437      25.939  57.201  76.250  1.00  0.00          C   \nATOM   3353  CG  GLU A 437      25.873  56.995  77.761  1.00  0.00          C   \nATOM   3354  CD  GLU A 437      26.740  57.979  78.534  1.00  0.00          C   \nATOM   3355  OE1 GLU A 437      26.551  59.206  78.382  1.00  0.00          O   \nATOM   3356  OE2 GLU A 437      27.613  57.519  79.299  1.00  0.00          O   \nATOM   3357  N   GLN A 438      23.986  56.027  73.303  1.00  0.00          N   \nATOM   3358  CA  GLN A 438      23.542  56.470  71.988  1.00  0.00          C   \nATOM   3359  C   GLN A 438      22.056  56.206  71.792  1.00  0.00          C   \nATOM   3360  O   GLN A 438      21.599  56.002  70.670  1.00  0.00          O   \nATOM   3361  CB  GLN A 438      24.331  55.766  70.891  1.00  0.00          C   \nATOM   3362  CG  GLN A 438      25.820  55.963  71.019  1.00  0.00          C   \nATOM   3363  CD  GLN A 438      26.557  55.588  69.761  1.00  0.00          C   \nATOM   3364  OE1 GLN A 438      26.241  54.583  69.121  1.00  0.00          O   \nATOM   3365  NE2 GLN A 438      27.555  56.389  69.399  1.00  0.00          N   \nATOM   3366  N   LEU A 439      21.310  56.210  72.890  1.00  0.00          N   \nATOM   3367  CA  LEU A 439      19.871  55.988  72.841  1.00  0.00          C   \nATOM   3368  C   LEU A 439      19.135  56.973  71.937  1.00  0.00          C   \nATOM   3369  O   LEU A 439      18.250  56.583  71.178  1.00  0.00          O   \nATOM   3370  CB  LEU A 439      19.278  56.074  74.252  1.00  0.00          C   \nATOM   3371  CG  LEU A 439      19.030  54.787  75.047  1.00  0.00          C   \nATOM   3372  CD1 LEU A 439      19.862  53.668  74.498  1.00  0.00          C   \nATOM   3373  CD2 LEU A 439      19.338  55.023  76.524  1.00  0.00          C   \nATOM   3374  N   GLU A 440      19.503  58.248  72.005  1.00  0.00          N   \nATOM   3375  CA  GLU A 440      18.821  59.262  71.211  1.00  0.00          C   \nATOM   3376  C   GLU A 440      19.400  59.580  69.840  1.00  0.00          C   \nATOM   3377  O   GLU A 440      18.973  60.531  69.191  1.00  0.00          O   \nATOM   3378  CB  GLU A 440      18.690  60.546  72.032  1.00  0.00          C   \nATOM   3379  CG  GLU A 440      19.931  60.936  72.798  1.00  0.00          C   \nATOM   3380  CD  GLU A 440      19.629  61.929  73.904  1.00  0.00          C   \nATOM   3381  OE1 GLU A 440      20.572  62.324  74.624  1.00  0.00          O   \nATOM   3382  OE2 GLU A 440      18.449  62.316  74.056  1.00  0.00          O   \nATOM   3383  N   LYS A 441      20.357  58.777  69.391  1.00  0.00          N   \nATOM   3384  CA  LYS A 441      20.962  58.987  68.085  1.00  0.00          C   \nATOM   3385  C   LYS A 441      20.068  58.359  67.012  1.00  0.00          C   \nATOM   3386  O   LYS A 441      19.798  57.160  67.051  1.00  0.00          O   \nATOM   3387  CB  LYS A 441      22.352  58.349  68.054  1.00  0.00          C   \nATOM   3388  CG  LYS A 441      23.087  58.492  66.727  1.00  0.00          C   \nATOM   3389  CD  LYS A 441      24.353  57.645  66.719  1.00  0.00          C   \nATOM   3390  CE  LYS A 441      25.060  57.690  65.371  1.00  0.00          C   \nATOM   3391  NZ  LYS A 441      26.261  56.800  65.368  1.00  0.00          N   \nATOM   3392  N   ALA A 442      19.613  59.173  66.062  1.00  0.00          N   \nATOM   3393  CA  ALA A 442      18.750  58.695  64.981  1.00  0.00          C   \nATOM   3394  C   ALA A 442      19.529  57.796  64.039  1.00  0.00          C   \nATOM   3395  O   ALA A 442      20.642  58.128  63.635  1.00  0.00          O   \nATOM   3396  CB  ALA A 442      18.171  59.871  64.207  1.00  0.00          C   \nATOM   3397  N   LEU A 443      18.933  56.663  63.683  1.00  0.00          N   \nATOM   3398  CA  LEU A 443      19.573  55.698  62.795  1.00  0.00          C   \nATOM   3399  C   LEU A 443      18.732  55.450  61.541  1.00  0.00          C   \nATOM   3400  O   LEU A 443      17.500  55.467  61.596  1.00  0.00          O   \nATOM   3401  CB  LEU A 443      19.780  54.374  63.539  1.00  0.00          C   \nATOM   3402  CG  LEU A 443      20.674  54.397  64.784  1.00  0.00          C   \nATOM   3403  CD1 LEU A 443      20.455  53.128  65.590  1.00  0.00          C   \nATOM   3404  CD2 LEU A 443      22.137  54.516  64.374  1.00  0.00          C   \nATOM   3405  N   ASP A 444      19.394  55.213  60.415  1.00  0.00          N   \nATOM   3406  CA  ASP A 444      18.677  54.950  59.175  1.00  0.00          C   \nATOM   3407  C   ASP A 444      18.335  53.474  59.017  1.00  0.00          C   \nATOM   3408  O   ASP A 444      19.125  52.602  59.390  1.00  0.00          O   \nATOM   3409  CB  ASP A 444      19.510  55.385  57.961  1.00  0.00          C   \nATOM   3410  CG  ASP A 444      19.822  56.869  57.965  1.00  0.00          C   \nATOM   3411  OD1 ASP A 444      18.990  57.657  58.468  1.00  0.00          O   \nATOM   3412  OD2 ASP A 444      20.892  57.250  57.449  1.00  0.00          O   \nATOM   3413  N   CYS A 445      17.152  53.208  58.468  1.00  0.00          N   \nATOM   3414  CA  CYS A 445      16.697  51.848  58.195  1.00  0.00          C   \nATOM   3415  C   CYS A 445      15.732  51.942  57.023  1.00  0.00          C   \nATOM   3416  O   CYS A 445      15.335  53.043  56.641  1.00  0.00          O   \nATOM   3417  CB  CYS A 445      15.993  51.239  59.417  1.00  0.00          C   \nATOM   3418  SG  CYS A 445      14.508  52.111  59.970  1.00  0.00          S   \nATOM   3419  N   GLN A 446      15.356  50.803  56.446  1.00  0.00          N   \nATOM   3420  CA  GLN A 446      14.433  50.821  55.317  1.00  0.00          C   \nATOM   3421  C   GLN A 446      13.144  50.070  55.594  1.00  0.00          C   \nATOM   3422  O   GLN A 446      13.142  49.026  56.248  1.00  0.00          O   \nATOM   3423  CB  GLN A 446      15.094  50.238  54.061  1.00  0.00          C   \nATOM   3424  CG  GLN A 446      16.343  50.968  53.600  1.00  0.00          C   \nATOM   3425  CD  GLN A 446      16.913  50.385  52.313  1.00  0.00          C   \nATOM   3426  OE1 GLN A 446      17.018  49.164  52.161  1.00  0.00          O   \nATOM   3427  NE2 GLN A 446      17.290  51.258  51.382  1.00  0.00          N   \nATOM   3428  N   ILE A 447      12.046  50.617  55.087  1.00  0.00          N   \nATOM   3429  CA  ILE A 447      10.725  50.016  55.242  1.00  0.00          C   \nATOM   3430  C   ILE A 447      10.134  50.007  53.844  1.00  0.00          C   \nATOM   3431  O   ILE A 447       9.850  51.065  53.279  1.00  0.00          O   \nATOM   3432  CB  ILE A 447       9.813  50.856  56.186  1.00  0.00          C   \nATOM   3433  CG1 ILE A 447      10.383  50.841  57.610  1.00  0.00          C   \nATOM   3434  CG2 ILE A 447       8.382  50.307  56.179  1.00  0.00          C   \nATOM   3435  CD1 ILE A 447       9.554  51.632  58.606  1.00  0.00          C   \nATOM   3436  N   TYR A 448       9.964  48.812  53.288  1.00  0.00          N   \nATOM   3437  CA  TYR A 448       9.435  48.647  51.938  1.00  0.00          C   \nATOM   3438  C   TYR A 448      10.336  49.371  50.947  1.00  0.00          C   \nATOM   3439  O   TYR A 448       9.862  49.954  49.968  1.00  0.00          O   \nATOM   3440  CB  TYR A 448       7.998  49.182  51.825  1.00  0.00          C   \nATOM   3441  CG  TYR A 448       6.945  48.355  52.533  1.00  0.00          C   \nATOM   3442  CD1 TYR A 448       7.219  47.057  52.974  1.00  0.00          C   \nATOM   3443  CD2 TYR A 448       5.660  48.870  52.748  1.00  0.00          C   \nATOM   3444  CE1 TYR A 448       6.239  46.291  53.616  1.00  0.00          C   \nATOM   3445  CE2 TYR A 448       4.673  48.117  53.382  1.00  0.00          C   \nATOM   3446  CZ  TYR A 448       4.966  46.829  53.816  1.00  0.00          C   \nATOM   3447  OH  TYR A 448       3.989  46.089  54.451  1.00  0.00          O   \nATOM   3448  N   GLY A 449      11.638  49.350  51.226  1.00  0.00          N   \nATOM   3449  CA  GLY A 449      12.597  49.970  50.332  1.00  0.00          C   \nATOM   3450  C   GLY A 449      12.914  51.431  50.577  1.00  0.00          C   \nATOM   3451  O   GLY A 449      13.971  51.900  50.166  1.00  0.00          O   \nATOM   3452  N   ALA A 450      12.006  52.154  51.225  1.00  0.00          N   \nATOM   3453  CA  ALA A 450      12.236  53.570  51.510  1.00  0.00          C   \nATOM   3454  C   ALA A 450      13.081  53.724  52.776  1.00  0.00          C   \nATOM   3455  O   ALA A 450      12.895  52.988  53.753  1.00  0.00          O   \nATOM   3456  CB  ALA A 450      10.909  54.296  51.684  1.00  0.00          C   \nATOM   3457  N   CYS A 451      13.999  54.686  52.751  1.00  0.00          N   \nATOM   3458  CA  CYS A 451      14.879  54.938  53.883  1.00  0.00          C   \nATOM   3459  C   CYS A 451      14.267  55.956  54.832  1.00  0.00          C   \nATOM   3460  O   CYS A 451      13.743  56.990  54.408  1.00  0.00          O   \nATOM   3461  CB  CYS A 451      16.251  55.434  53.398  1.00  0.00          C   \nATOM   3462  SG  CYS A 451      17.501  55.617  54.720  1.00  0.00          S   \nATOM   3463  N   TYR A 452      14.345  55.644  56.122  1.00  0.00          N   \nATOM   3464  CA  TYR A 452      13.816  56.486  57.185  1.00  0.00          C   \nATOM   3465  C   TYR A 452      14.889  56.725  58.252  1.00  0.00          C   \nATOM   3466  O   TYR A 452      15.758  55.883  58.480  1.00  0.00          O   \nATOM   3467  CB  TYR A 452      12.615  55.795  57.840  1.00  0.00          C   \nATOM   3468  CG  TYR A 452      11.401  55.657  56.945  1.00  0.00          C   \nATOM   3469  CD1 TYR A 452      10.378  56.607  56.974  1.00  0.00          C   \nATOM   3470  CD2 TYR A 452      11.281  54.585  56.063  1.00  0.00          C   \nATOM   3471  CE1 TYR A 452       9.261  56.490  56.143  1.00  0.00          C   \nATOM   3472  CE2 TYR A 452      10.173  54.455  55.229  1.00  0.00          C   \nATOM   3473  CZ  TYR A 452       9.167  55.409  55.272  1.00  0.00          C   \nATOM   3474  OH  TYR A 452       8.076  55.290  54.438  1.00  0.00          O   \nATOM   3475  N   SER A 453      14.827  57.884  58.895  1.00  0.00          N   \nATOM   3476  CA  SER A 453      15.756  58.214  59.969  1.00  0.00          C   \nATOM   3477  C   SER A 453      14.905  58.025  61.214  1.00  0.00          C   \nATOM   3478  O   SER A 453      13.869  58.684  61.364  1.00  0.00          O   \nATOM   3479  CB  SER A 453      16.220  59.665  59.867  1.00  0.00          C   \nATOM   3480  OG  SER A 453      17.084  59.982  60.944  1.00  0.00          O   \nATOM   3481  N   ILE A 454      15.324  57.123  62.098  1.00  0.00          N   \nATOM   3482  CA  ILE A 454      14.541  56.838  63.293  1.00  0.00          C   \nATOM   3483  C   ILE A 454      15.337  56.798  64.592  1.00  0.00          C   \nATOM   3484  O   ILE A 454      16.422  56.214  64.654  1.00  0.00          O   \nATOM   3485  CB  ILE A 454      13.799  55.492  63.125  1.00  0.00          C   \nATOM   3486  CG1 ILE A 454      12.801  55.607  61.966  1.00  0.00          C   \nATOM   3487  CG2 ILE A 454      13.106  55.095  64.432  1.00  0.00          C   \nATOM   3488  CD1 ILE A 454      12.118  54.322  61.638  1.00  0.00          C   \nATOM   3489  N   GLU A 455      14.773  57.413  65.627  1.00  0.00          N   \nATOM   3490  CA  GLU A 455      15.396  57.456  66.944  1.00  0.00          C   \nATOM   3491  C   GLU A 455      14.852  56.290  67.760  1.00  0.00          C   \nATOM   3492  O   GLU A 455      13.644  56.184  67.988  1.00  0.00          O   \nATOM   3493  CB  GLU A 455      15.079  58.785  67.644  1.00  0.00          C   \nATOM   3494  CG  GLU A 455      15.539  60.015  66.865  1.00  0.00          C   \nATOM   3495  CD  GLU A 455      15.322  61.307  67.620  1.00  0.00          C   \nATOM   3496  OE1 GLU A 455      14.193  61.535  68.103  1.00  0.00          O   \nATOM   3497  OE2 GLU A 455      16.281  62.104  67.722  1.00  0.00          O   \nATOM   3498  N   PRO A 456      15.743  55.387  68.196  1.00  0.00          N   \nATOM   3499  CA  PRO A 456      15.395  54.204  68.990  1.00  0.00          C   \nATOM   3500  C   PRO A 456      14.430  54.464  70.149  1.00  0.00          C   \nATOM   3501  O   PRO A 456      13.567  53.636  70.446  1.00  0.00          O   \nATOM   3502  CB  PRO A 456      16.756  53.715  69.464  1.00  0.00          C   \nATOM   3503  CG  PRO A 456      17.613  54.009  68.278  1.00  0.00          C   \nATOM   3504  CD  PRO A 456      17.189  55.413  67.911  1.00  0.00          C   \nATOM   3505  N   LEU A 457      14.580  55.617  70.793  1.00  0.00          N   \nATOM   3506  CA  LEU A 457      13.736  55.986  71.921  1.00  0.00          C   \nATOM   3507  C   LEU A 457      12.272  56.144  71.541  1.00  0.00          C   \nATOM   3508  O   LEU A 457      11.402  56.124  72.409  1.00  0.00          O   \nATOM   3509  CB  LEU A 457      14.255  57.273  72.571  1.00  0.00          C   \nATOM   3510  CG  LEU A 457      15.507  57.130  73.444  1.00  0.00          C   \nATOM   3511  CD1 LEU A 457      15.893  58.491  73.997  1.00  0.00          C   \nATOM   3512  CD2 LEU A 457      15.243  56.153  74.582  1.00  0.00          C   \nATOM   3513  N   ASP A 458      11.997  56.295  70.248  1.00  0.00          N   \nATOM   3514  CA  ASP A 458      10.617  56.430  69.787  1.00  0.00          C   \nATOM   3515  C   ASP A 458       9.990  55.086  69.457  1.00  0.00          C   \nATOM   3516  O   ASP A 458       8.837  55.033  69.029  1.00  0.00          O   \nATOM   3517  CB  ASP A 458      10.536  57.310  68.538  1.00  0.00          C   \nATOM   3518  CG  ASP A 458      10.840  58.758  68.829  1.00  0.00          C   \nATOM   3519  OD1 ASP A 458      10.460  59.227  69.926  1.00  0.00          O   \nATOM   3520  OD2 ASP A 458      11.443  59.426  67.960  1.00  0.00          O   \nATOM   3521  N   LEU A 459      10.736  54.002  69.657  1.00  0.00          N   \nATOM   3522  CA  LEU A 459      10.222  52.677  69.338  1.00  0.00          C   \nATOM   3523  C   LEU A 459       8.903  52.296  69.991  1.00  0.00          C   \nATOM   3524  O   LEU A 459       8.016  51.777  69.323  1.00  0.00          O   \nATOM   3525  CB  LEU A 459      11.288  51.609  69.621  1.00  0.00          C   \nATOM   3526  CG  LEU A 459      12.303  51.469  68.480  1.00  0.00          C   \nATOM   3527  CD1 LEU A 459      13.441  50.511  68.852  1.00  0.00          C   \nATOM   3528  CD2 LEU A 459      11.554  50.978  67.236  1.00  0.00          C   \nATOM   3529  N   PRO A 460       8.745  52.550  71.299  1.00  0.00          N   \nATOM   3530  CA  PRO A 460       7.491  52.205  71.974  1.00  0.00          C   \nATOM   3531  C   PRO A 460       6.280  52.835  71.281  1.00  0.00          C   \nATOM   3532  O   PRO A 460       5.261  52.179  71.073  1.00  0.00          O   \nATOM   3533  CB  PRO A 460       7.688  52.767  73.378  1.00  0.00          C   \nATOM   3534  CG  PRO A 460       9.172  52.689  73.569  1.00  0.00          C   \nATOM   3535  CD  PRO A 460       9.697  53.161  72.243  1.00  0.00          C   \nATOM   3536  N   GLN A 461       6.408  54.115  70.945  1.00  0.00          N   \nATOM   3537  CA  GLN A 461       5.355  54.878  70.276  1.00  0.00          C   \nATOM   3538  C   GLN A 461       5.074  54.305  68.895  1.00  0.00          C   \nATOM   3539  O   GLN A 461       3.924  54.076  68.528  1.00  0.00          O   \nATOM   3540  CB  GLN A 461       5.783  56.341  70.122  1.00  0.00          C   \nATOM   3541  CG  GLN A 461       6.101  57.034  71.428  1.00  0.00          C   \nATOM   3542  CD  GLN A 461       6.897  58.310  71.223  1.00  0.00          C   \nATOM   3543  OE1 GLN A 461       6.475  59.209  70.490  1.00  0.00          O   \nATOM   3544  NE2 GLN A 461       8.059  58.397  71.868  1.00  0.00          N   \nATOM   3545  N   ILE A 462       6.139  54.092  68.131  1.00  0.00          N   \nATOM   3546  CA  ILE A 462       6.028  53.540  66.791  1.00  0.00          C   \nATOM   3547  C   ILE A 462       5.346  52.170  66.830  1.00  0.00          C   \nATOM   3548  O   ILE A 462       4.417  51.893  66.060  1.00  0.00          O   \nATOM   3549  CB  ILE A 462       7.427  53.410  66.156  1.00  0.00          C   \nATOM   3550  CG1 ILE A 462       8.059  54.795  66.043  1.00  0.00          C   \nATOM   3551  CG2 ILE A 462       7.331  52.744  64.789  1.00  0.00          C   \nATOM   3552  CD1 ILE A 462       9.480  54.790  65.478  1.00  0.00          C   \nATOM   3553  N   ILE A 463       5.798  51.320  67.746  1.00  0.00          N   \nATOM   3554  CA  ILE A 463       5.228  49.987  67.868  1.00  0.00          C   \nATOM   3555  C   ILE A 463       3.749  50.036  68.245  1.00  0.00          C   \nATOM   3556  O   ILE A 463       2.946  49.299  67.676  1.00  0.00          O   \nATOM   3557  CB  ILE A 463       6.002  49.127  68.907  1.00  0.00          C   \nATOM   3558  CG1 ILE A 463       7.402  48.809  68.381  1.00  0.00          C   \nATOM   3559  CG2 ILE A 463       5.255  47.824  69.175  1.00  0.00          C   \nATOM   3560  CD1 ILE A 463       8.308  48.110  69.410  1.00  0.00          C   \nATOM   3561  N   GLU A 464       3.386  50.893  69.201  1.00  0.00          N   \nATOM   3562  CA  GLU A 464       1.984  50.998  69.613  1.00  0.00          C   \nATOM   3563  C   GLU A 464       1.151  51.417  68.410  1.00  0.00          C   \nATOM   3564  O   GLU A 464       0.137  50.797  68.093  1.00  0.00          O   \nATOM   3565  CB  GLU A 464       1.804  52.038  70.728  1.00  0.00          C   \nATOM   3566  CG  GLU A 464       0.382  52.074  71.309  1.00  0.00          C   \nATOM   3567  CD  GLU A 464       0.150  53.245  72.260  1.00  0.00          C   \nATOM   3568  OE1 GLU A 464       0.900  53.369  73.249  1.00  0.00          O   \nATOM   3569  OE2 GLU A 464      -0.785  54.043  72.022  1.00  0.00          O   \nATOM   3570  N   ARG A 465       1.597  52.471  67.742  1.00  0.00          N   \nATOM   3571  CA  ARG A 465       0.910  52.986  66.569  1.00  0.00          C   \nATOM   3572  C   ARG A 465       0.699  51.930  65.482  1.00  0.00          C   \nATOM   3573  O   ARG A 465      -0.414  51.751  64.992  1.00  0.00          O   \nATOM   3574  CB  ARG A 465       1.698  54.160  65.988  1.00  0.00          C   \nATOM   3575  CG  ARG A 465       1.641  55.423  66.839  1.00  0.00          C   \nATOM   3576  CD  ARG A 465       0.279  56.081  66.749  1.00  0.00          C   \nATOM   3577  NE  ARG A 465      -0.022  56.480  65.377  1.00  0.00          N   \nATOM   3578  CZ  ARG A 465       0.627  57.431  64.712  1.00  0.00          C   \nATOM   3579  NH1 ARG A 465       1.621  58.096  65.290  1.00  0.00          N   \nATOM   3580  NH2 ARG A 465       0.289  57.715  63.462  1.00  0.00          N   \nATOM   3581  N   LEU A 466       1.769  51.232  65.115  1.00  0.00          N   \nATOM   3582  CA  LEU A 466       1.708  50.217  64.071  1.00  0.00          C   \nATOM   3583  C   LEU A 466       1.104  48.877  64.477  1.00  0.00          C   \nATOM   3584  O   LEU A 466       0.393  48.255  63.695  1.00  0.00          O   \nATOM   3585  CB  LEU A 466       3.110  49.938  63.519  1.00  0.00          C   \nATOM   3586  CG  LEU A 466       4.002  51.029  62.943  1.00  0.00          C   \nATOM   3587  CD1 LEU A 466       5.214  50.345  62.316  1.00  0.00          C   \nATOM   3588  CD2 LEU A 466       3.269  51.846  61.901  1.00  0.00          C   \nATOM   3589  N   HIS A 467       1.384  48.427  65.693  1.00  0.00          N   \nATOM   3590  CA  HIS A 467       0.903  47.120  66.119  1.00  0.00          C   \nATOM   3591  C   HIS A 467      -0.128  47.094  67.230  1.00  0.00          C   \nATOM   3592  O   HIS A 467      -0.757  46.060  67.471  1.00  0.00          O   \nATOM   3593  CB  HIS A 467       2.098  46.265  66.540  1.00  0.00          C   \nATOM   3594  CG  HIS A 467       3.112  46.077  65.459  1.00  0.00          C   \nATOM   3595  ND1 HIS A 467       3.000  45.096  64.500  1.00  0.00          N   \nATOM   3596  CD2 HIS A 467       4.267  46.729  65.197  1.00  0.00          C   \nATOM   3597  CE1 HIS A 467       4.048  45.146  63.695  1.00  0.00          C   \nATOM   3598  NE2 HIS A 467       4.833  46.127  64.096  1.00  0.00          N   \nATOM   3599  N   GLY A 468      -0.304  48.216  67.911  1.00  0.00          N   \nATOM   3600  CA  GLY A 468      -1.251  48.245  69.008  1.00  0.00          C   \nATOM   3601  C   GLY A 468      -0.560  47.771  70.278  1.00  0.00          C   \nATOM   3602  O   GLY A 468       0.502  47.143  70.220  1.00  0.00          O   \nATOM   3603  N   LEU A 469      -1.163  48.070  71.425  1.00  0.00          N   \nATOM   3604  CA  LEU A 469      -0.615  47.697  72.718  1.00  0.00          C   \nATOM   3605  C   LEU A 469      -0.299  46.225  72.904  1.00  0.00          C   \nATOM   3606  O   LEU A 469       0.588  45.875  73.683  1.00  0.00          O   \nATOM   3607  CB  LEU A 469      -1.570  48.118  73.829  1.00  0.00          C   \nATOM   3608  CG  LEU A 469      -1.395  49.513  74.412  1.00  0.00          C   \nATOM   3609  CD1 LEU A 469      -2.409  49.693  75.544  1.00  0.00          C   \nATOM   3610  CD2 LEU A 469       0.015  49.691  74.938  1.00  0.00          C   \nATOM   3611  N   SER A 470      -1.021  45.360  72.205  1.00  0.00          N   \nATOM   3612  CA  SER A 470      -0.817  43.925  72.352  1.00  0.00          C   \nATOM   3613  C   SER A 470       0.584  43.456  71.987  1.00  0.00          C   \nATOM   3614  O   SER A 470       0.996  42.362  72.383  1.00  0.00          O   \nATOM   3615  CB  SER A 470      -1.850  43.159  71.525  1.00  0.00          C   \nATOM   3616  OG  SER A 470      -1.788  43.551  70.173  1.00  0.00          O   \nATOM   3617  N   ALA A 471       1.310  44.276  71.230  1.00  0.00          N   \nATOM   3618  CA  ALA A 471       2.667  43.930  70.819  1.00  0.00          C   \nATOM   3619  C   ALA A 471       3.563  43.762  72.033  1.00  0.00          C   \nATOM   3620  O   ALA A 471       4.570  43.042  71.986  1.00  0.00          O   \nATOM   3621  CB  ALA A 471       3.247  45.013  69.899  1.00  0.00          C   \nATOM   3622  N   PHE A 472       3.184  44.416  73.125  1.00  0.00          N   \nATOM   3623  CA  PHE A 472       3.973  44.362  74.347  1.00  0.00          C   \nATOM   3624  C   PHE A 472       3.507  43.314  75.327  1.00  0.00          C   \nATOM   3625  O   PHE A 472       4.083  43.183  76.401  1.00  0.00          O   \nATOM   3626  CB  PHE A 472       3.951  45.718  75.042  1.00  0.00          C   \nATOM   3627  CG  PHE A 472       4.293  46.855  74.142  1.00  0.00          C   \nATOM   3628  CD1 PHE A 472       5.523  46.905  73.508  1.00  0.00          C   \nATOM   3629  CD2 PHE A 472       3.383  47.890  73.935  1.00  0.00          C   \nATOM   3630  CE1 PHE A 472       5.851  47.970  72.677  1.00  0.00          C   \nATOM   3631  CE2 PHE A 472       3.703  48.957  73.109  1.00  0.00          C   \nATOM   3632  CZ  PHE A 472       4.946  48.997  72.479  1.00  0.00          C   \nATOM   3633  N   SER A 473       2.468  42.566  74.982  1.00  0.00          N   \nATOM   3634  CA  SER A 473       1.989  41.552  75.907  1.00  0.00          C   \nATOM   3635  C   SER A 473       1.694  40.205  75.267  1.00  0.00          C   \nATOM   3636  O   SER A 473       1.047  39.356  75.883  1.00  0.00          O   \nATOM   3637  CB  SER A 473       0.739  42.049  76.636  1.00  0.00          C   \nATOM   3638  OG  SER A 473      -0.359  42.088  75.750  1.00  0.00          O   \nATOM   3639  N   LEU A 474       2.159  39.988  74.043  1.00  0.00          N   \nATOM   3640  CA  LEU A 474       1.906  38.699  73.404  1.00  0.00          C   \nATOM   3641  C   LEU A 474       2.648  37.595  74.135  1.00  0.00          C   \nATOM   3642  O   LEU A 474       3.762  37.799  74.624  1.00  0.00          O   \nATOM   3643  CB  LEU A 474       2.336  38.711  71.930  1.00  0.00          C   \nATOM   3644  CG  LEU A 474       1.519  39.584  70.974  1.00  0.00          C   \nATOM   3645  CD1 LEU A 474       1.866  39.192  69.541  1.00  0.00          C   \nATOM   3646  CD2 LEU A 474       0.019  39.394  71.224  1.00  0.00          C   \nATOM   3647  N   HIS A 475       2.025  36.423  74.216  1.00  0.00          N   \nATOM   3648  CA  HIS A 475       2.634  35.276  74.880  1.00  0.00          C   \nATOM   3649  C   HIS A 475       1.907  34.033  74.389  1.00  0.00          C   \nATOM   3650  O   HIS A 475       0.937  34.136  73.634  1.00  0.00          O   \nATOM   3651  CB  HIS A 475       2.509  35.415  76.408  1.00  0.00          C   \nATOM   3652  CG  HIS A 475       1.094  35.488  76.885  1.00  0.00          C   \nATOM   3653  ND1 HIS A 475       0.369  34.369  77.232  1.00  0.00          N   \nATOM   3654  CD2 HIS A 475       0.242  36.537  76.985  1.00  0.00          C   \nATOM   3655  CE1 HIS A 475      -0.870  34.725  77.523  1.00  0.00          C   \nATOM   3656  NE2 HIS A 475      -0.972  36.035  77.382  1.00  0.00          N   \nATOM   3657  N   SER A 476       2.366  32.864  74.819  1.00  0.00          N   \nATOM   3658  CA  SER A 476       1.772  31.608  74.378  1.00  0.00          C   \nATOM   3659  C   SER A 476       1.773  31.570  72.853  1.00  0.00          C   \nATOM   3660  O   SER A 476       0.725  31.395  72.215  1.00  0.00          O   \nATOM   3661  CB  SER A 476       0.335  31.450  74.900  1.00  0.00          C   \nATOM   3662  OG  SER A 476       0.322  31.217  76.297  1.00  0.00          O   \nATOM   3663  N   TYR A 477       2.957  31.751  72.277  1.00  0.00          N   \nATOM   3664  CA  TYR A 477       3.122  31.698  70.835  1.00  0.00          C   \nATOM   3665  C   TYR A 477       2.811  30.277  70.377  1.00  0.00          C   \nATOM   3666  O   TYR A 477       2.704  29.368  71.196  1.00  0.00          O   \nATOM   3667  CB  TYR A 477       4.558  32.073  70.456  1.00  0.00          C   \nATOM   3668  CG  TYR A 477       4.885  33.532  70.694  1.00  0.00          C   \nATOM   3669  CD1 TYR A 477       5.449  33.964  71.898  1.00  0.00          C   \nATOM   3670  CD2 TYR A 477       4.614  34.484  69.714  1.00  0.00          C   \nATOM   3671  CE1 TYR A 477       5.738  35.331  72.112  1.00  0.00          C   \nATOM   3672  CE2 TYR A 477       4.891  35.840  69.915  1.00  0.00          C   \nATOM   3673  CZ  TYR A 477       5.453  36.254  71.112  1.00  0.00          C   \nATOM   3674  OH  TYR A 477       5.719  37.591  71.297  1.00  0.00          O   \nATOM   3675  N   SER A 478       2.687  30.071  69.073  1.00  0.00          N   \nATOM   3676  CA  SER A 478       2.357  28.746  68.557  1.00  0.00          C   \nATOM   3677  C   SER A 478       3.504  27.745  68.615  1.00  0.00          C   \nATOM   3678  O   SER A 478       4.684  28.111  68.669  1.00  0.00          O   \nATOM   3679  CB  SER A 478       1.876  28.852  67.109  1.00  0.00          C   \nATOM   3680  OG  SER A 478       2.962  29.172  66.251  1.00  0.00          O   \nATOM   3681  N   PRO A 479       3.162  26.450  68.613  1.00  0.00          N   \nATOM   3682  CA  PRO A 479       4.140  25.363  68.654  1.00  0.00          C   \nATOM   3683  C   PRO A 479       5.070  25.459  67.457  1.00  0.00          C   \nATOM   3684  O   PRO A 479       6.283  25.282  67.590  1.00  0.00          O   \nATOM   3685  CB  PRO A 479       3.269  24.112  68.582  1.00  0.00          C   \nATOM   3686  CG  PRO A 479       2.050  24.534  69.314  1.00  0.00          C   \nATOM   3687  CD  PRO A 479       1.793  25.929  68.791  1.00  0.00          C   \nATOM   3688  N   GLY A 480       4.488  25.736  66.288  1.00  0.00          N   \nATOM   3689  CA  GLY A 480       5.276  25.847  65.073  1.00  0.00          C   \nATOM   3690  C   GLY A 480       6.236  27.025  65.110  1.00  0.00          C   \nATOM   3691  O   GLY A 480       7.371  26.929  64.647  1.00  0.00          O   \nATOM   3692  N   GLU A 481       5.772  28.144  65.652  1.00  0.00          N   \nATOM   3693  CA  GLU A 481       6.594  29.347  65.755  1.00  0.00          C   \nATOM   3694  C   GLU A 481       7.711  29.103  66.775  1.00  0.00          C   \nATOM   3695  O   GLU A 481       8.885  29.358  66.500  1.00  0.00          O   \nATOM   3696  CB  GLU A 481       5.717  30.536  66.173  1.00  0.00          C   \nATOM   3697  CG  GLU A 481       6.463  31.864  66.321  1.00  0.00          C   \nATOM   3698  CD  GLU A 481       7.168  32.306  65.049  1.00  0.00          C   \nATOM   3699  OE1 GLU A 481       6.970  31.662  63.991  1.00  0.00          O   \nATOM   3700  OE2 GLU A 481       7.924  33.307  65.115  1.00  0.00          O   \nATOM   3701  N   ILE A 482       7.348  28.606  67.950  1.00  0.00          N   \nATOM   3702  CA  ILE A 482       8.335  28.310  68.982  1.00  0.00          C   \nATOM   3703  C   ILE A 482       9.392  27.330  68.444  1.00  0.00          C   \nATOM   3704  O   ILE A 482      10.587  27.518  68.661  1.00  0.00          O   \nATOM   3705  CB  ILE A 482       7.651  27.707  70.240  1.00  0.00          C   \nATOM   3706  CG1 ILE A 482       6.826  28.791  70.945  1.00  0.00          C   \nATOM   3707  CG2 ILE A 482       8.696  27.132  71.197  1.00  0.00          C   \nATOM   3708  CD1 ILE A 482       6.060  28.310  72.174  1.00  0.00          C   \nATOM   3709  N   ASN A 483       8.956  26.298  67.728  1.00  0.00          N   \nATOM   3710  CA  ASN A 483       9.890  25.319  67.174  1.00  0.00          C   \nATOM   3711  C   ASN A 483      10.880  25.939  66.199  1.00  0.00          C   \nATOM   3712  O   ASN A 483      12.065  25.609  66.218  1.00  0.00          O   \nATOM   3713  CB  ASN A 483       9.142  24.184  66.457  1.00  0.00          C   \nATOM   3714  CG  ASN A 483       8.598  23.144  67.417  1.00  0.00          C   \nATOM   3715  OD1 ASN A 483       8.925  23.151  68.609  1.00  0.00          O   \nATOM   3716  ND2 ASN A 483       7.771  22.234  66.900  1.00  0.00          N   \nATOM   3717  N   ARG A 484      10.388  26.825  65.337  1.00  0.00          N   \nATOM   3718  CA  ARG A 484      11.244  27.469  64.356  1.00  0.00          C   \nATOM   3719  C   ARG A 484      12.274  28.356  65.040  1.00  0.00          C   \nATOM   3720  O   ARG A 484      13.443  28.364  64.662  1.00  0.00          O   \nATOM   3721  CB  ARG A 484      10.421  28.309  63.371  1.00  0.00          C   \nATOM   3722  CG  ARG A 484      11.274  28.910  62.249  1.00  0.00          C   \nATOM   3723  CD  ARG A 484      10.424  29.449  61.123  1.00  0.00          C   \nATOM   3724  NE  ARG A 484       9.532  30.511  61.576  1.00  0.00          N   \nATOM   3725  CZ  ARG A 484       8.605  31.079  60.809  1.00  0.00          C   \nATOM   3726  NH1 ARG A 484       8.442  30.689  59.547  1.00  0.00          N   \nATOM   3727  NH2 ARG A 484       7.839  32.042  61.301  1.00  0.00          N   \nATOM   3728  N   VAL A 485      11.836  29.115  66.038  1.00  0.00          N   \nATOM   3729  CA  VAL A 485      12.751  29.976  66.766  1.00  0.00          C   \nATOM   3730  C   VAL A 485      13.816  29.098  67.435  1.00  0.00          C   \nATOM   3731  O   VAL A 485      15.008  29.367  67.328  1.00  0.00          O   \nATOM   3732  CB  VAL A 485      12.002  30.794  67.832  1.00  0.00          C   \nATOM   3733  CG1 VAL A 485      12.995  31.507  68.738  1.00  0.00          C   \nATOM   3734  CG2 VAL A 485      11.073  31.803  67.147  1.00  0.00          C   \nATOM   3735  N   ALA A 486      13.375  28.033  68.101  1.00  0.00          N   \nATOM   3736  CA  ALA A 486      14.292  27.115  68.777  1.00  0.00          C   \nATOM   3737  C   ALA A 486      15.314  26.532  67.815  1.00  0.00          C   \nATOM   3738  O   ALA A 486      16.509  26.511  68.116  1.00  0.00          O   \nATOM   3739  CB  ALA A 486      13.503  25.984  69.456  1.00  0.00          C   \nATOM   3740  N   SER A 487      14.850  26.069  66.656  1.00  0.00          N   \nATOM   3741  CA  SER A 487      15.740  25.489  65.646  1.00  0.00          C   \nATOM   3742  C   SER A 487      16.750  26.519  65.174  1.00  0.00          C   \nATOM   3743  O   SER A 487      17.941  26.220  65.031  1.00  0.00          O   \nATOM   3744  CB  SER A 487      14.952  24.997  64.428  1.00  0.00          C   \nATOM   3745  OG  SER A 487      14.007  24.016  64.803  1.00  0.00          O   \nATOM   3746  N   CYS A 488      16.272  27.733  64.917  1.00  0.00          N   \nATOM   3747  CA  CYS A 488      17.169  28.791  64.457  1.00  0.00          C   \nATOM   3748  C   CYS A 488      18.240  29.106  65.509  1.00  0.00          C   \nATOM   3749  O   CYS A 488      19.418  29.238  65.178  1.00  0.00          O   \nATOM   3750  CB  CYS A 488      16.370  30.057  64.118  1.00  0.00          C   \nATOM   3751  SG  CYS A 488      17.416  31.489  63.793  1.00  0.00          S   \nATOM   3752  N   LEU A 489      17.844  29.201  66.776  1.00  0.00          N   \nATOM   3753  CA  LEU A 489      18.806  29.518  67.825  1.00  0.00          C   \nATOM   3754  C   LEU A 489      19.899  28.460  67.961  1.00  0.00          C   \nATOM   3755  O   LEU A 489      21.060  28.793  68.199  1.00  0.00          O   \nATOM   3756  CB  LEU A 489      18.099  29.735  69.167  1.00  0.00          C   \nATOM   3757  CG  LEU A 489      17.005  30.816  69.216  1.00  0.00          C   \nATOM   3758  CD1 LEU A 489      16.853  31.294  70.648  1.00  0.00          C   \nATOM   3759  CD2 LEU A 489      17.352  31.994  68.312  1.00  0.00          C   \nATOM   3760  N   ARG A 490      19.537  27.191  67.807  1.00  0.00          N   \nATOM   3761  CA  ARG A 490      20.527  26.122  67.899  1.00  0.00          C   \nATOM   3762  C   ARG A 490      21.501  26.208  66.729  1.00  0.00          C   \nATOM   3763  O   ARG A 490      22.713  26.083  66.907  1.00  0.00          O   \nATOM   3764  CB  ARG A 490      19.854  24.746  67.880  1.00  0.00          C   \nATOM   3765  CG  ARG A 490      19.077  24.393  69.123  1.00  0.00          C   \nATOM   3766  CD  ARG A 490      18.702  22.916  69.088  1.00  0.00          C   \nATOM   3767  NE  ARG A 490      17.853  22.514  70.207  1.00  0.00          N   \nATOM   3768  CZ  ARG A 490      18.221  22.549  71.485  1.00  0.00          C   \nATOM   3769  NH1 ARG A 490      19.434  22.974  71.822  1.00  0.00          N   \nATOM   3770  NH2 ARG A 490      17.375  22.154  72.429  1.00  0.00          N   \nATOM   3771  N   LYS A 491      20.954  26.423  65.536  1.00  0.00          N   \nATOM   3772  CA  LYS A 491      21.729  26.515  64.310  1.00  0.00          C   \nATOM   3773  C   LYS A 491      22.814  27.585  64.375  1.00  0.00          C   \nATOM   3774  O   LYS A 491      23.982  27.319  64.068  1.00  0.00          O   \nATOM   3775  CB  LYS A 491      20.777  26.790  63.138  1.00  0.00          C   \nATOM   3776  CG  LYS A 491      21.445  27.158  61.821  1.00  0.00          C   \nATOM   3777  CD  LYS A 491      20.422  27.255  60.684  1.00  0.00          C   \nATOM   3778  CE  LYS A 491      19.474  28.447  60.851  1.00  0.00          C   \nATOM   3779  NZ  LYS A 491      18.467  28.553  59.740  1.00  0.00          N   \nATOM   3780  N   LEU A 492      22.418  28.791  64.776  1.00  0.00          N   \nATOM   3781  CA  LEU A 492      23.324  29.926  64.884  1.00  0.00          C   \nATOM   3782  C   LEU A 492      24.095  29.964  66.212  1.00  0.00          C   \nATOM   3783  O   LEU A 492      25.072  30.703  66.338  1.00  0.00          O   \nATOM   3784  CB  LEU A 492      22.544  31.235  64.732  1.00  0.00          C   \nATOM   3785  CG  LEU A 492      21.868  31.588  63.406  1.00  0.00          C   \nATOM   3786  CD1 LEU A 492      21.179  32.941  63.527  1.00  0.00          C   \nATOM   3787  CD2 LEU A 492      22.907  31.634  62.295  1.00  0.00          C   \nATOM   3788  N   GLY A 493      23.646  29.181  67.196  1.00  0.00          N   \nATOM   3789  CA  GLY A 493      24.308  29.155  68.493  1.00  0.00          C   \nATOM   3790  C   GLY A 493      24.019  30.379  69.347  1.00  0.00          C   \nATOM   3791  O   GLY A 493      24.925  30.985  69.913  1.00  0.00          O   \nATOM   3792  N   VAL A 494      22.743  30.731  69.446  1.00  0.00          N   \nATOM   3793  CA  VAL A 494      22.298  31.894  70.210  1.00  0.00          C   \nATOM   3794  C   VAL A 494      21.951  31.468  71.628  1.00  0.00          C   \nATOM   3795  O   VAL A 494      21.323  30.430  71.829  1.00  0.00          O   \nATOM   3796  CB  VAL A 494      21.018  32.512  69.566  1.00  0.00          C   \nATOM   3797  CG1 VAL A 494      20.493  33.650  70.420  1.00  0.00          C   \nATOM   3798  CG2 VAL A 494      21.315  32.985  68.138  1.00  0.00          C   \nATOM   3799  N   PRO A 495      22.341  32.271  72.630  1.00  0.00          N   \nATOM   3800  CA  PRO A 495      22.056  31.952  74.033  1.00  0.00          C   \nATOM   3801  C   PRO A 495      20.572  31.668  74.279  1.00  0.00          C   \nATOM   3802  O   PRO A 495      19.704  32.190  73.584  1.00  0.00          O   \nATOM   3803  CB  PRO A 495      22.522  33.198  74.770  1.00  0.00          C   \nATOM   3804  CG  PRO A 495      23.638  33.686  73.919  1.00  0.00          C   \nATOM   3805  CD  PRO A 495      23.085  33.535  72.526  1.00  0.00          C   \nATOM   3806  N   PRO A 496      20.267  30.843  75.291  1.00  0.00          N   \nATOM   3807  CA  PRO A 496      18.877  30.500  75.609  1.00  0.00          C   \nATOM   3808  C   PRO A 496      18.079  31.609  76.311  1.00  0.00          C   \nATOM   3809  O   PRO A 496      18.612  32.653  76.696  1.00  0.00          O   \nATOM   3810  CB  PRO A 496      19.028  29.232  76.451  1.00  0.00          C   \nATOM   3811  CG  PRO A 496      20.299  29.524  77.220  1.00  0.00          C   \nATOM   3812  CD  PRO A 496      21.207  30.077  76.136  1.00  0.00          C   \nATOM   3813  N   LEU A 497      16.785  31.364  76.456  1.00  0.00          N   \nATOM   3814  CA  LEU A 497      15.864  32.312  77.072  1.00  0.00          C   \nATOM   3815  C   LEU A 497      16.333  32.858  78.421  1.00  0.00          C   \nATOM   3816  O   LEU A 497      16.233  34.064  78.678  1.00  0.00          O   \nATOM   3817  CB  LEU A 497      14.504  31.643  77.225  1.00  0.00          C   \nATOM   3818  CG  LEU A 497      13.228  32.432  77.520  1.00  0.00          C   \nATOM   3819  CD1 LEU A 497      12.586  31.814  78.740  1.00  0.00          C   \nATOM   3820  CD2 LEU A 497      13.489  33.912  77.713  1.00  0.00          C   \nATOM   3821  N   ARG A 498      16.853  31.986  79.284  1.00  0.00          N   \nATOM   3822  CA  ARG A 498      17.304  32.435  80.598  1.00  0.00          C   \nATOM   3823  C   ARG A 498      18.317  33.562  80.500  1.00  0.00          C   \nATOM   3824  O   ARG A 498      18.295  34.492  81.307  1.00  0.00          O   \nATOM   3825  CB  ARG A 498      17.883  31.273  81.424  1.00  0.00          C   \nATOM   3826  CG  ARG A 498      18.889  30.394  80.706  1.00  0.00          C   \nATOM   3827  CD  ARG A 498      19.434  29.314  81.649  1.00  0.00          C   \nATOM   3828  NE  ARG A 498      20.229  28.299  80.952  1.00  0.00          N   \nATOM   3829  CZ  ARG A 498      19.735  27.420  80.083  1.00  0.00          C   \nATOM   3830  NH1 ARG A 498      20.538  26.536  79.499  1.00  0.00          N   \nATOM   3831  NH2 ARG A 498      18.438  27.418  79.796  1.00  0.00          N   \nATOM   3832  N   VAL A 499      19.194  33.503  79.504  1.00  0.00          N   \nATOM   3833  CA  VAL A 499      20.188  34.561  79.348  1.00  0.00          C   \nATOM   3834  C   VAL A 499      19.543  35.870  78.886  1.00  0.00          C   \nATOM   3835  O   VAL A 499      19.800  36.918  79.460  1.00  0.00          O   \nATOM   3836  CB  VAL A 499      21.314  34.142  78.363  1.00  0.00          C   \nATOM   3837  CG1 VAL A 499      22.314  35.290  78.170  1.00  0.00          C   \nATOM   3838  CG2 VAL A 499      22.039  32.916  78.913  1.00  0.00          C   \nATOM   3839  N   TRP A 500      18.676  35.814  77.881  1.00  0.00          N   \nATOM   3840  CA  TRP A 500      18.028  37.031  77.391  1.00  0.00          C   \nATOM   3841  C   TRP A 500      17.120  37.690  78.426  1.00  0.00          C   \nATOM   3842  O   TRP A 500      17.025  38.921  78.499  1.00  0.00          O   \nATOM   3843  CB  TRP A 500      17.237  36.730  76.106  1.00  0.00          C   \nATOM   3844  CG  TRP A 500      18.143  36.370  74.957  1.00  0.00          C   \nATOM   3845  CD1 TRP A 500      18.317  35.133  74.398  1.00  0.00          C   \nATOM   3846  CD2 TRP A 500      19.113  37.229  74.342  1.00  0.00          C   \nATOM   3847  NE1 TRP A 500      19.347  35.167  73.484  1.00  0.00          N   \nATOM   3848  CE2 TRP A 500      19.855  36.441  73.433  1.00  0.00          C   \nATOM   3849  CE3 TRP A 500      19.436  38.589  74.483  1.00  0.00          C   \nATOM   3850  CZ2 TRP A 500      20.906  36.967  72.661  1.00  0.00          C   \nATOM   3851  CZ3 TRP A 500      20.478  39.115  73.721  1.00  0.00          C   \nATOM   3852  CH2 TRP A 500      21.205  38.299  72.818  1.00  0.00          C   \nATOM   3853  N   ARG A 501      16.445  36.875  79.229  1.00  0.00          N   \nATOM   3854  CA  ARG A 501      15.558  37.421  80.255  1.00  0.00          C   \nATOM   3855  C   ARG A 501      16.408  38.178  81.276  1.00  0.00          C   \nATOM   3856  O   ARG A 501      16.072  39.291  81.683  1.00  0.00          O   \nATOM   3857  CB  ARG A 501      14.770  36.289  80.930  1.00  0.00          C   \nATOM   3858  CG  ARG A 501      13.790  36.736  82.014  1.00  0.00          C   \nATOM   3859  CD  ARG A 501      12.895  35.562  82.427  1.00  0.00          C   \nATOM   3860  NE  ARG A 501      13.702  34.374  82.703  1.00  0.00          N   \nATOM   3861  CZ  ARG A 501      13.351  33.131  82.379  1.00  0.00          C   \nATOM   3862  NH1 ARG A 501      12.198  32.900  81.763  1.00  0.00          N   \nATOM   3863  NH2 ARG A 501      14.161  32.118  82.663  1.00  0.00          N   \nATOM   3864  N   HIS A 502      17.524  37.579  81.671  1.00  0.00          N   \nATOM   3865  CA  HIS A 502      18.413  38.218  82.631  1.00  0.00          C   \nATOM   3866  C   HIS A 502      18.908  39.543  82.064  1.00  0.00          C   \nATOM   3867  O   HIS A 502      18.864  40.579  82.730  1.00  0.00          O   \nATOM   3868  CB  HIS A 502      19.603  37.315  82.915  1.00  0.00          C   \nATOM   3869  CG  HIS A 502      20.333  37.663  84.172  1.00  0.00          C   \nATOM   3870  ND1 HIS A 502      20.254  36.895  85.311  1.00  0.00          N   \nATOM   3871  CD2 HIS A 502      21.139  38.709  84.473  1.00  0.00          C   \nATOM   3872  CE1 HIS A 502      20.981  37.452  86.264  1.00  0.00          C   \nATOM   3873  NE2 HIS A 502      21.528  38.552  85.783  1.00  0.00          N   \nATOM   3874  N   ARG A 503      19.376  39.511  80.821  1.00  0.00          N   \nATOM   3875  CA  ARG A 503      19.868  40.714  80.169  1.00  0.00          C   \nATOM   3876  C   ARG A 503      18.773  41.780  80.021  1.00  0.00          C   \nATOM   3877  O   ARG A 503      19.035  42.984  80.162  1.00  0.00          O   \nATOM   3878  CB  ARG A 503      20.418  40.353  78.793  1.00  0.00          C   \nATOM   3879  CG  ARG A 503      21.550  39.357  78.846  1.00  0.00          C   \nATOM   3880  CD  ARG A 503      22.114  39.117  77.473  1.00  0.00          C   \nATOM   3881  NE  ARG A 503      23.310  38.286  77.535  1.00  0.00          N   \nATOM   3882  CZ  ARG A 503      23.895  37.758  76.471  1.00  0.00          C   \nATOM   3883  NH1 ARG A 503      24.981  37.006  76.616  1.00  0.00          N   \nATOM   3884  NH2 ARG A 503      23.383  37.975  75.261  1.00  0.00          N   \nATOM   3885  N   ALA A 504      17.549  41.336  79.737  1.00  0.00          N   \nATOM   3886  CA  ALA A 504      16.433  42.257  79.561  1.00  0.00          C   \nATOM   3887  C   ALA A 504      16.101  42.953  80.880  1.00  0.00          C   \nATOM   3888  O   ALA A 504      15.848  44.161  80.908  1.00  0.00          O   \nATOM   3889  CB  ALA A 504      15.208  41.497  79.023  1.00  0.00          C   \nATOM   3890  N   ARG A 505      16.110  42.197  81.973  1.00  0.00          N   \nATOM   3891  CA  ARG A 505      15.824  42.776  83.285  1.00  0.00          C   \nATOM   3892  C   ARG A 505      16.832  43.878  83.556  1.00  0.00          C   \nATOM   3893  O   ARG A 505      16.472  44.979  83.977  1.00  0.00          O   \nATOM   3894  CB  ARG A 505      15.894  41.697  84.368  1.00  0.00          C   \nATOM   3895  CG  ARG A 505      14.760  40.704  84.255  1.00  0.00          C   \nATOM   3896  CD  ARG A 505      14.766  39.663  85.366  1.00  0.00          C   \nATOM   3897  NE  ARG A 505      13.618  38.772  85.223  1.00  0.00          N   \nATOM   3898  CZ  ARG A 505      13.264  37.838  86.101  1.00  0.00          C   \nATOM   3899  NH1 ARG A 505      13.973  37.658  87.211  1.00  0.00          N   \nATOM   3900  NH2 ARG A 505      12.190  37.088  85.871  1.00  0.00          N   \nATOM   3901  N   SER A 506      18.100  43.581  83.286  1.00  0.00          N   \nATOM   3902  CA  SER A 506      19.173  44.551  83.473  1.00  0.00          C   \nATOM   3903  C   SER A 506      18.931  45.770  82.602  1.00  0.00          C   \nATOM   3904  O   SER A 506      18.951  46.903  83.079  1.00  0.00          O   \nATOM   3905  CB  SER A 506      20.513  43.938  83.088  1.00  0.00          C   \nATOM   3906  OG  SER A 506      21.504  44.940  83.019  1.00  0.00          O   \nATOM   3907  N   VAL A 507      18.731  45.528  81.312  1.00  0.00          N   \nATOM   3908  CA  VAL A 507      18.474  46.600  80.354  1.00  0.00          C   \nATOM   3909  C   VAL A 507      17.281  47.459  80.781  1.00  0.00          C   \nATOM   3910  O   VAL A 507      17.335  48.695  80.753  1.00  0.00          O   \nATOM   3911  CB  VAL A 507      18.185  46.013  78.950  1.00  0.00          C   \nATOM   3912  CG1 VAL A 507      17.660  47.097  78.024  1.00  0.00          C   \nATOM   3913  CG2 VAL A 507      19.452  45.382  78.387  1.00  0.00          C   \nATOM   3914  N   ARG A 508      16.203  46.793  81.171  1.00  0.00          N   \nATOM   3915  CA  ARG A 508      14.987  47.469  81.610  1.00  0.00          C   \nATOM   3916  C   ARG A 508      15.291  48.382  82.797  1.00  0.00          C   \nATOM   3917  O   ARG A 508      14.958  49.568  82.781  1.00  0.00          O   \nATOM   3918  CB  ARG A 508      13.932  46.425  82.003  1.00  0.00          C   \nATOM   3919  CG  ARG A 508      12.642  46.986  82.614  1.00  0.00          C   \nATOM   3920  CD  ARG A 508      11.603  45.881  82.820  1.00  0.00          C   \nATOM   3921  NE  ARG A 508      10.386  46.359  83.489  1.00  0.00          N   \nATOM   3922  CZ  ARG A 508      10.313  46.671  84.783  1.00  0.00          C   \nATOM   3923  NH1 ARG A 508      11.386  46.553  85.556  1.00  0.00          N   \nATOM   3924  NH2 ARG A 508       9.175  47.104  85.311  1.00  0.00          N   \nATOM   3925  N   ALA A 509      15.924  47.822  83.825  1.00  0.00          N   \nATOM   3926  CA  ALA A 509      16.264  48.587  85.022  1.00  0.00          C   \nATOM   3927  C   ALA A 509      17.057  49.837  84.659  1.00  0.00          C   \nATOM   3928  O   ALA A 509      16.786  50.927  85.165  1.00  0.00          O   \nATOM   3929  CB  ALA A 509      17.059  47.716  85.988  1.00  0.00          C   \nATOM   3930  N   ARG A 510      18.033  49.686  83.773  1.00  0.00          N   \nATOM   3931  CA  ARG A 510      18.829  50.831  83.364  1.00  0.00          C   \nATOM   3932  C   ARG A 510      17.989  51.891  82.651  1.00  0.00          C   \nATOM   3933  O   ARG A 510      18.161  53.092  82.881  1.00  0.00          O   \nATOM   3934  CB  ARG A 510      19.975  50.386  82.445  1.00  0.00          C   \nATOM   3935  CG  ARG A 510      21.262  50.069  83.181  1.00  0.00          C   \nATOM   3936  CD  ARG A 510      21.595  48.585  83.186  1.00  0.00          C   \nATOM   3937  NE  ARG A 510      22.102  48.079  81.910  1.00  0.00          N   \nATOM   3938  CZ  ARG A 510      23.090  48.635  81.210  1.00  0.00          C   \nATOM   3939  NH1 ARG A 510      23.689  49.732  81.642  1.00  0.00          N   \nATOM   3940  NH2 ARG A 510      23.510  48.070  80.089  1.00  0.00          N   \nATOM   3941  N   LEU A 511      17.081  51.448  81.784  1.00  0.00          N   \nATOM   3942  CA  LEU A 511      16.245  52.377  81.033  1.00  0.00          C   \nATOM   3943  C   LEU A 511      15.276  53.142  81.932  1.00  0.00          C   \nATOM   3944  O   LEU A 511      15.081  54.344  81.765  1.00  0.00          O   \nATOM   3945  CB  LEU A 511      15.472  51.626  79.940  1.00  0.00          C   \nATOM   3946  CG  LEU A 511      16.312  51.019  78.812  1.00  0.00          C   \nATOM   3947  CD1 LEU A 511      15.450  50.064  77.969  1.00  0.00          C   \nATOM   3948  CD2 LEU A 511      16.890  52.140  77.955  1.00  0.00          C   \nATOM   3949  N   LEU A 512      14.673  52.439  82.879  1.00  0.00          N   \nATOM   3950  CA  LEU A 512      13.740  53.058  83.803  1.00  0.00          C   \nATOM   3951  C   LEU A 512      14.441  54.183  84.559  1.00  0.00          C   \nATOM   3952  O   LEU A 512      13.875  55.254  84.770  1.00  0.00          O   \nATOM   3953  CB  LEU A 512      13.213  52.021  84.794  1.00  0.00          C   \nATOM   3954  CG  LEU A 512      12.174  51.022  84.284  1.00  0.00          C   \nATOM   3955  CD1 LEU A 512      11.949  49.929  85.330  1.00  0.00          C   \nATOM   3956  CD2 LEU A 512      10.873  51.752  83.983  1.00  0.00          C   \nATOM   3957  N   SER A 513      15.685  53.939  84.955  1.00  0.00          N   \nATOM   3958  CA  SER A 513      16.456  54.931  85.694  1.00  0.00          C   \nATOM   3959  C   SER A 513      16.737  56.173  84.868  1.00  0.00          C   \nATOM   3960  O   SER A 513      16.858  57.270  85.413  1.00  0.00          O   \nATOM   3961  CB  SER A 513      17.772  54.316  86.173  1.00  0.00          C   \nATOM   3962  OG  SER A 513      17.554  53.321  87.162  1.00  0.00          O   \nATOM   3963  N   GLN A 514      16.828  56.006  83.552  1.00  0.00          N   \nATOM   3964  CA  GLN A 514      17.116  57.117  82.655  1.00  0.00          C   \nATOM   3965  C   GLN A 514      15.959  58.119  82.578  1.00  0.00          C   \nATOM   3966  O   GLN A 514      16.160  59.285  82.251  1.00  0.00          O   \nATOM   3967  CB  GLN A 514      17.509  56.578  81.265  1.00  0.00          C   \nATOM   3968  CG  GLN A 514      18.537  57.412  80.532  1.00  0.00          C   \nATOM   3969  CD  GLN A 514      19.799  56.633  80.268  1.00  0.00          C   \nATOM   3970  OE1 GLN A 514      20.575  56.959  79.364  1.00  0.00          O   \nATOM   3971  NE2 GLN A 514      20.016  55.588  81.060  1.00  0.00          N   \nATOM   3972  N   GLY A 515      14.756  57.645  82.880  1.00  0.00          N   \nATOM   3973  CA  GLY A 515      13.598  58.518  82.848  1.00  0.00          C   \nATOM   3974  C   GLY A 515      13.146  58.870  81.444  1.00  0.00          C   \nATOM   3975  O   GLY A 515      13.776  58.462  80.462  1.00  0.00          O   \nATOM   3976  N   GLY A 516      12.056  59.626  81.342  1.00  0.00          N   \nATOM   3977  CA  GLY A 516      11.553  60.023  80.038  1.00  0.00          C   \nATOM   3978  C   GLY A 516      11.294  58.858  79.100  1.00  0.00          C   \nATOM   3979  O   GLY A 516      10.875  57.784  79.528  1.00  0.00          O   \nATOM   3980  N   ARG A 517      11.537  59.072  77.812  1.00  0.00          N   \nATOM   3981  CA  ARG A 517      11.320  58.037  76.806  1.00  0.00          C   \nATOM   3982  C   ARG A 517      12.069  56.733  77.092  1.00  0.00          C   \nATOM   3983  O   ARG A 517      11.551  55.642  76.834  1.00  0.00          O   \nATOM   3984  CB  ARG A 517      11.726  58.546  75.417  1.00  0.00          C   \nATOM   3985  CG  ARG A 517      10.726  59.484  74.759  1.00  0.00          C   \nATOM   3986  CD  ARG A 517      11.258  60.894  74.694  1.00  0.00          C   \nATOM   3987  NE  ARG A 517      12.494  61.014  73.915  1.00  0.00          N   \nATOM   3988  CZ  ARG A 517      12.600  60.726  72.622  1.00  0.00          C   \nATOM   3989  NH1 ARG A 517      11.541  60.287  71.954  1.00  0.00          N   \nATOM   3990  NH2 ARG A 517      13.753  60.909  71.987  1.00  0.00          N   \nATOM   3991  N   ALA A 518      13.292  56.840  77.605  1.00  0.00          N   \nATOM   3992  CA  ALA A 518      14.073  55.644  77.903  1.00  0.00          C   \nATOM   3993  C   ALA A 518      13.303  54.803  78.909  1.00  0.00          C   \nATOM   3994  O   ALA A 518      13.282  53.567  78.827  1.00  0.00          O   \nATOM   3995  CB  ALA A 518      15.438  56.026  78.459  1.00  0.00          C   \nATOM   3996  N   ALA A 519      12.654  55.488  79.850  1.00  0.00          N   \nATOM   3997  CA  ALA A 519      11.870  54.819  80.885  1.00  0.00          C   \nATOM   3998  C   ALA A 519      10.712  54.081  80.233  1.00  0.00          C   \nATOM   3999  O   ALA A 519      10.376  52.955  80.610  1.00  0.00          O   \nATOM   4000  CB  ALA A 519      11.346  55.846  81.886  1.00  0.00          C   \nATOM   4001  N   THR A 520      10.101  54.731  79.250  1.00  0.00          N   \nATOM   4002  CA  THR A 520       8.991  54.130  78.527  1.00  0.00          C   \nATOM   4003  C   THR A 520       9.494  52.892  77.784  1.00  0.00          C   \nATOM   4004  O   THR A 520       8.845  51.850  77.781  1.00  0.00          O   \nATOM   4005  CB  THR A 520       8.381  55.129  77.533  1.00  0.00          C   \nATOM   4006  OG1 THR A 520       7.915  56.274  78.259  1.00  0.00          O   \nATOM   4007  CG2 THR A 520       7.199  54.497  76.779  1.00  0.00          C   \nATOM   4008  N   CYS A 521      10.658  53.008  77.165  1.00  0.00          N   \nATOM   4009  CA  CYS A 521      11.228  51.877  76.455  1.00  0.00          C   \nATOM   4010  C   CYS A 521      11.350  50.697  77.413  1.00  0.00          C   \nATOM   4011  O   CYS A 521      10.930  49.584  77.098  1.00  0.00          O   \nATOM   4012  CB  CYS A 521      12.603  52.246  75.886  1.00  0.00          C   \nATOM   4013  SG  CYS A 521      12.538  53.242  74.363  1.00  0.00          S   \nATOM   4014  N   GLY A 522      11.896  50.953  78.600  1.00  0.00          N   \nATOM   4015  CA  GLY A 522      12.075  49.889  79.572  1.00  0.00          C   \nATOM   4016  C   GLY A 522      10.768  49.238  79.956  1.00  0.00          C   \nATOM   4017  O   GLY A 522      10.633  48.006  80.000  1.00  0.00          O   \nATOM   4018  N   LYS A 523       9.794  50.098  80.223  1.00  0.00          N   \nATOM   4019  CA  LYS A 523       8.458  49.710  80.636  1.00  0.00          C   \nATOM   4020  C   LYS A 523       7.704  48.818  79.632  1.00  0.00          C   \nATOM   4021  O   LYS A 523       7.245  47.722  79.977  1.00  0.00          O   \nATOM   4022  CB  LYS A 523       7.676  51.003  80.926  1.00  0.00          C   \nATOM   4023  CG  LYS A 523       6.367  50.863  81.683  1.00  0.00          C   \nATOM   4024  CD  LYS A 523       5.981  52.229  82.273  1.00  0.00          C   \nATOM   4025  CE  LYS A 523       4.586  52.242  82.895  1.00  0.00          C   \nATOM   4026  NZ  LYS A 523       3.489  52.316  81.881  1.00  0.00          N   \nATOM   4027  N   TYR A 524       7.588  49.274  78.391  1.00  0.00          N   \nATOM   4028  CA  TYR A 524       6.848  48.522  77.378  1.00  0.00          C   \nATOM   4029  C   TYR A 524       7.623  47.429  76.658  1.00  0.00          C   \nATOM   4030  O   TYR A 524       7.131  46.314  76.496  1.00  0.00          O   \nATOM   4031  CB  TYR A 524       6.277  49.480  76.320  1.00  0.00          C   \nATOM   4032  CG  TYR A 524       5.269  50.475  76.869  1.00  0.00          C   \nATOM   4033  CD1 TYR A 524       5.667  51.503  77.716  1.00  0.00          C   \nATOM   4034  CD2 TYR A 524       3.913  50.350  76.578  1.00  0.00          C   \nATOM   4035  CE1 TYR A 524       4.741  52.382  78.268  1.00  0.00          C   \nATOM   4036  CE2 TYR A 524       2.976  51.222  77.126  1.00  0.00          C   \nATOM   4037  CZ  TYR A 524       3.398  52.234  77.972  1.00  0.00          C   \nATOM   4038  OH  TYR A 524       2.480  53.081  78.541  1.00  0.00          O   \nATOM   4039  N   LEU A 525       8.837  47.751  76.224  1.00  0.00          N   \nATOM   4040  CA  LEU A 525       9.633  46.797  75.469  1.00  0.00          C   \nATOM   4041  C   LEU A 525      10.042  45.554  76.216  1.00  0.00          C   \nATOM   4042  O   LEU A 525      10.173  44.487  75.617  1.00  0.00          O   \nATOM   4043  CB  LEU A 525      10.889  47.467  74.909  1.00  0.00          C   \nATOM   4044  CG  LEU A 525      10.687  48.672  73.995  1.00  0.00          C   \nATOM   4045  CD1 LEU A 525      12.042  49.173  73.508  1.00  0.00          C   \nATOM   4046  CD2 LEU A 525       9.796  48.287  72.815  1.00  0.00          C   \nATOM   4047  N   PHE A 526      10.222  45.676  77.525  1.00  0.00          N   \nATOM   4048  CA  PHE A 526      10.689  44.547  78.309  1.00  0.00          C   \nATOM   4049  C   PHE A 526       9.775  44.053  79.437  1.00  0.00          C   \nATOM   4050  O   PHE A 526      10.225  43.358  80.353  1.00  0.00          O   \nATOM   4051  CB  PHE A 526      12.076  44.903  78.840  1.00  0.00          C   \nATOM   4052  CG  PHE A 526      13.056  45.266  77.754  1.00  0.00          C   \nATOM   4053  CD1 PHE A 526      13.605  44.280  76.940  1.00  0.00          C   \nATOM   4054  CD2 PHE A 526      13.430  46.594  77.544  1.00  0.00          C   \nATOM   4055  CE1 PHE A 526      14.524  44.604  75.928  1.00  0.00          C   \nATOM   4056  CE2 PHE A 526      14.343  46.939  76.540  1.00  0.00          C   \nATOM   4057  CZ  PHE A 526      14.893  45.938  75.727  1.00  0.00          C   \nATOM   4058  N   ASN A 527       8.489  44.390  79.352  1.00  0.00          N   \nATOM   4059  CA  ASN A 527       7.523  43.964  80.364  1.00  0.00          C   \nATOM   4060  C   ASN A 527       7.477  42.433  80.395  1.00  0.00          C   \nATOM   4061  O   ASN A 527       7.148  41.841  81.418  1.00  0.00          O   \nATOM   4062  CB  ASN A 527       6.133  44.506  80.033  1.00  0.00          C   \nATOM   4063  CG  ASN A 527       5.181  44.456  81.216  1.00  0.00          C   \nATOM   4064  OD1 ASN A 527       3.966  44.482  81.032  1.00  0.00          O   \nATOM   4065  ND2 ASN A 527       5.722  44.409  82.431  1.00  0.00          N   \nATOM   4066  N   TRP A 528       7.806  41.800  79.270  1.00  0.00          N   \nATOM   4067  CA  TRP A 528       7.812  40.347  79.189  1.00  0.00          C   \nATOM   4068  C   TRP A 528       8.868  39.697  80.073  1.00  0.00          C   \nATOM   4069  O   TRP A 528       8.756  38.518  80.405  1.00  0.00          O   \nATOM   4070  CB  TRP A 528       8.069  39.884  77.752  1.00  0.00          C   \nATOM   4071  CG  TRP A 528       9.371  40.385  77.159  1.00  0.00          C   \nATOM   4072  CD1 TRP A 528       9.546  41.501  76.387  1.00  0.00          C   \nATOM   4073  CD2 TRP A 528      10.673  39.792  77.303  1.00  0.00          C   \nATOM   4074  NE1 TRP A 528      10.872  41.636  76.038  1.00  0.00          N   \nATOM   4075  CE2 TRP A 528      11.586  40.598  76.582  1.00  0.00          C   \nATOM   4076  CE3 TRP A 528      11.158  38.652  77.960  1.00  0.00          C   \nATOM   4077  CZ2 TRP A 528      12.957  40.309  76.508  1.00  0.00          C   \nATOM   4078  CZ3 TRP A 528      12.526  38.361  77.885  1.00  0.00          C   \nATOM   4079  CH2 TRP A 528      13.405  39.188  77.159  1.00  0.00          C   \nATOM   4080  N   ALA A 529       9.903  40.451  80.435  1.00  0.00          N   \nATOM   4081  CA  ALA A 529      10.993  39.912  81.252  1.00  0.00          C   \nATOM   4082  C   ALA A 529      10.746  39.827  82.760  1.00  0.00          C   \nATOM   4083  O   ALA A 529      11.387  39.031  83.445  1.00  0.00          O   \nATOM   4084  CB  ALA A 529      12.274  40.717  80.992  1.00  0.00          C   \nATOM   4085  N   VAL A 530       9.842  40.647  83.287  1.00  0.00          N   \nATOM   4086  CA  VAL A 530       9.557  40.630  84.722  1.00  0.00          C   \nATOM   4087  C   VAL A 530       8.410  39.680  85.065  1.00  0.00          C   \nATOM   4088  O   VAL A 530       7.540  39.425  84.234  1.00  0.00          O   \nATOM   4089  CB  VAL A 530       9.200  42.031  85.226  1.00  0.00          C   \nATOM   4090  CG1 VAL A 530      10.344  42.980  84.954  1.00  0.00          C   \nATOM   4091  CG2 VAL A 530       7.933  42.511  84.561  1.00  0.00          C   \nATOM   4092  N   LYS A 531       8.396  39.148  86.285  1.00  0.00          N   \nATOM   4093  CA  LYS A 531       7.313  38.234  86.639  1.00  0.00          C   \nATOM   4094  C   LYS A 531       6.035  38.964  87.000  1.00  0.00          C   \nATOM   4095  O   LYS A 531       4.945  38.489  86.691  1.00  0.00          O   \nATOM   4096  CB  LYS A 531       7.723  37.275  87.758  1.00  0.00          C   \nATOM   4097  CG  LYS A 531       8.279  37.890  89.012  1.00  0.00          C   \nATOM   4098  CD  LYS A 531       8.683  36.758  89.942  1.00  0.00          C   \nATOM   4099  CE  LYS A 531       9.308  37.250  91.229  1.00  0.00          C   \nATOM   4100  NZ  LYS A 531       9.705  36.090  92.078  1.00  0.00          N   \nATOM   4101  N   THR A 532       6.162  40.120  87.638  1.00  0.00          N   \nATOM   4102  CA  THR A 532       4.986  40.911  87.985  1.00  0.00          C   \nATOM   4103  C   THR A 532       4.850  41.994  86.919  1.00  0.00          C   \nATOM   4104  O   THR A 532       5.431  43.071  87.043  1.00  0.00          O   \nATOM   4105  CB  THR A 532       5.131  41.582  89.372  1.00  0.00          C   \nATOM   4106  OG1 THR A 532       5.345  40.578  90.374  1.00  0.00          O   \nATOM   4107  CG2 THR A 532       3.873  42.384  89.707  1.00  0.00          C   \nATOM   4108  N   LYS A 533       4.090  41.699  85.869  1.00  0.00          N   \nATOM   4109  CA  LYS A 533       3.898  42.636  84.760  1.00  0.00          C   \nATOM   4110  C   LYS A 533       3.295  43.988  85.124  1.00  0.00          C   \nATOM   4111  O   LYS A 533       2.487  44.093  86.046  1.00  0.00          O   \nATOM   4112  CB  LYS A 533       3.023  41.999  83.678  1.00  0.00          C   \nATOM   4113  CG  LYS A 533       3.765  41.141  82.677  1.00  0.00          C   \nATOM   4114  CD  LYS A 533       4.367  39.904  83.310  1.00  0.00          C   \nATOM   4115  CE  LYS A 533       5.043  39.048  82.253  1.00  0.00          C   \nATOM   4116  NZ  LYS A 533       5.665  37.841  82.841  1.00  0.00          N   \nATOM   4117  N   LEU A 534       3.683  45.019  84.377  1.00  0.00          N   \nATOM   4118  CA  LEU A 534       3.158  46.362  84.589  1.00  0.00          C   \nATOM   4119  C   LEU A 534       1.853  46.549  83.820  1.00  0.00          C   \nATOM   4120  O   LEU A 534       1.523  45.762  82.931  1.00  0.00          O   \nATOM   4121  CB  LEU A 534       4.167  47.424  84.142  1.00  0.00          C   \nATOM   4122  CG  LEU A 534       4.958  48.107  85.259  1.00  0.00          C   \nATOM   4123  CD1 LEU A 534       5.737  47.062  86.042  1.00  0.00          C   \nATOM   4124  CD2 LEU A 534       5.889  49.155  84.666  1.00  0.00          C   \nATOM   4125  N   LYS A 535       1.127  47.605  84.181  1.00  0.00          N   \nATOM   4126  CA  LYS A 535      -0.156  47.958  83.578  1.00  0.00          C   \nATOM   4127  C   LYS A 535      -0.096  48.076  82.059  1.00  0.00          C   \nATOM   4128  O   LYS A 535      -0.810  47.370  81.345  1.00  0.00          O   \nATOM   4129  CB  LYS A 535      -0.651  49.282  84.175  1.00  0.00          C   \nATOM   4130  CG  LYS A 535       0.373  50.419  84.075  1.00  0.00          C   \nATOM   4131  CD  LYS A 535       0.011  51.622  84.946  1.00  0.00          C   \nATOM   4132  CE  LYS A 535      -1.251  52.326  84.466  1.00  0.00          C   \nATOM   4133  NZ  LYS A 535      -1.537  53.554  85.266  1.00  0.00          N   \nATOM   4134  N   LEU A 536       0.765  48.964  81.572  1.00  0.00          N   \nATOM   4135  CA  LEU A 536       0.904  49.194  80.140  1.00  0.00          C   \nATOM   4136  C   LEU A 536      -0.374  49.813  79.592  1.00  0.00          C   \nATOM   4137  O   LEU A 536      -1.418  49.166  79.518  1.00  0.00          O   \nATOM   4138  CB  LEU A 536       1.220  47.887  79.404  1.00  0.00          C   \nATOM   4139  CG  LEU A 536       2.707  47.530  79.315  1.00  0.00          C   \nATOM   4140  CD1 LEU A 536       3.306  47.420  80.707  1.00  0.00          C   \nATOM   4141  CD2 LEU A 536       2.865  46.227  78.558  1.00  0.00          C   \nATOM   4142  N   THR A 537      -0.271  51.078  79.209  1.00  0.00          N   \nATOM   4143  CA  THR A 537      -1.399  51.824  78.680  1.00  0.00          C   \nATOM   4144  C   THR A 537      -0.904  52.694  77.534  1.00  0.00          C   \nATOM   4145  O   THR A 537       0.297  52.810  77.311  1.00  0.00          O   \nATOM   4146  CB  THR A 537      -2.012  52.727  79.775  1.00  0.00          C   \nATOM   4147  OG1 THR A 537      -1.011  53.626  80.277  1.00  0.00          O   \nATOM   4148  CG2 THR A 537      -2.542  51.880  80.926  1.00  0.00          C   \nATOM   4149  N   PRO A 538      -1.823  53.315  76.785  1.00  0.00          N   \nATOM   4150  CA  PRO A 538      -1.428  54.173  75.664  1.00  0.00          C   \nATOM   4151  C   PRO A 538      -0.295  55.149  75.992  1.00  0.00          C   \nATOM   4152  O   PRO A 538       0.134  55.252  77.139  1.00  0.00          O   \nATOM   4153  CB  PRO A 538      -2.732  54.871  75.306  1.00  0.00          C   \nATOM   4154  CG  PRO A 538      -3.726  53.764  75.515  1.00  0.00          C   \nATOM   4155  CD  PRO A 538      -3.288  53.163  76.838  1.00  0.00          C   \nATOM   4156  N   ILE A 539       0.181  55.865  74.977  1.00  0.00          N   \nATOM   4157  CA  ILE A 539       1.274  56.817  75.155  1.00  0.00          C   \nATOM   4158  C   ILE A 539       1.027  58.131  74.403  1.00  0.00          C   \nATOM   4159  O   ILE A 539       0.782  58.130  73.196  1.00  0.00          O   \nATOM   4160  CB  ILE A 539       2.607  56.207  74.670  1.00  0.00          C   \nATOM   4161  CG1 ILE A 539       2.863  54.883  75.395  1.00  0.00          C   \nATOM   4162  CG2 ILE A 539       3.747  57.187  74.907  1.00  0.00          C   \nATOM   4163  CD1 ILE A 539       4.077  54.132  74.891  1.00  0.00          C   \nATOM   4164  N   PRO A 540       1.102  59.270  75.117  1.00  0.00          N   \nATOM   4165  CA  PRO A 540       0.894  60.623  74.585  1.00  0.00          C   \nATOM   4166  C   PRO A 540       1.644  60.983  73.299  1.00  0.00          C   \nATOM   4167  O   PRO A 540       1.023  61.207  72.258  1.00  0.00          O   \nATOM   4168  CB  PRO A 540       1.302  61.514  75.756  1.00  0.00          C   \nATOM   4169  CG  PRO A 540       0.867  60.707  76.937  1.00  0.00          C   \nATOM   4170  CD  PRO A 540       1.362  59.324  76.569  1.00  0.00          C   \nATOM   4171  N   ALA A 541       2.971  61.048  73.377  1.00  0.00          N   \nATOM   4172  CA  ALA A 541       3.802  61.404  72.225  1.00  0.00          C   \nATOM   4173  C   ALA A 541       3.510  60.597  70.960  1.00  0.00          C   \nATOM   4174  O   ALA A 541       3.810  61.047  69.852  1.00  0.00          O   \nATOM   4175  CB  ALA A 541       5.277  61.272  72.590  1.00  0.00          C   \nATOM   4176  N   ALA A 542       2.927  59.413  71.126  1.00  0.00          N   \nATOM   4177  CA  ALA A 542       2.606  58.540  69.999  1.00  0.00          C   \nATOM   4178  C   ALA A 542       1.934  59.267  68.840  1.00  0.00          C   \nATOM   4179  O   ALA A 542       2.449  59.275  67.721  1.00  0.00          O   \nATOM   4180  CB  ALA A 542       1.720  57.389  70.469  1.00  0.00          C   \nATOM   4181  N   SER A 543       0.784  59.874  69.117  1.00  0.00          N   \nATOM   4182  CA  SER A 543       0.023  60.595  68.101  1.00  0.00          C   \nATOM   4183  C   SER A 543       0.831  61.683  67.394  1.00  0.00          C   \nATOM   4184  O   SER A 543       0.475  62.108  66.294  1.00  0.00          O   \nATOM   4185  CB  SER A 543      -1.228  61.215  68.728  1.00  0.00          C   \nATOM   4186  OG  SER A 543      -0.881  62.146  69.738  1.00  0.00          O   \nATOM   4187  N   GLN A 544       1.913  62.134  68.025  1.00  0.00          N   \nATOM   4188  CA  GLN A 544       2.761  63.171  67.441  1.00  0.00          C   \nATOM   4189  C   GLN A 544       3.493  62.688  66.197  1.00  0.00          C   \nATOM   4190  O   GLN A 544       3.636  63.433  65.227  1.00  0.00          O   \nATOM   4191  CB  GLN A 544       3.785  63.673  68.463  1.00  0.00          C   \nATOM   4192  CG  GLN A 544       3.230  64.653  69.484  1.00  0.00          C   \nATOM   4193  CD  GLN A 544       4.324  65.313  70.310  1.00  0.00          C   \nATOM   4194  OE1 GLN A 544       4.056  66.202  71.122  1.00  0.00          O   \nATOM   4195  NE2 GLN A 544       5.565  64.879  70.107  1.00  0.00          N   \nATOM   4196  N   LEU A 545       3.963  61.445  66.228  1.00  0.00          N   \nATOM   4197  CA  LEU A 545       4.677  60.868  65.093  1.00  0.00          C   \nATOM   4198  C   LEU A 545       3.817  60.880  63.827  1.00  0.00          C   \nATOM   4199  O   LEU A 545       2.698  60.359  63.811  1.00  0.00          O   \nATOM   4200  CB  LEU A 545       5.104  59.433  65.418  1.00  0.00          C   \nATOM   4201  CG  LEU A 545       6.196  59.247  66.475  1.00  0.00          C   \nATOM   4202  CD1 LEU A 545       6.225  57.801  66.927  1.00  0.00          C   \nATOM   4203  CD2 LEU A 545       7.547  59.664  65.902  1.00  0.00          C   \nATOM   4204  N   ASP A 546       4.348  61.484  62.769  1.00  0.00          N   \nATOM   4205  CA  ASP A 546       3.646  61.565  61.493  1.00  0.00          C   \nATOM   4206  C   ASP A 546       4.009  60.344  60.657  1.00  0.00          C   \nATOM   4207  O   ASP A 546       4.957  60.375  59.870  1.00  0.00          O   \nATOM   4208  CB  ASP A 546       4.045  62.847  60.753  1.00  0.00          C   \nATOM   4209  CG  ASP A 546       3.354  62.987  59.411  1.00  0.00          C   \nATOM   4210  OD1 ASP A 546       3.543  62.105  58.548  1.00  0.00          O   \nATOM   4211  OD2 ASP A 546       2.623  63.981  59.215  1.00  0.00          O   \nATOM   4212  N   LEU A 547       3.249  59.269  60.836  1.00  0.00          N   \nATOM   4213  CA  LEU A 547       3.497  58.026  60.117  1.00  0.00          C   \nATOM   4214  C   LEU A 547       2.845  57.958  58.741  1.00  0.00          C   \nATOM   4215  O   LEU A 547       2.680  56.871  58.183  1.00  0.00          O   \nATOM   4216  CB  LEU A 547       3.045  56.836  60.968  1.00  0.00          C   \nATOM   4217  CG  LEU A 547       4.044  56.287  61.997  1.00  0.00          C   \nATOM   4218  CD1 LEU A 547       4.790  57.414  62.697  1.00  0.00          C   \nATOM   4219  CD2 LEU A 547       3.291  55.432  63.000  1.00  0.00          C   \nATOM   4220  N   SER A 548       2.478  59.112  58.191  1.00  0.00          N   \nATOM   4221  CA  SER A 548       1.868  59.143  56.866  1.00  0.00          C   \nATOM   4222  C   SER A 548       2.987  58.985  55.834  1.00  0.00          C   \nATOM   4223  O   SER A 548       3.969  59.729  55.851  1.00  0.00          O   \nATOM   4224  CB  SER A 548       1.112  60.461  56.641  1.00  0.00          C   \nATOM   4225  OG  SER A 548       1.993  61.567  56.547  1.00  0.00          O   \nATOM   4226  N   GLY A 549       2.841  58.001  54.952  1.00  0.00          N   \nATOM   4227  CA  GLY A 549       3.854  57.749  53.941  1.00  0.00          C   \nATOM   4228  C   GLY A 549       4.643  56.482  54.224  1.00  0.00          C   \nATOM   4229  O   GLY A 549       5.341  55.963  53.354  1.00  0.00          O   \nATOM   4230  N   TRP A 550       4.531  55.978  55.447  1.00  0.00          N   \nATOM   4231  CA  TRP A 550       5.239  54.767  55.847  1.00  0.00          C   \nATOM   4232  C   TRP A 550       4.823  53.518  55.070  1.00  0.00          C   \nATOM   4233  O   TRP A 550       5.671  52.765  54.574  1.00  0.00          O   \nATOM   4234  CB  TRP A 550       5.031  54.524  57.341  1.00  0.00          C   \nATOM   4235  CG  TRP A 550       6.023  55.227  58.192  1.00  0.00          C   \nATOM   4236  CD1 TRP A 550       6.432  56.524  58.076  1.00  0.00          C   \nATOM   4237  CD2 TRP A 550       6.732  54.676  59.305  1.00  0.00          C   \nATOM   4238  NE1 TRP A 550       7.359  56.815  59.048  1.00  0.00          N   \nATOM   4239  CE2 TRP A 550       7.565  55.697  59.816  1.00  0.00          C   \nATOM   4240  CE3 TRP A 550       6.752  53.415  59.917  1.00  0.00          C   \nATOM   4241  CZ2 TRP A 550       8.403  55.500  60.921  1.00  0.00          C   \nATOM   4242  CZ3 TRP A 550       7.585  53.219  61.016  1.00  0.00          C   \nATOM   4243  CH2 TRP A 550       8.403  54.259  61.502  1.00  0.00          C   \nATOM   4244  N   PHE A 551       3.517  53.298  54.979  1.00  0.00          N   \nATOM   4245  CA  PHE A 551       2.997  52.137  54.281  1.00  0.00          C   \nATOM   4246  C   PHE A 551       2.034  52.551  53.176  1.00  0.00          C   \nATOM   4247  O   PHE A 551       0.836  52.247  53.216  1.00  0.00          O   \nATOM   4248  CB  PHE A 551       2.310  51.216  55.283  1.00  0.00          C   \nATOM   4249  CG  PHE A 551       3.206  50.779  56.405  1.00  0.00          C   \nATOM   4250  CD1 PHE A 551       4.210  49.844  56.185  1.00  0.00          C   \nATOM   4251  CD2 PHE A 551       3.069  51.329  57.678  1.00  0.00          C   \nATOM   4252  CE1 PHE A 551       5.062  49.454  57.213  1.00  0.00          C   \nATOM   4253  CE2 PHE A 551       3.915  50.947  58.714  1.00  0.00          C   \nATOM   4254  CZ  PHE A 551       4.916  50.008  58.478  1.00  0.00          C   \nATOM   4255  N   VAL A 552       2.576  53.246  52.184  1.00  0.00          N   \nATOM   4256  CA  VAL A 552       1.791  53.716  51.055  1.00  0.00          C   \nATOM   4257  C   VAL A 552       2.201  52.981  49.785  1.00  0.00          C   \nATOM   4258  O   VAL A 552       1.360  52.485  49.042  1.00  0.00          O   \nATOM   4259  CB  VAL A 552       2.001  55.240  50.827  1.00  0.00          C   \nATOM   4260  CG1 VAL A 552       1.371  55.662  49.514  1.00  0.00          C   \nATOM   4261  CG2 VAL A 552       1.416  56.029  51.991  1.00  0.00          C   \nATOM   4262  N   ALA A 553       3.506  52.914  49.545  1.00  0.00          N   \nATOM   4263  CA  ALA A 553       4.018  52.268  48.349  1.00  0.00          C   \nATOM   4264  C   ALA A 553       5.382  51.620  48.568  1.00  0.00          C   \nATOM   4265  O   ALA A 553       6.072  51.909  49.548  1.00  0.00          O   \nATOM   4266  CB  ALA A 553       4.110  53.296  47.224  1.00  0.00          C   \nATOM   4267  N   GLY A 554       5.757  50.731  47.650  1.00  0.00          N   \nATOM   4268  CA  GLY A 554       7.054  50.083  47.723  1.00  0.00          C   \nATOM   4269  C   GLY A 554       8.049  50.901  46.909  1.00  0.00          C   \nATOM   4270  O   GLY A 554       7.679  51.505  45.901  1.00  0.00          O   \nATOM   4271  N   TYR A 555       9.305  50.938  47.337  1.00  0.00          N   \nATOM   4272  CA  TYR A 555      10.314  51.717  46.624  1.00  0.00          C   \nATOM   4273  C   TYR A 555      11.672  51.013  46.557  1.00  0.00          C   \nATOM   4274  O   TYR A 555      12.704  51.670  46.363  1.00  0.00          O   \nATOM   4275  CB  TYR A 555      10.499  53.065  47.310  1.00  0.00          C   \nATOM   4276  CG  TYR A 555       9.242  53.886  47.412  1.00  0.00          C   \nATOM   4277  CD1 TYR A 555       8.772  54.614  46.324  1.00  0.00          C   \nATOM   4278  CD2 TYR A 555       8.532  53.955  48.615  1.00  0.00          C   \nATOM   4279  CE1 TYR A 555       7.627  55.401  46.433  1.00  0.00          C   \nATOM   4280  CE2 TYR A 555       7.396  54.729  48.735  1.00  0.00          C   \nATOM   4281  CZ  TYR A 555       6.948  55.453  47.642  1.00  0.00          C   \nATOM   4282  OH  TYR A 555       5.832  56.243  47.772  1.00  0.00          O   \nATOM   4283  N   SER A 556      11.679  49.691  46.719  1.00  0.00          N   \nATOM   4284  CA  SER A 556      12.929  48.940  46.680  1.00  0.00          C   \nATOM   4285  C   SER A 556      13.758  49.333  45.460  1.00  0.00          C   \nATOM   4286  O   SER A 556      13.260  49.330  44.331  1.00  0.00          O   \nATOM   4287  CB  SER A 556      12.645  47.440  46.646  1.00  0.00          C   \nATOM   4288  OG  SER A 556      13.857  46.717  46.746  1.00  0.00          O   \nATOM   4289  N   GLY A 557      15.022  49.668  45.692  1.00  0.00          N   \nATOM   4290  CA  GLY A 557      15.894  50.075  44.606  1.00  0.00          C   \nATOM   4291  C   GLY A 557      15.539  51.449  44.049  1.00  0.00          C   \nATOM   4292  O   GLY A 557      16.215  51.940  43.136  1.00  0.00          O   \nATOM   4293  N   GLY A 558      14.513  52.083  44.623  1.00  0.00          N   \nATOM   4294  CA  GLY A 558      14.046  53.377  44.142  1.00  0.00          C   \nATOM   4295  C   GLY A 558      14.755  54.627  44.634  1.00  0.00          C   \nATOM   4296  O   GLY A 558      14.358  55.746  44.296  1.00  0.00          O   \nATOM   4297  N   ASP A 559      15.789  54.450  45.447  1.00  0.00          N   \nATOM   4298  CA  ASP A 559      16.543  55.587  45.953  1.00  0.00          C   \nATOM   4299  C   ASP A 559      15.634  56.625  46.615  1.00  0.00          C   \nATOM   4300  O   ASP A 559      15.762  57.823  46.353  1.00  0.00          O   \nATOM   4301  CB  ASP A 559      17.330  56.225  44.796  1.00  0.00          C   \nATOM   4302  CG  ASP A 559      18.351  57.247  45.269  1.00  0.00          C   \nATOM   4303  OD1 ASP A 559      19.076  56.967  46.265  1.00  0.00          O   \nATOM   4304  OD2 ASP A 559      18.435  58.324  44.632  1.00  0.00          O   \nATOM   4305  N   ILE A 560      14.728  56.163  47.479  1.00  0.00          N   \nATOM   4306  CA  ILE A 560      13.791  57.046  48.176  1.00  0.00          C   \nATOM   4307  C   ILE A 560      14.113  57.202  49.668  1.00  0.00          C   \nATOM   4308  O   ILE A 560      14.351  56.216  50.373  1.00  0.00          O   \nATOM   4309  CB  ILE A 560      12.334  56.529  48.043  1.00  0.00          C   \nATOM   4310  CG1 ILE A 560      11.942  56.431  46.564  1.00  0.00          C   \nATOM   4311  CG2 ILE A 560      11.382  57.436  48.820  1.00  0.00          C   \nATOM   4312  CD1 ILE A 560      12.036  57.741  45.794  1.00  0.00          C   \nATOM   4313  N   TYR A 561      14.085  58.447  50.136  1.00  0.00          N   \nATOM   4314  CA  TYR A 561      14.377  58.788  51.528  1.00  0.00          C   \nATOM   4315  C   TYR A 561      13.297  59.691  52.136  1.00  0.00          C   \nATOM   4316  O   TYR A 561      12.778  60.580  51.469  1.00  0.00          O   \nATOM   4317  CB  TYR A 561      15.723  59.518  51.600  1.00  0.00          C   \nATOM   4318  CG  TYR A 561      16.094  60.033  52.978  1.00  0.00          C   \nATOM   4319  CD1 TYR A 561      16.775  59.222  53.891  1.00  0.00          C   \nATOM   4320  CD2 TYR A 561      15.766  61.332  53.369  1.00  0.00          C   \nATOM   4321  CE1 TYR A 561      17.126  59.697  55.162  1.00  0.00          C   \nATOM   4322  CE2 TYR A 561      16.106  61.818  54.639  1.00  0.00          C   \nATOM   4323  CZ  TYR A 561      16.787  60.996  55.527  1.00  0.00          C   \nATOM   4324  OH  TYR A 561      17.138  61.468  56.774  1.00  0.00          O   \nATOM   4325  N   HIS A 562      12.971  59.458  53.404  1.00  0.00          N   \nATOM   4326  CA  HIS A 562      11.988  60.268  54.122  1.00  0.00          C   \nATOM   4327  C   HIS A 562      12.562  60.661  55.485  1.00  0.00          C   \nATOM   4328  O   HIS A 562      13.171  59.833  56.163  1.00  0.00          O   \nATOM   4329  CB  HIS A 562      10.687  59.494  54.348  1.00  0.00          C   \nATOM   4330  CG  HIS A 562       9.902  59.230  53.100  1.00  0.00          C   \nATOM   4331  ND1 HIS A 562       9.952  58.027  52.428  1.00  0.00          N   \nATOM   4332  CD2 HIS A 562       9.029  60.008  52.417  1.00  0.00          C   \nATOM   4333  CE1 HIS A 562       9.140  58.074  51.386  1.00  0.00          C   \nATOM   4334  NE2 HIS A 562       8.568  59.264  51.356  1.00  0.00          N   \nATOM   4335  N   SER A 563      12.351  61.918  55.877  1.00  0.00          N   \nATOM   4336  CA  SER A 563      12.819  62.476  57.157  1.00  0.00          C   \nATOM   4337  C   SER A 563      13.796  61.603  57.946  1.00  0.00          C   \nATOM   4338  O   SER A 563      14.962  62.031  58.088  1.00  0.00          O   \nATOM   4339  CB  SER A 563      11.624  62.811  58.062  1.00  0.00          C   \nATOM   4340  OG  SER A 563      11.038  61.636  58.605  1.00  0.00          O   \nATOM   4341  H   SER A   1      14.865  37.645  21.864  1.00  0.00          H   \nATOM   4342  H   SER A   1      14.701  36.607  23.156  1.00  0.00          H   \nATOM   4343  H   SER A   1      16.183  36.791  22.419  1.00  0.00          H   \nATOM   4344  H   SER A   1      13.908  40.808  24.965  1.00  0.00          H   \nATOM   4345  H   MET A   2      18.142  38.276  24.091  1.00  0.00          H   \nATOM   4346  H   SER A   3      18.084  37.264  28.559  1.00  0.00          H   \nATOM   4347  H   SER A   3      18.516  36.926  31.803  1.00  0.00          H   \nATOM   4348  H   TYR A   4      20.386  38.522  29.032  1.00  0.00          H   \nATOM   4349  H   TYR A   4      19.795  42.983  35.352  1.00  0.00          H   \nATOM   4350  H   THR A   5      23.482  41.428  28.003  1.00  0.00          H   \nATOM   4351  H   THR A   5      24.307  42.588  24.714  1.00  0.00          H   \nATOM   4352  H   TRP A   6      26.786  38.888  27.373  1.00  0.00          H   \nATOM   4353  H   TRP A   6      24.579  37.432  31.679  1.00  0.00          H   \nATOM   4354  H   THR A   7      30.017  41.814  28.783  1.00  0.00          H   \nATOM   4355  H   THR A   7      32.397  44.282  28.511  1.00  0.00          H   \nATOM   4356  H   GLY A   8      31.650  40.554  29.571  1.00  0.00          H   \nATOM   4357  H   ALA A   9      33.417  42.045  29.955  1.00  0.00          H   \nATOM   4358  H   LEU A  10      35.525  42.501  34.029  1.00  0.00          H   \nATOM   4359  H   ILE A  11      32.259  41.482  37.121  1.00  0.00          H   \nATOM   4360  H   THR A  12      32.760  45.444  38.354  1.00  0.00          H   \nATOM   4361  H   THR A  12      35.010  47.784  38.549  1.00  0.00          H   \nATOM   4362  H   CYS A  14      32.622  48.117  45.021  1.00  0.00          H   \nATOM   4363  H   CYS A  14      31.743  53.189  43.872  1.00  0.00          H   \nATOM   4364  H   ALA A  15      34.889  48.289  46.024  1.00  0.00          H   \nATOM   4365  H   ALA A  16      37.227  48.638  49.603  1.00  0.00          H   \nATOM   4366  H   GLU A  17      36.443  44.097  49.807  1.00  0.00          H   \nATOM   4367  H   GLU A  18      33.336  43.230  52.794  1.00  0.00          H   \nATOM   4368  H   SER A  19      34.599  38.983  53.574  1.00  0.00          H   \nATOM   4369  H   SER A  19      34.586  35.796  51.936  1.00  0.00          H   \nATOM   4370  H   LYS A  20      34.340  37.706  55.147  1.00  0.00          H   \nATOM   4371  H   LYS A  20      35.029  32.748  52.761  1.00  0.00          H   \nATOM   4372  H   LYS A  20      34.161  34.159  52.934  1.00  0.00          H   \nATOM   4373  H   LYS A  20      34.274  33.089  54.206  1.00  0.00          H   \nATOM   4374  H   LEU A  21      31.818  36.057  58.599  1.00  0.00          H   \nATOM   4375  H   ILE A  23      35.109  39.912  63.553  1.00  0.00          H   \nATOM   4376  H   ASN A  24      32.820  40.812  67.247  1.00  0.00          H   \nATOM   4377  H   ASN A  24      32.320  45.653  67.822  1.00  0.00          H   \nATOM   4378  H   ASN A  24      30.780  45.086  68.285  1.00  0.00          H   \nATOM   4379  H   ALA A  25      34.379  43.313  70.413  1.00  0.00          H   \nATOM   4380  H   LEU A  26      31.774  42.541  70.531  1.00  0.00          H   \nATOM   4381  H   SER A  27      31.021  40.643  69.517  1.00  0.00          H   \nATOM   4382  H   SER A  27      31.794  39.653  66.490  1.00  0.00          H   \nATOM   4383  H   ASN A  28      30.862  37.793  70.661  1.00  0.00          H   \nATOM   4384  H   ASN A  28      33.861  34.997  70.731  1.00  0.00          H   \nATOM   4385  H   ASN A  28      33.330  36.091  71.927  1.00  0.00          H   \nATOM   4386  H   SER A  29      28.672  38.231  72.363  1.00  0.00          H   \nATOM   4387  H   SER A  29      26.792  40.855  73.436  1.00  0.00          H   \nATOM   4388  H   LEU A  30      26.531  37.937  70.817  1.00  0.00          H   \nATOM   4389  H   LEU A  31      26.409  36.017  68.995  1.00  0.00          H   \nATOM   4390  H   ARG A  32      26.462  31.636  68.613  1.00  0.00          H   \nATOM   4391  H   ARG A  32      27.732  30.664  74.060  1.00  0.00          H   \nATOM   4392  H   ARG A  32      30.913  30.959  72.631  1.00  0.00          H   \nATOM   4393  H   ARG A  32      31.561  31.817  73.954  1.00  0.00          H   \nATOM   4394  H   ARG A  32      30.229  32.276  75.802  1.00  0.00          H   \nATOM   4395  H   ARG A  32      28.591  31.802  75.807  1.00  0.00          H   \nATOM   4396  H   HIS A  33      27.209  30.579  66.701  1.00  0.00          H   \nATOM   4397  H   HIS A  34      30.423  31.472  65.071  1.00  0.00          H   \nATOM   4398  H   ASN A  35      31.083  30.521  62.429  1.00  0.00          H   \nATOM   4399  H   ASN A  35      33.285  28.042  62.009  1.00  0.00          H   \nATOM   4400  H   ASN A  35      34.710  28.966  62.157  1.00  0.00          H   \nATOM   4401  H   MET A  36      28.688  30.842  61.133  1.00  0.00          H   \nATOM   4402  H   VAL A  37      28.202  32.691  59.961  1.00  0.00          H   \nATOM   4403  H   TYR A  38      26.737  35.165  56.425  1.00  0.00          H   \nATOM   4404  H   TYR A  38      24.300  33.242  50.529  1.00  0.00          H   \nATOM   4405  H   ALA A  39      29.651  37.400  54.064  1.00  0.00          H   \nATOM   4406  H   THR A  40      26.785  38.998  51.067  1.00  0.00          H   \nATOM   4407  H   THR A  40      25.189  39.494  48.095  1.00  0.00          H   \nATOM   4408  H   THR A  41      29.780  38.165  47.664  1.00  0.00          H   \nATOM   4409  H   THR A  41      32.679  38.747  46.541  1.00  0.00          H   \nATOM   4410  H   SER A  42      31.778  41.579  45.704  1.00  0.00          H   \nATOM   4411  H   SER A  42      33.986  42.879  43.483  1.00  0.00          H   \nATOM   4412  H   ARG A  43      33.019  39.147  45.140  1.00  0.00          H   \nATOM   4413  H   ARG A  43      35.480  36.679  48.023  1.00  0.00          H   \nATOM   4414  H   ARG A  43      35.785  35.462  49.934  1.00  0.00          H   \nATOM   4415  H   ARG A  43      36.983  34.249  49.984  1.00  0.00          H   \nATOM   4416  H   ARG A  43      38.084  34.775  46.725  1.00  0.00          H   \nATOM   4417  H   ARG A  43      38.300  33.856  48.145  1.00  0.00          H   \nATOM   4418  H   SER A  44      31.107  37.296  44.319  1.00  0.00          H   \nATOM   4419  H   SER A  44      28.282  35.891  45.736  1.00  0.00          H   \nATOM   4420  H   ALA A  45      30.581  37.870  42.052  1.00  0.00          H   \nATOM   4421  H   GLY A  46      30.753  36.524  39.576  1.00  0.00          H   \nATOM   4422  H   LEU A  47      29.314  34.254  39.821  1.00  0.00          H   \nATOM   4423  H   ARG A  48      26.865  35.258  39.976  1.00  0.00          H   \nATOM   4424  H   ARG A  48      23.078  36.700  42.429  1.00  0.00          H   \nATOM   4425  H   ARG A  48      20.226  35.518  40.819  1.00  0.00          H   \nATOM   4426  H   ARG A  48      19.605  34.739  42.203  1.00  0.00          H   \nATOM   4427  H   ARG A  48      20.763  34.829  44.194  1.00  0.00          H   \nATOM   4428  H   ARG A  48      22.250  35.660  44.279  1.00  0.00          H   \nATOM   4429  H   GLN A  49      26.365  36.411  37.713  1.00  0.00          H   \nATOM   4430  H   GLN A  49      30.484  38.568  35.895  1.00  0.00          H   \nATOM   4431  H   GLN A  49      29.398  37.881  37.016  1.00  0.00          H   \nATOM   4432  H   LYS A  50      26.141  34.146  36.130  1.00  0.00          H   \nATOM   4433  H   LYS A  50      30.984  31.902  33.485  1.00  0.00          H   \nATOM   4434  H   LYS A  50      30.407  32.356  34.980  1.00  0.00          H   \nATOM   4435  H   LYS A  50      29.493  32.635  33.616  1.00  0.00          H   \nATOM   4436  H   LYS A  51      23.779  33.123  37.025  1.00  0.00          H   \nATOM   4437  H   LYS A  51      23.915  29.280  42.064  1.00  0.00          H   \nATOM   4438  H   LYS A  51      23.216  29.235  40.553  1.00  0.00          H   \nATOM   4439  H   LYS A  51      22.260  29.396  41.908  1.00  0.00          H   \nATOM   4440  H   VAL A  52      21.948  35.003  36.355  1.00  0.00          H   \nATOM   4441  H   THR A  53      21.635  34.552  33.703  1.00  0.00          H   \nATOM   4442  H   THR A  53      24.237  36.344  31.829  1.00  0.00          H   \nATOM   4443  H   PHE A  54      19.020  35.410  30.387  1.00  0.00          H   \nATOM   4444  H   ASP A  55      17.372  32.451  27.601  1.00  0.00          H   \nATOM   4445  H   ARG A  56      14.390  35.688  26.216  1.00  0.00          H   \nATOM   4446  H   ARG A  56      15.133  37.801  27.466  1.00  0.00          H   \nATOM   4447  H   ARG A  56      13.761  40.567  30.013  1.00  0.00          H   \nATOM   4448  H   ARG A  56      13.757  38.931  30.497  1.00  0.00          H   \nATOM   4449  H   ARG A  56      15.057  39.943  26.841  1.00  0.00          H   \nATOM   4450  H   ARG A  56      14.445  41.133  27.897  1.00  0.00          H   \nATOM   4451  H   LEU A  57      10.476  33.784  27.112  1.00  0.00          H   \nATOM   4452  H   GLN A  58       7.588  35.082  24.114  1.00  0.00          H   \nATOM   4453  H   GLN A  58       5.507  39.650  29.108  1.00  0.00          H   \nATOM   4454  H   GLN A  58       5.385  37.972  29.386  1.00  0.00          H   \nATOM   4455  H   VAL A  59       3.955  33.982  26.031  1.00  0.00          H   \nATOM   4456  H   LEU A  60       1.402  35.975  23.674  1.00  0.00          H   \nATOM   4457  H   ASP A  61      -1.922  35.510  26.457  1.00  0.00          H   \nATOM   4458  H   ASP A  62      -6.154  35.140  25.024  1.00  0.00          H   \nATOM   4459  H   HIS A  63      -6.601  35.500  27.716  1.00  0.00          H   \nATOM   4460  H   TYR A  64      -4.639  36.928  28.582  1.00  0.00          H   \nATOM   4461  H   TYR A  64       1.780  42.798  28.452  1.00  0.00          H   \nATOM   4462  H   ARG A  65      -4.854  39.358  27.218  1.00  0.00          H   \nATOM   4463  H   ARG A  65      -5.929  40.075  22.900  1.00  0.00          H   \nATOM   4464  H   ARG A  65      -2.471  38.104  23.090  1.00  0.00          H   \nATOM   4465  H   ARG A  65      -2.504  39.774  23.434  1.00  0.00          H   \nATOM   4466  H   ARG A  65      -5.883  37.837  22.552  1.00  0.00          H   \nATOM   4467  H   ARG A  65      -4.413  36.984  22.685  1.00  0.00          H   \nATOM   4468  H   ASP A  66      -7.238  40.084  28.280  1.00  0.00          H   \nATOM   4469  H   VAL A  67      -6.882  40.628  30.902  1.00  0.00          H   \nATOM   4470  H   LEU A  68      -5.178  42.615  30.678  1.00  0.00          H   \nATOM   4471  H   LYS A  69      -6.731  44.594  29.557  1.00  0.00          H   \nATOM   4472  H   LYS A  69     -12.747  48.072  24.980  1.00  0.00          H   \nATOM   4473  H   LYS A  69     -12.832  46.519  25.578  1.00  0.00          H   \nATOM   4474  H   LYS A  69     -11.584  46.954  24.565  1.00  0.00          H   \nATOM   4475  H   GLU A  70      -8.369  45.173  31.691  1.00  0.00          H   \nATOM   4476  H   MET A  71      -6.668  46.189  33.501  1.00  0.00          H   \nATOM   4477  H   LYS A  72      -5.908  48.513  32.251  1.00  0.00          H   \nATOM   4478  H   LYS A  72      -2.202  49.723  26.100  1.00  0.00          H   \nATOM   4479  H   LYS A  72      -3.654  50.532  26.197  1.00  0.00          H   \nATOM   4480  H   LYS A  72      -3.620  48.870  26.285  1.00  0.00          H   \nATOM   4481  H   ALA A  73      -8.249  49.899  32.338  1.00  0.00          H   \nATOM   4482  H   LYS A  74      -8.440  50.528  34.963  1.00  0.00          H   \nATOM   4483  H   LYS A  74     -11.424  46.528  38.133  1.00  0.00          H   \nATOM   4484  H   LYS A  74      -9.959  46.520  37.341  1.00  0.00          H   \nATOM   4485  H   LYS A  74     -11.007  47.800  37.143  1.00  0.00          H   \nATOM   4486  H   ALA A  75      -6.290  51.969  35.228  1.00  0.00          H   \nATOM   4487  H   SER A  76      -7.112  54.234  33.949  1.00  0.00          H   \nATOM   4488  H   SER A  76      -8.238  55.018  30.793  1.00  0.00          H   \nATOM   4489  H   THR A  77      -8.024  55.796  35.989  1.00  0.00          H   \nATOM   4490  H   THR A  77      -8.318  56.525  39.130  1.00  0.00          H   \nATOM   4491  H   VAL A  78      -6.370  57.199  36.838  1.00  0.00          H   \nATOM   4492  H   LYS A  79      -4.895  60.672  39.440  1.00  0.00          H   \nATOM   4493  H   LYS A  79      -5.274  69.079  37.546  1.00  0.00          H   \nATOM   4494  H   LYS A  79      -6.217  68.281  38.664  1.00  0.00          H   \nATOM   4495  H   LYS A  79      -6.486  68.056  37.035  1.00  0.00          H   \nATOM   4496  H   ALA A  80      -1.761  62.424  37.237  1.00  0.00          H   \nATOM   4497  H   LYS A  81       1.068  63.831  40.413  1.00  0.00          H   \nATOM   4498  H   LYS A  81       1.896  66.035  46.012  1.00  0.00          H   \nATOM   4499  H   LYS A  81       1.193  65.415  44.635  1.00  0.00          H   \nATOM   4500  H   LYS A  81       0.753  66.923  45.189  1.00  0.00          H   \nATOM   4501  H   LEU A  82       3.100  67.067  38.059  1.00  0.00          H   \nATOM   4502  H   LEU A  83       7.038  65.554  39.558  1.00  0.00          H   \nATOM   4503  H   SER A  84       8.229  69.016  42.391  1.00  0.00          H   \nATOM   4504  H   SER A  84       9.497  71.824  43.971  1.00  0.00          H   \nATOM   4505  H   VAL A  85      11.855  70.983  40.329  1.00  0.00          H   \nATOM   4506  H   GLU A  86      12.924  71.525  42.925  1.00  0.00          H   \nATOM   4507  H   GLU A  87      11.194  69.943  44.515  1.00  0.00          H   \nATOM   4508  H   ALA A  88      11.793  67.449  43.642  1.00  0.00          H   \nATOM   4509  H   CYS A  89      14.358  67.389  44.408  1.00  0.00          H   \nATOM   4510  H   CYS A  89      17.824  69.668  44.015  1.00  0.00          H   \nATOM   4511  H   LYS A  90      14.106  67.214  47.208  1.00  0.00          H   \nATOM   4512  H   LYS A  90      10.817  72.359  49.753  1.00  0.00          H   \nATOM   4513  H   LYS A  90      10.357  71.128  50.776  1.00  0.00          H   \nATOM   4514  H   LYS A  90      10.178  70.953  49.129  1.00  0.00          H   \nATOM   4515  H   LEU A  91      13.684  64.487  47.381  1.00  0.00          H   \nATOM   4516  H   THR A  92      15.902  63.380  47.867  1.00  0.00          H   \nATOM   4517  H   THR A  92      18.188  63.342  45.141  1.00  0.00          H   \nATOM   4518  H   HIS A  95      22.186  59.080  51.881  1.00  0.00          H   \nATOM   4519  H   SER A  96      22.560  59.376  49.390  1.00  0.00          H   \nATOM   4520  H   SER A  96      20.594  58.503  45.499  1.00  0.00          H   \nATOM   4521  H   ALA A  97      23.772  56.472  45.979  1.00  0.00          H   \nATOM   4522  H   LYS A  98      27.727  58.623  45.068  1.00  0.00          H   \nATOM   4523  H   LYS A  98      34.314  59.743  43.057  1.00  0.00          H   \nATOM   4524  H   LYS A  98      33.942  60.461  44.513  1.00  0.00          H   \nATOM   4525  H   LYS A  98      33.600  61.247  43.085  1.00  0.00          H   \nATOM   4526  H   SER A  99      26.728  62.293  42.436  1.00  0.00          H   \nATOM   4527  H   SER A  99      25.916  64.003  38.436  1.00  0.00          H   \nATOM   4528  H   LYS A 100      27.482  60.893  38.086  1.00  0.00          H   \nATOM   4529  H   LYS A 100      29.112  54.281  35.545  1.00  0.00          H   \nATOM   4530  H   LYS A 100      29.960  55.341  34.579  1.00  0.00          H   \nATOM   4531  H   LYS A 100      28.300  55.445  34.672  1.00  0.00          H   \nATOM   4532  H   PHE A 101      28.147  63.157  38.001  1.00  0.00          H   \nATOM   4533  H   GLY A 102      30.465  64.514  39.140  1.00  0.00          H   \nATOM   4534  H   TYR A 103      28.815  65.184  41.163  1.00  0.00          H   \nATOM   4535  H   TYR A 103      23.418  65.605  37.932  1.00  0.00          H   \nATOM   4536  H   GLY A 104      27.360  66.133  45.210  1.00  0.00          H   \nATOM   4537  H   ALA A 105      25.452  62.570  46.967  1.00  0.00          H   \nATOM   4538  H   LYS A 106      25.292  64.467  48.841  1.00  0.00          H   \nATOM   4539  H   LYS A 106      22.695  63.921  52.207  1.00  0.00          H   \nATOM   4540  H   LYS A 106      23.197  65.462  51.826  1.00  0.00          H   \nATOM   4541  H   LYS A 106      22.978  65.027  53.419  1.00  0.00          H   \nATOM   4542  H   ASP A 107      25.501  66.849  47.676  1.00  0.00          H   \nATOM   4543  H   VAL A 108      23.172  66.845  46.511  1.00  0.00          H   \nATOM   4544  H   ARG A 109      21.222  66.824  48.222  1.00  0.00          H   \nATOM   4545  H   ARG A 109      19.687  65.537  53.305  1.00  0.00          H   \nATOM   4546  H   ARG A 109      17.591  64.066  50.939  1.00  0.00          H   \nATOM   4547  H   ARG A 109      16.173  64.352  51.842  1.00  0.00          H   \nATOM   4548  H   ARG A 109      16.298  65.395  53.878  1.00  0.00          H   \nATOM   4549  H   ARG A 109      17.806  65.909  54.486  1.00  0.00          H   \nATOM   4550  H   ASN A 110      21.584  69.338  48.885  1.00  0.00          H   \nATOM   4551  H   ASN A 110      24.856  70.572  50.400  1.00  0.00          H   \nATOM   4552  H   ASN A 110      24.642  69.994  51.990  1.00  0.00          H   \nATOM   4553  H   LEU A 111      20.093  70.591  47.355  1.00  0.00          H   \nATOM   4554  H   SER A 112      22.234  71.666  46.149  1.00  0.00          H   \nATOM   4555  H   SER A 112      25.569  70.998  44.450  1.00  0.00          H   \nATOM   4556  H   SER A 113      24.074  75.372  44.072  1.00  0.00          H   \nATOM   4557  H   SER A 113      25.907  77.391  41.668  1.00  0.00          H   \nATOM   4558  H   LYS A 114      25.588  73.879  42.218  1.00  0.00          H   \nATOM   4559  H   LYS A 114      30.979  70.359  37.947  1.00  0.00          H   \nATOM   4560  H   LYS A 114      30.891  71.757  38.848  1.00  0.00          H   \nATOM   4561  H   LYS A 114      29.524  71.153  38.112  1.00  0.00          H   \nATOM   4562  H   ALA A 115      24.601  71.297  42.210  1.00  0.00          H   \nATOM   4563  H   VAL A 116      21.859  71.713  41.700  1.00  0.00          H   \nATOM   4564  H   ASN A 117      22.256  72.799  39.152  1.00  0.00          H   \nATOM   4565  H   ASN A 117      23.546  76.697  38.466  1.00  0.00          H   \nATOM   4566  H   ASN A 117      24.369  75.216  38.660  1.00  0.00          H   \nATOM   4567  H   HIS A 118      23.184  70.751  37.620  1.00  0.00          H   \nATOM   4568  H   ILE A 119      21.141  69.002  37.763  1.00  0.00          H   \nATOM   4569  H   HIS A 120      19.265  70.386  36.313  1.00  0.00          H   \nATOM   4570  H   SER A 121      20.253  70.059  33.842  1.00  0.00          H   \nATOM   4571  H   SER A 121      23.259  69.153  32.493  1.00  0.00          H   \nATOM   4572  H   VAL A 122      20.032  67.656  33.332  1.00  0.00          H   \nATOM   4573  H   TRP A 123      17.420  67.206  33.253  1.00  0.00          H   \nATOM   4574  H   TRP A 123      10.661  68.859  31.923  1.00  0.00          H   \nATOM   4575  H   LYS A 124      16.700  68.721  31.041  1.00  0.00          H   \nATOM   4576  H   LYS A 124      18.088  74.447  25.252  1.00  0.00          H   \nATOM   4577  H   LYS A 124      19.202  73.789  26.301  1.00  0.00          H   \nATOM   4578  H   LYS A 124      17.892  74.628  26.896  1.00  0.00          H   \nATOM   4579  H   ASP A 125      17.962  67.230  29.097  1.00  0.00          H   \nATOM   4580  H   LEU A 126      16.348  65.056  29.219  1.00  0.00          H   \nATOM   4581  H   LEU A 127      14.028  65.913  28.109  1.00  0.00          H   \nATOM   4582  H   GLU A 128      14.774  66.267  25.625  1.00  0.00          H   \nATOM   4583  H   ASP A 129      15.435  64.164  24.588  1.00  0.00          H   \nATOM   4584  H   THR A 130      14.540  61.024  21.871  1.00  0.00          H   \nATOM   4585  H   THR A 130      12.471  59.120  21.135  1.00  0.00          H   \nATOM   4586  H   VAL A 131      16.052  59.133  22.398  1.00  0.00          H   \nATOM   4587  H   THR A 132      17.987  58.534  22.716  1.00  0.00          H   \nATOM   4588  H   THR A 132      19.464  59.451  21.894  1.00  0.00          H   \nATOM   4589  H   ILE A 134      19.685  54.677  28.198  1.00  0.00          H   \nATOM   4590  H   ASP A 135      23.436  56.365  30.075  1.00  0.00          H   \nATOM   4591  H   THR A 136      24.775  52.433  32.130  1.00  0.00          H   \nATOM   4592  H   THR A 136      22.545  49.576  33.256  1.00  0.00          H   \nATOM   4593  H   THR A 137      24.452  52.174  36.475  1.00  0.00          H   \nATOM   4594  H   THR A 137      26.351  53.706  36.954  1.00  0.00          H   \nATOM   4595  H   ILE A 138      27.428  48.865  36.593  1.00  0.00          H   \nATOM   4596  H   MET A 139      25.605  46.722  39.912  1.00  0.00          H   \nATOM   4597  H   ALA A 140      28.581  44.768  42.488  1.00  0.00          H   \nATOM   4598  H   LYS A 141      25.072  42.881  44.434  1.00  0.00          H   \nATOM   4599  H   LYS A 141      18.581  46.021  45.231  1.00  0.00          H   \nATOM   4600  H   LYS A 141      19.200  45.811  46.763  1.00  0.00          H   \nATOM   4601  H   LYS A 141      18.704  44.491  45.878  1.00  0.00          H   \nATOM   4602  H   ASN A 142      26.022  44.740  48.576  1.00  0.00          H   \nATOM   4603  H   ASN A 142      30.370  43.783  47.968  1.00  0.00          H   \nATOM   4604  H   ASN A 142      29.449  42.608  48.792  1.00  0.00          H   \nATOM   4605  H   GLU A 143      25.711  40.985  50.220  1.00  0.00          H   \nATOM   4606  H   VAL A 144      23.476  41.010  54.039  1.00  0.00          H   \nATOM   4607  H   PHE A 145      24.691  36.718  54.904  1.00  0.00          H   \nATOM   4608  H   CYS A 146      21.777  33.583  55.916  1.00  0.00          H   \nATOM   4609  H   CYS A 146      21.763  30.220  59.894  1.00  0.00          H   \nATOM   4610  H   VAL A 147      24.993  30.652  56.832  1.00  0.00          H   \nATOM   4611  H   GLN A 148      24.052  28.272  53.025  1.00  0.00          H   \nATOM   4612  H   GLN A 148      22.657  22.506  49.801  1.00  0.00          H   \nATOM   4613  H   GLN A 148      21.469  23.723  49.919  1.00  0.00          H   \nATOM   4614  H   ARG A 154      24.579  28.664  50.283  1.00  0.00          H   \nATOM   4615  H   ARG A 154      25.972  28.545  51.188  1.00  0.00          H   \nATOM   4616  H   ARG A 154      26.012  28.230  49.553  1.00  0.00          H   \nATOM   4617  H   ARG A 154      28.889  32.536  50.303  1.00  0.00          H   \nATOM   4618  H   ARG A 154      32.508  33.058  51.894  1.00  0.00          H   \nATOM   4619  H   ARG A 154      31.694  31.576  52.118  1.00  0.00          H   \nATOM   4620  H   ARG A 154      29.978  34.517  50.005  1.00  0.00          H   \nATOM   4621  H   ARG A 154      31.524  34.744  50.686  1.00  0.00          H   \nATOM   4622  H   LYS A 155      24.377  32.260  49.572  1.00  0.00          H   \nATOM   4623  H   LYS A 155      18.480  32.378  50.373  1.00  0.00          H   \nATOM   4624  H   LYS A 155      18.426  33.501  49.144  1.00  0.00          H   \nATOM   4625  H   LYS A 155      18.802  33.980  50.694  1.00  0.00          H   \nATOM   4626  H   ALA A 157      26.911  36.862  44.855  1.00  0.00          H   \nATOM   4627  H   ARG A 158      23.380  39.819  45.539  1.00  0.00          H   \nATOM   4628  H   ARG A 158      19.999  41.298  46.518  1.00  0.00          H   \nATOM   4629  H   ARG A 158      16.659  40.533  45.929  1.00  0.00          H   \nATOM   4630  H   ARG A 158      16.063  41.793  46.910  1.00  0.00          H   \nATOM   4631  H   ARG A 158      19.207  42.973  47.824  1.00  0.00          H   \nATOM   4632  H   ARG A 158      17.525  43.194  47.998  1.00  0.00          H   \nATOM   4633  H   LEU A 159      21.768  40.975  41.428  1.00  0.00          H   \nATOM   4634  H   ILE A 160      23.687  44.977  41.450  1.00  0.00          H   \nATOM   4635  H   VAL A 161      20.485  47.375  39.592  1.00  0.00          H   \nATOM   4636  H   PHE A 162      22.722  50.607  37.930  1.00  0.00          H   \nATOM   4637  H   ASP A 164      22.685  57.387  35.715  1.00  0.00          H   \nATOM   4638  H   LEU A 165      23.812  59.560  39.646  1.00  0.00          H   \nATOM   4639  H   GLY A 166      22.620  62.038  38.435  1.00  0.00          H   \nATOM   4640  H   VAL A 167      20.770  61.015  36.762  1.00  0.00          H   \nATOM   4641  H   ARG A 168      19.230  59.800  38.456  1.00  0.00          H   \nATOM   4642  H   ARG A 168      19.772  57.670  43.225  1.00  0.00          H   \nATOM   4643  H   ARG A 168      21.702  56.743  40.471  1.00  0.00          H   \nATOM   4644  H   ARG A 168      23.161  57.290  41.163  1.00  0.00          H   \nATOM   4645  H   ARG A 168      23.158  58.257  43.242  1.00  0.00          H   \nATOM   4646  H   ARG A 168      21.703  58.386  44.122  1.00  0.00          H   \nATOM   4647  H   VAL A 169      18.025  61.899  39.853  1.00  0.00          H   \nATOM   4648  H   CYS A 170      16.494  62.873  37.943  1.00  0.00          H   \nATOM   4649  H   CYS A 170      16.796  64.634  34.153  1.00  0.00          H   \nATOM   4650  H   GLU A 171      14.747  60.713  37.639  1.00  0.00          H   \nATOM   4651  H   LYS A 172      13.124  60.928  39.865  1.00  0.00          H   \nATOM   4652  H   LYS A 172      15.721  58.819  44.803  1.00  0.00          H   \nATOM   4653  H   LYS A 172      16.115  60.008  43.705  1.00  0.00          H   \nATOM   4654  H   LYS A 172      14.895  58.927  43.360  1.00  0.00          H   \nATOM   4655  H   MET A 173      11.845  63.310  39.484  1.00  0.00          H   \nATOM   4656  H   ALA A 174      10.239  62.754  37.242  1.00  0.00          H   \nATOM   4657  H   LEU A 175       8.852  60.819  38.227  1.00  0.00          H   \nATOM   4658  H   TYR A 176       8.011  60.803  40.332  1.00  0.00          H   \nATOM   4659  H   TYR A 176       7.807  58.855  48.805  1.00  0.00          H   \nATOM   4660  H   ASP A 177       5.476  61.309  41.325  1.00  0.00          H   \nATOM   4661  H   VAL A 178       4.055  59.644  39.994  1.00  0.00          H   \nATOM   4662  H   VAL A 179       4.934  56.959  40.572  1.00  0.00          H   \nATOM   4663  H   SER A 180       3.875  57.102  42.926  1.00  0.00          H   \nATOM   4664  H   SER A 180       5.260  58.699  45.965  1.00  0.00          H   \nATOM   4665  H   THR A 181       1.495  56.769  42.952  1.00  0.00          H   \nATOM   4666  H   THR A 181      -1.590  57.497  41.721  1.00  0.00          H   \nATOM   4667  H   LEU A 182       0.276  55.746  41.472  1.00  0.00          H   \nATOM   4668  H   GLN A 184      -1.565  52.106  44.207  1.00  0.00          H   \nATOM   4669  H   GLN A 184      -7.766  51.833  46.666  1.00  0.00          H   \nATOM   4670  H   GLN A 184      -6.751  50.563  47.180  1.00  0.00          H   \nATOM   4671  H   VAL A 185      -3.879  52.819  43.025  1.00  0.00          H   \nATOM   4672  H   VAL A 186      -4.122  51.206  40.897  1.00  0.00          H   \nATOM   4673  H   MET A 187      -4.233  48.781  41.936  1.00  0.00          H   \nATOM   4674  H   GLY A 188      -6.075  48.728  43.419  1.00  0.00          H   \nATOM   4675  H   SER A 189      -8.214  46.420  46.783  1.00  0.00          H   \nATOM   4676  H   SER A 189      -9.284  43.610  48.361  1.00  0.00          H   \nATOM   4677  H   SER A 190      -6.028  44.773  45.980  1.00  0.00          H   \nATOM   4678  H   SER A 190      -5.924  41.624  44.113  1.00  0.00          H   \nATOM   4679  H   TYR A 191      -3.687  45.939  46.362  1.00  0.00          H   \nATOM   4680  H   TYR A 191       4.040  49.864  45.416  1.00  0.00          H   \nATOM   4681  H   GLY A 192       0.479  44.653  47.260  1.00  0.00          H   \nATOM   4682  H   PHE A 193       1.977  46.880  48.408  1.00  0.00          H   \nATOM   4683  H   GLN A 194       0.154  48.200  50.227  1.00  0.00          H   \nATOM   4684  H   GLN A 194      -4.674  47.853  48.405  1.00  0.00          H   \nATOM   4685  H   GLN A 194      -3.145  47.255  48.864  1.00  0.00          H   \nATOM   4686  H   TYR A 195       0.227  47.733  52.424  1.00  0.00          H   \nATOM   4687  H   TYR A 195      -5.484  44.793  50.441  1.00  0.00          H   \nATOM   4688  H   SER A 196      -0.435  48.166  56.922  1.00  0.00          H   \nATOM   4689  H   SER A 196      -0.486  48.657  60.197  1.00  0.00          H   \nATOM   4690  H   GLY A 198       0.924  45.541  60.749  1.00  0.00          H   \nATOM   4691  H   GLN A 199      -1.299  46.039  59.221  1.00  0.00          H   \nATOM   4692  H   GLN A 199      -3.735  51.039  57.654  1.00  0.00          H   \nATOM   4693  H   GLN A 199      -4.607  49.951  58.635  1.00  0.00          H   \nATOM   4694  H   ARG A 200      -0.861  44.239  57.412  1.00  0.00          H   \nATOM   4695  H   ARG A 200       2.542  39.392  54.134  1.00  0.00          H   \nATOM   4696  H   ARG A 200       2.163  41.480  50.761  1.00  0.00          H   \nATOM   4697  H   ARG A 200       1.909  42.252  52.260  1.00  0.00          H   \nATOM   4698  H   ARG A 200       2.701  39.244  50.692  1.00  0.00          H   \nATOM   4699  H   ARG A 200       2.845  38.359  52.142  1.00  0.00          H   \nATOM   4700  H   VAL A 201      -1.087  41.811  58.318  1.00  0.00          H   \nATOM   4701  H   GLU A 202      -3.697  41.995  58.976  1.00  0.00          H   \nATOM   4702  H   PHE A 203      -4.862  42.200  56.389  1.00  0.00          H   \nATOM   4703  H   LEU A 204      -4.270  39.771  55.456  1.00  0.00          H   \nATOM   4704  H   VAL A 205      -5.575  38.079  57.175  1.00  0.00          H   \nATOM   4705  H   ASN A 206      -8.218  38.742  56.914  1.00  0.00          H   \nATOM   4706  H   ASN A 206     -10.063  42.190  56.764  1.00  0.00          H   \nATOM   4707  H   ASN A 206     -10.509  42.179  58.410  1.00  0.00          H   \nATOM   4708  H   THR A 207      -8.526  38.063  54.330  1.00  0.00          H   \nATOM   4709  H   THR A 207      -7.364  38.835  51.062  1.00  0.00          H   \nATOM   4710  H   TRP A 208      -8.127  35.505  54.310  1.00  0.00          H   \nATOM   4711  H   TRP A 208      -7.912  28.935  56.807  1.00  0.00          H   \nATOM   4712  H   LYS A 209     -10.306  34.761  55.700  1.00  0.00          H   \nATOM   4713  H   LYS A 209     -10.969  35.887  62.729  1.00  0.00          H   \nATOM   4714  H   LYS A 209     -12.500  35.944  62.076  1.00  0.00          H   \nATOM   4715  H   LYS A 209     -11.310  36.957  61.499  1.00  0.00          H   \nATOM   4716  H   SER A 210     -12.067  34.940  53.827  1.00  0.00          H   \nATOM   4717  H   SER A 210     -12.375  36.622  50.796  1.00  0.00          H   \nATOM   4718  H   LYS A 211     -12.745  32.657  52.789  1.00  0.00          H   \nATOM   4719  H   LYS A 211     -10.609  35.646  47.594  1.00  0.00          H   \nATOM   4720  H   LYS A 211     -12.140  35.458  48.224  1.00  0.00          H   \nATOM   4721  H   LYS A 211     -11.629  34.485  46.972  1.00  0.00          H   \nATOM   4722  H   LYS A 212     -15.257  29.698  50.389  1.00  0.00          H   \nATOM   4723  H   LYS A 212     -20.245  30.691  47.905  1.00  0.00          H   \nATOM   4724  H   LYS A 212     -19.274  29.416  47.449  1.00  0.00          H   \nATOM   4725  H   LYS A 212     -18.717  30.535  48.550  1.00  0.00          H   \nATOM   4726  H   ASN A 213     -14.002  27.514  50.586  1.00  0.00          H   \nATOM   4727  H   ASN A 213     -14.844  23.727  49.436  1.00  0.00          H   \nATOM   4728  H   ASN A 213     -16.058  23.275  50.545  1.00  0.00          H   \nATOM   4729  H   MET A 215      -8.087  28.737  49.779  1.00  0.00          H   \nATOM   4730  H   GLY A 216      -4.078  27.611  49.907  1.00  0.00          H   \nATOM   4731  H   PHE A 217      -1.865  30.685  47.743  1.00  0.00          H   \nATOM   4732  H   SER A 218       2.361  29.851  47.891  1.00  0.00          H   \nATOM   4733  H   SER A 218       5.368  30.931  49.101  1.00  0.00          H   \nATOM   4734  H   TYR A 219       3.494  33.191  45.190  1.00  0.00          H   \nATOM   4735  H   TYR A 219       6.037  30.930  37.073  1.00  0.00          H   \nATOM   4736  H   ASP A 220       6.608  30.840  43.728  1.00  0.00          H   \nATOM   4737  H   THR A 221       8.328  34.333  41.847  1.00  0.00          H   \nATOM   4738  H   THR A 221       7.021  35.880  38.777  1.00  0.00          H   \nATOM   4739  H   ARG A 222      10.637  32.256  38.497  1.00  0.00          H   \nATOM   4740  H   ARG A 222      14.030  29.101  36.862  1.00  0.00          H   \nATOM   4741  H   ARG A 222      14.249  30.639  33.186  1.00  0.00          H   \nATOM   4742  H   ARG A 222      14.817  31.550  34.511  1.00  0.00          H   \nATOM   4743  H   ARG A 222      13.381  28.544  33.518  1.00  0.00          H   \nATOM   4744  H   ARG A 222      13.300  27.889  35.090  1.00  0.00          H   \nATOM   4745  H   CYS A 223      14.311  34.173  40.292  1.00  0.00          H   \nATOM   4746  H   CYS A 223      18.214  34.006  37.913  1.00  0.00          H   \nATOM   4747  H   PHE A 224      12.757  36.881  39.850  1.00  0.00          H   \nATOM   4748  H   ASP A 225      13.573  39.663  38.785  1.00  0.00          H   \nATOM   4749  H   SER A 226      14.974  38.858  36.451  1.00  0.00          H   \nATOM   4750  H   SER A 226      18.154  37.495  35.243  1.00  0.00          H   \nATOM   4751  H   THR A 227      12.940  38.119  34.986  1.00  0.00          H   \nATOM   4752  H   THR A 227       9.732  38.130  34.473  1.00  0.00          H   \nATOM   4753  H   VAL A 228      11.968  39.988  33.810  1.00  0.00          H   \nATOM   4754  H   THR A 229       9.348  41.094  30.381  1.00  0.00          H   \nATOM   4755  H   THR A 229       7.839  40.522  27.420  1.00  0.00          H   \nATOM   4756  H   GLU A 230      11.338  42.601  26.543  1.00  0.00          H   \nATOM   4757  H   ASN A 231       8.789  42.984  25.792  1.00  0.00          H   \nATOM   4758  H   ASN A 231       3.407  42.107  27.288  1.00  0.00          H   \nATOM   4759  H   ASN A 231       5.033  41.811  27.706  1.00  0.00          H   \nATOM   4760  H   ASP A 232       7.394  42.920  28.206  1.00  0.00          H   \nATOM   4761  H   ILE A 233       8.394  45.338  29.269  1.00  0.00          H   \nATOM   4762  H   ARG A 234       7.226  47.108  27.666  1.00  0.00          H   \nATOM   4763  H   ARG A 234       9.544  47.980  22.842  1.00  0.00          H   \nATOM   4764  H   ARG A 234       9.211  51.949  22.938  1.00  0.00          H   \nATOM   4765  H   ARG A 234       7.923  50.986  23.506  1.00  0.00          H   \nATOM   4766  H   ARG A 234      11.244  49.248  22.090  1.00  0.00          H   \nATOM   4767  H   ARG A 234      11.122  50.948  22.120  1.00  0.00          H   \nATOM   4768  H   VAL A 235       4.867  46.893  28.145  1.00  0.00          H   \nATOM   4769  H   GLU A 236       4.640  47.795  30.547  1.00  0.00          H   \nATOM   4770  H   GLU A 237       5.124  50.475  30.128  1.00  0.00          H   \nATOM   4771  H   SER A 238       2.837  51.028  28.769  1.00  0.00          H   \nATOM   4772  H   SER A 238      -0.004  49.127  27.659  1.00  0.00          H   \nATOM   4773  H   ILE A 239       1.179  51.132  30.782  1.00  0.00          H   \nATOM   4774  H   TYR A 240       1.833  52.983  32.562  1.00  0.00          H   \nATOM   4775  H   TYR A 240       7.089  51.191  35.560  1.00  0.00          H   \nATOM   4776  H   GLN A 241       1.374  55.146  30.739  1.00  0.00          H   \nATOM   4777  H   GLN A 241       4.740  57.547  28.173  1.00  0.00          H   \nATOM   4778  H   GLN A 241       4.591  59.235  28.368  1.00  0.00          H   \nATOM   4779  H   CYS A 242      -1.364  55.363  31.333  1.00  0.00          H   \nATOM   4780  H   CYS A 242      -4.581  52.550  30.779  1.00  0.00          H   \nATOM   4781  H   CYS A 243      -1.875  57.149  32.970  1.00  0.00          H   \nATOM   4782  H   CYS A 243      -0.421  57.391  36.716  1.00  0.00          H   \nATOM   4783  H   ASP A 244      -4.119  60.677  34.972  1.00  0.00          H   \nATOM   4784  H   LEU A 245      -2.177  63.377  32.141  1.00  0.00          H   \nATOM   4785  H   ALA A 246       1.238  65.582  33.945  1.00  0.00          H   \nATOM   4786  H   GLU A 248       4.326  68.149  30.380  1.00  0.00          H   \nATOM   4787  H   ALA A 249       4.697  66.077  31.770  1.00  0.00          H   \nATOM   4788  H   ARG A 250       3.987  63.992  30.402  1.00  0.00          H   \nATOM   4789  H   ARG A 250      -1.061  63.673  28.821  1.00  0.00          H   \nATOM   4790  H   ARG A 250       0.318  60.471  28.832  1.00  0.00          H   \nATOM   4791  H   ARG A 250      -0.999  59.771  29.659  1.00  0.00          H   \nATOM   4792  H   ARG A 250      -2.806  62.733  29.913  1.00  0.00          H   \nATOM   4793  H   ARG A 250      -2.789  61.068  30.279  1.00  0.00          H   \nATOM   4794  H   GLN A 251       5.802  63.884  28.384  1.00  0.00          H   \nATOM   4795  H   GLN A 251       9.613  63.532  22.820  1.00  0.00          H   \nATOM   4796  H   GLN A 251      10.568  64.353  23.970  1.00  0.00          H   \nATOM   4797  H   ALA A 252       8.091  63.517  29.746  1.00  0.00          H   \nATOM   4798  H   ILE A 253       7.616  60.977  30.725  1.00  0.00          H   \nATOM   4799  H   LYS A 254       7.530  59.498  28.556  1.00  0.00          H   \nATOM   4800  H   LYS A 254       9.150  58.453  20.949  1.00  0.00          H   \nATOM   4801  H   LYS A 254       9.634  58.346  22.539  1.00  0.00          H   \nATOM   4802  H   LYS A 254       9.157  59.813  21.911  1.00  0.00          H   \nATOM   4803  H   SER A 255       9.951  59.687  27.639  1.00  0.00          H   \nATOM   4804  H   SER A 255      14.046  61.337  27.049  1.00  0.00          H   \nATOM   4805  H   LEU A 256      11.499  58.629  29.676  1.00  0.00          H   \nATOM   4806  H   THR A 257      10.713  56.119  29.357  1.00  0.00          H   \nATOM   4807  H   THR A 257       7.811  54.028  29.635  1.00  0.00          H   \nATOM   4808  H   GLU A 258      11.525  55.128  26.861  1.00  0.00          H   \nATOM   4809  H   ARG A 259      14.142  55.468  27.123  1.00  0.00          H   \nATOM   4810  H   ARG A 259      18.424  58.244  26.178  1.00  0.00          H   \nATOM   4811  H   ARG A 259      16.526  61.142  26.486  1.00  0.00          H   \nATOM   4812  H   ARG A 259      17.872  62.153  26.755  1.00  0.00          H   \nATOM   4813  H   ARG A 259      20.218  59.598  26.537  1.00  0.00          H   \nATOM   4814  H   ARG A 259      19.986  61.269  26.784  1.00  0.00          H   \nATOM   4815  H   LEU A 260      14.967  54.292  29.184  1.00  0.00          H   \nATOM   4816  H   TYR A 261      13.906  53.070  30.897  1.00  0.00          H   \nATOM   4817  H   TYR A 261      11.165  56.319  35.712  1.00  0.00          H   \nATOM   4818  H   ILE A 262      13.474  50.861  29.040  1.00  0.00          H   \nATOM   4819  H   GLY A 263      15.905  49.837  28.839  1.00  0.00          H   \nATOM   4820  H   GLY A 264      19.834  47.896  28.557  1.00  0.00          H   \nATOM   4821  H   LEU A 266      26.053  47.878  29.698  1.00  0.00          H   \nATOM   4822  H   THR A 267      27.102  49.885  33.363  1.00  0.00          H   \nATOM   4823  H   THR A 267      28.925  52.674  31.611  1.00  0.00          H   \nATOM   4824  H   ASN A 268      31.381  48.727  33.977  1.00  0.00          H   \nATOM   4825  H   ASN A 268      35.143  46.729  34.588  1.00  0.00          H   \nATOM   4826  H   ASN A 268      33.937  45.667  35.157  1.00  0.00          H   \nATOM   4827  H   SER A 269      32.056  48.621  38.487  1.00  0.00          H   \nATOM   4828  H   SER A 269      34.144  48.857  41.270  1.00  0.00          H   \nATOM   4829  H   LYS A 270      34.346  49.529  37.139  1.00  0.00          H   \nATOM   4830  H   LYS A 270      39.645  45.744  35.500  1.00  0.00          H   \nATOM   4831  H   LYS A 270      39.834  45.763  37.155  1.00  0.00          H   \nATOM   4832  H   LYS A 270      40.538  46.950  36.223  1.00  0.00          H   \nATOM   4833  H   GLY A 271      33.838  51.146  35.479  1.00  0.00          H   \nATOM   4834  H   GLN A 272      34.470  49.796  33.615  1.00  0.00          H   \nATOM   4835  H   GLN A 272      38.686  47.063  31.162  1.00  0.00          H   \nATOM   4836  H   GLN A 272      38.876  45.711  32.184  1.00  0.00          H   \nATOM   4837  H   ASN A 273      33.521  48.700  29.242  1.00  0.00          H   \nATOM   4838  H   ASN A 273      28.077  47.623  27.884  1.00  0.00          H   \nATOM   4839  H   ASN A 273      29.035  48.485  29.000  1.00  0.00          H   \nATOM   4840  H   CYS A 274      29.817  47.027  30.974  1.00  0.00          H   \nATOM   4841  H   CYS A 274      30.236  45.069  34.905  1.00  0.00          H   \nATOM   4842  H   GLY A 275      28.427  45.927  29.506  1.00  0.00          H   \nATOM   4843  H   TYR A 276      25.123  43.458  28.099  1.00  0.00          H   \nATOM   4844  H   TYR A 276      20.512  50.220  24.618  1.00  0.00          H   \nATOM   4845  H   ARG A 277      21.817  46.165  28.932  1.00  0.00          H   \nATOM   4846  H   ARG A 277      19.938  47.997  32.793  1.00  0.00          H   \nATOM   4847  H   ARG A 277      16.175  48.646  31.641  1.00  0.00          H   \nATOM   4848  H   ARG A 277      16.617  47.094  32.193  1.00  0.00          H   \nATOM   4849  H   ARG A 277      17.733  50.305  31.511  1.00  0.00          H   \nATOM   4850  H   ARG A 277      19.350  50.005  31.964  1.00  0.00          H   \nATOM   4851  H   ARG A 278      18.622  43.020  29.315  1.00  0.00          H   \nATOM   4852  H   ARG A 278      19.484  43.181  22.965  1.00  0.00          H   \nATOM   4853  H   ARG A 278      21.648  42.675  22.646  1.00  0.00          H   \nATOM   4854  H   ARG A 278      22.470  41.673  23.753  1.00  0.00          H   \nATOM   4855  H   ARG A 278      21.389  40.873  25.610  1.00  0.00          H   \nATOM   4856  H   ARG A 278      19.746  41.250  25.872  1.00  0.00          H   \nATOM   4857  H   CYS A 279      17.052  44.007  30.609  1.00  0.00          H   \nATOM   4858  H   CYS A 279      17.416  40.668  33.406  1.00  0.00          H   \nATOM   4859  H   ARG A 280      13.892  43.145  33.476  1.00  0.00          H   \nATOM   4860  H   ARG A 280      10.566  45.244  37.136  1.00  0.00          H   \nATOM   4861  H   ARG A 280       9.189  47.673  34.290  1.00  0.00          H   \nATOM   4862  H   ARG A 280      10.796  47.867  34.827  1.00  0.00          H   \nATOM   4863  H   ARG A 280       7.891  45.975  35.159  1.00  0.00          H   \nATOM   4864  H   ARG A 280       8.538  44.923  36.335  1.00  0.00          H   \nATOM   4865  H   ALA A 281      14.978  47.405  34.917  1.00  0.00          H   \nATOM   4866  H   SER A 282      18.083  46.381  38.006  1.00  0.00          H   \nATOM   4867  H   SER A 282      19.635  45.232  40.790  1.00  0.00          H   \nATOM   4868  H   GLY A 283      18.055  48.753  39.107  1.00  0.00          H   \nATOM   4869  H   VAL A 284      15.504  50.450  39.682  1.00  0.00          H   \nATOM   4870  H   LEU A 285      13.845  54.577  39.125  1.00  0.00          H   \nATOM   4871  H   THR A 286      11.546  53.224  38.460  1.00  0.00          H   \nATOM   4872  H   THR A 286      10.440  52.251  35.605  1.00  0.00          H   \nATOM   4873  H   THR A 287      11.780  50.863  39.955  1.00  0.00          H   \nATOM   4874  H   THR A 287      13.612  49.987  39.669  1.00  0.00          H   \nATOM   4875  H   SER A 288      10.290  49.518  42.065  1.00  0.00          H   \nATOM   4876  H   SER A 288       7.760  48.689  45.633  1.00  0.00          H   \nATOM   4877  H   CYS A 289       7.643  50.164  41.604  1.00  0.00          H   \nATOM   4878  H   CYS A 289       3.826  52.735  39.485  1.00  0.00          H   \nATOM   4879  H   GLY A 290       7.445  49.356  38.935  1.00  0.00          H   \nATOM   4880  H   ASN A 291       7.992  46.543  39.195  1.00  0.00          H   \nATOM   4881  H   ASN A 291      11.624  43.756  39.852  1.00  0.00          H   \nATOM   4882  H   ASN A 291      10.595  43.870  41.206  1.00  0.00          H   \nATOM   4883  H   THR A 292       5.798  45.862  40.757  1.00  0.00          H   \nATOM   4884  H   THR A 292       5.492  46.124  42.807  1.00  0.00          H   \nATOM   4885  H   LEU A 293       3.548  46.470  39.246  1.00  0.00          H   \nATOM   4886  H   THR A 294       3.995  44.785  37.054  1.00  0.00          H   \nATOM   4887  H   THR A 294       6.072  44.377  34.343  1.00  0.00          H   \nATOM   4888  H   CYS A 295       4.034  42.329  38.287  1.00  0.00          H   \nATOM   4889  H   CYS A 295       4.437  38.908  42.006  1.00  0.00          H   \nATOM   4890  H   TYR A 296       1.616  42.105  39.140  1.00  0.00          H   \nATOM   4891  H   TYR A 296      -6.922  43.534  39.403  1.00  0.00          H   \nATOM   4892  H   LEU A 297       0.200  42.195  36.882  1.00  0.00          H   \nATOM   4893  H   LYS A 298       0.924  39.899  35.400  1.00  0.00          H   \nATOM   4894  H   LYS A 298       6.678  39.249  31.740  1.00  0.00          H   \nATOM   4895  H   LYS A 298       6.459  37.704  32.322  1.00  0.00          H   \nATOM   4896  H   LYS A 298       6.706  38.966  33.381  1.00  0.00          H   \nATOM   4897  H   ALA A 299       0.188  37.939  37.280  1.00  0.00          H   \nATOM   4898  H   SER A 300      -2.506  38.374  37.564  1.00  0.00          H   \nATOM   4899  H   SER A 300      -4.959  40.839  38.298  1.00  0.00          H   \nATOM   4900  H   ALA A 301      -3.492  37.767  35.043  1.00  0.00          H   \nATOM   4901  H   ALA A 302      -2.678  35.199  35.028  1.00  0.00          H   \nATOM   4902  H   CYS A 303      -4.424  34.123  36.808  1.00  0.00          H   \nATOM   4903  H   ARG A 304      -6.747  33.967  35.552  1.00  0.00          H   \nATOM   4904  H   ARG A 304      -9.467  37.693  35.606  1.00  0.00          H   \nATOM   4905  H   ARG A 304     -10.269  38.197  32.262  1.00  0.00          H   \nATOM   4906  H   ARG A 304     -10.911  39.756  32.517  1.00  0.00          H   \nATOM   4907  H   ARG A 304     -10.942  40.655  34.611  1.00  0.00          H   \nATOM   4908  H   ARG A 304     -10.325  39.773  35.933  1.00  0.00          H   \nATOM   4909  H   ALA A 305      -6.251  32.449  33.485  1.00  0.00          H   \nATOM   4910  H   ALA A 306      -5.828  30.067  34.924  1.00  0.00          H   \nATOM   4911  H   LYS A 307      -8.348  29.417  35.199  1.00  0.00          H   \nATOM   4912  H   LYS A 307      -8.496  23.154  33.643  1.00  0.00          H   \nATOM   4913  H   LYS A 307      -9.661  23.470  32.495  1.00  0.00          H   \nATOM   4914  H   LYS A 307     -10.107  23.137  34.066  1.00  0.00          H   \nATOM   4915  H   LEU A 308      -8.468  29.689  37.901  1.00  0.00          H   \nATOM   4916  H   GLN A 309     -10.663  28.464  41.709  1.00  0.00          H   \nATOM   4917  H   GLN A 309     -16.807  29.148  43.682  1.00  0.00          H   \nATOM   4918  H   GLN A 309     -15.444  28.187  44.040  1.00  0.00          H   \nATOM   4919  H   ASP A 310     -13.161  32.191  41.687  1.00  0.00          H   \nATOM   4920  H   CYS A 311     -10.309  33.534  41.992  1.00  0.00          H   \nATOM   4921  H   THR A 312      -7.823  34.719  45.582  1.00  0.00          H   \nATOM   4922  H   THR A 312     -10.020  36.110  46.587  1.00  0.00          H   \nATOM   4923  H   MET A 313      -6.186  38.786  44.955  1.00  0.00          H   \nATOM   4924  H   LEU A 314      -1.974  38.080  46.406  1.00  0.00          H   \nATOM   4925  H   VAL A 315      -0.615  41.881  46.317  1.00  0.00          H   \nATOM   4926  H   ASN A 316       3.678  41.297  45.919  1.00  0.00          H   \nATOM   4927  H   ASN A 316       4.757  44.624  49.879  1.00  0.00          H   \nATOM   4928  H   ASN A 316       3.264  44.293  50.633  1.00  0.00          H   \nATOM   4929  H   GLY A 317       5.045  44.601  44.554  1.00  0.00          H   \nATOM   4930  H   ASP A 318       8.583  41.855  43.539  1.00  0.00          H   \nATOM   4931  H   ASP A 319       6.938  40.537  44.928  1.00  0.00          H   \nATOM   4932  H   LEU A 320       5.034  36.489  45.380  1.00  0.00          H   \nATOM   4933  H   VAL A 321       0.760  36.920  46.174  1.00  0.00          H   \nATOM   4934  H   VAL A 322      -0.952  33.068  46.644  1.00  0.00          H   \nATOM   4935  H   ILE A 323      -5.147  34.263  46.923  1.00  0.00          H   \nATOM   4936  H   CYS A 324      -7.069  30.518  47.599  1.00  0.00          H   \nATOM   4937  H   CYS A 324      -7.173  28.727  43.659  1.00  0.00          H   \nATOM   4938  H   GLU A 325     -10.842  29.986  45.424  1.00  0.00          H   \nATOM   4939  H   SER A 326     -11.314  26.584  48.249  1.00  0.00          H   \nATOM   4940  H   SER A 326      -9.534  22.497  48.787  1.00  0.00          H   \nATOM   4941  H   ALA A 327     -10.065  23.045  45.518  1.00  0.00          H   \nATOM   4942  H   GLY A 328     -10.595  21.415  46.783  1.00  0.00          H   \nATOM   4943  H   THR A 329      -8.424  18.061  47.757  1.00  0.00          H   \nATOM   4944  H   THR A 329      -4.179  18.604  48.097  1.00  0.00          H   \nATOM   4945  H   GLN A 330      -7.500  16.884  45.391  1.00  0.00          H   \nATOM   4946  H   GLN A 330      -4.263  12.641  42.078  1.00  0.00          H   \nATOM   4947  H   GLN A 330      -5.914  12.309  42.344  1.00  0.00          H   \nATOM   4948  H   GLU A 331      -8.438  18.274  43.259  1.00  0.00          H   \nATOM   4949  H   ASP A 332      -7.108  20.382  43.796  1.00  0.00          H   \nATOM   4950  H   ALA A 333      -4.522  20.013  43.346  1.00  0.00          H   \nATOM   4951  H   ALA A 334      -4.570  19.857  40.703  1.00  0.00          H   \nATOM   4952  H   SER A 335      -5.438  22.236  39.715  1.00  0.00          H   \nATOM   4953  H   SER A 335      -7.901  24.323  40.256  1.00  0.00          H   \nATOM   4954  H   LEU A 336      -3.521  24.113  40.661  1.00  0.00          H   \nATOM   4955  H   ARG A 337      -1.598  23.131  38.978  1.00  0.00          H   \nATOM   4956  H   ARG A 337       2.289  18.466  35.702  1.00  0.00          H   \nATOM   4957  H   ARG A 337       0.036  19.871  32.725  1.00  0.00          H   \nATOM   4958  H   ARG A 337      -0.354  20.268  34.337  1.00  0.00          H   \nATOM   4959  H   ARG A 337       1.845  18.533  32.276  1.00  0.00          H   \nATOM   4960  H   ARG A 337       2.802  17.932  33.553  1.00  0.00          H   \nATOM   4961  H   VAL A 338      -2.273  23.961  36.656  1.00  0.00          H   \nATOM   4962  H   PHE A 339      -2.235  26.372  36.964  1.00  0.00          H   \nATOM   4963  H   THR A 340       0.386  26.862  37.193  1.00  0.00          H   \nATOM   4964  H   THR A 340       1.679  26.717  38.851  1.00  0.00          H   \nATOM   4965  H   GLU A 341       1.147  26.330  34.727  1.00  0.00          H   \nATOM   4966  H   ALA A 342       0.184  28.364  33.382  1.00  0.00          H   \nATOM   4967  H   MET A 343       1.343  30.607  34.388  1.00  0.00          H   \nATOM   4968  H   THR A 344       3.761  29.994  33.345  1.00  0.00          H   \nATOM   4969  H   THR A 344       6.307  27.504  33.557  1.00  0.00          H   \nATOM   4970  H   ARG A 345       3.486  30.285  30.713  1.00  0.00          H   \nATOM   4971  H   ARG A 345      -0.407  30.188  27.895  1.00  0.00          H   \nATOM   4972  H   ARG A 345       0.763  26.951  28.458  1.00  0.00          H   \nATOM   4973  H   ARG A 345      -0.526  26.535  29.493  1.00  0.00          H   \nATOM   4974  H   ARG A 345      -2.108  29.622  29.282  1.00  0.00          H   \nATOM   4975  H   ARG A 345      -2.168  28.060  29.964  1.00  0.00          H   \nATOM   4976  H   TYR A 346       3.516  32.887  30.840  1.00  0.00          H   \nATOM   4977  H   TYR A 346      -3.075  34.718  29.577  1.00  0.00          H   \nATOM   4978  H   SER A 347       5.903  33.488  30.341  1.00  0.00          H   \nATOM   4979  H   SER A 347       9.794  36.852  30.041  1.00  0.00          H   \nATOM   4980  H   ALA A 348       6.559  33.412  32.865  1.00  0.00          H   \nATOM   4981  H   GLY A 351       9.931  29.916  40.942  1.00  0.00          H   \nATOM   4982  H   ASP A 352      10.724  28.658  43.144  1.00  0.00          H   \nATOM   4983  H   GLN A 355       3.270  25.955  46.283  1.00  0.00          H   \nATOM   4984  H   GLN A 355       5.757  23.998  49.784  1.00  0.00          H   \nATOM   4985  H   GLN A 355       5.620  22.699  50.880  1.00  0.00          H   \nATOM   4986  H   GLU A 357      -2.025  25.987  49.472  1.00  0.00          H   \nATOM   4987  H   TYR A 358      -2.648  24.029  53.402  1.00  0.00          H   \nATOM   4988  H   TYR A 358      -6.253  21.816  47.623  1.00  0.00          H   \nATOM   4989  H   ASP A 359      -2.681  25.563  54.765  1.00  0.00          H   \nATOM   4990  H   LEU A 360      -4.211  28.615  57.052  1.00  0.00          H   \nATOM   4991  H   GLU A 361      -1.883  29.156  58.608  1.00  0.00          H   \nATOM   4992  H   LEU A 362       0.030  28.140  57.033  1.00  0.00          H   \nATOM   4993  H   ILE A 363       1.084  29.917  55.563  1.00  0.00          H   \nATOM   4994  H   THR A 364       4.186  30.643  52.557  1.00  0.00          H   \nATOM   4995  H   THR A 364       5.911  30.345  54.661  1.00  0.00          H   \nATOM   4996  H   SER A 365       4.716  34.895  53.276  1.00  0.00          H   \nATOM   4997  H   SER A 365       1.767  36.731  51.877  1.00  0.00          H   \nATOM   4998  H   CYS A 366       6.460  37.235  50.154  1.00  0.00          H   \nATOM   4999  H   CYS A 366       5.248  41.972  51.288  1.00  0.00          H   \nATOM   5000  H   SER A 367       8.357  36.368  51.690  1.00  0.00          H   \nATOM   5001  H   SER A 367      12.280  37.397  52.498  1.00  0.00          H   \nATOM   5002  H   SER A 368       6.936  36.442  54.118  1.00  0.00          H   \nATOM   5003  H   SER A 368       7.015  39.554  56.597  1.00  0.00          H   \nATOM   5004  H   ASN A 369       5.335  35.623  58.209  1.00  0.00          H   \nATOM   5005  H   ASN A 369       3.012  31.972  59.777  1.00  0.00          H   \nATOM   5006  H   ASN A 369       4.357  31.859  60.819  1.00  0.00          H   \nATOM   5007  H   VAL A 370       1.290  34.069  57.916  1.00  0.00          H   \nATOM   5008  H   SER A 371       0.831  35.168  62.317  1.00  0.00          H   \nATOM   5009  H   SER A 371       3.325  31.810  64.877  1.00  0.00          H   \nATOM   5010  H   VAL A 372       0.221  32.173  65.414  1.00  0.00          H   \nATOM   5011  H   ALA A 373      -1.473  34.998  68.339  1.00  0.00          H   \nATOM   5012  H   HIS A 374      -0.427  35.035  72.482  1.00  0.00          H   \nATOM   5013  H   ASP A 375      -3.856  37.637  73.454  1.00  0.00          H   \nATOM   5014  H   ALA A 376      -2.619  40.885  76.242  1.00  0.00          H   \nATOM   5015  H   SER A 377      -5.021  39.612  76.822  1.00  0.00          H   \nATOM   5016  H   SER A 377      -8.134  41.573  77.057  1.00  0.00          H   \nATOM   5017  H   GLY A 378      -5.025  37.388  76.779  1.00  0.00          H   \nATOM   5018  H   LYS A 379      -6.739  37.239  75.253  1.00  0.00          H   \nATOM   5019  H   LYS A 379      -8.363  42.483  71.567  1.00  0.00          H   \nATOM   5020  H   LYS A 379      -9.835  41.854  72.029  1.00  0.00          H   \nATOM   5021  H   LYS A 379      -9.342  41.742  70.442  1.00  0.00          H   \nATOM   5022  H   ARG A 380      -7.833  34.935  71.400  1.00  0.00          H   \nATOM   5023  H   ARG A 380      -8.009  31.800  67.285  1.00  0.00          H   \nATOM   5024  H   ARG A 380      -4.913  29.402  68.048  1.00  0.00          H   \nATOM   5025  H   ARG A 380      -5.782  30.083  69.347  1.00  0.00          H   \nATOM   5026  H   ARG A 380      -6.871  30.869  65.575  1.00  0.00          H   \nATOM   5027  H   ARG A 380      -5.556  29.829  65.887  1.00  0.00          H   \nATOM   5028  H   VAL A 381      -3.869  35.834  69.347  1.00  0.00          H   \nATOM   5029  H   TYR A 382      -4.373  38.167  65.775  1.00  0.00          H   \nATOM   5030  H   TYR A 382      -6.845  31.853  63.565  1.00  0.00          H   \nATOM   5031  H   TYR A 383      -0.405  36.999  63.846  1.00  0.00          H   \nATOM   5032  H   TYR A 383       5.150  34.566  66.544  1.00  0.00          H   \nATOM   5033  H   LEU A 384       2.249  40.200  62.486  1.00  0.00          H   \nATOM   5034  H   THR A 385       4.666  36.798  60.720  1.00  0.00          H   \nATOM   5035  H   THR A 385       8.065  36.154  63.768  1.00  0.00          H   \nATOM   5036  H   ARG A 386       8.731  35.820  61.846  1.00  0.00          H   \nATOM   5037  H   ARG A 386      12.738  36.158  58.575  1.00  0.00          H   \nATOM   5038  H   ARG A 386      13.408  38.339  55.302  1.00  0.00          H   \nATOM   5039  H   ARG A 386      12.342  38.931  56.494  1.00  0.00          H   \nATOM   5040  H   ARG A 386      14.448  36.320  55.589  1.00  0.00          H   \nATOM   5041  H   ARG A 386      14.162  35.399  56.995  1.00  0.00          H   \nATOM   5042  H   ASP A 387      10.645  33.511  58.707  1.00  0.00          H   \nATOM   5043  H   THR A 389      15.580  33.053  60.654  1.00  0.00          H   \nATOM   5044  H   THR A 389      17.295  30.267  59.563  1.00  0.00          H   \nATOM   5045  H   THR A 390      16.946  34.802  58.535  1.00  0.00          H   \nATOM   5046  H   THR A 390      17.453  35.616  55.141  1.00  0.00          H   \nATOM   5047  H   LEU A 392      18.188  36.828  61.935  1.00  0.00          H   \nATOM   5048  H   ALA A 393      20.552  36.482  61.009  1.00  0.00          H   \nATOM   5049  H   ARG A 394      21.137  38.819  59.994  1.00  0.00          H   \nATOM   5050  H   ARG A 394      21.047  39.800  55.395  1.00  0.00          H   \nATOM   5051  H   ARG A 394      18.065  41.715  53.577  1.00  0.00          H   \nATOM   5052  H   ARG A 394      18.344  41.992  55.236  1.00  0.00          H   \nATOM   5053  H   ARG A 394      19.414  40.265  52.416  1.00  0.00          H   \nATOM   5054  H   ARG A 394      20.692  39.465  53.212  1.00  0.00          H   \nATOM   5055  H   ALA A 395      21.232  40.267  62.165  1.00  0.00          H   \nATOM   5056  H   ALA A 396      23.424  39.451  63.242  1.00  0.00          H   \nATOM   5057  H   TRP A 397      25.386  40.402  61.667  1.00  0.00          H   \nATOM   5058  H   TRP A 397      29.420  44.526  57.572  1.00  0.00          H   \nATOM   5059  H   GLU A 398      24.972  43.055  62.035  1.00  0.00          H   \nATOM   5060  H   THR A 399      26.310  43.149  64.316  1.00  0.00          H   \nATOM   5061  H   THR A 399      25.020  42.355  66.103  1.00  0.00          H   \nATOM   5062  H   ALA A 400      28.682  43.687  64.264  1.00  0.00          H   \nATOM   5063  H   ARG A 401      28.966  45.433  63.094  1.00  0.00          H   \nATOM   5064  H   ARG A 401      33.941  45.398  58.586  1.00  0.00          H   \nATOM   5065  H   ARG A 401      33.744  46.128  61.977  1.00  0.00          H   \nATOM   5066  H   ARG A 401      35.191  45.317  62.368  1.00  0.00          H   \nATOM   5067  H   ARG A 401      35.861  44.330  59.095  1.00  0.00          H   \nATOM   5068  H   ARG A 401      36.410  44.282  60.709  1.00  0.00          H   \nATOM   5069  H   HIS A 402      29.996  49.418  61.655  1.00  0.00          H   \nATOM   5070  H   THR A 403      25.783  49.872  60.165  1.00  0.00          H   \nATOM   5071  H   THR A 403      23.684  49.008  58.176  1.00  0.00          H   \nATOM   5072  H   VAL A 405      22.804  50.067  55.978  1.00  0.00          H   \nATOM   5073  H   ASN A 406      19.826  50.764  58.965  1.00  0.00          H   \nATOM   5074  H   ASN A 406      20.595  52.571  60.668  1.00  0.00          H   \nATOM   5075  H   ASN A 406      22.198  52.808  61.198  1.00  0.00          H   \nATOM   5076  H   SER A 407      20.294  46.748  59.391  1.00  0.00          H   \nATOM   5077  H   SER A 407      19.960  43.189  59.337  1.00  0.00          H   \nATOM   5078  H   TRP A 408      19.501  45.874  61.954  1.00  0.00          H   \nATOM   5079  H   TRP A 408      21.759  49.098  64.570  1.00  0.00          H   \nATOM   5080  H   LEU A 409      17.775  47.797  62.978  1.00  0.00          H   \nATOM   5081  H   GLY A 410      15.836  48.077  61.335  1.00  0.00          H   \nATOM   5082  H   ASN A 411      14.776  45.626  61.554  1.00  0.00          H   \nATOM   5083  H   ASN A 411      15.980  42.006  59.486  1.00  0.00          H   \nATOM   5084  H   ASN A 411      16.435  42.524  61.045  1.00  0.00          H   \nATOM   5085  H   ILE A 412      13.718  45.756  63.993  1.00  0.00          H   \nATOM   5086  H   ILE A 413      11.767  47.373  63.810  1.00  0.00          H   \nATOM   5087  H   MET A 414      10.076  46.242  61.977  1.00  0.00          H   \nATOM   5088  H   TYR A 415       9.498  44.033  63.159  1.00  0.00          H   \nATOM   5089  H   TYR A 415       9.535  40.479  57.103  1.00  0.00          H   \nATOM   5090  H   ALA A 416       7.599  44.372  65.001  1.00  0.00          H   \nATOM   5091  H   THR A 418       7.124  40.415  66.963  1.00  0.00          H   \nATOM   5092  H   THR A 418      10.031  38.808  65.631  1.00  0.00          H   \nATOM   5093  H   LEU A 419       7.765  36.083  68.484  1.00  0.00          H   \nATOM   5094  H   TRP A 420      10.500  35.950  68.072  1.00  0.00          H   \nATOM   5095  H   TRP A 420       9.492  35.955  64.119  1.00  0.00          H   \nATOM   5096  H   ALA A 421      11.186  38.613  67.554  1.00  0.00          H   \nATOM   5097  H   ARG A 422      10.844  39.851  69.832  1.00  0.00          H   \nATOM   5098  H   ARG A 422       7.680  41.948  73.450  1.00  0.00          H   \nATOM   5099  H   ARG A 422       5.831  39.147  72.511  1.00  0.00          H   \nATOM   5100  H   ARG A 422       4.867  39.155  73.917  1.00  0.00          H   \nATOM   5101  H   ARG A 422       6.414  41.935  75.333  1.00  0.00          H   \nATOM   5102  H   ARG A 422       5.202  40.753  75.536  1.00  0.00          H   \nATOM   5103  H   MET A 423      12.072  38.549  71.837  1.00  0.00          H   \nATOM   5104  H   ILE A 424      14.652  38.699  70.982  1.00  0.00          H   \nATOM   5105  H   LEU A 425      15.793  40.343  69.176  1.00  0.00          H   \nATOM   5106  H   MET A 426      14.946  42.725  70.456  1.00  0.00          H   \nATOM   5107  H   THR A 427      16.041  42.535  73.000  1.00  0.00          H   \nATOM   5108  H   THR A 427      15.563  41.506  74.364  1.00  0.00          H   \nATOM   5109  H   HIS A 428      18.755  42.225  72.463  1.00  0.00          H   \nATOM   5110  H   PHE A 429      19.479  44.600  70.874  1.00  0.00          H   \nATOM   5111  H   PHE A 430      18.765  46.593  72.560  1.00  0.00          H   \nATOM   5112  H   SER A 431      20.410  46.322  74.438  1.00  0.00          H   \nATOM   5113  H   SER A 431      23.577  44.281  76.756  1.00  0.00          H   \nATOM   5114  H   ILE A 432      22.819  46.896  73.399  1.00  0.00          H   \nATOM   5115  H   LEU A 433      22.406  49.522  72.630  1.00  0.00          H   \nATOM   5116  H   LEU A 434      22.166  50.683  75.205  1.00  0.00          H   \nATOM   5117  H   ALA A 435      24.623  50.276  76.480  1.00  0.00          H   \nATOM   5118  H   GLN A 436      26.033  51.997  74.708  1.00  0.00          H   \nATOM   5119  H   GLN A 436      30.961  50.698  73.742  1.00  0.00          H   \nATOM   5120  H   GLN A 436      29.393  50.028  73.758  1.00  0.00          H   \nATOM   5121  H   GLU A 437      25.494  54.145  75.740  1.00  0.00          H   \nATOM   5122  H   GLN A 438      23.746  55.122  73.608  1.00  0.00          H   \nATOM   5123  H   GLN A 438      28.073  56.193  68.585  1.00  0.00          H   \nATOM   5124  H   GLN A 438      27.776  57.178  69.945  1.00  0.00          H   \nATOM   5125  H   LEU A 439      21.741  56.367  73.761  1.00  0.00          H   \nATOM   5126  H   GLU A 440      20.244  58.510  72.598  1.00  0.00          H   \nATOM   5127  H   LYS A 441      20.662  58.027  69.951  1.00  0.00          H   \nATOM   5128  H   LYS A 441      26.717  56.847  64.457  1.00  0.00          H   \nATOM   5129  H   LYS A 441      25.973  55.840  65.555  1.00  0.00          H   \nATOM   5130  H   LYS A 441      26.913  57.106  66.090  1.00  0.00          H   \nATOM   5131  H   ALA A 442      19.864  60.125  66.087  1.00  0.00          H   \nATOM   5132  H   LEU A 443      18.032  56.469  64.029  1.00  0.00          H   \nATOM   5133  H   ASP A 444      20.378  55.215  60.418  1.00  0.00          H   \nATOM   5134  H   CYS A 445      16.559  53.959  58.237  1.00  0.00          H   \nATOM   5135  H   CYS A 445      13.985  51.474  61.052  1.00  0.00          H   \nATOM   5136  H   GLN A 446      15.702  49.945  56.783  1.00  0.00          H   \nATOM   5137  H   GLN A 446      17.665  50.938  50.530  1.00  0.00          H   \nATOM   5138  H   GLN A 446      17.193  52.224  51.545  1.00  0.00          H   \nATOM   5139  H   ILE A 447      12.126  51.461  54.587  1.00  0.00          H   \nATOM   5140  H   TYR A 448      10.204  48.009  53.805  1.00  0.00          H   \nATOM   5141  H   TYR A 448       3.136  46.454  54.583  1.00  0.00          H   \nATOM   5142  H   GLY A 449      11.953  48.909  52.048  1.00  0.00          H   \nATOM   5143  H   ALA A 450      11.167  51.730  51.517  1.00  0.00          H   \nATOM   5144  H   CYS A 451      14.086  55.243  51.944  1.00  0.00          H   \nATOM   5145  H   CYS A 451      18.669  56.058  54.181  1.00  0.00          H   \nATOM   5146  H   TYR A 452      14.784  54.799  56.372  1.00  0.00          H   \nATOM   5147  H   TYR A 452       8.012  54.562  53.851  1.00  0.00          H   \nATOM   5148  H   SER A 453      14.136  58.538  58.640  1.00  0.00          H   \nATOM   5149  H   SER A 453      17.371  60.905  60.867  1.00  0.00          H   \nATOM   5150  H   ILE A 454      16.170  56.644  61.943  1.00  0.00          H   \nATOM   5151  H   GLU A 455      13.903  57.855  65.499  1.00  0.00          H   \nATOM   5152  H   LEU A 457      15.283  56.240  70.499  1.00  0.00          H   \nATOM   5153  H   ASP A 458      12.732  56.316  69.594  1.00  0.00          H   \nATOM   5154  H   LEU A 459      11.644  54.097  70.024  1.00  0.00          H   \nATOM   5155  H   GLN A 461       7.254  54.572  71.157  1.00  0.00          H   \nATOM   5156  H   GLN A 461       8.617  59.203  71.771  1.00  0.00          H   \nATOM   5157  H   GLN A 461       8.360  57.656  72.441  1.00  0.00          H   \nATOM   5158  H   ILE A 462       7.031  54.315  68.483  1.00  0.00          H   \nATOM   5159  H   ILE A 463       6.526  51.595  68.348  1.00  0.00          H   \nATOM   5160  H   GLU A 464       4.066  51.459  69.633  1.00  0.00          H   \nATOM   5161  H   ARG A 465       2.418  52.919  68.049  1.00  0.00          H   \nATOM   5162  H   ARG A 465      -0.755  56.014  64.913  1.00  0.00          H   \nATOM   5163  H   ARG A 465       2.098  58.799  64.792  1.00  0.00          H   \nATOM   5164  H   ARG A 465       1.881  57.886  66.216  1.00  0.00          H   \nATOM   5165  H   ARG A 465       0.770  58.419  62.970  1.00  0.00          H   \nATOM   5166  H   ARG A 465      -0.443  57.222  63.026  1.00  0.00          H   \nATOM   5167  H   LEU A 466       2.629  51.406  65.562  1.00  0.00          H   \nATOM   5168  H   HIS A 467       1.918  48.978  66.309  1.00  0.00          H   \nATOM   5169  H   GLY A 468       0.205  49.025  67.674  1.00  0.00          H   \nATOM   5170  H   LEU A 469      -2.014  48.564  71.397  1.00  0.00          H   \nATOM   5171  H   SER A 470      -1.704  45.692  71.578  1.00  0.00          H   \nATOM   5172  H   SER A 470      -2.448  43.057  69.662  1.00  0.00          H   \nATOM   5173  H   ALA A 471       0.923  45.134  70.941  1.00  0.00          H   \nATOM   5174  H   PHE A 472       2.355  44.947  73.108  1.00  0.00          H   \nATOM   5175  H   SER A 473       2.027  42.696  74.111  1.00  0.00          H   \nATOM   5176  H   SER A 473      -1.145  42.404  76.223  1.00  0.00          H   \nATOM   5177  H   LEU A 474       2.668  40.687  73.572  1.00  0.00          H   \nATOM   5178  H   HIS A 475       1.130  36.327  73.818  1.00  0.00          H   \nATOM   5179  H   SER A 476       3.123  32.845  75.448  1.00  0.00          H   \nATOM   5180  H   SER A 476      -0.594  31.121  76.600  1.00  0.00          H   \nATOM   5181  H   TYR A 477       3.743  31.926  72.843  1.00  0.00          H   \nATOM   5182  H   TYR A 477       5.524  38.206  70.618  1.00  0.00          H   \nATOM   5183  H   SER A 478       2.819  30.819  68.447  1.00  0.00          H   \nATOM   5184  H   SER A 478       2.644  29.236  65.337  1.00  0.00          H   \nATOM   5185  H   GLY A 480       3.513  25.867  66.249  1.00  0.00          H   \nATOM   5186  H   GLU A 481       4.849  28.164  65.994  1.00  0.00          H   \nATOM   5187  H   ILE A 482       6.395  28.434  68.129  1.00  0.00          H   \nATOM   5188  H   ASN A 483       7.990  26.192  67.569  1.00  0.00          H   \nATOM   5189  H   ASN A 483       7.538  22.267  65.944  1.00  0.00          H   \nATOM   5190  H   ASN A 483       7.394  21.529  67.474  1.00  0.00          H   \nATOM   5191  H   ARG A 484       9.429  27.047  65.364  1.00  0.00          H   \nATOM   5192  H   ARG A 484       9.621  30.826  62.504  1.00  0.00          H   \nATOM   5193  H   ARG A 484       7.754  31.114  58.985  1.00  0.00          H   \nATOM   5194  H   ARG A 484       9.009  29.976  59.175  1.00  0.00          H   \nATOM   5195  H   ARG A 484       7.153  32.463  60.734  1.00  0.00          H   \nATOM   5196  H   ARG A 484       7.956  32.336  62.233  1.00  0.00          H   \nATOM   5197  H   VAL A 485      10.884  29.094  66.286  1.00  0.00          H   \nATOM   5198  H   ALA A 486      12.407  27.859  68.140  1.00  0.00          H   \nATOM   5199  H   SER A 487      13.884  26.117  66.473  1.00  0.00          H   \nATOM   5200  H   SER A 487      13.520  23.719  64.018  1.00  0.00          H   \nATOM   5201  H   CYS A 488      15.313  27.919  65.037  1.00  0.00          H   \nATOM   5202  H   CYS A 488      16.635  32.564  63.502  1.00  0.00          H   \nATOM   5203  H   LEU A 489      16.898  29.057  67.006  1.00  0.00          H   \nATOM   5204  H   ARG A 490      18.594  26.970  67.630  1.00  0.00          H   \nATOM   5205  H   ARG A 490      16.945  22.195  70.001  1.00  0.00          H   \nATOM   5206  H   ARG A 490      20.064  23.268  71.125  1.00  0.00          H   \nATOM   5207  H   ARG A 490      19.703  22.995  72.769  1.00  0.00          H   \nATOM   5208  H   ARG A 490      16.475  21.838  72.183  1.00  0.00          H   \nATOM   5209  H   ARG A 490      17.647  22.177  73.375  1.00  0.00          H   \nATOM   5210  H   LYS A 491      19.976  26.525  65.481  1.00  0.00          H   \nATOM   5211  H   LYS A 491      17.865  29.360  59.902  1.00  0.00          H   \nATOM   5212  H   LYS A 491      17.899  27.706  59.714  1.00  0.00          H   \nATOM   5213  H   LYS A 491      18.953  28.665  58.850  1.00  0.00          H   \nATOM   5214  H   LEU A 492      21.471  28.921  65.010  1.00  0.00          H   \nATOM   5215  H   GLY A 493      22.856  28.613  67.043  1.00  0.00          H   \nATOM   5216  H   VAL A 494      22.067  30.184  68.984  1.00  0.00          H   \nATOM   5217  H   LEU A 497      16.430  30.504  76.134  1.00  0.00          H   \nATOM   5218  H   ARG A 498      16.933  31.037  79.035  1.00  0.00          H   \nATOM   5219  H   ARG A 498      21.195  28.265  81.139  1.00  0.00          H   \nATOM   5220  H   ARG A 498      20.171  25.887  78.857  1.00  0.00          H   \nATOM   5221  H   ARG A 498      21.500  26.531  79.709  1.00  0.00          H   \nATOM   5222  H   ARG A 498      18.077  26.766  79.153  1.00  0.00          H   \nATOM   5223  H   ARG A 498      17.838  28.069  80.227  1.00  0.00          H   \nATOM   5224  H   VAL A 499      19.174  32.748  78.873  1.00  0.00          H   \nATOM   5225  H   TRP A 500      18.468  34.947  77.463  1.00  0.00          H   \nATOM   5226  H   TRP A 500      19.667  34.402  72.954  1.00  0.00          H   \nATOM   5227  H   ARG A 501      16.542  35.900  79.133  1.00  0.00          H   \nATOM   5228  H   ARG A 501      14.565  34.502  83.160  1.00  0.00          H   \nATOM   5229  H   ARG A 501      11.943  31.979  81.525  1.00  0.00          H   \nATOM   5230  H   ARG A 501      11.598  33.649  81.544  1.00  0.00          H   \nATOM   5231  H   ARG A 501      13.902  31.199  82.424  1.00  0.00          H   \nATOM   5232  H   ARG A 501      15.020  32.286  83.113  1.00  0.00          H   \nATOM   5233  H   HIS A 502      17.754  36.692  81.311  1.00  0.00          H   \nATOM   5234  H   ARG A 503      19.390  38.657  80.332  1.00  0.00          H   \nATOM   5235  H   ARG A 503      23.705  38.107  78.419  1.00  0.00          H   \nATOM   5236  H   ARG A 503      25.417  36.613  75.825  1.00  0.00          H   \nATOM   5237  H   ARG A 503      25.350  36.842  77.514  1.00  0.00          H   \nATOM   5238  H   ARG A 503      23.814  37.585  74.467  1.00  0.00          H   \nATOM   5239  H   ARG A 503      22.573  38.526  75.161  1.00  0.00          H   \nATOM   5240  H   ALA A 504      17.396  40.368  79.644  1.00  0.00          H   \nATOM   5241  H   ARG A 505      16.312  41.236  81.899  1.00  0.00          H   \nATOM   5242  H   ARG A 505      13.064  38.870  84.415  1.00  0.00          H   \nATOM   5243  H   ARG A 505      14.768  38.212  87.383  1.00  0.00          H   \nATOM   5244  H   ARG A 505      13.703  36.970  87.862  1.00  0.00          H   \nATOM   5245  H   ARG A 505      11.664  37.220  85.049  1.00  0.00          H   \nATOM   5246  H   ARG A 505      11.921  36.401  86.522  1.00  0.00          H   \nATOM   5247  H   SER A 506      18.320  42.682  82.951  1.00  0.00          H   \nATOM   5248  H   SER A 506      22.352  44.539  82.773  1.00  0.00          H   \nATOM   5249  H   VAL A 507      18.757  44.598  80.991  1.00  0.00          H   \nATOM   5250  H   ARG A 508      16.224  45.809  81.164  1.00  0.00          H   \nATOM   5251  H   ARG A 508       9.570  46.455  82.946  1.00  0.00          H   \nATOM   5252  H   ARG A 508      12.235  46.233  85.174  1.00  0.00          H   \nATOM   5253  H   ARG A 508      11.333  46.786  86.511  1.00  0.00          H   \nATOM   5254  H   ARG A 508       8.374  47.195  84.746  1.00  0.00          H   \nATOM   5255  H   ARG A 508       9.132  47.334  86.267  1.00  0.00          H   \nATOM   5256  H   ALA A 509      16.169  46.870  83.778  1.00  0.00          H   \nATOM   5257  H   ARG A 510      18.218  48.797  83.392  1.00  0.00          H   \nATOM   5258  H   ARG A 510      21.682  47.268  81.542  1.00  0.00          H   \nATOM   5259  H   ARG A 510      24.418  50.134  81.117  1.00  0.00          H   \nATOM   5260  H   ARG A 510      23.406  50.146  82.489  1.00  0.00          H   \nATOM   5261  H   ARG A 510      24.240  48.483  79.573  1.00  0.00          H   \nATOM   5262  H   ARG A 510      23.092  47.237  79.771  1.00  0.00          H   \nATOM   5263  H   LEU A 511      16.972  50.479  81.648  1.00  0.00          H   \nATOM   5264  H   LEU A 512      14.862  51.476  82.958  1.00  0.00          H   \nATOM   5265  H   SER A 513      16.095  53.069  84.745  1.00  0.00          H   \nATOM   5266  H   SER A 513      18.406  52.952  87.443  1.00  0.00          H   \nATOM   5267  H   GLN A 514      16.697  55.107  83.173  1.00  0.00          H   \nATOM   5268  H   GLN A 514      20.823  55.038  80.939  1.00  0.00          H   \nATOM   5269  H   GLN A 514      19.369  55.364  81.768  1.00  0.00          H   \nATOM   5270  H   GLY A 515      14.650  56.698  83.126  1.00  0.00          H   \nATOM   5271  H   GLY A 516      11.587  59.919  82.156  1.00  0.00          H   \nATOM   5272  H   ARG A 517      11.873  59.953  77.528  1.00  0.00          H   \nATOM   5273  H   ARG A 517      13.301  61.330  74.382  1.00  0.00          H   \nATOM   5274  H   ARG A 517      11.617  60.073  70.996  1.00  0.00          H   \nATOM   5275  H   ARG A 517      10.679  60.175  72.416  1.00  0.00          H   \nATOM   5276  H   ARG A 517      13.827  60.694  71.029  1.00  0.00          H   \nATOM   5277  H   ARG A 517      14.533  61.259  72.475  1.00  0.00          H   \nATOM   5278  H   ALA A 518      13.672  57.730  77.784  1.00  0.00          H   \nATOM   5279  H   ALA A 519      12.704  56.471  79.847  1.00  0.00          H   \nATOM   5280  H   THR A 520      10.404  55.635  79.006  1.00  0.00          H   \nATOM   5281  H   THR A 520       7.527  56.913  77.641  1.00  0.00          H   \nATOM   5282  H   CYS A 521      11.140  53.866  77.186  1.00  0.00          H   \nATOM   5283  H   CYS A 521      13.803  53.513  73.944  1.00  0.00          H   \nATOM   5284  H   GLY A 522      12.180  51.870  78.820  1.00  0.00          H   \nATOM   5285  H   LYS A 523       9.990  51.059  80.136  1.00  0.00          H   \nATOM   5286  H   LYS A 523       2.587  52.321  82.356  1.00  0.00          H   \nATOM   5287  H   LYS A 523       3.543  51.507  81.262  1.00  0.00          H   \nATOM   5288  H   LYS A 523       3.590  53.170  81.333  1.00  0.00          H   \nATOM   5289  H   TYR A 524       8.006  50.132  78.149  1.00  0.00          H   \nATOM   5290  H   TYR A 524       1.569  52.985  78.344  1.00  0.00          H   \nATOM   5291  H   LEU A 525       9.201  48.645  76.416  1.00  0.00          H   \nATOM   5292  H   PHE A 526      10.038  46.536  77.967  1.00  0.00          H   \nATOM   5293  H   ASN A 527       8.183  44.937  78.593  1.00  0.00          H   \nATOM   5294  H   ASN A 527       6.701  44.389  82.534  1.00  0.00          H   \nATOM   5295  H   ASN A 527       5.143  44.394  83.227  1.00  0.00          H   \nATOM   5296  H   TRP A 528       8.051  42.327  78.475  1.00  0.00          H   \nATOM   5297  H   TRP A 528      11.247  42.361  75.487  1.00  0.00          H   \nATOM   5298  H   ALA A 529       9.937  41.392  80.147  1.00  0.00          H   \nATOM   5299  H   VAL A 530       9.357  41.275  82.703  1.00  0.00          H   \nATOM   5300  H   LYS A 531       9.102  39.366  86.936  1.00  0.00          H   \nATOM   5301  H   LYS A 531      10.125  36.430  92.943  1.00  0.00          H   \nATOM   5302  H   LYS A 531      10.381  35.516  91.574  1.00  0.00          H   \nATOM   5303  H   LYS A 531       8.880  35.531  92.297  1.00  0.00          H   \nATOM   5304  H   THR A 532       7.058  40.447  87.880  1.00  0.00          H   \nATOM   5305  H   THR A 532       6.144  40.071  90.159  1.00  0.00          H   \nATOM   5306  H   LYS A 533       3.642  40.823  85.836  1.00  0.00          H   \nATOM   5307  H   LYS A 533       6.108  37.293  82.103  1.00  0.00          H   \nATOM   5308  H   LYS A 533       4.948  37.278  83.299  1.00  0.00          H   \nATOM   5309  H   LYS A 533       6.368  38.120  83.526  1.00  0.00          H   \nATOM   5310  H   LEU A 534       4.343  44.870  83.662  1.00  0.00          H   \nATOM   5311  H   LYS A 535       1.476  48.183  84.898  1.00  0.00          H   \nATOM   5312  H   LYS A 535      -2.387  53.997  84.917  1.00  0.00          H   \nATOM   5313  H   LYS A 535      -0.755  54.204  85.182  1.00  0.00          H   \nATOM   5314  H   LYS A 535      -1.664  53.300  86.246  1.00  0.00          H   \nATOM   5315  H   LEU A 536       1.324  49.481  82.196  1.00  0.00          H   \nATOM   5316  H   THR A 537       0.601  51.528  79.286  1.00  0.00          H   \nATOM   5317  H   THR A 537      -0.669  54.168  79.549  1.00  0.00          H   \nATOM   5318  H   ILE A 539      -0.214  55.750  74.082  1.00  0.00          H   \nATOM   5319  H   ALA A 541       3.408  60.850  74.237  1.00  0.00          H   \nATOM   5320  H   ALA A 542       2.705  59.115  72.038  1.00  0.00          H   \nATOM   5321  H   SER A 543       0.435  59.836  70.037  1.00  0.00          H   \nATOM   5322  H   SER A 543      -1.688  62.525  70.120  1.00  0.00          H   \nATOM   5323  H   GLN A 544       2.147  61.760  68.905  1.00  0.00          H   \nATOM   5324  H   GLN A 544       5.736  64.167  69.449  1.00  0.00          H   \nATOM   5325  H   GLN A 544       6.312  65.270  70.615  1.00  0.00          H   \nATOM   5326  H   LEU A 545       3.826  60.900  67.036  1.00  0.00          H   \nATOM   5327  H   ASP A 546       5.242  61.889  62.849  1.00  0.00          H   \nATOM   5328  H   LEU A 547       2.498  59.313  61.471  1.00  0.00          H   \nATOM   5329  H   SER A 548       2.619  59.953  58.683  1.00  0.00          H   \nATOM   5330  H   SER A 548       1.480  62.378  56.406  1.00  0.00          H   \nATOM   5331  H   GLY A 549       2.036  57.435  54.984  1.00  0.00          H   \nATOM   5332  H   TRP A 550       3.954  56.434  56.102  1.00  0.00          H   \nATOM   5333  H   TRP A 550       7.803  57.685  59.173  1.00  0.00          H   \nATOM   5334  H   PHE A 551       2.890  53.934  55.393  1.00  0.00          H   \nATOM   5335  H   VAL A 552       3.539  53.449  52.214  1.00  0.00          H   \nATOM   5336  H   ALA A 553       4.137  53.309  50.189  1.00  0.00          H   \nATOM   5337  H   GLY A 554       5.145  50.510  46.911  1.00  0.00          H   \nATOM   5338  H   TYR A 555       9.559  50.435  48.144  1.00  0.00          H   \nATOM   5339  H   TYR A 555       5.520  56.737  47.040  1.00  0.00          H   \nATOM   5340  H   SER A 556      10.830  49.215  46.867  1.00  0.00          H   \nATOM   5341  H   SER A 556      13.669  45.766  46.724  1.00  0.00          H   \nATOM   5342  H   GLY A 557      15.372  49.640  46.612  1.00  0.00          H   \nATOM   5343  H   GLY A 558      14.060  51.665  45.391  1.00  0.00          H   \nATOM   5344  H   ASP A 559      16.049  53.538  45.711  1.00  0.00          H   \nATOM   5345  H   ILE A 560      14.688  55.195  47.654  1.00  0.00          H   \nATOM   5346  H   TYR A 561      13.856  59.175  49.514  1.00  0.00          H   \nATOM   5347  H   TYR A 561      16.912  62.342  57.025  1.00  0.00          H   \nATOM   5348  H   HIS A 562      13.408  58.713  53.877  1.00  0.00          H   \nATOM   5349  H   SER A 563      11.850  62.511  55.272  1.00  0.00          H   \nATOM   5350  H   SER A 563      10.288  61.880  59.170  1.00  0.00          H   \nTER    5351      SER A 563\nENDMDL\nCONECT 2317 2378\nCONECT 2378 2317\nEND\n",
+                    "type": "blob",
+                    "binary": false
+                  }
+                ],
+                "kwargs": {
+                  "ext": "pdb",
+                  "defaultRepresentation": true
+                },
+                "type": "call_method"
+              }
+            ]
+          }
+        },
+        "a5a64350a6974d84b4346b77f6bd86d0": {
+          "model_module": "nglview-js-widgets",
+          "model_name": "NGLModel",
+          "state": {
+            "_view_name": "NGLView",
+            "_scene_rotation": {},
+            "_ngl_view_id": [],
+            "_synced_model_ids": [],
+            "_camera_orientation": [],
+            "frame": 0,
+            "_view_module": "nglview-js-widgets",
+            "_ibtn_fullscreen": "IPY_MODEL_209b7293a2e446e78e00571d8774e12b",
+            "_camera_str": "orthographic",
+            "_ngl_serialize": false,
+            "picked": {},
+            "_model_module": "nglview-js-widgets",
+            "_igui": null,
+            "_iplayer": "IPY_MODEL_5ff8f647c58e42cd81008eb6fd9a71bc",
+            "layout": "IPY_MODEL_d374d1fde21c45c4b835a664ba643810",
+            "_view_width": "",
+            "_ngl_coordinate_resource": {},
+            "_view_module_version": "2.7.5",
+            "_player_dict": {},
+            "_synced_repr_model_ids": [],
+            "_ngl_version": "",
+            "max_frame": 0,
+            "_dom_classes": [],
+            "_model_name": "NGLModel",
+            "_scene_position": {},
+            "_model_module_version": "2.7.5",
+            "gui_style": null,
+            "background": "white",
+            "_view_count": null,
+            "_view_height": "",
+            "_ngl_repr_dict": {},
+            "_ngl_original_stage_parameters": {},
+            "_ngl_full_stage_parameters": {},
+            "n_components": 0,
+            "_ngl_color_dict": {},
+            "_gui_theme": null,
+            "_ngl_msg_archive": [
+              {
+                "reconstruc_color_scheme": false,
+                "methodName": "loadFile",
+                "target": "Stage",
+                "args": [
+                  {
+                    "data": "MODEL        0\nATOM      1  N   SER A   1      15.324  37.268  22.693  1.00  0.00          N   \nATOM      2  CA  SER A   1      15.651  38.380  23.628  1.00  0.00          C   \nATOM      3  C   SER A   1      16.340  37.889  24.890  1.00  0.00          C   \nATOM      4  O   SER A   1      15.662  37.449  25.835  1.00  0.00          O   \nATOM      5  CB  SER A   1      14.375  39.133  24.030  1.00  0.00          C   \nATOM      6  OG  SER A   1      14.718  40.337  24.714  1.00  0.00          O   \nATOM      7  N   MET A   2      17.676  37.947  24.893  1.00  0.00          N   \nATOM      8  CA  MET A   2      18.467  37.535  26.056  1.00  0.00          C   \nATOM      9  C   MET A   2      18.329  38.628  27.108  1.00  0.00          C   \nATOM     10  O   MET A   2      18.402  39.819  26.772  1.00  0.00          O   \nATOM     11  CB  MET A   2      19.951  37.383  25.685  1.00  0.00          C   \nATOM     12  CG  MET A   2      20.246  36.266  24.686  1.00  0.00          C   \nATOM     13  SD  MET A   2      19.701  34.709  25.344  1.00  0.00          S   \nATOM     14  CE  MET A   2      20.967  34.406  26.652  1.00  0.00          C   \nATOM     15  N   SER A   3      18.132  38.228  28.363  1.00  0.00          N   \nATOM     16  CA  SER A   3      17.986  39.184  29.453  1.00  0.00          C   \nATOM     17  C   SER A   3      19.224  40.075  29.525  1.00  0.00          C   \nATOM     18  O   SER A   3      19.119  41.279  29.800  1.00  0.00          O   \nATOM     19  CB  SER A   3      17.758  38.436  30.779  1.00  0.00          C   \nATOM     20  OG  SER A   3      18.692  37.371  30.959  1.00  0.00          O   \nATOM     21  N   TYR A   4      20.387  39.483  29.246  1.00  0.00          N   \nATOM     22  CA  TYR A   4      21.667  40.201  29.244  1.00  0.00          C   \nATOM     23  C   TYR A   4      22.617  39.643  28.188  1.00  0.00          C   \nATOM     24  O   TYR A   4      22.530  38.475  27.821  1.00  0.00          O   \nATOM     25  CB  TYR A   4      22.400  40.032  30.583  1.00  0.00          C   \nATOM     26  CG  TYR A   4      21.694  40.566  31.800  1.00  0.00          C   \nATOM     27  CD1 TYR A   4      21.799  41.911  32.153  1.00  0.00          C   \nATOM     28  CD2 TYR A   4      20.910  39.728  32.597  1.00  0.00          C   \nATOM     29  CE1 TYR A   4      21.139  42.410  33.274  1.00  0.00          C   \nATOM     30  CE2 TYR A   4      20.243  40.227  33.726  1.00  0.00          C   \nATOM     31  CZ  TYR A   4      20.366  41.566  34.049  1.00  0.00          C   \nATOM     32  OH  TYR A   4      19.710  42.075  35.136  1.00  0.00          O   \nATOM     33  N   THR A   5      23.526  40.486  27.719  1.00  0.00          N   \nATOM     34  CA  THR A   5      24.591  40.084  26.800  1.00  0.00          C   \nATOM     35  C   THR A   5      25.816  40.634  27.541  1.00  0.00          C   \nATOM     36  O   THR A   5      25.823  41.802  27.951  1.00  0.00          O   \nATOM     37  CB  THR A   5      24.494  40.753  25.418  1.00  0.00          C   \nATOM     38  OG1 THR A   5      24.368  42.164  25.584  1.00  0.00          O   \nATOM     39  CG2 THR A   5      23.312  40.227  24.654  1.00  0.00          C   \nATOM     40  N   TRP A   6      26.834  39.802  27.736  1.00  0.00          N   \nATOM     41  CA  TRP A   6      28.022  40.216  28.478  1.00  0.00          C   \nATOM     42  C   TRP A   6      29.258  40.373  27.601  1.00  0.00          C   \nATOM     43  O   TRP A   6      29.378  39.696  26.594  1.00  0.00          O   \nATOM     44  CB  TRP A   6      28.299  39.201  29.583  1.00  0.00          C   \nATOM     45  CG  TRP A   6      27.121  38.979  30.493  1.00  0.00          C   \nATOM     46  CD1 TRP A   6      26.265  37.906  30.498  1.00  0.00          C   \nATOM     47  CD2 TRP A   6      26.661  39.861  31.523  1.00  0.00          C   \nATOM     48  NE1 TRP A   6      25.303  38.067  31.476  1.00  0.00          N   \nATOM     49  CE2 TRP A   6      25.525  39.261  32.119  1.00  0.00          C   \nATOM     50  CE3 TRP A   6      27.101  41.103  32.006  1.00  0.00          C   \nATOM     51  CZ2 TRP A   6      24.827  39.858  33.165  1.00  0.00          C   \nATOM     52  CZ3 TRP A   6      26.405  41.696  33.051  1.00  0.00          C   \nATOM     53  CH2 TRP A   6      25.281  41.073  33.618  1.00  0.00          C   \nATOM     54  N   THR A   7      30.167  41.273  27.974  1.00  0.00          N   \nATOM     55  CA  THR A   7      31.389  41.462  27.188  1.00  0.00          C   \nATOM     56  C   THR A   7      32.529  40.626  27.765  1.00  0.00          C   \nATOM     57  O   THR A   7      33.474  40.291  27.063  1.00  0.00          O   \nATOM     58  CB  THR A   7      31.867  42.927  27.175  1.00  0.00          C   \nATOM     59  OG1 THR A   7      32.096  43.360  28.516  1.00  0.00          O   \nATOM     60  CG2 THR A   7      30.837  43.833  26.520  1.00  0.00          C   \nATOM     61  N   GLY A   8      32.437  40.286  29.043  1.00  0.00          N   \nATOM     62  CA  GLY A   8      33.501  39.518  29.660  1.00  0.00          C   \nATOM     63  C   GLY A   8      34.160  40.354  30.740  1.00  0.00          C   \nATOM     64  O   GLY A   8      34.856  39.832  31.600  1.00  0.00          O   \nATOM     65  N   ALA A   9      33.952  41.666  30.689  1.00  0.00          N   \nATOM     66  CA  ALA A   9      34.506  42.549  31.705  1.00  0.00          C   \nATOM     67  C   ALA A   9      33.832  42.157  33.020  1.00  0.00          C   \nATOM     68  O   ALA A   9      32.645  41.824  33.047  1.00  0.00          O   \nATOM     69  CB  ALA A   9      34.201  43.992  31.373  1.00  0.00          C   \nATOM     70  N   LEU A  10      34.590  42.203  34.106  1.00  0.00          N   \nATOM     71  CA  LEU A  10      34.068  41.822  35.412  1.00  0.00          C   \nATOM     72  C   LEU A  10      33.234  42.901  36.104  1.00  0.00          C   \nATOM     73  O   LEU A  10      33.382  44.089  35.839  1.00  0.00          O   \nATOM     74  CB  LEU A  10      35.226  41.428  36.341  1.00  0.00          C   \nATOM     75  CG  LEU A  10      36.135  40.284  35.885  1.00  0.00          C   \nATOM     76  CD1 LEU A  10      37.324  40.165  36.832  1.00  0.00          C   \nATOM     77  CD2 LEU A  10      35.340  38.984  35.834  1.00  0.00          C   \nATOM     78  N   ILE A  11      32.352  42.454  36.994  1.00  0.00          N   \nATOM     79  CA  ILE A  11      31.521  43.343  37.785  1.00  0.00          C   \nATOM     80  C   ILE A  11      32.437  43.578  38.975  1.00  0.00          C   \nATOM     81  O   ILE A  11      32.647  42.698  39.808  1.00  0.00          O   \nATOM     82  CB  ILE A  11      30.227  42.644  38.225  1.00  0.00          C   \nATOM     83  CG1 ILE A  11      29.373  42.330  36.990  1.00  0.00          C   \nATOM     84  CG2 ILE A  11      29.486  43.511  39.216  1.00  0.00          C   \nATOM     85  CD1 ILE A  11      28.181  41.424  37.266  1.00  0.00          C   \nATOM     86  N   THR A  12      33.000  44.770  39.030  1.00  0.00          N   \nATOM     87  CA  THR A  12      33.962  45.116  40.058  1.00  0.00          C   \nATOM     88  C   THR A  12      33.421  45.785  41.304  1.00  0.00          C   \nATOM     89  O   THR A  12      32.416  46.496  41.262  1.00  0.00          O   \nATOM     90  CB  THR A  12      35.007  46.030  39.467  1.00  0.00          C   \nATOM     91  OG1 THR A  12      34.350  47.186  38.933  1.00  0.00          O   \nATOM     92  CG2 THR A  12      35.763  45.313  38.352  1.00  0.00          C   \nATOM     93  N   PRO A  13      34.098  45.562  42.441  1.00  0.00          N   \nATOM     94  CA  PRO A  13      33.675  46.162  43.707  1.00  0.00          C   \nATOM     95  C   PRO A  13      34.058  47.636  43.694  1.00  0.00          C   \nATOM     96  O   PRO A  13      34.976  48.032  42.976  1.00  0.00          O   \nATOM     97  CB  PRO A  13      34.466  45.364  44.744  1.00  0.00          C   \nATOM     98  CG  PRO A  13      35.744  45.042  44.008  1.00  0.00          C   \nATOM     99  CD  PRO A  13      35.238  44.644  42.639  1.00  0.00          C   \nATOM    100  N   CYS A  14      33.363  48.458  44.469  1.00  0.00          N   \nATOM    101  CA  CYS A  14      33.711  49.872  44.492  1.00  0.00          C   \nATOM    102  C   CYS A  14      34.445  50.217  45.774  1.00  0.00          C   \nATOM    103  O   CYS A  14      34.518  51.385  46.164  1.00  0.00          O   \nATOM    104  CB  CYS A  14      32.470  50.748  44.365  1.00  0.00          C   \nATOM    105  SG  CYS A  14      32.885  52.457  43.973  1.00  0.00          S   \nATOM    106  N   ALA A  15      34.990  49.187  46.414  1.00  0.00          N   \nATOM    107  CA  ALA A  15      35.731  49.319  47.665  1.00  0.00          C   \nATOM    108  C   ALA A  15      35.921  47.928  48.260  1.00  0.00          C   \nATOM    109  O   ALA A  15      35.303  46.960  47.809  1.00  0.00          O   \nATOM    110  CB  ALA A  15      34.966  50.208  48.649  1.00  0.00          C   \nATOM    111  N   ALA A  16      36.770  47.829  49.277  1.00  0.00          N   \nATOM    112  CA  ALA A  16      37.039  46.548  49.921  1.00  0.00          C   \nATOM    113  C   ALA A  16      35.745  45.884  50.379  1.00  0.00          C   \nATOM    114  O   ALA A  16      34.830  46.548  50.872  1.00  0.00          O   \nATOM    115  CB  ALA A  16      37.979  46.744  51.108  1.00  0.00          C   \nATOM    116  N   GLU A  17      35.677  44.568  50.207  1.00  0.00          N   \nATOM    117  CA  GLU A  17      34.497  43.804  50.595  1.00  0.00          C   \nATOM    118  C   GLU A  17      34.858  42.718  51.599  1.00  0.00          C   \nATOM    119  O   GLU A  17      35.829  42.003  51.411  1.00  0.00          O   \nATOM    120  CB  GLU A  17      33.867  43.128  49.370  1.00  0.00          C   \nATOM    121  CG  GLU A  17      33.337  44.058  48.298  1.00  0.00          C   \nATOM    122  CD  GLU A  17      32.922  43.292  47.043  1.00  0.00          C   \nATOM    123  OE1 GLU A  17      33.800  42.688  46.389  1.00  0.00          O   \nATOM    124  OE2 GLU A  17      31.720  43.286  46.716  1.00  0.00          O   \nATOM    125  N   GLU A  18      34.081  42.599  52.666  1.00  0.00          N   \nATOM    126  CA  GLU A  18      34.309  41.552  53.652  1.00  0.00          C   \nATOM    127  C   GLU A  18      33.254  40.467  53.405  1.00  0.00          C   \nATOM    128  O   GLU A  18      32.092  40.786  53.188  1.00  0.00          O   \nATOM    129  CB  GLU A  18      34.123  42.089  55.071  1.00  0.00          C   \nATOM    130  CG  GLU A  18      35.138  43.120  55.528  1.00  0.00          C   \nATOM    131  CD  GLU A  18      34.861  43.593  56.945  1.00  0.00          C   \nATOM    132  OE1 GLU A  18      33.861  44.316  57.158  1.00  0.00          O   \nATOM    133  OE2 GLU A  18      35.637  43.228  57.849  1.00  0.00          O   \nATOM    134  N   SER A  19      33.649  39.198  53.428  1.00  0.00          N   \nATOM    135  CA  SER A  19      32.681  38.120  53.237  1.00  0.00          C   \nATOM    136  C   SER A  19      32.448  37.374  54.548  1.00  0.00          C   \nATOM    137  O   SER A  19      31.349  36.871  54.800  1.00  0.00          O   \nATOM    138  CB  SER A  19      33.160  37.125  52.180  1.00  0.00          C   \nATOM    139  OG  SER A  19      34.303  36.419  52.624  1.00  0.00          O   \nATOM    140  N   LYS A  20      33.480  37.291  55.385  1.00  0.00          N   \nATOM    141  CA  LYS A  20      33.339  36.587  56.652  1.00  0.00          C   \nATOM    142  C   LYS A  20      32.833  37.538  57.718  1.00  0.00          C   \nATOM    143  O   LYS A  20      33.151  38.721  57.702  1.00  0.00          O   \nATOM    144  CB  LYS A  20      34.675  35.977  57.098  1.00  0.00          C   \nATOM    145  CG  LYS A  20      35.166  34.829  56.220  1.00  0.00          C   \nATOM    146  CD  LYS A  20      35.703  35.336  54.877  1.00  0.00          C   \nATOM    147  CE  LYS A  20      36.005  34.194  53.890  1.00  0.00          C   \nATOM    148  NZ  LYS A  20      34.768  33.491  53.409  1.00  0.00          N   \nATOM    149  N   LEU A  21      32.041  37.015  58.643  1.00  0.00          N   \nATOM    150  CA  LEU A  21      31.493  37.824  59.723  1.00  0.00          C   \nATOM    151  C   LEU A  21      32.568  38.443  60.616  1.00  0.00          C   \nATOM    152  O   LEU A  21      33.398  37.735  61.196  1.00  0.00          O   \nATOM    153  CB  LEU A  21      30.571  36.975  60.601  1.00  0.00          C   \nATOM    154  CG  LEU A  21      29.990  37.699  61.820  1.00  0.00          C   \nATOM    155  CD1 LEU A  21      28.959  38.727  61.348  1.00  0.00          C   \nATOM    156  CD2 LEU A  21      29.353  36.703  62.778  1.00  0.00          C   \nATOM    157  N   PRO A  22      32.573  39.779  60.735  1.00  0.00          N   \nATOM    158  CA  PRO A  22      33.573  40.422  61.590  1.00  0.00          C   \nATOM    159  C   PRO A  22      33.191  40.135  63.044  1.00  0.00          C   \nATOM    160  O   PRO A  22      32.012  40.135  63.393  1.00  0.00          O   \nATOM    161  CB  PRO A  22      33.426  41.906  61.244  1.00  0.00          C   \nATOM    162  CG  PRO A  22      32.859  41.894  59.853  1.00  0.00          C   \nATOM    163  CD  PRO A  22      31.842  40.784  59.946  1.00  0.00          C   \nATOM    164  N   ILE A  23      34.183  39.889  63.885  1.00  0.00          N   \nATOM    165  CA  ILE A  23      33.942  39.586  65.283  1.00  0.00          C   \nATOM    166  C   ILE A  23      34.525  40.667  66.192  1.00  0.00          C   \nATOM    167  O   ILE A  23      35.661  41.097  65.990  1.00  0.00          O   \nATOM    168  CB  ILE A  23      34.619  38.237  65.663  1.00  0.00          C   \nATOM    169  CG1 ILE A  23      34.179  37.138  64.695  1.00  0.00          C   \nATOM    170  CG2 ILE A  23      34.272  37.846  67.097  1.00  0.00          C   \nATOM    171  CD1 ILE A  23      32.677  36.944  64.628  1.00  0.00          C   \nATOM    172  N   ASN A  24      33.749  41.129  67.169  1.00  0.00          N   \nATOM    173  CA  ASN A  24      34.266  42.102  68.126  1.00  0.00          C   \nATOM    174  C   ASN A  24      33.946  41.566  69.526  1.00  0.00          C   \nATOM    175  O   ASN A  24      33.588  40.394  69.669  1.00  0.00          O   \nATOM    176  CB  ASN A  24      33.704  43.524  67.869  1.00  0.00          C   \nATOM    177  CG  ASN A  24      32.217  43.679  68.204  1.00  0.00          C   \nATOM    178  OD1 ASN A  24      31.516  42.720  68.542  1.00  0.00          O   \nATOM    179  ND2 ASN A  24      31.730  44.913  68.094  1.00  0.00          N   \nATOM    180  N   ALA A  25      34.094  42.382  70.559  1.00  0.00          N   \nATOM    181  CA  ALA A  25      33.835  41.904  71.910  1.00  0.00          C   \nATOM    182  C   ALA A  25      32.367  41.594  72.194  1.00  0.00          C   \nATOM    183  O   ALA A  25      32.053  40.940  73.183  1.00  0.00          O   \nATOM    184  CB  ALA A  25      34.346  42.913  72.920  1.00  0.00          C   \nATOM    185  N   LEU A  26      31.470  42.048  71.327  1.00  0.00          N   \nATOM    186  CA  LEU A  26      30.039  41.829  71.534  1.00  0.00          C   \nATOM    187  C   LEU A  26      29.457  40.652  70.764  1.00  0.00          C   \nATOM    188  O   LEU A  26      28.394  40.133  71.107  1.00  0.00          O   \nATOM    189  CB  LEU A  26      29.269  43.088  71.133  1.00  0.00          C   \nATOM    190  CG  LEU A  26      29.663  44.381  71.842  1.00  0.00          C   \nATOM    191  CD1 LEU A  26      28.939  45.564  71.206  1.00  0.00          C   \nATOM    192  CD2 LEU A  26      29.306  44.255  73.322  1.00  0.00          C   \nATOM    193  N   SER A  27      30.152  40.228  69.724  1.00  0.00          N   \nATOM    194  CA  SER A  27      29.649  39.157  68.882  1.00  0.00          C   \nATOM    195  C   SER A  27      29.162  37.913  69.597  1.00  0.00          C   \nATOM    196  O   SER A  27      28.041  37.456  69.352  1.00  0.00          O   \nATOM    197  CB  SER A  27      30.711  38.768  67.860  1.00  0.00          C   \nATOM    198  OG  SER A  27      31.119  39.906  67.139  1.00  0.00          O   \nATOM    199  N   ASN A  28      29.995  37.365  70.476  1.00  0.00          N   \nATOM    200  CA  ASN A  28      29.634  36.138  71.165  1.00  0.00          C   \nATOM    201  C   ASN A  28      28.393  36.261  72.020  1.00  0.00          C   \nATOM    202  O   ASN A  28      27.688  35.280  72.199  1.00  0.00          O   \nATOM    203  CB  ASN A  28      30.814  35.598  71.990  1.00  0.00          C   \nATOM    204  CG  ASN A  28      31.883  34.947  71.114  1.00  0.00          C   \nATOM    205  OD1 ASN A  28      31.585  34.067  70.302  1.00  0.00          O   \nATOM    206  ND2 ASN A  28      33.134  35.383  71.271  1.00  0.00          N   \nATOM    207  N   SER A  29      28.092  37.453  72.529  1.00  0.00          N   \nATOM    208  CA  SER A  29      26.884  37.584  73.339  1.00  0.00          C   \nATOM    209  C   SER A  29      25.633  37.299  72.496  1.00  0.00          C   \nATOM    210  O   SER A  29      24.622  36.838  73.026  1.00  0.00          O   \nATOM    211  CB  SER A  29      26.793  38.973  73.979  1.00  0.00          C   \nATOM    212  OG  SER A  29      26.852  39.988  73.004  1.00  0.00          O   \nATOM    213  N   LEU A  30      25.703  37.558  71.191  1.00  0.00          N   \nATOM    214  CA  LEU A  30      24.569  37.289  70.301  1.00  0.00          C   \nATOM    215  C   LEU A  30      24.556  35.845  69.756  1.00  0.00          C   \nATOM    216  O   LEU A  30      23.569  35.131  69.916  1.00  0.00          O   \nATOM    217  CB  LEU A  30      24.543  38.270  69.118  1.00  0.00          C   \nATOM    218  CG  LEU A  30      23.432  38.019  68.083  1.00  0.00          C   \nATOM    219  CD1 LEU A  30      22.055  38.035  68.776  1.00  0.00          C   \nATOM    220  CD2 LEU A  30      23.514  39.069  66.976  1.00  0.00          C   \nATOM    221  N   LEU A  31      25.638  35.416  69.110  1.00  0.00          N   \nATOM    222  CA  LEU A  31      25.689  34.056  68.569  1.00  0.00          C   \nATOM    223  C   LEU A  31      27.121  33.531  68.572  1.00  0.00          C   \nATOM    224  O   LEU A  31      28.077  34.315  68.532  1.00  0.00          O   \nATOM    225  CB  LEU A  31      25.119  34.018  67.140  1.00  0.00          C   \nATOM    226  CG  LEU A  31      25.858  34.679  65.962  1.00  0.00          C   \nATOM    227  CD1 LEU A  31      27.064  33.858  65.547  1.00  0.00          C   \nATOM    228  CD2 LEU A  31      24.904  34.773  64.770  1.00  0.00          C   \nATOM    229  N   ARG A  32      27.265  32.206  68.607  1.00  0.00          N   \nATOM    230  CA  ARG A  32      28.582  31.580  68.636  1.00  0.00          C   \nATOM    231  C   ARG A  32      29.076  30.943  67.337  1.00  0.00          C   \nATOM    232  O   ARG A  32      30.280  30.884  67.109  1.00  0.00          O   \nATOM    233  CB  ARG A  32      28.634  30.552  69.769  1.00  0.00          C   \nATOM    234  CG  ARG A  32      28.662  31.176  71.154  1.00  0.00          C   \nATOM    235  CD  ARG A  32      28.596  30.101  72.220  1.00  0.00          C   \nATOM    236  NE  ARG A  32      28.578  30.700  73.557  1.00  0.00          N   \nATOM    237  CZ  ARG A  32      29.622  31.291  74.146  1.00  0.00          C   \nATOM    238  NH1 ARG A  32      30.802  31.362  73.522  1.00  0.00          N   \nATOM    239  NH2 ARG A  32      29.467  31.838  75.359  1.00  0.00          N   \nATOM    240  N   HIS A  33      28.164  30.487  66.481  1.00  0.00          N   \nATOM    241  CA  HIS A  33      28.550  29.853  65.222  1.00  0.00          C   \nATOM    242  C   HIS A  33      28.765  30.904  64.134  1.00  0.00          C   \nATOM    243  O   HIS A  33      27.991  31.021  63.176  1.00  0.00          O   \nATOM    244  CB  HIS A  33      27.479  28.846  64.795  1.00  0.00          C   \nATOM    245  CG  HIS A  33      27.174  27.808  65.832  1.00  0.00          C   \nATOM    246  ND1 HIS A  33      26.039  27.026  65.794  1.00  0.00          N   \nATOM    247  CD2 HIS A  33      27.854  27.429  66.941  1.00  0.00          C   \nATOM    248  CE1 HIS A  33      26.031  26.212  66.832  1.00  0.00          C   \nATOM    249  NE2 HIS A  33      27.121  26.434  67.545  1.00  0.00          N   \nATOM    250  N   HIS A  34      29.846  31.654  64.295  1.00  0.00          N   \nATOM    251  CA  HIS A  34      30.233  32.734  63.392  1.00  0.00          C   \nATOM    252  C   HIS A  34      30.383  32.332  61.923  1.00  0.00          C   \nATOM    253  O   HIS A  34      30.072  33.121  61.027  1.00  0.00          O   \nATOM    254  CB  HIS A  34      31.545  33.341  63.890  1.00  0.00          C   \nATOM    255  CG  HIS A  34      31.510  33.732  65.334  1.00  0.00          C   \nATOM    256  ND1 HIS A  34      32.647  33.831  66.105  1.00  0.00          N   \nATOM    257  CD2 HIS A  34      30.474  34.035  66.153  1.00  0.00          C   \nATOM    258  CE1 HIS A  34      32.313  34.174  67.338  1.00  0.00          C   \nATOM    259  NE2 HIS A  34      31.001  34.304  67.392  1.00  0.00          N   \nATOM    260  N   ASN A  35      30.863  31.115  61.676  1.00  0.00          N   \nATOM    261  CA  ASN A  35      31.070  30.644  60.306  1.00  0.00          C   \nATOM    262  C   ASN A  35      29.790  30.360  59.536  1.00  0.00          C   \nATOM    263  O   ASN A  35      29.827  29.988  58.369  1.00  0.00          O   \nATOM    264  CB  ASN A  35      31.968  29.409  60.308  1.00  0.00          C   \nATOM    265  CG  ASN A  35      33.346  29.701  60.879  1.00  0.00          C   \nATOM    266  OD1 ASN A  35      33.978  30.704  60.529  1.00  0.00          O   \nATOM    267  ND2 ASN A  35      33.821  28.828  61.757  1.00  0.00          N   \nATOM    268  N   MET A  36      28.656  30.548  60.194  1.00  0.00          N   \nATOM    269  CA  MET A  36      27.366  30.331  59.562  1.00  0.00          C   \nATOM    270  C   MET A  36      26.974  31.604  58.809  1.00  0.00          C   \nATOM    271  O   MET A  36      26.179  31.573  57.870  1.00  0.00          O   \nATOM    272  CB  MET A  36      26.297  30.056  60.623  1.00  0.00          C   \nATOM    273  CG  MET A  36      26.490  28.802  61.448  1.00  0.00          C   \nATOM    274  SD  MET A  36      26.183  27.344  60.478  1.00  0.00          S   \nATOM    275  CE  MET A  36      24.396  27.451  60.266  1.00  0.00          C   \nATOM    276  N   VAL A  37      27.542  32.726  59.232  1.00  0.00          N   \nATOM    277  CA  VAL A  37      27.201  34.008  58.631  1.00  0.00          C   \nATOM    278  C   VAL A  37      28.212  34.509  57.612  1.00  0.00          C   \nATOM    279  O   VAL A  37      29.406  34.375  57.805  1.00  0.00          O   \nATOM    280  CB  VAL A  37      27.009  35.058  59.732  1.00  0.00          C   \nATOM    281  CG1 VAL A  37      26.601  36.397  59.127  1.00  0.00          C   \nATOM    282  CG2 VAL A  37      25.946  34.563  60.714  1.00  0.00          C   \nATOM    283  N   TYR A  38      27.714  35.088  56.523  1.00  0.00          N   \nATOM    284  CA  TYR A  38      28.576  35.611  55.470  1.00  0.00          C   \nATOM    285  C   TYR A  38      27.879  36.742  54.728  1.00  0.00          C   \nATOM    286  O   TYR A  38      26.653  36.897  54.802  1.00  0.00          O   \nATOM    287  CB  TYR A  38      28.921  34.509  54.462  1.00  0.00          C   \nATOM    288  CG  TYR A  38      27.723  34.058  53.650  1.00  0.00          C   \nATOM    289  CD1 TYR A  38      26.708  33.286  54.226  1.00  0.00          C   \nATOM    290  CD2 TYR A  38      27.579  34.448  52.316  1.00  0.00          C   \nATOM    291  CE1 TYR A  38      25.582  32.918  53.491  1.00  0.00          C   \nATOM    292  CE2 TYR A  38      26.456  34.090  51.573  1.00  0.00          C   \nATOM    293  CZ  TYR A  38      25.469  33.328  52.163  1.00  0.00          C   \nATOM    294  OH  TYR A  38      24.374  32.965  51.421  1.00  0.00          O   \nATOM    295  N   ALA A  39      28.678  37.544  54.030  1.00  0.00          N   \nATOM    296  CA  ALA A  39      28.151  38.629  53.217  1.00  0.00          C   \nATOM    297  C   ALA A  39      28.515  38.261  51.784  1.00  0.00          C   \nATOM    298  O   ALA A  39      29.570  37.671  51.548  1.00  0.00          O   \nATOM    299  CB  ALA A  39      28.795  39.939  53.590  1.00  0.00          C   \nATOM    300  N   THR A  40      27.641  38.573  50.831  1.00  0.00          N   \nATOM    301  CA  THR A  40      27.951  38.285  49.439  1.00  0.00          C   \nATOM    302  C   THR A  40      28.877  39.400  48.959  1.00  0.00          C   \nATOM    303  O   THR A  40      28.820  40.518  49.477  1.00  0.00          O   \nATOM    304  CB  THR A  40      26.682  38.269  48.541  1.00  0.00          C   \nATOM    305  OG1 THR A  40      25.978  39.514  48.659  1.00  0.00          O   \nATOM    306  CG2 THR A  40      25.755  37.119  48.942  1.00  0.00          C   \nATOM    307  N   THR A  41      29.745  39.090  47.998  1.00  0.00          N   \nATOM    308  CA  THR A  41      30.650  40.092  47.425  1.00  0.00          C   \nATOM    309  C   THR A  41      30.731  39.881  45.916  1.00  0.00          C   \nATOM    310  O   THR A  41      30.230  38.881  45.394  1.00  0.00          O   \nATOM    311  CB  THR A  41      32.087  39.983  47.970  1.00  0.00          C   \nATOM    312  OG1 THR A  41      32.680  38.762  47.511  1.00  0.00          O   \nATOM    313  CG2 THR A  41      32.089  40.004  49.474  1.00  0.00          C   \nATOM    314  N   SER A  42      31.384  40.813  45.227  1.00  0.00          N   \nATOM    315  CA  SER A  42      31.527  40.721  43.778  1.00  0.00          C   \nATOM    316  C   SER A  42      32.219  39.440  43.321  1.00  0.00          C   \nATOM    317  O   SER A  42      32.096  39.049  42.160  1.00  0.00          O   \nATOM    318  CB  SER A  42      32.278  41.938  43.229  1.00  0.00          C   \nATOM    319  OG  SER A  42      33.540  42.102  43.854  1.00  0.00          O   \nATOM    320  N   ARG A  43      32.939  38.788  44.227  1.00  0.00          N   \nATOM    321  CA  ARG A  43      33.617  37.534  43.873  1.00  0.00          C   \nATOM    322  C   ARG A  43      32.650  36.495  43.316  1.00  0.00          C   \nATOM    323  O   ARG A  43      33.059  35.622  42.558  1.00  0.00          O   \nATOM    324  CB  ARG A  43      34.269  36.873  45.082  1.00  0.00          C   \nATOM    325  CG  ARG A  43      35.536  37.562  45.564  1.00  0.00          C   \nATOM    326  CD  ARG A  43      36.527  36.574  46.189  1.00  0.00          C   \nATOM    327  NE  ARG A  43      36.209  36.199  47.568  1.00  0.00          N   \nATOM    328  CZ  ARG A  43      36.850  35.246  48.242  1.00  0.00          C   \nATOM    329  NH1 ARG A  43      36.510  34.961  49.495  1.00  0.00          N   \nATOM    330  NH2 ARG A  43      37.829  34.567  47.653  1.00  0.00          N   \nATOM    331  N   SER A  44      31.378  36.577  43.704  1.00  0.00          N   \nATOM    332  CA  SER A  44      30.375  35.618  43.233  1.00  0.00          C   \nATOM    333  C   SER A  44      29.480  36.194  42.140  1.00  0.00          C   \nATOM    334  O   SER A  44      28.525  35.538  41.709  1.00  0.00          O   \nATOM    335  CB  SER A  44      29.513  35.145  44.405  1.00  0.00          C   \nATOM    336  OG  SER A  44      28.819  36.220  44.998  1.00  0.00          O   \nATOM    337  N   ALA A  45      29.798  37.404  41.679  1.00  0.00          N   \nATOM    338  CA  ALA A  45      29.009  38.057  40.628  1.00  0.00          C   \nATOM    339  C   ALA A  45      28.884  37.223  39.353  1.00  0.00          C   \nATOM    340  O   ALA A  45      27.852  37.252  38.686  1.00  0.00          O   \nATOM    341  CB  ALA A  45      29.606  39.423  40.289  1.00  0.00          C   \nATOM    342  N   GLY A  46      29.945  36.496  39.014  1.00  0.00          N   \nATOM    343  CA  GLY A  46      29.928  35.665  37.830  1.00  0.00          C   \nATOM    344  C   GLY A  46      28.845  34.608  37.907  1.00  0.00          C   \nATOM    345  O   GLY A  46      28.137  34.344  36.927  1.00  0.00          O   \nATOM    346  N   LEU A  47      28.717  33.998  39.081  1.00  0.00          N   \nATOM    347  CA  LEU A  47      27.717  32.961  39.303  1.00  0.00          C   \nATOM    348  C   LEU A  47      26.308  33.524  39.130  1.00  0.00          C   \nATOM    349  O   LEU A  47      25.410  32.844  38.615  1.00  0.00          O   \nATOM    350  CB  LEU A  47      27.874  32.378  40.708  1.00  0.00          C   \nATOM    351  CG  LEU A  47      29.172  31.598  40.957  1.00  0.00          C   \nATOM    352  CD1 LEU A  47      29.246  31.151  42.420  1.00  0.00          C   \nATOM    353  CD2 LEU A  47      29.214  30.381  40.033  1.00  0.00          C   \nATOM    354  N   ARG A  48      26.116  34.766  39.569  1.00  0.00          N   \nATOM    355  CA  ARG A  48      24.811  35.408  39.455  1.00  0.00          C   \nATOM    356  C   ARG A  48      24.516  35.685  37.990  1.00  0.00          C   \nATOM    357  O   ARG A  48      23.416  35.411  37.514  1.00  0.00          O   \nATOM    358  CB  ARG A  48      24.787  36.716  40.251  1.00  0.00          C   \nATOM    359  CG  ARG A  48      23.522  37.555  40.062  1.00  0.00          C   \nATOM    360  CD  ARG A  48      22.277  36.800  40.482  1.00  0.00          C   \nATOM    361  NE  ARG A  48      22.310  36.411  41.885  1.00  0.00          N   \nATOM    362  CZ  ARG A  48      21.360  35.687  42.474  1.00  0.00          C   \nATOM    363  NH1 ARG A  48      20.305  35.279  41.771  1.00  0.00          N   \nATOM    364  NH2 ARG A  48      21.467  35.364  43.761  1.00  0.00          N   \nATOM    365  N   GLN A  49      25.504  36.219  37.276  1.00  0.00          N   \nATOM    366  CA  GLN A  49      25.338  36.526  35.857  1.00  0.00          C   \nATOM    367  C   GLN A  49      24.852  35.296  35.115  1.00  0.00          C   \nATOM    368  O   GLN A  49      23.965  35.376  34.268  1.00  0.00          O   \nATOM    369  CB  GLN A  49      26.655  37.011  35.250  1.00  0.00          C   \nATOM    370  CG  GLN A  49      27.165  38.296  35.879  1.00  0.00          C   \nATOM    371  CD  GLN A  49      28.546  38.677  35.382  1.00  0.00          C   \nATOM    372  OE1 GLN A  49      28.699  39.247  34.301  1.00  0.00          O   \nATOM    373  NE2 GLN A  49      29.563  38.347  36.165  1.00  0.00          N   \nATOM    374  N   LYS A  50      25.434  34.152  35.445  1.00  0.00          N   \nATOM    375  CA  LYS A  50      25.042  32.907  34.809  1.00  0.00          C   \nATOM    376  C   LYS A  50      23.590  32.567  35.106  1.00  0.00          C   \nATOM    377  O   LYS A  50      22.861  32.135  34.221  1.00  0.00          O   \nATOM    378  CB  LYS A  50      25.958  31.770  35.269  1.00  0.00          C   \nATOM    379  CG  LYS A  50      27.304  31.793  34.564  1.00  0.00          C   \nATOM    380  CD  LYS A  50      28.246  30.721  35.077  1.00  0.00          C   \nATOM    381  CE  LYS A  50      29.504  30.654  34.221  1.00  0.00          C   \nATOM    382  NZ  LYS A  50      30.148  31.993  34.063  1.00  0.00          N   \nATOM    383  N   LYS A  51      23.162  32.768  36.345  1.00  0.00          N   \nATOM    384  CA  LYS A  51      21.778  32.464  36.704  1.00  0.00          C   \nATOM    385  C   LYS A  51      20.774  33.403  36.035  1.00  0.00          C   \nATOM    386  O   LYS A  51      19.739  32.945  35.562  1.00  0.00          O   \nATOM    387  CB  LYS A  51      21.565  32.545  38.221  1.00  0.00          C   \nATOM    388  CG  LYS A  51      22.409  31.586  39.046  1.00  0.00          C   \nATOM    389  CD  LYS A  51      22.182  31.804  40.542  1.00  0.00          C   \nATOM    390  CE  LYS A  51      23.269  31.127  41.389  1.00  0.00          C   \nATOM    391  NZ  LYS A  51      23.156  29.642  41.486  1.00  0.00          N   \nATOM    392  N   VAL A  52      21.081  34.705  35.997  1.00  0.00          N   \nATOM    393  CA  VAL A  52      20.159  35.702  35.435  1.00  0.00          C   \nATOM    394  C   VAL A  52      20.151  35.881  33.922  1.00  0.00          C   \nATOM    395  O   VAL A  52      19.344  36.645  33.400  1.00  0.00          O   \nATOM    396  CB  VAL A  52      20.393  37.108  36.064  1.00  0.00          C   \nATOM    397  CG1 VAL A  52      20.196  37.038  37.586  1.00  0.00          C   \nATOM    398  CG2 VAL A  52      21.796  37.618  35.714  1.00  0.00          C   \nATOM    399  N   THR A  53      21.034  35.170  33.227  1.00  0.00          N   \nATOM    400  CA  THR A  53      21.134  35.283  31.776  1.00  0.00          C   \nATOM    401  C   THR A  53      20.429  34.147  31.032  1.00  0.00          C   \nATOM    402  O   THR A  53      20.871  33.003  31.078  1.00  0.00          O   \nATOM    403  CB  THR A  53      22.616  35.315  31.345  1.00  0.00          C   \nATOM    404  OG1 THR A  53      23.305  36.321  32.097  1.00  0.00          O   \nATOM    405  CG2 THR A  53      22.736  35.632  29.870  1.00  0.00          C   \nATOM    406  N   PHE A  54      19.328  34.475  30.359  1.00  0.00          N   \nATOM    407  CA  PHE A  54      18.555  33.505  29.579  1.00  0.00          C   \nATOM    408  C   PHE A  54      17.618  34.183  28.580  1.00  0.00          C   \nATOM    409  O   PHE A  54      17.361  35.393  28.658  1.00  0.00          O   \nATOM    410  CB  PHE A  54      17.756  32.565  30.498  1.00  0.00          C   \nATOM    411  CG  PHE A  54      16.970  33.268  31.574  1.00  0.00          C   \nATOM    412  CD1 PHE A  54      15.760  33.892  31.281  1.00  0.00          C   \nATOM    413  CD2 PHE A  54      17.448  33.306  32.883  1.00  0.00          C   \nATOM    414  CE1 PHE A  54      15.030  34.543  32.276  1.00  0.00          C   \nATOM    415  CE2 PHE A  54      16.726  33.961  33.897  1.00  0.00          C   \nATOM    416  CZ  PHE A  54      15.510  34.581  33.590  1.00  0.00          C   \nATOM    417  N   ASP A  55      17.120  33.402  27.629  1.00  0.00          N   \nATOM    418  CA  ASP A  55      16.209  33.931  26.630  1.00  0.00          C   \nATOM    419  C   ASP A  55      14.769  33.964  27.171  1.00  0.00          C   \nATOM    420  O   ASP A  55      14.358  33.094  27.936  1.00  0.00          O   \nATOM    421  CB  ASP A  55      16.288  33.068  25.371  1.00  0.00          C   \nATOM    422  CG  ASP A  55      16.009  33.854  24.102  1.00  0.00          C   \nATOM    423  OD1 ASP A  55      16.205  33.285  23.012  1.00  0.00          O   \nATOM    424  OD2 ASP A  55      15.594  35.035  24.190  1.00  0.00          O   \nATOM    425  N   ARG A  56      14.014  34.987  26.796  1.00  0.00          N   \nATOM    426  CA  ARG A  56      12.631  35.077  27.239  1.00  0.00          C   \nATOM    427  C   ARG A  56      11.712  34.927  26.037  1.00  0.00          C   \nATOM    428  O   ARG A  56      11.988  35.436  24.949  1.00  0.00          O   \nATOM    429  CB  ARG A  56      12.340  36.417  27.934  1.00  0.00          C   \nATOM    430  CG  ARG A  56      12.997  36.609  29.343  1.00  0.00          C   \nATOM    431  CD  ARG A  56      14.487  36.866  29.235  1.00  0.00          C   \nATOM    432  NE  ARG A  56      14.737  37.994  28.347  1.00  0.00          N   \nATOM    433  CZ  ARG A  56      14.470  39.269  28.637  1.00  0.00          C   \nATOM    434  NH1 ARG A  56      13.950  39.620  29.820  1.00  0.00          N   \nATOM    435  NH2 ARG A  56      14.675  40.194  27.713  1.00  0.00          N   \nATOM    436  N   LEU A  57      10.623  34.204  26.234  1.00  0.00          N   \nATOM    437  CA  LEU A  57       9.638  34.016  25.182  1.00  0.00          C   \nATOM    438  C   LEU A  57       8.300  34.342  25.833  1.00  0.00          C   \nATOM    439  O   LEU A  57       8.130  34.132  27.040  1.00  0.00          O   \nATOM    440  CB  LEU A  57       9.674  32.565  24.697  1.00  0.00          C   \nATOM    441  CG  LEU A  57       9.865  32.317  23.199  1.00  0.00          C   \nATOM    442  CD1 LEU A  57      10.906  33.261  22.608  1.00  0.00          C   \nATOM    443  CD2 LEU A  57      10.278  30.860  22.994  1.00  0.00          C   \nATOM    444  N   GLN A  58       7.375  34.893  25.056  1.00  0.00          N   \nATOM    445  CA  GLN A  58       6.053  35.223  25.567  1.00  0.00          C   \nATOM    446  C   GLN A  58       4.938  34.923  24.578  1.00  0.00          C   \nATOM    447  O   GLN A  58       5.000  35.288  23.407  1.00  0.00          O   \nATOM    448  CB  GLN A  58       5.960  36.700  25.968  1.00  0.00          C   \nATOM    449  CG  GLN A  58       6.566  37.036  27.309  1.00  0.00          C   \nATOM    450  CD  GLN A  58       6.329  38.487  27.680  1.00  0.00          C   \nATOM    451  OE1 GLN A  58       6.722  39.382  26.946  1.00  0.00          O   \nATOM    452  NE2 GLN A  58       5.684  38.724  28.825  1.00  0.00          N   \nATOM    453  N   VAL A  59       3.913  34.261  25.088  1.00  0.00          N   \nATOM    454  CA  VAL A  59       2.732  33.928  24.321  1.00  0.00          C   \nATOM    455  C   VAL A  59       1.597  34.588  25.107  1.00  0.00          C   \nATOM    456  O   VAL A  59       1.221  34.126  26.196  1.00  0.00          O   \nATOM    457  CB  VAL A  59       2.541  32.404  24.250  1.00  0.00          C   \nATOM    458  CG1 VAL A  59       1.217  32.095  23.626  1.00  0.00          C   \nATOM    459  CG2 VAL A  59       3.667  31.775  23.428  1.00  0.00          C   \nATOM    460  N   LEU A  60       1.071  35.681  24.553  1.00  0.00          N   \nATOM    461  CA  LEU A  60       0.019  36.458  25.207  1.00  0.00          C   \nATOM    462  C   LEU A  60      -1.381  36.186  24.658  1.00  0.00          C   \nATOM    463  O   LEU A  60      -1.676  36.469  23.494  1.00  0.00          O   \nATOM    464  CB  LEU A  60       0.354  37.948  25.101  1.00  0.00          C   \nATOM    465  CG  LEU A  60       1.745  38.367  25.606  1.00  0.00          C   \nATOM    466  CD1 LEU A  60       1.918  39.876  25.429  1.00  0.00          C   \nATOM    467  CD2 LEU A  60       1.919  37.976  27.070  1.00  0.00          C   \nATOM    468  N   ASP A  61      -2.235  35.657  25.535  1.00  0.00          N   \nATOM    469  CA  ASP A  61      -3.609  35.283  25.205  1.00  0.00          C   \nATOM    470  C   ASP A  61      -4.642  36.348  25.571  1.00  0.00          C   \nATOM    471  O   ASP A  61      -4.301  37.441  26.037  1.00  0.00          O   \nATOM    472  CB  ASP A  61      -3.947  33.961  25.895  1.00  0.00          C   \nATOM    473  CG  ASP A  61      -4.051  34.100  27.402  1.00  0.00          C   \nATOM    474  OD1 ASP A  61      -3.549  35.103  27.956  1.00  0.00          O   \nATOM    475  OD2 ASP A  61      -4.640  33.208  28.036  1.00  0.00          O   \nATOM    476  N   ASP A  62      -5.915  36.032  25.364  1.00  0.00          N   \nATOM    477  CA  ASP A  62      -6.956  37.013  25.643  1.00  0.00          C   \nATOM    478  C   ASP A  62      -7.148  37.348  27.130  1.00  0.00          C   \nATOM    479  O   ASP A  62      -7.532  38.474  27.466  1.00  0.00          O   \nATOM    480  CB  ASP A  62      -8.271  36.570  24.980  1.00  0.00          C   \nATOM    481  CG  ASP A  62      -8.165  36.555  23.449  1.00  0.00          C   \nATOM    482  OD1 ASP A  62      -7.920  37.624  22.853  1.00  0.00          O   \nATOM    483  OD2 ASP A  62      -8.301  35.476  22.837  1.00  0.00          O   \nATOM    484  N   HIS A  63      -6.881  36.394  28.019  1.00  0.00          N   \nATOM    485  CA  HIS A  63      -7.007  36.682  29.454  1.00  0.00          C   \nATOM    486  C   HIS A  63      -5.986  37.764  29.815  1.00  0.00          C   \nATOM    487  O   HIS A  63      -6.264  38.671  30.615  1.00  0.00          O   \nATOM    488  CB  HIS A  63      -6.744  35.435  30.304  1.00  0.00          C   \nATOM    489  CG  HIS A  63      -7.843  34.416  30.250  1.00  0.00          C   \nATOM    490  ND1 HIS A  63      -9.171  34.741  30.441  1.00  0.00          N   \nATOM    491  CD2 HIS A  63      -7.807  33.074  30.075  1.00  0.00          C   \nATOM    492  CE1 HIS A  63      -9.904  33.642  30.387  1.00  0.00          C   \nATOM    493  NE2 HIS A  63      -9.099  32.616  30.167  1.00  0.00          N   \nATOM    494  N   TYR A  64      -4.807  37.672  29.205  1.00  0.00          N   \nATOM    495  CA  TYR A  64      -3.757  38.660  29.447  1.00  0.00          C   \nATOM    496  C   TYR A  64      -4.253  40.046  29.011  1.00  0.00          C   \nATOM    497  O   TYR A  64      -4.162  41.005  29.769  1.00  0.00          O   \nATOM    498  CB  TYR A  64      -2.490  38.302  28.668  1.00  0.00          C   \nATOM    499  CG  TYR A  64      -1.363  39.321  28.803  1.00  0.00          C   \nATOM    500  CD1 TYR A  64      -0.439  39.249  29.856  1.00  0.00          C   \nATOM    501  CD2 TYR A  64      -1.227  40.357  27.877  1.00  0.00          C   \nATOM    502  CE1 TYR A  64       0.592  40.184  29.967  1.00  0.00          C   \nATOM    503  CE2 TYR A  64      -0.207  41.295  27.980  1.00  0.00          C   \nATOM    504  CZ  TYR A  64       0.697  41.201  29.022  1.00  0.00          C   \nATOM    505  OH  TYR A  64       1.717  42.117  29.092  1.00  0.00          O   \nATOM    506  N   ARG A  65      -4.786  40.152  27.796  1.00  0.00          N   \nATOM    507  CA  ARG A  65      -5.272  41.449  27.319  1.00  0.00          C   \nATOM    508  C   ARG A  65      -6.530  41.949  28.041  1.00  0.00          C   \nATOM    509  O   ARG A  65      -6.692  43.158  28.224  1.00  0.00          O   \nATOM    510  CB  ARG A  65      -5.474  41.427  25.793  1.00  0.00          C   \nATOM    511  CG  ARG A  65      -4.148  41.427  25.000  1.00  0.00          C   \nATOM    512  CD  ARG A  65      -4.374  41.373  23.485  1.00  0.00          C   \nATOM    513  NE  ARG A  65      -4.963  40.098  23.089  1.00  0.00          N   \nATOM    514  CZ  ARG A  65      -4.277  38.965  22.967  1.00  0.00          C   \nATOM    515  NH1 ARG A  65      -2.973  38.946  23.182  1.00  0.00          N   \nATOM    516  NH2 ARG A  65      -4.912  37.832  22.713  1.00  0.00          N   \nATOM    517  N   ASP A  66      -7.407  41.036  28.465  1.00  0.00          N   \nATOM    518  CA  ASP A  66      -8.613  41.420  29.198  1.00  0.00          C   \nATOM    519  C   ASP A  66      -8.212  42.085  30.526  1.00  0.00          C   \nATOM    520  O   ASP A  66      -8.741  43.137  30.905  1.00  0.00          O   \nATOM    521  CB  ASP A  66      -9.501  40.200  29.526  1.00  0.00          C   \nATOM    522  CG  ASP A  66     -10.254  39.648  28.313  1.00  0.00          C   \nATOM    523  OD1 ASP A  66     -10.344  40.329  27.271  1.00  0.00          O   \nATOM    524  OD2 ASP A  66     -10.778  38.517  28.416  1.00  0.00          O   \nATOM    525  N   VAL A  67      -7.281  41.462  31.240  1.00  0.00          N   \nATOM    526  CA  VAL A  67      -6.846  42.007  32.521  1.00  0.00          C   \nATOM    527  C   VAL A  67      -6.146  43.342  32.286  1.00  0.00          C   \nATOM    528  O   VAL A  67      -6.376  44.317  33.012  1.00  0.00          O   \nATOM    529  CB  VAL A  67      -5.893  41.016  33.267  1.00  0.00          C   \nATOM    530  CG1 VAL A  67      -5.251  41.715  34.464  1.00  0.00          C   \nATOM    531  CG2 VAL A  67      -6.684  39.753  33.746  1.00  0.00          C   \nATOM    532  N   LEU A  68      -5.316  43.403  31.252  1.00  0.00          N   \nATOM    533  CA  LEU A  68      -4.610  44.649  30.975  1.00  0.00          C   \nATOM    534  C   LEU A  68      -5.606  45.779  30.728  1.00  0.00          C   \nATOM    535  O   LEU A  68      -5.451  46.894  31.249  1.00  0.00          O   \nATOM    536  CB  LEU A  68      -3.695  44.478  29.758  1.00  0.00          C   \nATOM    537  CG  LEU A  68      -2.917  45.710  29.292  1.00  0.00          C   \nATOM    538  CD1 LEU A  68      -2.181  46.326  30.479  1.00  0.00          C   \nATOM    539  CD2 LEU A  68      -1.933  45.306  28.166  1.00  0.00          C   \nATOM    540  N   LYS A  69      -6.639  45.496  29.942  1.00  0.00          N   \nATOM    541  CA  LYS A  69      -7.639  46.520  29.647  1.00  0.00          C   \nATOM    542  C   LYS A  69      -8.281  47.029  30.945  1.00  0.00          C   \nATOM    543  O   LYS A  69      -8.503  48.224  31.100  1.00  0.00          O   \nATOM    544  CB  LYS A  69      -8.709  45.976  28.688  1.00  0.00          C   \nATOM    545  CG  LYS A  69      -9.781  46.992  28.302  1.00  0.00          C   \nATOM    546  CD  LYS A  69     -10.542  46.551  27.046  1.00  0.00          C   \nATOM    547  CE  LYS A  69     -11.387  47.685  26.491  1.00  0.00          C   \nATOM    548  NZ  LYS A  69     -12.202  47.275  25.310  1.00  0.00          N   \nATOM    549  N   GLU A  70      -8.565  46.120  31.874  1.00  0.00          N   \nATOM    550  CA  GLU A  70      -9.164  46.489  33.169  1.00  0.00          C   \nATOM    551  C   GLU A  70      -8.218  47.374  33.963  1.00  0.00          C   \nATOM    552  O   GLU A  70      -8.634  48.366  34.559  1.00  0.00          O   \nATOM    553  CB  GLU A  70      -9.484  45.239  33.995  1.00  0.00          C   \nATOM    554  CG  GLU A  70     -10.682  44.439  33.485  1.00  0.00          C   \nATOM    555  CD  GLU A  70     -10.774  43.044  34.100  1.00  0.00          C   \nATOM    556  OE1 GLU A  70     -10.562  42.898  35.324  1.00  0.00          O   \nATOM    557  OE2 GLU A  70     -11.068  42.089  33.353  1.00  0.00          O   \nATOM    558  N   MET A  71      -6.943  47.004  33.980  1.00  0.00          N   \nATOM    559  CA  MET A  71      -5.944  47.786  34.697  1.00  0.00          C   \nATOM    560  C   MET A  71      -5.846  49.206  34.135  1.00  0.00          C   \nATOM    561  O   MET A  71      -5.763  50.173  34.897  1.00  0.00          O   \nATOM    562  CB  MET A  71      -4.588  47.089  34.619  1.00  0.00          C   \nATOM    563  CG  MET A  71      -4.522  45.867  35.521  1.00  0.00          C   \nATOM    564  SD  MET A  71      -3.080  44.867  35.252  1.00  0.00          S   \nATOM    565  CE  MET A  71      -1.805  45.804  36.133  1.00  0.00          C   \nATOM    566  N   LYS A  72      -5.854  49.325  32.805  1.00  0.00          N   \nATOM    567  CA  LYS A  72      -5.785  50.629  32.151  1.00  0.00          C   \nATOM    568  C   LYS A  72      -7.019  51.488  32.460  1.00  0.00          C   \nATOM    569  O   LYS A  72      -6.913  52.707  32.626  1.00  0.00          O   \nATOM    570  CB  LYS A  72      -5.656  50.451  30.639  1.00  0.00          C   \nATOM    571  CG  LYS A  72      -4.289  49.981  30.164  1.00  0.00          C   \nATOM    572  CD  LYS A  72      -4.344  49.815  28.655  1.00  0.00          C   \nATOM    573  CE  LYS A  72      -2.982  49.809  28.016  1.00  0.00          C   \nATOM    574  NZ  LYS A  72      -3.126  49.727  26.532  1.00  0.00          N   \nATOM    575  N   ALA A  73      -8.192  50.866  32.514  1.00  0.00          N   \nATOM    576  CA  ALA A  73      -9.400  51.630  32.838  1.00  0.00          C   \nATOM    577  C   ALA A  73      -9.231  52.233  34.239  1.00  0.00          C   \nATOM    578  O   ALA A  73      -9.566  53.391  34.473  1.00  0.00          O   \nATOM    579  CB  ALA A  73     -10.632  50.730  32.794  1.00  0.00          C   \nATOM    580  N   LYS A  74      -8.697  51.455  35.173  1.00  0.00          N   \nATOM    581  CA  LYS A  74      -8.497  51.991  36.511  1.00  0.00          C   \nATOM    582  C   LYS A  74      -7.410  53.059  36.485  1.00  0.00          C   \nATOM    583  O   LYS A  74      -7.551  54.108  37.119  1.00  0.00          O   \nATOM    584  CB  LYS A  74      -8.139  50.878  37.498  1.00  0.00          C   \nATOM    585  CG  LYS A  74      -9.308  49.926  37.782  1.00  0.00          C   \nATOM    586  CD  LYS A  74      -8.850  48.700  38.558  1.00  0.00          C   \nATOM    587  CE  LYS A  74     -10.019  47.807  38.969  1.00  0.00          C   \nATOM    588  NZ  LYS A  74     -10.653  47.108  37.803  1.00  0.00          N   \nATOM    589  N   ALA A  75      -6.342  52.814  35.730  1.00  0.00          N   \nATOM    590  CA  ALA A  75      -5.241  53.776  35.631  1.00  0.00          C   \nATOM    591  C   ALA A  75      -5.707  55.122  35.072  1.00  0.00          C   \nATOM    592  O   ALA A  75      -5.155  56.164  35.416  1.00  0.00          O   \nATOM    593  CB  ALA A  75      -4.108  53.213  34.742  1.00  0.00          C   \nATOM    594  N   SER A  76      -6.707  55.096  34.198  1.00  0.00          N   \nATOM    595  CA  SER A  76      -7.211  56.324  33.604  1.00  0.00          C   \nATOM    596  C   SER A  76      -7.885  57.249  34.606  1.00  0.00          C   \nATOM    597  O   SER A  76      -8.124  58.411  34.290  1.00  0.00          O   \nATOM    598  CB  SER A  76      -8.211  56.010  32.488  1.00  0.00          C   \nATOM    599  OG  SER A  76      -7.597  55.211  31.495  1.00  0.00          O   \nATOM    600  N   THR A  77      -8.215  56.741  35.792  1.00  0.00          N   \nATOM    601  CA  THR A  77      -8.861  57.580  36.805  1.00  0.00          C   \nATOM    602  C   THR A  77      -7.818  58.331  37.628  1.00  0.00          C   \nATOM    603  O   THR A  77      -8.140  59.264  38.369  1.00  0.00          O   \nATOM    604  CB  THR A  77      -9.734  56.742  37.758  1.00  0.00          C   \nATOM    605  OG1 THR A  77      -8.900  55.945  38.614  1.00  0.00          O   \nATOM    606  CG2 THR A  77     -10.640  55.838  36.954  1.00  0.00          C   \nATOM    607  N   VAL A  78      -6.560  57.930  37.469  1.00  0.00          N   \nATOM    608  CA  VAL A  78      -5.452  58.538  38.199  1.00  0.00          C   \nATOM    609  C   VAL A  78      -4.932  59.849  37.615  1.00  0.00          C   \nATOM    610  O   VAL A  78      -4.766  59.983  36.400  1.00  0.00          O   \nATOM    611  CB  VAL A  78      -4.250  57.559  38.284  1.00  0.00          C   \nATOM    612  CG1 VAL A  78      -3.033  58.266  38.880  1.00  0.00          C   \nATOM    613  CG2 VAL A  78      -4.624  56.351  39.130  1.00  0.00          C   \nATOM    614  N   LYS A  79      -4.687  60.820  38.489  1.00  0.00          N   \nATOM    615  CA  LYS A  79      -4.118  62.100  38.078  1.00  0.00          C   \nATOM    616  C   LYS A  79      -2.846  62.233  38.904  1.00  0.00          C   \nATOM    617  O   LYS A  79      -2.886  62.174  40.134  1.00  0.00          O   \nATOM    618  CB  LYS A  79      -5.068  63.265  38.371  1.00  0.00          C   \nATOM    619  CG  LYS A  79      -4.471  64.614  37.994  1.00  0.00          C   \nATOM    620  CD  LYS A  79      -5.492  65.743  38.042  1.00  0.00          C   \nATOM    621  CE  LYS A  79      -4.816  67.079  37.746  1.00  0.00          C   \nATOM    622  NZ  LYS A  79      -5.773  68.212  37.748  1.00  0.00          N   \nATOM    623  N   ALA A  80      -1.719  62.381  38.220  1.00  0.00          N   \nATOM    624  CA  ALA A  80      -0.419  62.482  38.875  1.00  0.00          C   \nATOM    625  C   ALA A  80       0.243  63.810  38.586  1.00  0.00          C   \nATOM    626  O   ALA A  80       0.106  64.358  37.498  1.00  0.00          O   \nATOM    627  CB  ALA A  80       0.496  61.338  38.407  1.00  0.00          C   \nATOM    628  N   LYS A  81       0.979  64.317  39.562  1.00  0.00          N   \nATOM    629  CA  LYS A  81       1.651  65.580  39.382  1.00  0.00          C   \nATOM    630  C   LYS A  81       3.146  65.427  39.182  1.00  0.00          C   \nATOM    631  O   LYS A  81       3.785  64.499  39.689  1.00  0.00          O   \nATOM    632  CB  LYS A  81       1.389  66.509  40.575  1.00  0.00          C   \nATOM    633  CG  LYS A  81       1.991  66.061  41.907  1.00  0.00          C   \nATOM    634  CD  LYS A  81       2.147  67.269  42.851  1.00  0.00          C   \nATOM    635  CE  LYS A  81       2.608  66.887  44.258  1.00  0.00          C   \nATOM    636  NZ  LYS A  81       1.526  66.265  45.090  1.00  0.00          N   \nATOM    637  N   LEU A  82       3.688  66.372  38.434  1.00  0.00          N   \nATOM    638  CA  LEU A  82       5.100  66.436  38.139  1.00  0.00          C   \nATOM    639  C   LEU A  82       5.752  67.077  39.360  1.00  0.00          C   \nATOM    640  O   LEU A  82       5.335  68.143  39.799  1.00  0.00          O   \nATOM    641  CB  LEU A  82       5.289  67.315  36.908  1.00  0.00          C   \nATOM    642  CG  LEU A  82       6.583  67.376  36.112  1.00  0.00          C   \nATOM    643  CD1 LEU A  82       6.966  65.993  35.615  1.00  0.00          C   \nATOM    644  CD2 LEU A  82       6.373  68.332  34.940  1.00  0.00          C   \nATOM    645  N   LEU A  83       6.754  66.421  39.927  1.00  0.00          N   \nATOM    646  CA  LEU A  83       7.439  66.967  41.088  1.00  0.00          C   \nATOM    647  C   LEU A  83       8.415  68.053  40.638  1.00  0.00          C   \nATOM    648  O   LEU A  83       8.919  68.022  39.513  1.00  0.00          O   \nATOM    649  CB  LEU A  83       8.211  65.860  41.816  1.00  0.00          C   \nATOM    650  CG  LEU A  83       7.838  65.479  43.251  1.00  0.00          C   \nATOM    651  CD1 LEU A  83       6.437  64.863  43.302  1.00  0.00          C   \nATOM    652  CD2 LEU A  83       8.871  64.492  43.780  1.00  0.00          C   \nATOM    653  N   SER A  84       8.676  69.020  41.514  1.00  0.00          N   \nATOM    654  CA  SER A  84       9.619  70.089  41.189  1.00  0.00          C   \nATOM    655  C   SER A  84      11.017  69.557  41.466  1.00  0.00          C   \nATOM    656  O   SER A  84      11.176  68.570  42.182  1.00  0.00          O   \nATOM    657  CB  SER A  84       9.370  71.319  42.071  1.00  0.00          C   \nATOM    658  OG  SER A  84       9.661  71.034  43.433  1.00  0.00          O   \nATOM    659  N   VAL A  85      12.030  70.205  40.907  1.00  0.00          N   \nATOM    660  CA  VAL A  85      13.400  69.773  41.143  1.00  0.00          C   \nATOM    661  C   VAL A  85      13.680  69.689  42.639  1.00  0.00          C   \nATOM    662  O   VAL A  85      14.179  68.675  43.133  1.00  0.00          O   \nATOM    663  CB  VAL A  85      14.412  70.741  40.505  1.00  0.00          C   \nATOM    664  CG1 VAL A  85      15.833  70.347  40.896  1.00  0.00          C   \nATOM    665  CG2 VAL A  85      14.254  70.722  38.987  1.00  0.00          C   \nATOM    666  N   GLU A  86      13.344  70.751  43.365  1.00  0.00          N   \nATOM    667  CA  GLU A  86      13.588  70.787  44.801  1.00  0.00          C   \nATOM    668  C   GLU A  86      12.900  69.643  45.533  1.00  0.00          C   \nATOM    669  O   GLU A  86      13.509  68.996  46.388  1.00  0.00          O   \nATOM    670  CB  GLU A  86      13.144  72.132  45.384  1.00  0.00          C   \nATOM    671  CG  GLU A  86      11.805  72.621  44.871  1.00  0.00          C   \nATOM    672  CD  GLU A  86      11.945  73.708  43.818  1.00  0.00          C   \nATOM    673  OE1 GLU A  86      12.721  73.521  42.849  1.00  0.00          O   \nATOM    674  OE2 GLU A  86      11.268  74.753  43.965  1.00  0.00          O   \nATOM    675  N   GLU A  87      11.635  69.391  45.200  1.00  0.00          N   \nATOM    676  CA  GLU A  87      10.895  68.307  45.838  1.00  0.00          C   \nATOM    677  C   GLU A  87      11.608  66.986  45.583  1.00  0.00          C   \nATOM    678  O   GLU A  87      11.815  66.188  46.500  1.00  0.00          O   \nATOM    679  CB  GLU A  87       9.467  68.231  45.287  1.00  0.00          C   \nATOM    680  CG  GLU A  87       8.586  69.408  45.670  1.00  0.00          C   \nATOM    681  CD  GLU A  87       7.224  69.360  45.001  1.00  0.00          C   \nATOM    682  OE1 GLU A  87       7.160  69.433  43.756  1.00  0.00          O   \nATOM    683  OE2 GLU A  87       6.216  69.247  45.724  1.00  0.00          O   \nATOM    684  N   ALA A  88      11.984  66.768  44.327  1.00  0.00          N   \nATOM    685  CA  ALA A  88      12.671  65.546  43.942  1.00  0.00          C   \nATOM    686  C   ALA A  88      13.969  65.443  44.718  1.00  0.00          C   \nATOM    687  O   ALA A  88      14.313  64.373  45.221  1.00  0.00          O   \nATOM    688  CB  ALA A  88      12.947  65.543  42.445  1.00  0.00          C   \nATOM    689  N   CYS A  89      14.685  66.560  44.826  1.00  0.00          N   \nATOM    690  CA  CYS A  89      15.946  66.572  45.554  1.00  0.00          C   \nATOM    691  C   CYS A  89      15.781  66.134  47.007  1.00  0.00          C   \nATOM    692  O   CYS A  89      16.594  65.376  47.517  1.00  0.00          O   \nATOM    693  CB  CYS A  89      16.580  67.964  45.516  1.00  0.00          C   \nATOM    694  SG  CYS A  89      17.293  68.419  43.931  1.00  0.00          S   \nATOM    695  N   LYS A  90      14.731  66.610  47.670  1.00  0.00          N   \nATOM    696  CA  LYS A  90      14.480  66.258  49.071  1.00  0.00          C   \nATOM    697  C   LYS A  90      14.223  64.754  49.297  1.00  0.00          C   \nATOM    698  O   LYS A  90      14.393  64.246  50.411  1.00  0.00          O   \nATOM    699  CB  LYS A  90      13.285  67.053  49.607  1.00  0.00          C   \nATOM    700  CG  LYS A  90      13.450  68.570  49.609  1.00  0.00          C   \nATOM    701  CD  LYS A  90      12.109  69.237  49.943  1.00  0.00          C   \nATOM    702  CE  LYS A  90      12.142  70.760  49.778  1.00  0.00          C   \nATOM    703  NZ  LYS A  90      10.763  71.347  49.866  1.00  0.00          N   \nATOM    704  N   LEU A  91      13.811  64.043  48.251  1.00  0.00          N   \nATOM    705  CA  LEU A  91      13.544  62.608  48.377  1.00  0.00          C   \nATOM    706  C   LEU A  91      14.802  61.753  48.246  1.00  0.00          C   \nATOM    707  O   LEU A  91      14.752  60.533  48.407  1.00  0.00          O   \nATOM    708  CB  LEU A  91      12.525  62.163  47.330  1.00  0.00          C   \nATOM    709  CG  LEU A  91      11.081  62.617  47.551  1.00  0.00          C   \nATOM    710  CD1 LEU A  91      10.261  62.285  46.321  1.00  0.00          C   \nATOM    711  CD2 LEU A  91      10.503  61.935  48.792  1.00  0.00          C   \nATOM    712  N   THR A  92      15.929  62.401  47.966  1.00  0.00          N   \nATOM    713  CA  THR A  92      17.204  61.710  47.800  1.00  0.00          C   \nATOM    714  C   THR A  92      17.913  61.348  49.111  1.00  0.00          C   \nATOM    715  O   THR A  92      18.076  62.193  49.993  1.00  0.00          O   \nATOM    716  CB  THR A  92      18.164  62.563  46.961  1.00  0.00          C   \nATOM    717  OG1 THR A  92      17.583  62.803  45.674  1.00  0.00          O   \nATOM    718  CG2 THR A  92      19.496  61.860  46.795  1.00  0.00          C   \nATOM    719  N   PRO A  93      18.338  60.078  49.252  1.00  0.00          N   \nATOM    720  CA  PRO A  93      19.036  59.604  50.454  1.00  0.00          C   \nATOM    721  C   PRO A  93      20.332  60.362  50.686  1.00  0.00          C   \nATOM    722  O   PRO A  93      21.134  60.532  49.768  1.00  0.00          O   \nATOM    723  CB  PRO A  93      19.280  58.134  50.147  1.00  0.00          C   \nATOM    724  CG  PRO A  93      18.044  57.769  49.379  1.00  0.00          C   \nATOM    725  CD  PRO A  93      17.928  58.937  48.414  1.00  0.00          C   \nATOM    726  N   PRO A  94      20.556  60.828  51.924  1.00  0.00          N   \nATOM    727  CA  PRO A  94      21.771  61.576  52.262  1.00  0.00          C   \nATOM    728  C   PRO A  94      23.055  60.889  51.811  1.00  0.00          C   \nATOM    729  O   PRO A  94      24.063  61.551  51.569  1.00  0.00          O   \nATOM    730  CB  PRO A  94      21.671  61.715  53.777  1.00  0.00          C   \nATOM    731  CG  PRO A  94      20.189  61.865  53.971  1.00  0.00          C   \nATOM    732  CD  PRO A  94      19.640  60.773  53.077  1.00  0.00          C   \nATOM    733  N   HIS A  95      23.018  59.568  51.684  1.00  0.00          N   \nATOM    734  CA  HIS A  95      24.203  58.835  51.254  1.00  0.00          C   \nATOM    735  C   HIS A  95      24.024  58.116  49.921  1.00  0.00          C   \nATOM    736  O   HIS A  95      24.719  57.148  49.631  1.00  0.00          O   \nATOM    737  CB  HIS A  95      24.634  57.844  52.341  1.00  0.00          C   \nATOM    738  CG  HIS A  95      25.070  58.506  53.611  1.00  0.00          C   \nATOM    739  ND1 HIS A  95      24.190  59.163  54.450  1.00  0.00          N   \nATOM    740  CD2 HIS A  95      26.295  58.657  54.167  1.00  0.00          C   \nATOM    741  CE1 HIS A  95      24.855  59.687  55.462  1.00  0.00          C   \nATOM    742  NE2 HIS A  95      26.137  59.395  55.314  1.00  0.00          N   \nATOM    743  N   SER A  96      23.095  58.600  49.108  1.00  0.00          N   \nATOM    744  CA  SER A  96      22.856  58.000  47.805  1.00  0.00          C   \nATOM    745  C   SER A  96      24.140  58.163  46.988  1.00  0.00          C   \nATOM    746  O   SER A  96      24.882  59.126  47.187  1.00  0.00          O   \nATOM    747  CB  SER A  96      21.693  58.705  47.111  1.00  0.00          C   \nATOM    748  OG  SER A  96      21.333  58.037  45.920  1.00  0.00          O   \nATOM    749  N   ALA A  97      24.404  57.219  46.087  1.00  0.00          N   \nATOM    750  CA  ALA A  97      25.606  57.260  45.253  1.00  0.00          C   \nATOM    751  C   ALA A  97      25.783  58.618  44.585  1.00  0.00          C   \nATOM    752  O   ALA A  97      24.832  59.189  44.039  1.00  0.00          O   \nATOM    753  CB  ALA A  97      25.549  56.164  44.189  1.00  0.00          C   \nATOM    754  N   LYS A  98      27.008  59.129  44.625  1.00  0.00          N   \nATOM    755  CA  LYS A  98      27.310  60.423  44.027  1.00  0.00          C   \nATOM    756  C   LYS A  98      27.234  60.359  42.499  1.00  0.00          C   \nATOM    757  O   LYS A  98      27.459  59.306  41.888  1.00  0.00          O   \nATOM    758  CB  LYS A  98      28.712  60.876  44.445  1.00  0.00          C   \nATOM    759  CG  LYS A  98      29.829  59.941  43.962  1.00  0.00          C   \nATOM    760  CD  LYS A  98      31.219  60.517  44.226  1.00  0.00          C   \nATOM    761  CE  LYS A  98      32.291  59.695  43.516  1.00  0.00          C   \nATOM    762  NZ  LYS A  98      33.645  60.338  43.545  1.00  0.00          N   \nATOM    763  N   SER A  99      26.914  61.495  41.890  1.00  0.00          N   \nATOM    764  CA  SER A  99      26.830  61.596  40.444  1.00  0.00          C   \nATOM    765  C   SER A  99      28.228  61.517  39.839  1.00  0.00          C   \nATOM    766  O   SER A  99      29.215  61.802  40.515  1.00  0.00          O   \nATOM    767  CB  SER A  99      26.207  62.930  40.055  1.00  0.00          C   \nATOM    768  OG  SER A  99      26.318  63.150  38.662  1.00  0.00          O   \nATOM    769  N   LYS A 100      28.306  61.133  38.568  1.00  0.00          N   \nATOM    770  CA  LYS A 100      29.585  61.060  37.872  1.00  0.00          C   \nATOM    771  C   LYS A 100      29.897  62.446  37.319  1.00  0.00          C   \nATOM    772  O   LYS A 100      30.972  62.677  36.758  1.00  0.00          O   \nATOM    773  CB  LYS A 100      29.517  60.062  36.709  1.00  0.00          C   \nATOM    774  CG  LYS A 100      29.410  58.598  37.132  1.00  0.00          C   \nATOM    775  CD  LYS A 100      29.344  57.654  35.915  1.00  0.00          C   \nATOM    776  CE  LYS A 100      29.276  56.188  36.344  1.00  0.00          C   \nATOM    777  NZ  LYS A 100      29.152  55.238  35.193  1.00  0.00          N   \nATOM    778  N   PHE A 101      28.965  63.377  37.499  1.00  0.00          N   \nATOM    779  CA  PHE A 101      29.143  64.716  36.962  1.00  0.00          C   \nATOM    780  C   PHE A 101      29.531  65.827  37.938  1.00  0.00          C   \nATOM    781  O   PHE A 101      29.184  66.992  37.739  1.00  0.00          O   \nATOM    782  CB  PHE A 101      27.889  65.081  36.161  1.00  0.00          C   \nATOM    783  CG  PHE A 101      27.614  64.123  35.033  1.00  0.00          C   \nATOM    784  CD1 PHE A 101      28.395  64.149  33.875  1.00  0.00          C   \nATOM    785  CD2 PHE A 101      26.639  63.133  35.158  1.00  0.00          C   \nATOM    786  CE1 PHE A 101      28.210  63.199  32.861  1.00  0.00          C   \nATOM    787  CE2 PHE A 101      26.447  62.181  34.154  1.00  0.00          C   \nATOM    788  CZ  PHE A 101      27.235  62.211  33.004  1.00  0.00          C   \nATOM    789  N   GLY A 102      30.250  65.464  38.996  1.00  0.00          N   \nATOM    790  CA  GLY A 102      30.720  66.465  39.943  1.00  0.00          C   \nATOM    791  C   GLY A 102      29.936  66.855  41.188  1.00  0.00          C   \nATOM    792  O   GLY A 102      30.180  67.930  41.742  1.00  0.00          O   \nATOM    793  N   TYR A 103      29.003  66.024  41.641  1.00  0.00          N   \nATOM    794  CA  TYR A 103      28.257  66.360  42.850  1.00  0.00          C   \nATOM    795  C   TYR A 103      27.675  65.120  43.505  1.00  0.00          C   \nATOM    796  O   TYR A 103      27.585  64.060  42.881  1.00  0.00          O   \nATOM    797  CB  TYR A 103      27.152  67.375  42.539  1.00  0.00          C   \nATOM    798  CG  TYR A 103      26.114  66.882  41.563  1.00  0.00          C   \nATOM    799  CD1 TYR A 103      24.955  66.238  42.008  1.00  0.00          C   \nATOM    800  CD2 TYR A 103      26.291  67.049  40.191  1.00  0.00          C   \nATOM    801  CE1 TYR A 103      24.002  65.779  41.104  1.00  0.00          C   \nATOM    802  CE2 TYR A 103      25.344  66.587  39.283  1.00  0.00          C   \nATOM    803  CZ  TYR A 103      24.206  65.955  39.747  1.00  0.00          C   \nATOM    804  OH  TYR A 103      23.281  65.487  38.851  1.00  0.00          O   \nATOM    805  N   GLY A 104      27.282  65.258  44.766  1.00  0.00          N   \nATOM    806  CA  GLY A 104      26.739  64.127  45.491  1.00  0.00          C   \nATOM    807  C   GLY A 104      25.333  64.323  46.011  1.00  0.00          C   \nATOM    808  O   GLY A 104      24.679  65.323  45.717  1.00  0.00          O   \nATOM    809  N   ALA A 105      24.872  63.344  46.782  1.00  0.00          N   \nATOM    810  CA  ALA A 105      23.540  63.378  47.360  1.00  0.00          C   \nATOM    811  C   ALA A 105      23.376  64.594  48.259  1.00  0.00          C   \nATOM    812  O   ALA A 105      22.302  65.190  48.330  1.00  0.00          O   \nATOM    813  CB  ALA A 105      23.288  62.107  48.151  1.00  0.00          C   \nATOM    814  N   LYS A 106      24.451  64.968  48.942  1.00  0.00          N   \nATOM    815  CA  LYS A 106      24.397  66.112  49.838  1.00  0.00          C   \nATOM    816  C   LYS A 106      24.158  67.395  49.058  1.00  0.00          C   \nATOM    817  O   LYS A 106      23.351  68.228  49.464  1.00  0.00          O   \nATOM    818  CB  LYS A 106      25.696  66.227  50.633  1.00  0.00          C   \nATOM    819  CG  LYS A 106      25.483  66.612  52.087  1.00  0.00          C   \nATOM    820  CD  LYS A 106      25.603  65.398  53.011  1.00  0.00          C   \nATOM    821  CE  LYS A 106      24.697  64.259  52.575  1.00  0.00          C   \nATOM    822  NZ  LYS A 106      23.281  64.702  52.501  1.00  0.00          N   \nATOM    823  N   ASP A 107      24.864  67.551  47.942  1.00  0.00          N   \nATOM    824  CA  ASP A 107      24.720  68.735  47.102  1.00  0.00          C   \nATOM    825  C   ASP A 107      23.299  68.838  46.568  1.00  0.00          C   \nATOM    826  O   ASP A 107      22.774  69.932  46.371  1.00  0.00          O   \nATOM    827  CB  ASP A 107      25.699  68.679  45.926  1.00  0.00          C   \nATOM    828  CG  ASP A 107      27.143  68.682  46.374  1.00  0.00          C   \nATOM    829  OD1 ASP A 107      27.518  69.577  47.162  1.00  0.00          O   \nATOM    830  OD2 ASP A 107      27.902  67.797  45.936  1.00  0.00          O   \nATOM    831  N   VAL A 108      22.688  67.683  46.331  1.00  0.00          N   \nATOM    832  CA  VAL A 108      21.329  67.610  45.815  1.00  0.00          C   \nATOM    833  C   VAL A 108      20.329  68.027  46.892  1.00  0.00          C   \nATOM    834  O   VAL A 108      19.439  68.847  46.652  1.00  0.00          O   \nATOM    835  CB  VAL A 108      21.020  66.170  45.326  1.00  0.00          C   \nATOM    836  CG1 VAL A 108      19.533  66.005  45.031  1.00  0.00          C   \nATOM    837  CG2 VAL A 108      21.832  65.881  44.067  1.00  0.00          C   \nATOM    838  N   ARG A 109      20.488  67.465  48.083  1.00  0.00          N   \nATOM    839  CA  ARG A 109      19.596  67.781  49.184  1.00  0.00          C   \nATOM    840  C   ARG A 109      19.695  69.255  49.548  1.00  0.00          C   \nATOM    841  O   ARG A 109      18.732  69.835  50.041  1.00  0.00          O   \nATOM    842  CB  ARG A 109      19.923  66.912  50.399  1.00  0.00          C   \nATOM    843  CG  ARG A 109      19.654  65.438  50.179  1.00  0.00          C   \nATOM    844  CD  ARG A 109      19.957  64.636  51.421  1.00  0.00          C   \nATOM    845  NE  ARG A 109      19.191  65.107  52.571  1.00  0.00          N   \nATOM    846  CZ  ARG A 109      17.873  64.997  52.698  1.00  0.00          C   \nATOM    847  NH1 ARG A 109      17.150  64.422  51.744  1.00  0.00          N   \nATOM    848  NH2 ARG A 109      17.274  65.475  53.780  1.00  0.00          N   \nATOM    849  N   ASN A 110      20.853  69.859  49.290  1.00  0.00          N   \nATOM    850  CA  ASN A 110      21.072  71.273  49.590  1.00  0.00          C   \nATOM    851  C   ASN A 110      20.656  72.178  48.436  1.00  0.00          C   \nATOM    852  O   ASN A 110      20.773  73.405  48.519  1.00  0.00          O   \nATOM    853  CB  ASN A 110      22.549  71.539  49.903  1.00  0.00          C   \nATOM    854  CG  ASN A 110      23.014  70.850  51.168  1.00  0.00          C   \nATOM    855  OD1 ASN A 110      22.251  70.705  52.127  1.00  0.00          O   \nATOM    856  ND2 ASN A 110      24.281  70.436  51.188  1.00  0.00          N   \nATOM    857  N   LEU A 111      20.175  71.572  47.360  1.00  0.00          N   \nATOM    858  CA  LEU A 111      19.767  72.327  46.188  1.00  0.00          C   \nATOM    859  C   LEU A 111      20.949  73.136  45.673  1.00  0.00          C   \nATOM    860  O   LEU A 111      20.777  74.261  45.205  1.00  0.00          O   \nATOM    861  CB  LEU A 111      18.604  73.266  46.528  1.00  0.00          C   \nATOM    862  CG  LEU A 111      17.363  72.659  47.193  1.00  0.00          C   \nATOM    863  CD1 LEU A 111      16.304  73.735  47.344  1.00  0.00          C   \nATOM    864  CD2 LEU A 111      16.817  71.506  46.360  1.00  0.00          C   \nATOM    865  N   SER A 112      22.151  72.567  45.761  1.00  0.00          N   \nATOM    866  CA  SER A 112      23.346  73.262  45.290  1.00  0.00          C   \nATOM    867  C   SER A 112      23.146  73.613  43.822  1.00  0.00          C   \nATOM    868  O   SER A 112      22.587  72.825  43.060  1.00  0.00          O   \nATOM    869  CB  SER A 112      24.588  72.380  45.447  1.00  0.00          C   \nATOM    870  OG  SER A 112      24.777  71.542  44.319  1.00  0.00          O   \nATOM    871  N   SER A 113      23.611  74.793  43.424  1.00  0.00          N   \nATOM    872  CA  SER A 113      23.450  75.247  42.047  1.00  0.00          C   \nATOM    873  C   SER A 113      24.017  74.262  41.036  1.00  0.00          C   \nATOM    874  O   SER A 113      23.429  74.041  39.985  1.00  0.00          O   \nATOM    875  CB  SER A 113      24.108  76.620  41.850  1.00  0.00          C   \nATOM    876  OG  SER A 113      25.519  76.511  41.793  1.00  0.00          O   \nATOM    877  N   LYS A 114      25.158  73.669  41.358  1.00  0.00          N   \nATOM    878  CA  LYS A 114      25.784  72.712  40.457  1.00  0.00          C   \nATOM    879  C   LYS A 114      24.855  71.517  40.236  1.00  0.00          C   \nATOM    880  O   LYS A 114      24.596  71.129  39.101  1.00  0.00          O   \nATOM    881  CB  LYS A 114      27.114  72.236  41.036  1.00  0.00          C   \nATOM    882  CG  LYS A 114      27.945  71.387  40.080  1.00  0.00          C   \nATOM    883  CD  LYS A 114      29.228  70.899  40.756  1.00  0.00          C   \nATOM    884  CE  LYS A 114      30.096  70.104  39.795  1.00  0.00          C   \nATOM    885  NZ  LYS A 114      30.396  70.906  38.580  1.00  0.00          N   \nATOM    886  N   ALA A 115      24.356  70.942  41.325  1.00  0.00          N   \nATOM    887  CA  ALA A 115      23.453  69.796  41.241  1.00  0.00          C   \nATOM    888  C   ALA A 115      22.179  70.158  40.482  1.00  0.00          C   \nATOM    889  O   ALA A 115      21.812  69.491  39.516  1.00  0.00          O   \nATOM    890  CB  ALA A 115      23.107  69.294  42.647  1.00  0.00          C   \nATOM    891  N   VAL A 116      21.510  71.220  40.923  1.00  0.00          N   \nATOM    892  CA  VAL A 116      20.278  71.678  40.294  1.00  0.00          C   \nATOM    893  C   VAL A 116      20.458  71.979  38.812  1.00  0.00          C   \nATOM    894  O   VAL A 116      19.571  71.698  38.000  1.00  0.00          O   \nATOM    895  CB  VAL A 116      19.741  72.941  40.991  1.00  0.00          C   \nATOM    896  CG1 VAL A 116      18.520  73.471  40.253  1.00  0.00          C   \nATOM    897  CG2 VAL A 116      19.396  72.622  42.432  1.00  0.00          C   \nATOM    898  N   ASN A 117      21.594  72.572  38.460  1.00  0.00          N   \nATOM    899  CA  ASN A 117      21.875  72.892  37.068  1.00  0.00          C   \nATOM    900  C   ASN A 117      22.017  71.621  36.231  1.00  0.00          C   \nATOM    901  O   ASN A 117      21.465  71.530  35.138  1.00  0.00          O   \nATOM    902  CB  ASN A 117      23.146  73.737  36.961  1.00  0.00          C   \nATOM    903  CG  ASN A 117      22.895  75.199  37.289  1.00  0.00          C   \nATOM    904  OD1 ASN A 117      22.010  75.823  36.707  1.00  0.00          O   \nATOM    905  ND2 ASN A 117      23.671  75.752  38.219  1.00  0.00          N   \nATOM    906  N   HIS A 118      22.753  70.638  36.742  1.00  0.00          N   \nATOM    907  CA  HIS A 118      22.922  69.393  36.001  1.00  0.00          C   \nATOM    908  C   HIS A 118      21.594  68.654  35.844  1.00  0.00          C   \nATOM    909  O   HIS A 118      21.272  68.150  34.767  1.00  0.00          O   \nATOM    910  CB  HIS A 118      23.930  68.486  36.694  1.00  0.00          C   \nATOM    911  CG  HIS A 118      24.155  67.191  35.977  1.00  0.00          C   \nATOM    912  ND1 HIS A 118      23.668  65.988  36.440  1.00  0.00          N   \nATOM    913  CD2 HIS A 118      24.781  66.920  34.805  1.00  0.00          C   \nATOM    914  CE1 HIS A 118      23.985  65.029  35.586  1.00  0.00          C   \nATOM    915  NE2 HIS A 118      24.658  65.567  34.587  1.00  0.00          N   \nATOM    916  N   ILE A 119      20.827  68.591  36.925  1.00  0.00          N   \nATOM    917  CA  ILE A 119      19.529  67.926  36.900  1.00  0.00          C   \nATOM    918  C   ILE A 119      18.646  68.543  35.812  1.00  0.00          C   \nATOM    919  O   ILE A 119      17.949  67.832  35.073  1.00  0.00          O   \nATOM    920  CB  ILE A 119      18.846  68.038  38.287  1.00  0.00          C   \nATOM    921  CG1 ILE A 119      19.628  67.196  39.302  1.00  0.00          C   \nATOM    922  CG2 ILE A 119      17.395  67.588  38.209  1.00  0.00          C   \nATOM    923  CD1 ILE A 119      19.240  67.424  40.741  1.00  0.00          C   \nATOM    924  N   HIS A 120      18.688  69.870  35.705  1.00  0.00          N   \nATOM    925  CA  HIS A 120      17.894  70.574  34.703  1.00  0.00          C   \nATOM    926  C   HIS A 120      18.327  70.238  33.286  1.00  0.00          C   \nATOM    927  O   HIS A 120      17.493  70.194  32.375  1.00  0.00          O   \nATOM    928  CB  HIS A 120      17.973  72.091  34.926  1.00  0.00          C   \nATOM    929  CG  HIS A 120      16.943  72.616  35.879  1.00  0.00          C   \nATOM    930  ND1 HIS A 120      17.187  73.669  36.736  1.00  0.00          N   \nATOM    931  CD2 HIS A 120      15.653  72.257  36.086  1.00  0.00          C   \nATOM    932  CE1 HIS A 120      16.094  73.935  37.429  1.00  0.00          C   \nATOM    933  NE2 HIS A 120      15.148  73.092  37.053  1.00  0.00          N   \nATOM    934  N   SER A 121      19.623  70.005  33.087  1.00  0.00          N   \nATOM    935  CA  SER A 121      20.113  69.670  31.754  1.00  0.00          C   \nATOM    936  C   SER A 121      19.740  68.221  31.427  1.00  0.00          C   \nATOM    937  O   SER A 121      19.426  67.890  30.285  1.00  0.00          O   \nATOM    938  CB  SER A 121      21.630  69.869  31.664  1.00  0.00          C   \nATOM    939  OG  SER A 121      22.302  69.012  32.565  1.00  0.00          O   \nATOM    940  N   VAL A 122      19.761  67.358  32.434  1.00  0.00          N   \nATOM    941  CA  VAL A 122      19.382  65.965  32.211  1.00  0.00          C   \nATOM    942  C   VAL A 122      17.902  65.925  31.791  1.00  0.00          C   \nATOM    943  O   VAL A 122      17.535  65.239  30.834  1.00  0.00          O   \nATOM    944  CB  VAL A 122      19.574  65.135  33.486  1.00  0.00          C   \nATOM    945  CG1 VAL A 122      19.056  63.719  33.274  1.00  0.00          C   \nATOM    946  CG2 VAL A 122      21.038  65.111  33.859  1.00  0.00          C   \nATOM    947  N   TRP A 123      17.064  66.676  32.504  1.00  0.00          N   \nATOM    948  CA  TRP A 123      15.629  66.740  32.210  1.00  0.00          C   \nATOM    949  C   TRP A 123      15.389  67.226  30.779  1.00  0.00          C   \nATOM    950  O   TRP A 123      14.597  66.644  30.023  1.00  0.00          O   \nATOM    951  CB  TRP A 123      14.920  67.668  33.215  1.00  0.00          C   \nATOM    952  CG  TRP A 123      13.423  67.719  33.036  1.00  0.00          C   \nATOM    953  CD1 TRP A 123      12.711  68.657  32.346  1.00  0.00          C   \nATOM    954  CD2 TRP A 123      12.466  66.756  33.508  1.00  0.00          C   \nATOM    955  NE1 TRP A 123      11.375  68.338  32.355  1.00  0.00          N   \nATOM    956  CE2 TRP A 123      11.196  67.180  33.061  1.00  0.00          C   \nATOM    957  CE3 TRP A 123      12.562  65.578  34.264  1.00  0.00          C   \nATOM    958  CZ2 TRP A 123      10.022  66.463  33.342  1.00  0.00          C   \nATOM    959  CZ3 TRP A 123      11.399  64.866  34.543  1.00  0.00          C   \nATOM    960  CH2 TRP A 123      10.143  65.314  34.083  1.00  0.00          C   \nATOM    961  N   LYS A 124      16.084  68.293  30.403  1.00  0.00          N   \nATOM    962  CA  LYS A 124      15.947  68.837  29.061  1.00  0.00          C   \nATOM    963  C   LYS A 124      16.341  67.771  28.033  1.00  0.00          C   \nATOM    964  O   LYS A 124      15.660  67.596  27.024  1.00  0.00          O   \nATOM    965  CB  LYS A 124      16.826  70.079  28.915  1.00  0.00          C   \nATOM    966  CG  LYS A 124      16.670  70.791  27.593  1.00  0.00          C   \nATOM    967  CD  LYS A 124      17.566  72.015  27.534  1.00  0.00          C   \nATOM    968  CE  LYS A 124      17.425  72.737  26.205  1.00  0.00          C   \nATOM    969  NZ  LYS A 124      18.214  74.000  26.160  1.00  0.00          N   \nATOM    970  N   ASP A 125      17.435  67.052  28.285  1.00  0.00          N   \nATOM    971  CA  ASP A 125      17.856  66.000  27.359  1.00  0.00          C   \nATOM    972  C   ASP A 125      16.785  64.912  27.259  1.00  0.00          C   \nATOM    973  O   ASP A 125      16.574  64.342  26.194  1.00  0.00          O   \nATOM    974  CB  ASP A 125      19.167  65.359  27.801  1.00  0.00          C   \nATOM    975  CG  ASP A 125      19.679  64.341  26.795  1.00  0.00          C   \nATOM    976  OD1 ASP A 125      20.084  64.753  25.693  1.00  0.00          O   \nATOM    977  OD2 ASP A 125      19.666  63.126  27.095  1.00  0.00          O   \nATOM    978  N   LEU A 126      16.127  64.602  28.374  1.00  0.00          N   \nATOM    979  CA  LEU A 126      15.080  63.590  28.340  1.00  0.00          C   \nATOM    980  C   LEU A 126      13.952  64.025  27.417  1.00  0.00          C   \nATOM    981  O   LEU A 126      13.436  63.229  26.631  1.00  0.00          O   \nATOM    982  CB  LEU A 126      14.524  63.341  29.741  1.00  0.00          C   \nATOM    983  CG  LEU A 126      15.387  62.456  30.633  1.00  0.00          C   \nATOM    984  CD1 LEU A 126      14.875  62.507  32.062  1.00  0.00          C   \nATOM    985  CD2 LEU A 126      15.378  61.031  30.069  1.00  0.00          C   \nATOM    986  N   LEU A 127      13.574  65.297  27.490  1.00  0.00          N   \nATOM    987  CA  LEU A 127      12.483  65.777  26.649  1.00  0.00          C   \nATOM    988  C   LEU A 127      12.867  65.864  25.172  1.00  0.00          C   \nATOM    989  O   LEU A 127      12.034  65.641  24.300  1.00  0.00          O   \nATOM    990  CB  LEU A 127      11.988  67.147  27.137  1.00  0.00          C   \nATOM    991  CG  LEU A 127      11.459  67.257  28.577  1.00  0.00          C   \nATOM    992  CD1 LEU A 127      11.206  68.727  28.920  1.00  0.00          C   \nATOM    993  CD2 LEU A 127      10.180  66.429  28.738  1.00  0.00          C   \nATOM    994  N   GLU A 128      14.131  66.152  24.889  1.00  0.00          N   \nATOM    995  CA  GLU A 128      14.591  66.302  23.508  1.00  0.00          C   \nATOM    996  C   GLU A 128      15.065  65.025  22.821  1.00  0.00          C   \nATOM    997  O   GLU A 128      15.045  64.932  21.597  1.00  0.00          O   \nATOM    998  CB  GLU A 128      15.713  67.344  23.465  1.00  0.00          C   \nATOM    999  CG  GLU A 128      15.300  68.703  24.037  1.00  0.00          C   \nATOM   1000  CD  GLU A 128      16.471  69.660  24.224  1.00  0.00          C   \nATOM   1001  OE1 GLU A 128      16.217  70.811  24.641  1.00  0.00          O   \nATOM   1002  OE2 GLU A 128      17.635  69.264  23.961  1.00  0.00          O   \nATOM   1003  N   ASP A 129      15.477  64.040  23.612  1.00  0.00          N   \nATOM   1004  CA  ASP A 129      15.991  62.777  23.082  1.00  0.00          C   \nATOM   1005  C   ASP A 129      15.193  61.607  23.679  1.00  0.00          C   \nATOM   1006  O   ASP A 129      15.195  61.408  24.883  1.00  0.00          O   \nATOM   1007  CB  ASP A 129      17.480  62.673  23.452  1.00  0.00          C   \nATOM   1008  CG  ASP A 129      18.132  61.386  22.965  1.00  0.00          C   \nATOM   1009  OD1 ASP A 129      17.474  60.329  22.994  1.00  0.00          O   \nATOM   1010  OD2 ASP A 129      19.320  61.433  22.575  1.00  0.00          O   \nATOM   1011  N   THR A 130      14.514  60.835  22.837  1.00  0.00          N   \nATOM   1012  CA  THR A 130      13.730  59.708  23.336  1.00  0.00          C   \nATOM   1013  C   THR A 130      14.242  58.366  22.819  1.00  0.00          C   \nATOM   1014  O   THR A 130      13.514  57.378  22.850  1.00  0.00          O   \nATOM   1015  CB  THR A 130      12.227  59.853  22.954  1.00  0.00          C   \nATOM   1016  OG1 THR A 130      12.100  59.923  21.533  1.00  0.00          O   \nATOM   1017  CG2 THR A 130      11.637  61.127  23.558  1.00  0.00          C   \nATOM   1018  N   VAL A 131      15.496  58.321  22.367  1.00  0.00          N   \nATOM   1019  CA  VAL A 131      16.061  57.077  21.823  1.00  0.00          C   \nATOM   1020  C   VAL A 131      17.425  56.642  22.353  1.00  0.00          C   \nATOM   1021  O   VAL A 131      17.716  55.450  22.398  1.00  0.00          O   \nATOM   1022  CB  VAL A 131      16.203  57.132  20.266  1.00  0.00          C   \nATOM   1023  CG1 VAL A 131      14.837  57.303  19.610  1.00  0.00          C   \nATOM   1024  CG2 VAL A 131      17.145  58.263  19.864  1.00  0.00          C   \nATOM   1025  N   THR A 132      18.266  57.590  22.742  1.00  0.00          N   \nATOM   1026  CA  THR A 132      19.600  57.242  23.208  1.00  0.00          C   \nATOM   1027  C   THR A 132      19.655  56.496  24.528  1.00  0.00          C   \nATOM   1028  O   THR A 132      19.257  57.016  25.562  1.00  0.00          O   \nATOM   1029  CB  THR A 132      20.468  58.495  23.306  1.00  0.00          C   \nATOM   1030  OG1 THR A 132      20.387  59.207  22.067  1.00  0.00          O   \nATOM   1031  CG2 THR A 132      21.937  58.117  23.563  1.00  0.00          C   \nATOM   1032  N   PRO A 133      20.157  55.252  24.508  1.00  0.00          N   \nATOM   1033  CA  PRO A 133      20.233  54.501  25.759  1.00  0.00          C   \nATOM   1034  C   PRO A 133      20.987  55.296  26.814  1.00  0.00          C   \nATOM   1035  O   PRO A 133      21.981  55.974  26.519  1.00  0.00          O   \nATOM   1036  CB  PRO A 133      20.978  53.229  25.356  1.00  0.00          C   \nATOM   1037  CG  PRO A 133      20.506  53.017  23.946  1.00  0.00          C   \nATOM   1038  CD  PRO A 133      20.609  54.424  23.374  1.00  0.00          C   \nATOM   1039  N   ILE A 134      20.493  55.217  28.043  1.00  0.00          N   \nATOM   1040  CA  ILE A 134      21.105  55.900  29.164  1.00  0.00          C   \nATOM   1041  C   ILE A 134      22.032  54.939  29.884  1.00  0.00          C   \nATOM   1042  O   ILE A 134      21.699  53.769  30.085  1.00  0.00          O   \nATOM   1043  CB  ILE A 134      20.013  56.441  30.110  1.00  0.00          C   \nATOM   1044  CG1 ILE A 134      19.388  57.692  29.469  1.00  0.00          C   \nATOM   1045  CG2 ILE A 134      20.585  56.705  31.502  1.00  0.00          C   \nATOM   1046  CD1 ILE A 134      18.234  58.292  30.248  1.00  0.00          C   \nATOM   1047  N   ASP A 135      23.211  55.424  30.256  1.00  0.00          N   \nATOM   1048  CA  ASP A 135      24.165  54.566  30.929  1.00  0.00          C   \nATOM   1049  C   ASP A 135      23.725  54.135  32.326  1.00  0.00          C   \nATOM   1050  O   ASP A 135      22.947  54.828  33.012  1.00  0.00          O   \nATOM   1051  CB  ASP A 135      25.534  55.249  31.006  1.00  0.00          C   \nATOM   1052  CG  ASP A 135      26.604  54.328  31.552  1.00  0.00          C   \nATOM   1053  OD1 ASP A 135      26.815  53.239  30.966  1.00  0.00          O   \nATOM   1054  OD2 ASP A 135      27.224  54.690  32.574  1.00  0.00          O   \nATOM   1055  N   THR A 136      24.204  52.962  32.733  1.00  0.00          N   \nATOM   1056  CA  THR A 136      23.894  52.454  34.057  1.00  0.00          C   \nATOM   1057  C   THR A 136      25.134  51.809  34.632  1.00  0.00          C   \nATOM   1058  O   THR A 136      26.022  51.362  33.903  1.00  0.00          O   \nATOM   1059  CB  THR A 136      22.794  51.376  34.046  1.00  0.00          C   \nATOM   1060  OG1 THR A 136      23.236  50.257  33.265  1.00  0.00          O   \nATOM   1061  CG2 THR A 136      21.492  51.936  33.481  1.00  0.00          C   \nATOM   1062  N   THR A 137      25.182  51.769  35.953  1.00  0.00          N   \nATOM   1063  CA  THR A 137      26.282  51.144  36.644  1.00  0.00          C   \nATOM   1064  C   THR A 137      25.750  49.810  37.156  1.00  0.00          C   \nATOM   1065  O   THR A 137      24.611  49.726  37.616  1.00  0.00          O   \nATOM   1066  CB  THR A 137      26.720  51.975  37.848  1.00  0.00          C   \nATOM   1067  OG1 THR A 137      27.100  53.288  37.406  1.00  0.00          O   \nATOM   1068  CG2 THR A 137      27.889  51.285  38.570  1.00  0.00          C   \nATOM   1069  N   ILE A 138      26.555  48.761  37.036  1.00  0.00          N   \nATOM   1070  CA  ILE A 138      26.162  47.470  37.549  1.00  0.00          C   \nATOM   1071  C   ILE A 138      27.131  47.149  38.692  1.00  0.00          C   \nATOM   1072  O   ILE A 138      28.337  47.278  38.540  1.00  0.00          O   \nATOM   1073  CB  ILE A 138      26.214  46.366  36.461  1.00  0.00          C   \nATOM   1074  CG1 ILE A 138      25.774  45.028  37.079  1.00  0.00          C   \nATOM   1075  CG2 ILE A 138      27.631  46.275  35.852  1.00  0.00          C   \nATOM   1076  CD1 ILE A 138      25.410  43.952  36.074  1.00  0.00          C   \nATOM   1077  N   MET A 139      26.586  46.771  39.845  1.00  0.00          N   \nATOM   1078  CA  MET A 139      27.389  46.426  41.016  1.00  0.00          C   \nATOM   1079  C   MET A 139      26.816  45.159  41.634  1.00  0.00          C   \nATOM   1080  O   MET A 139      25.640  44.828  41.433  1.00  0.00          O   \nATOM   1081  CB  MET A 139      27.302  47.522  42.076  1.00  0.00          C   \nATOM   1082  CG  MET A 139      27.791  48.892  41.661  1.00  0.00          C   \nATOM   1083  SD  MET A 139      29.573  49.053  41.777  1.00  0.00          S   \nATOM   1084  CE  MET A 139      29.810  49.242  43.552  1.00  0.00          C   \nATOM   1085  N   ALA A 140      27.650  44.461  42.396  1.00  0.00          N   \nATOM   1086  CA  ALA A 140      27.235  43.261  43.098  1.00  0.00          C   \nATOM   1087  C   ALA A 140      26.877  43.755  44.497  1.00  0.00          C   \nATOM   1088  O   ALA A 140      27.677  44.446  45.124  1.00  0.00          O   \nATOM   1089  CB  ALA A 140      28.387  42.273  43.170  1.00  0.00          C   \nATOM   1090  N   LYS A 141      25.683  43.423  44.983  1.00  0.00          N   \nATOM   1091  CA  LYS A 141      25.259  43.850  46.323  1.00  0.00          C   \nATOM   1092  C   LYS A 141      25.989  43.100  47.428  1.00  0.00          C   \nATOM   1093  O   LYS A 141      26.299  41.920  47.289  1.00  0.00          O   \nATOM   1094  CB  LYS A 141      23.755  43.629  46.526  1.00  0.00          C   \nATOM   1095  CG  LYS A 141      22.849  44.435  45.612  1.00  0.00          C   \nATOM   1096  CD  LYS A 141      21.404  44.381  46.096  1.00  0.00          C   \nATOM   1097  CE  LYS A 141      20.512  45.280  45.255  1.00  0.00          C   \nATOM   1098  NZ  LYS A 141      19.141  45.411  45.827  1.00  0.00          N   \nATOM   1099  N   ASN A 142      26.266  43.788  48.526  1.00  0.00          N   \nATOM   1100  CA  ASN A 142      26.928  43.154  49.662  1.00  0.00          C   \nATOM   1101  C   ASN A 142      25.801  42.940  50.658  1.00  0.00          C   \nATOM   1102  O   ASN A 142      25.366  43.884  51.302  1.00  0.00          O   \nATOM   1103  CB  ASN A 142      27.979  44.079  50.281  1.00  0.00          C   \nATOM   1104  CG  ASN A 142      29.037  44.516  49.287  1.00  0.00          C   \nATOM   1105  OD1 ASN A 142      29.281  45.716  49.119  1.00  0.00          O   \nATOM   1106  ND2 ASN A 142      29.674  43.552  48.625  1.00  0.00          N   \nATOM   1107  N   GLU A 143      25.319  41.705  50.766  1.00  0.00          N   \nATOM   1108  CA  GLU A 143      24.223  41.395  51.675  1.00  0.00          C   \nATOM   1109  C   GLU A 143      24.574  40.224  52.564  1.00  0.00          C   \nATOM   1110  O   GLU A 143      25.272  39.297  52.140  1.00  0.00          O   \nATOM   1111  CB  GLU A 143      22.956  41.082  50.869  1.00  0.00          C   \nATOM   1112  CG  GLU A 143      22.493  42.256  50.031  1.00  0.00          C   \nATOM   1113  CD  GLU A 143      21.208  41.976  49.283  1.00  0.00          C   \nATOM   1114  OE1 GLU A 143      21.217  41.113  48.376  1.00  0.00          O   \nATOM   1115  OE2 GLU A 143      20.189  42.625  49.608  1.00  0.00          O   \nATOM   1116  N   VAL A 144      24.062  40.259  53.790  1.00  0.00          N   \nATOM   1117  CA  VAL A 144      24.339  39.220  54.782  1.00  0.00          C   \nATOM   1118  C   VAL A 144      23.262  38.120  54.894  1.00  0.00          C   \nATOM   1119  O   VAL A 144      22.057  38.400  54.935  1.00  0.00          O   \nATOM   1120  CB  VAL A 144      24.577  39.893  56.168  1.00  0.00          C   \nATOM   1121  CG1 VAL A 144      24.795  38.844  57.263  1.00  0.00          C   \nATOM   1122  CG2 VAL A 144      25.793  40.808  56.075  1.00  0.00          C   \nATOM   1123  N   PHE A 145      23.719  36.868  54.947  1.00  0.00          N   \nATOM   1124  CA  PHE A 145      22.837  35.710  55.066  1.00  0.00          C   \nATOM   1125  C   PHE A 145      23.466  34.655  55.969  1.00  0.00          C   \nATOM   1126  O   PHE A 145      24.610  34.787  56.401  1.00  0.00          O   \nATOM   1127  CB  PHE A 145      22.602  35.070  53.693  1.00  0.00          C   \nATOM   1128  CG  PHE A 145      21.961  35.989  52.693  1.00  0.00          C   \nATOM   1129  CD1 PHE A 145      20.588  36.211  52.716  1.00  0.00          C   \nATOM   1130  CD2 PHE A 145      22.738  36.638  51.731  1.00  0.00          C   \nATOM   1131  CE1 PHE A 145      19.980  37.073  51.789  1.00  0.00          C   \nATOM   1132  CE2 PHE A 145      22.152  37.501  50.796  1.00  0.00          C   \nATOM   1133  CZ  PHE A 145      20.765  37.720  50.825  1.00  0.00          C   \nATOM   1134  N   CYS A 146      22.703  33.605  56.249  1.00  0.00          N   \nATOM   1135  CA  CYS A 146      23.201  32.488  57.034  1.00  0.00          C   \nATOM   1136  C   CYS A 146      23.319  31.375  55.993  1.00  0.00          C   \nATOM   1137  O   CYS A 146      22.471  31.272  55.107  1.00  0.00          O   \nATOM   1138  CB  CYS A 146      22.210  32.087  58.129  1.00  0.00          C   \nATOM   1139  SG  CYS A 146      22.676  30.525  58.933  1.00  0.00          S   \nATOM   1140  N   VAL A 147      24.357  30.550  56.087  1.00  0.00          N   \nATOM   1141  CA  VAL A 147      24.567  29.493  55.100  1.00  0.00          C   \nATOM   1142  C   VAL A 147      23.385  28.567  54.874  1.00  0.00          C   \nATOM   1143  O   VAL A 147      22.555  28.364  55.765  1.00  0.00          O   \nATOM   1144  CB  VAL A 147      25.809  28.626  55.437  1.00  0.00          C   \nATOM   1145  CG1 VAL A 147      27.073  29.488  55.395  1.00  0.00          C   \nATOM   1146  CG2 VAL A 147      25.641  27.980  56.803  1.00  0.00          C   \nATOM   1147  N   GLN A 148      23.337  28.027  53.656  1.00  0.00          N   \nATOM   1148  CA  GLN A 148      22.310  27.100  53.179  1.00  0.00          C   \nATOM   1149  C   GLN A 148      21.378  26.547  54.248  1.00  0.00          C   \nATOM   1150  O   GLN A 148      20.148  26.699  54.090  1.00  0.00          O   \nATOM   1151  CB  GLN A 148      22.979  25.932  52.449  1.00  0.00          C   \nATOM   1152  CG  GLN A 148      22.044  24.776  52.162  1.00  0.00          C   \nATOM   1153  CD  GLN A 148      22.741  23.620  51.474  1.00  0.00          C   \nATOM   1154  OE1 GLN A 148      23.712  23.062  51.995  1.00  0.00          O   \nATOM   1155  NE2 GLN A 148      22.246  23.251  50.296  1.00  0.00          N   \nATOM   1156  N   ARG A 154      25.589  28.807  50.280  1.00  0.00          N   \nATOM   1157  CA  ARG A 154      25.887  30.246  50.010  1.00  0.00          C   \nATOM   1158  C   ARG A 154      25.306  30.714  48.685  1.00  0.00          C   \nATOM   1159  O   ARG A 154      25.536  30.099  47.644  1.00  0.00          O   \nATOM   1160  CB  ARG A 154      27.398  30.490  50.000  1.00  0.00          C   \nATOM   1161  CG  ARG A 154      28.050  30.337  51.359  1.00  0.00          C   \nATOM   1162  CD  ARG A 154      29.499  30.801  51.343  1.00  0.00          C   \nATOM   1163  NE  ARG A 154      29.633  32.167  50.831  1.00  0.00          N   \nATOM   1164  CZ  ARG A 154      30.696  32.942  51.034  1.00  0.00          C   \nATOM   1165  NH1 ARG A 154      31.721  32.486  51.743  1.00  0.00          N   \nATOM   1166  NH2 ARG A 154      30.736  34.175  50.531  1.00  0.00          N   \nATOM   1167  N   LYS A 155      24.550  31.805  48.716  1.00  0.00          N   \nATOM   1168  CA  LYS A 155      23.979  32.326  47.490  1.00  0.00          C   \nATOM   1169  C   LYS A 155      24.888  33.415  46.927  1.00  0.00          C   \nATOM   1170  O   LYS A 155      25.577  34.111  47.676  1.00  0.00          O   \nATOM   1171  CB  LYS A 155      22.574  32.876  47.750  1.00  0.00          C   \nATOM   1172  CG  LYS A 155      22.482  33.952  48.814  1.00  0.00          C   \nATOM   1173  CD  LYS A 155      21.027  34.238  49.179  1.00  0.00          C   \nATOM   1174  CE  LYS A 155      20.345  32.998  49.728  1.00  0.00          C   \nATOM   1175  NZ  LYS A 155      18.899  33.233  50.007  1.00  0.00          N   \nATOM   1176  N   PRO A 156      24.926  33.557  45.595  1.00  0.00          N   \nATOM   1177  CA  PRO A 156      25.770  34.580  44.974  1.00  0.00          C   \nATOM   1178  C   PRO A 156      25.151  35.959  45.177  1.00  0.00          C   \nATOM   1179  O   PRO A 156      23.963  36.066  45.427  1.00  0.00          O   \nATOM   1180  CB  PRO A 156      25.777  34.171  43.508  1.00  0.00          C   \nATOM   1181  CG  PRO A 156      24.402  33.632  43.323  1.00  0.00          C   \nATOM   1182  CD  PRO A 156      24.224  32.767  44.569  1.00  0.00          C   \nATOM   1183  N   ALA A 157      25.959  37.006  45.061  1.00  0.00          N   \nATOM   1184  CA  ALA A 157      25.461  38.362  45.234  1.00  0.00          C   \nATOM   1185  C   ALA A 157      24.401  38.718  44.201  1.00  0.00          C   \nATOM   1186  O   ALA A 157      24.465  38.276  43.059  1.00  0.00          O   \nATOM   1187  CB  ALA A 157      26.605  39.340  45.112  1.00  0.00          C   \nATOM   1188  N   ARG A 158      23.426  39.527  44.600  1.00  0.00          N   \nATOM   1189  CA  ARG A 158      22.427  39.980  43.645  1.00  0.00          C   \nATOM   1190  C   ARG A 158      23.086  41.142  42.913  1.00  0.00          C   \nATOM   1191  O   ARG A 158      24.070  41.710  43.399  1.00  0.00          O   \nATOM   1192  CB  ARG A 158      21.157  40.460  44.352  1.00  0.00          C   \nATOM   1193  CG  ARG A 158      20.307  39.332  44.940  1.00  0.00          C   \nATOM   1194  CD  ARG A 158      18.952  39.856  45.388  1.00  0.00          C   \nATOM   1195  NE  ARG A 158      19.090  41.001  46.286  1.00  0.00          N   \nATOM   1196  CZ  ARG A 158      18.066  41.666  46.809  1.00  0.00          C   \nATOM   1197  NH1 ARG A 158      16.822  41.299  46.525  1.00  0.00          N   \nATOM   1198  NH2 ARG A 158      18.285  42.700  47.613  1.00  0.00          N   \nATOM   1199  N   LEU A 159      22.551  41.479  41.748  1.00  0.00          N   \nATOM   1200  CA  LEU A 159      23.066  42.559  40.919  1.00  0.00          C   \nATOM   1201  C   LEU A 159      22.151  43.789  40.953  1.00  0.00          C   \nATOM   1202  O   LEU A 159      20.946  43.682  40.706  1.00  0.00          O   \nATOM   1203  CB  LEU A 159      23.179  42.079  39.475  1.00  0.00          C   \nATOM   1204  CG  LEU A 159      24.408  41.316  38.984  1.00  0.00          C   \nATOM   1205  CD1 LEU A 159      25.048  40.491  40.079  1.00  0.00          C   \nATOM   1206  CD2 LEU A 159      23.972  40.460  37.809  1.00  0.00          C   \nATOM   1207  N   ILE A 160      22.723  44.951  41.251  1.00  0.00          N   \nATOM   1208  CA  ILE A 160      21.954  46.187  41.289  1.00  0.00          C   \nATOM   1209  C   ILE A 160      22.387  46.971  40.060  1.00  0.00          C   \nATOM   1210  O   ILE A 160      23.580  47.074  39.792  1.00  0.00          O   \nATOM   1211  CB  ILE A 160      22.249  47.002  42.583  1.00  0.00          C   \nATOM   1212  CG1 ILE A 160      21.459  48.310  42.587  1.00  0.00          C   \nATOM   1213  CG2 ILE A 160      23.712  47.295  42.700  1.00  0.00          C   \nATOM   1214  CD1 ILE A 160      19.975  48.135  42.931  1.00  0.00          C   \nATOM   1215  N   VAL A 161      21.421  47.500  39.312  1.00  0.00          N   \nATOM   1216  CA  VAL A 161      21.694  48.263  38.086  1.00  0.00          C   \nATOM   1217  C   VAL A 161      21.002  49.615  38.251  1.00  0.00          C   \nATOM   1218  O   VAL A 161      19.788  49.679  38.477  1.00  0.00          O   \nATOM   1219  CB  VAL A 161      21.143  47.503  36.846  1.00  0.00          C   \nATOM   1220  CG1 VAL A 161      21.311  48.352  35.574  1.00  0.00          C   \nATOM   1221  CG2 VAL A 161      21.881  46.152  36.695  1.00  0.00          C   \nATOM   1222  N   PHE A 162      21.767  50.699  38.152  1.00  0.00          N   \nATOM   1223  CA  PHE A 162      21.196  52.020  38.371  1.00  0.00          C   \nATOM   1224  C   PHE A 162      21.804  53.128  37.521  1.00  0.00          C   \nATOM   1225  O   PHE A 162      22.976  53.073  37.165  1.00  0.00          O   \nATOM   1226  CB  PHE A 162      21.342  52.396  39.856  1.00  0.00          C   \nATOM   1227  CG  PHE A 162      22.787  52.491  40.333  1.00  0.00          C   \nATOM   1228  CD1 PHE A 162      23.488  51.351  40.742  1.00  0.00          C   \nATOM   1229  CD2 PHE A 162      23.438  53.723  40.383  1.00  0.00          C   \nATOM   1230  CE1 PHE A 162      24.827  51.447  41.199  1.00  0.00          C   \nATOM   1231  CE2 PHE A 162      24.765  53.829  40.833  1.00  0.00          C   \nATOM   1232  CZ  PHE A 162      25.459  52.689  41.243  1.00  0.00          C   \nATOM   1233  N   PRO A 163      20.999  54.150  37.184  1.00  0.00          N   \nATOM   1234  CA  PRO A 163      21.456  55.281  36.378  1.00  0.00          C   \nATOM   1235  C   PRO A 163      22.086  56.319  37.295  1.00  0.00          C   \nATOM   1236  O   PRO A 163      22.016  56.203  38.521  1.00  0.00          O   \nATOM   1237  CB  PRO A 163      20.167  55.787  35.735  1.00  0.00          C   \nATOM   1238  CG  PRO A 163      19.172  55.581  36.832  1.00  0.00          C   \nATOM   1239  CD  PRO A 163      19.541  54.217  37.397  1.00  0.00          C   \nATOM   1240  N   ASP A 164      22.682  57.341  36.698  1.00  0.00          N   \nATOM   1241  CA  ASP A 164      23.334  58.400  37.461  1.00  0.00          C   \nATOM   1242  C   ASP A 164      22.355  59.169  38.337  1.00  0.00          C   \nATOM   1243  O   ASP A 164      21.180  59.276  38.004  1.00  0.00          O   \nATOM   1244  CB  ASP A 164      24.016  59.389  36.518  1.00  0.00          C   \nATOM   1245  CG  ASP A 164      24.865  60.388  37.262  1.00  0.00          C   \nATOM   1246  OD1 ASP A 164      25.943  59.982  37.751  1.00  0.00          O   \nATOM   1247  OD2 ASP A 164      24.441  61.560  37.381  1.00  0.00          O   \nATOM   1248  N   LEU A 165      22.857  59.701  39.452  1.00  0.00          N   \nATOM   1249  CA  LEU A 165      22.063  60.486  40.406  1.00  0.00          C   \nATOM   1250  C   LEU A 165      21.172  61.537  39.739  1.00  0.00          C   \nATOM   1251  O   LEU A 165      20.034  61.731  40.148  1.00  0.00          O   \nATOM   1252  CB  LEU A 165      23.001  61.171  41.410  1.00  0.00          C   \nATOM   1253  CG  LEU A 165      22.474  62.220  42.398  1.00  0.00          C   \nATOM   1254  CD1 LEU A 165      21.342  61.661  43.266  1.00  0.00          C   \nATOM   1255  CD2 LEU A 165      23.647  62.662  43.272  1.00  0.00          C   \nATOM   1256  N   GLY A 166      21.695  62.218  38.719  1.00  0.00          N   \nATOM   1257  CA  GLY A 166      20.910  63.225  38.022  1.00  0.00          C   \nATOM   1258  C   GLY A 166      19.693  62.605  37.355  1.00  0.00          C   \nATOM   1259  O   GLY A 166      18.597  63.178  37.356  1.00  0.00          O   \nATOM   1260  N   VAL A 167      19.877  61.430  36.766  1.00  0.00          N   \nATOM   1261  CA  VAL A 167      18.750  60.751  36.122  1.00  0.00          C   \nATOM   1262  C   VAL A 167      17.749  60.319  37.199  1.00  0.00          C   \nATOM   1263  O   VAL A 167      16.540  60.399  36.995  1.00  0.00          O   \nATOM   1264  CB  VAL A 167      19.206  59.499  35.335  1.00  0.00          C   \nATOM   1265  CG1 VAL A 167      17.996  58.726  34.842  1.00  0.00          C   \nATOM   1266  CG2 VAL A 167      20.062  59.916  34.145  1.00  0.00          C   \nATOM   1267  N   ARG A 168      18.254  59.856  38.342  1.00  0.00          N   \nATOM   1268  CA  ARG A 168      17.372  59.429  39.431  1.00  0.00          C   \nATOM   1269  C   ARG A 168      16.493  60.599  39.918  1.00  0.00          C   \nATOM   1270  O   ARG A 168      15.305  60.435  40.173  1.00  0.00          O   \nATOM   1271  CB  ARG A 168      18.195  58.850  40.585  1.00  0.00          C   \nATOM   1272  CG  ARG A 168      19.022  57.625  40.187  1.00  0.00          C   \nATOM   1273  CD  ARG A 168      19.376  56.722  41.382  1.00  0.00          C   \nATOM   1274  NE  ARG A 168      20.215  57.359  42.402  1.00  0.00          N   \nATOM   1275  CZ  ARG A 168      21.527  57.550  42.303  1.00  0.00          C   \nATOM   1276  NH1 ARG A 168      22.187  57.161  41.219  1.00  0.00          N   \nATOM   1277  NH2 ARG A 168      22.186  58.113  43.309  1.00  0.00          N   \nATOM   1278  N   VAL A 169      17.067  61.785  40.047  1.00  0.00          N   \nATOM   1279  CA  VAL A 169      16.262  62.920  40.480  1.00  0.00          C   \nATOM   1280  C   VAL A 169      15.172  63.152  39.422  1.00  0.00          C   \nATOM   1281  O   VAL A 169      14.001  63.378  39.745  1.00  0.00          O   \nATOM   1282  CB  VAL A 169      17.155  64.174  40.679  1.00  0.00          C   \nATOM   1283  CG1 VAL A 169      16.297  65.403  40.966  1.00  0.00          C   \nATOM   1284  CG2 VAL A 169      18.115  63.925  41.850  1.00  0.00          C   \nATOM   1285  N   CYS A 170      15.550  63.059  38.153  1.00  0.00          N   \nATOM   1286  CA  CYS A 170      14.597  63.227  37.065  1.00  0.00          C   \nATOM   1287  C   CYS A 170      13.472  62.177  37.111  1.00  0.00          C   \nATOM   1288  O   CYS A 170      12.318  62.502  36.843  1.00  0.00          O   \nATOM   1289  CB  CYS A 170      15.329  63.174  35.725  1.00  0.00          C   \nATOM   1290  SG  CYS A 170      16.162  64.734  35.352  1.00  0.00          S   \nATOM   1291  N   GLU A 171      13.804  60.928  37.454  1.00  0.00          N   \nATOM   1292  CA  GLU A 171      12.795  59.877  37.560  1.00  0.00          C   \nATOM   1293  C   GLU A 171      11.724  60.258  38.598  1.00  0.00          C   \nATOM   1294  O   GLU A 171      10.534  60.030  38.385  1.00  0.00          O   \nATOM   1295  CB  GLU A 171      13.418  58.548  38.011  1.00  0.00          C   \nATOM   1296  CG  GLU A 171      14.314  57.843  37.018  1.00  0.00          C   \nATOM   1297  CD  GLU A 171      14.729  56.471  37.538  1.00  0.00          C   \nATOM   1298  OE1 GLU A 171      13.882  55.547  37.521  1.00  0.00          O   \nATOM   1299  OE2 GLU A 171      15.889  56.323  37.986  1.00  0.00          O   \nATOM   1300  N   LYS A 172      12.157  60.798  39.734  1.00  0.00          N   \nATOM   1301  CA  LYS A 172      11.230  61.206  40.798  1.00  0.00          C   \nATOM   1302  C   LYS A 172      10.304  62.308  40.301  1.00  0.00          C   \nATOM   1303  O   LYS A 172       9.086  62.251  40.508  1.00  0.00          O   \nATOM   1304  CB  LYS A 172      11.990  61.728  42.020  1.00  0.00          C   \nATOM   1305  CG  LYS A 172      12.903  60.713  42.694  1.00  0.00          C   \nATOM   1306  CD  LYS A 172      13.737  61.385  43.772  1.00  0.00          C   \nATOM   1307  CE  LYS A 172      14.358  60.377  44.738  1.00  0.00          C   \nATOM   1308  NZ  LYS A 172      15.351  59.460  44.101  1.00  0.00          N   \nATOM   1309  N   MET A 173      10.873  63.311  39.639  1.00  0.00          N   \nATOM   1310  CA  MET A 173      10.058  64.408  39.145  1.00  0.00          C   \nATOM   1311  C   MET A 173       8.944  63.911  38.234  1.00  0.00          C   \nATOM   1312  O   MET A 173       7.783  64.286  38.387  1.00  0.00          O   \nATOM   1313  CB  MET A 173      10.920  65.435  38.407  1.00  0.00          C   \nATOM   1314  CG  MET A 173      11.911  66.162  39.309  1.00  0.00          C   \nATOM   1315  SD  MET A 173      12.639  67.620  38.525  1.00  0.00          S   \nATOM   1316  CE  MET A 173      13.823  66.865  37.501  1.00  0.00          C   \nATOM   1317  N   ALA A 174       9.300  63.046  37.300  1.00  0.00          N   \nATOM   1318  CA  ALA A 174       8.329  62.520  36.360  1.00  0.00          C   \nATOM   1319  C   ALA A 174       7.464  61.384  36.881  1.00  0.00          C   \nATOM   1320  O   ALA A 174       6.323  61.222  36.446  1.00  0.00          O   \nATOM   1321  CB  ALA A 174       9.041  62.052  35.104  1.00  0.00          C   \nATOM   1322  N   LEU A 175       7.977  60.612  37.826  1.00  0.00          N   \nATOM   1323  CA  LEU A 175       7.214  59.446  38.259  1.00  0.00          C   \nATOM   1324  C   LEU A 175       6.988  59.171  39.734  1.00  0.00          C   \nATOM   1325  O   LEU A 175       6.352  58.167  40.067  1.00  0.00          O   \nATOM   1326  CB  LEU A 175       7.848  58.192  37.620  1.00  0.00          C   \nATOM   1327  CG  LEU A 175       7.675  58.050  36.103  1.00  0.00          C   \nATOM   1328  CD1 LEU A 175       8.796  57.222  35.499  1.00  0.00          C   \nATOM   1329  CD2 LEU A 175       6.324  57.400  35.816  1.00  0.00          C   \nATOM   1330  N   TYR A 176       7.508  60.009  40.625  1.00  0.00          N   \nATOM   1331  CA  TYR A 176       7.320  59.732  42.046  1.00  0.00          C   \nATOM   1332  C   TYR A 176       5.845  59.678  42.429  1.00  0.00          C   \nATOM   1333  O   TYR A 176       5.406  58.786  43.160  1.00  0.00          O   \nATOM   1334  CB  TYR A 176       8.014  60.784  42.915  1.00  0.00          C   \nATOM   1335  CG  TYR A 176       7.867  60.490  44.388  1.00  0.00          C   \nATOM   1336  CD1 TYR A 176       8.565  59.433  44.978  1.00  0.00          C   \nATOM   1337  CD2 TYR A 176       6.997  61.234  45.183  1.00  0.00          C   \nATOM   1338  CE1 TYR A 176       8.399  59.123  46.324  1.00  0.00          C   \nATOM   1339  CE2 TYR A 176       6.822  60.931  46.524  1.00  0.00          C   \nATOM   1340  CZ  TYR A 176       7.523  59.875  47.089  1.00  0.00          C   \nATOM   1341  OH  TYR A 176       7.336  59.567  48.418  1.00  0.00          O   \nATOM   1342  N   ASP A 177       5.081  60.637  41.927  1.00  0.00          N   \nATOM   1343  CA  ASP A 177       3.669  60.708  42.254  1.00  0.00          C   \nATOM   1344  C   ASP A 177       2.932  59.519  41.649  1.00  0.00          C   \nATOM   1345  O   ASP A 177       2.053  58.926  42.283  1.00  0.00          O   \nATOM   1346  CB  ASP A 177       3.088  62.034  41.760  1.00  0.00          C   \nATOM   1347  CG  ASP A 177       1.786  62.386  42.444  1.00  0.00          C   \nATOM   1348  OD1 ASP A 177       1.584  61.957  43.599  1.00  0.00          O   \nATOM   1349  OD2 ASP A 177       0.966  63.101  41.834  1.00  0.00          O   \nATOM   1350  N   VAL A 178       3.331  59.145  40.438  1.00  0.00          N   \nATOM   1351  CA  VAL A 178       2.723  58.016  39.754  1.00  0.00          C   \nATOM   1352  C   VAL A 178       2.939  56.693  40.494  1.00  0.00          C   \nATOM   1353  O   VAL A 178       1.977  55.984  40.791  1.00  0.00          O   \nATOM   1354  CB  VAL A 178       3.275  57.883  38.302  1.00  0.00          C   \nATOM   1355  CG1 VAL A 178       2.895  56.526  37.706  1.00  0.00          C   \nATOM   1356  CG2 VAL A 178       2.709  58.986  37.443  1.00  0.00          C   \nATOM   1357  N   VAL A 179       4.190  56.356  40.801  1.00  0.00          N   \nATOM   1358  CA  VAL A 179       4.454  55.095  41.476  1.00  0.00          C   \nATOM   1359  C   VAL A 179       3.946  55.099  42.909  1.00  0.00          C   \nATOM   1360  O   VAL A 179       3.758  54.046  43.515  1.00  0.00          O   \nATOM   1361  CB  VAL A 179       5.976  54.729  41.466  1.00  0.00          C   \nATOM   1362  CG1 VAL A 179       6.472  54.641  40.029  1.00  0.00          C   \nATOM   1363  CG2 VAL A 179       6.779  55.741  42.259  1.00  0.00          C   \nATOM   1364  N   SER A 180       3.715  56.286  43.453  1.00  0.00          N   \nATOM   1365  CA  SER A 180       3.227  56.397  44.824  1.00  0.00          C   \nATOM   1366  C   SER A 180       1.714  56.270  44.890  1.00  0.00          C   \nATOM   1367  O   SER A 180       1.167  55.933  45.933  1.00  0.00          O   \nATOM   1368  CB  SER A 180       3.630  57.741  45.434  1.00  0.00          C   \nATOM   1369  OG  SER A 180       5.034  57.839  45.577  1.00  0.00          O   \nATOM   1370  N   THR A 181       1.026  56.533  43.785  1.00  0.00          N   \nATOM   1371  CA  THR A 181      -0.420  56.469  43.816  1.00  0.00          C   \nATOM   1372  C   THR A 181      -1.104  55.463  42.893  1.00  0.00          C   \nATOM   1373  O   THR A 181      -2.115  54.879  43.269  1.00  0.00          O   \nATOM   1374  CB  THR A 181      -1.015  57.874  43.580  1.00  0.00          C   \nATOM   1375  OG1 THR A 181      -1.050  58.163  42.175  1.00  0.00          O   \nATOM   1376  CG2 THR A 181      -0.165  58.929  44.273  1.00  0.00          C   \nATOM   1377  N   LEU A 182      -0.556  55.270  41.697  1.00  0.00          N   \nATOM   1378  CA  LEU A 182      -1.152  54.368  40.698  1.00  0.00          C   \nATOM   1379  C   LEU A 182      -1.441  52.921  41.120  1.00  0.00          C   \nATOM   1380  O   LEU A 182      -2.535  52.407  40.882  1.00  0.00          O   \nATOM   1381  CB  LEU A 182      -0.287  54.360  39.416  1.00  0.00          C   \nATOM   1382  CG  LEU A 182      -0.713  53.521  38.191  1.00  0.00          C   \nATOM   1383  CD1 LEU A 182       0.169  53.868  37.001  1.00  0.00          C   \nATOM   1384  CD2 LEU A 182      -0.582  52.049  38.491  1.00  0.00          C   \nATOM   1385  N   PRO A 183      -0.463  52.245  41.744  1.00  0.00          N   \nATOM   1386  CA  PRO A 183      -0.615  50.851  42.183  1.00  0.00          C   \nATOM   1387  C   PRO A 183      -1.872  50.533  42.988  1.00  0.00          C   \nATOM   1388  O   PRO A 183      -2.572  49.561  42.700  1.00  0.00          O   \nATOM   1389  CB  PRO A 183       0.656  50.599  42.996  1.00  0.00          C   \nATOM   1390  CG  PRO A 183       1.646  51.532  42.373  1.00  0.00          C   \nATOM   1391  CD  PRO A 183       0.830  52.792  42.195  1.00  0.00          C   \nATOM   1392  N   GLN A 184      -2.153  51.342  44.008  1.00  0.00          N   \nATOM   1393  CA  GLN A 184      -3.332  51.097  44.831  1.00  0.00          C   \nATOM   1394  C   GLN A 184      -4.622  51.248  44.032  1.00  0.00          C   \nATOM   1395  O   GLN A 184      -5.575  50.484  44.212  1.00  0.00          O   \nATOM   1396  CB  GLN A 184      -3.357  52.032  46.036  1.00  0.00          C   \nATOM   1397  CG  GLN A 184      -4.446  51.674  47.058  1.00  0.00          C   \nATOM   1398  CD  GLN A 184      -5.803  52.280  46.723  1.00  0.00          C   \nATOM   1399  OE1 GLN A 184      -5.893  53.453  46.353  1.00  0.00          O   \nATOM   1400  NE2 GLN A 184      -6.866  51.490  46.869  1.00  0.00          N   \nATOM   1401  N   VAL A 185      -4.658  52.228  43.142  1.00  0.00          N   \nATOM   1402  CA  VAL A 185      -5.850  52.426  42.341  1.00  0.00          C   \nATOM   1403  C   VAL A 185      -6.075  51.275  41.366  1.00  0.00          C   \nATOM   1404  O   VAL A 185      -7.199  50.793  41.206  1.00  0.00          O   \nATOM   1405  CB  VAL A 185      -5.776  53.747  41.563  1.00  0.00          C   \nATOM   1406  CG1 VAL A 185      -6.926  53.842  40.556  1.00  0.00          C   \nATOM   1407  CG2 VAL A 185      -5.849  54.901  42.534  1.00  0.00          C   \nATOM   1408  N   VAL A 186      -5.009  50.816  40.722  1.00  0.00          N   \nATOM   1409  CA  VAL A 186      -5.153  49.735  39.756  1.00  0.00          C   \nATOM   1410  C   VAL A 186      -5.440  48.371  40.389  1.00  0.00          C   \nATOM   1411  O   VAL A 186      -6.249  47.592  39.871  1.00  0.00          O   \nATOM   1412  CB  VAL A 186      -3.886  49.643  38.863  1.00  0.00          C   \nATOM   1413  CG1 VAL A 186      -3.925  48.386  37.990  1.00  0.00          C   \nATOM   1414  CG2 VAL A 186      -3.785  50.893  38.003  1.00  0.00          C   \nATOM   1415  N   MET A 187      -4.806  48.096  41.523  1.00  0.00          N   \nATOM   1416  CA  MET A 187      -4.946  46.793  42.167  1.00  0.00          C   \nATOM   1417  C   MET A 187      -5.790  46.739  43.440  1.00  0.00          C   \nATOM   1418  O   MET A 187      -6.017  45.667  43.987  1.00  0.00          O   \nATOM   1419  CB  MET A 187      -3.542  46.227  42.418  1.00  0.00          C   \nATOM   1420  CG  MET A 187      -2.767  45.984  41.108  1.00  0.00          C   \nATOM   1421  SD  MET A 187      -0.943  45.829  41.254  1.00  0.00          S   \nATOM   1422  CE  MET A 187      -0.886  44.778  42.738  1.00  0.00          C   \nATOM   1423  N   GLY A 188      -6.268  47.890  43.898  1.00  0.00          N   \nATOM   1424  CA  GLY A 188      -7.076  47.912  45.106  1.00  0.00          C   \nATOM   1425  C   GLY A 188      -6.438  47.191  46.282  1.00  0.00          C   \nATOM   1426  O   GLY A 188      -5.225  47.268  46.498  1.00  0.00          O   \nATOM   1427  N   SER A 189      -7.263  46.470  47.034  1.00  0.00          N   \nATOM   1428  CA  SER A 189      -6.809  45.751  48.216  1.00  0.00          C   \nATOM   1429  C   SER A 189      -5.669  44.776  47.957  1.00  0.00          C   \nATOM   1430  O   SER A 189      -4.980  44.370  48.903  1.00  0.00          O   \nATOM   1431  CB  SER A 189      -7.988  45.008  48.853  1.00  0.00          C   \nATOM   1432  OG  SER A 189      -8.540  44.070  47.941  1.00  0.00          O   \nATOM   1433  N   SER A 190      -5.456  44.407  46.693  1.00  0.00          N   \nATOM   1434  CA  SER A 190      -4.386  43.466  46.344  1.00  0.00          C   \nATOM   1435  C   SER A 190      -2.977  44.061  46.384  1.00  0.00          C   \nATOM   1436  O   SER A 190      -1.984  43.326  46.417  1.00  0.00          O   \nATOM   1437  CB  SER A 190      -4.641  42.849  44.966  1.00  0.00          C   \nATOM   1438  OG  SER A 190      -5.781  42.003  44.994  1.00  0.00          O   \nATOM   1439  N   TYR A 191      -2.876  45.382  46.390  1.00  0.00          N   \nATOM   1440  CA  TYR A 191      -1.559  46.010  46.438  1.00  0.00          C   \nATOM   1441  C   TYR A 191      -0.993  45.893  47.854  1.00  0.00          C   \nATOM   1442  O   TYR A 191      -1.499  46.525  48.776  1.00  0.00          O   \nATOM   1443  CB  TYR A 191      -1.664  47.481  46.031  1.00  0.00          C   \nATOM   1444  CG  TYR A 191      -0.330  48.187  46.017  1.00  0.00          C   \nATOM   1445  CD1 TYR A 191       0.758  47.643  45.336  1.00  0.00          C   \nATOM   1446  CD2 TYR A 191      -0.166  49.414  46.659  1.00  0.00          C   \nATOM   1447  CE1 TYR A 191       1.984  48.312  45.288  1.00  0.00          C   \nATOM   1448  CE2 TYR A 191       1.049  50.094  46.618  1.00  0.00          C   \nATOM   1449  CZ  TYR A 191       2.118  49.542  45.931  1.00  0.00          C   \nATOM   1450  OH  TYR A 191       3.310  50.228  45.878  1.00  0.00          O   \nATOM   1451  N   GLY A 192       0.064  45.106  48.030  1.00  0.00          N   \nATOM   1452  CA  GLY A 192       0.603  44.919  49.368  1.00  0.00          C   \nATOM   1453  C   GLY A 192       1.292  46.063  50.107  1.00  0.00          C   \nATOM   1454  O   GLY A 192       1.207  46.146  51.333  1.00  0.00          O   \nATOM   1455  N   PHE A 193       1.969  46.948  49.390  1.00  0.00          N   \nATOM   1456  CA  PHE A 193       2.703  48.022  50.049  1.00  0.00          C   \nATOM   1457  C   PHE A 193       1.874  49.103  50.735  1.00  0.00          C   \nATOM   1458  O   PHE A 193       2.437  50.053  51.299  1.00  0.00          O   \nATOM   1459  CB  PHE A 193       3.677  48.665  49.061  1.00  0.00          C   \nATOM   1460  CG  PHE A 193       4.782  47.745  48.619  1.00  0.00          C   \nATOM   1461  CD1 PHE A 193       5.614  47.129  49.561  1.00  0.00          C   \nATOM   1462  CD2 PHE A 193       4.981  47.477  47.264  1.00  0.00          C   \nATOM   1463  CE1 PHE A 193       6.636  46.245  49.154  1.00  0.00          C   \nATOM   1464  CE2 PHE A 193       5.996  46.603  46.843  1.00  0.00          C   \nATOM   1465  CZ  PHE A 193       6.826  45.982  47.791  1.00  0.00          C   \nATOM   1466  N   GLN A 194       0.550  48.970  50.694  1.00  0.00          N   \nATOM   1467  CA  GLN A 194      -0.324  49.953  51.334  1.00  0.00          C   \nATOM   1468  C   GLN A 194      -0.562  49.542  52.780  1.00  0.00          C   \nATOM   1469  O   GLN A 194      -1.060  50.330  53.585  1.00  0.00          O   \nATOM   1470  CB  GLN A 194      -1.679  50.038  50.612  1.00  0.00          C   \nATOM   1471  CG  GLN A 194      -2.554  48.791  50.807  1.00  0.00          C   \nATOM   1472  CD  GLN A 194      -3.815  48.805  49.950  1.00  0.00          C   \nATOM   1473  OE1 GLN A 194      -4.710  49.630  50.147  1.00  0.00          O   \nATOM   1474  NE2 GLN A 194      -3.884  47.893  48.991  1.00  0.00          N   \nATOM   1475  N   TYR A 195      -0.195  48.306  53.105  1.00  0.00          N   \nATOM   1476  CA  TYR A 195      -0.405  47.782  54.448  1.00  0.00          C   \nATOM   1477  C   TYR A 195       0.793  47.798  55.387  1.00  0.00          C   \nATOM   1478  O   TYR A 195       1.942  47.659  54.969  1.00  0.00          O   \nATOM   1479  CB  TYR A 195      -0.903  46.323  54.381  1.00  0.00          C   \nATOM   1480  CG  TYR A 195      -2.124  46.091  53.512  1.00  0.00          C   \nATOM   1481  CD1 TYR A 195      -3.330  46.738  53.782  1.00  0.00          C   \nATOM   1482  CD2 TYR A 195      -2.077  45.211  52.433  1.00  0.00          C   \nATOM   1483  CE1 TYR A 195      -4.462  46.514  53.001  1.00  0.00          C   \nATOM   1484  CE2 TYR A 195      -3.212  44.971  51.640  1.00  0.00          C   \nATOM   1485  CZ  TYR A 195      -4.401  45.626  51.931  1.00  0.00          C   \nATOM   1486  OH  TYR A 195      -5.526  45.391  51.161  1.00  0.00          O   \nATOM   1487  N   SER A 196       0.497  47.974  56.671  1.00  0.00          N   \nATOM   1488  CA  SER A 196       1.501  47.892  57.714  1.00  0.00          C   \nATOM   1489  C   SER A 196       1.462  46.381  57.988  1.00  0.00          C   \nATOM   1490  O   SER A 196       0.553  45.698  57.512  1.00  0.00          O   \nATOM   1491  CB  SER A 196       1.036  48.657  58.956  1.00  0.00          C   \nATOM   1492  OG  SER A 196      -0.208  48.161  59.411  1.00  0.00          O   \nATOM   1493  N   PRO A 197       2.433  45.834  58.743  1.00  0.00          N   \nATOM   1494  CA  PRO A 197       2.397  44.389  59.007  1.00  0.00          C   \nATOM   1495  C   PRO A 197       1.046  43.930  59.554  1.00  0.00          C   \nATOM   1496  O   PRO A 197       0.533  42.881  59.162  1.00  0.00          O   \nATOM   1497  CB  PRO A 197       3.509  44.190  60.036  1.00  0.00          C   \nATOM   1498  CG  PRO A 197       4.491  45.261  59.672  1.00  0.00          C   \nATOM   1499  CD  PRO A 197       3.598  46.460  59.393  1.00  0.00          C   \nATOM   1500  N   GLY A 198       0.477  44.710  60.468  1.00  0.00          N   \nATOM   1501  CA  GLY A 198      -0.802  44.344  61.055  1.00  0.00          C   \nATOM   1502  C   GLY A 198      -1.931  44.320  60.043  1.00  0.00          C   \nATOM   1503  O   GLY A 198      -2.760  43.403  60.035  1.00  0.00          O   \nATOM   1504  N   GLN A 199      -1.974  45.324  59.180  1.00  0.00          N   \nATOM   1505  CA  GLN A 199      -3.022  45.368  58.170  1.00  0.00          C   \nATOM   1506  C   GLN A 199      -2.855  44.239  57.160  1.00  0.00          C   \nATOM   1507  O   GLN A 199      -3.837  43.755  56.591  1.00  0.00          O   \nATOM   1508  CB  GLN A 199      -3.007  46.713  57.464  1.00  0.00          C   \nATOM   1509  CG  GLN A 199      -3.193  47.854  58.428  1.00  0.00          C   \nATOM   1510  CD  GLN A 199      -2.951  49.193  57.784  1.00  0.00          C   \nATOM   1511  OE1 GLN A 199      -1.967  49.379  57.063  1.00  0.00          O   \nATOM   1512  NE2 GLN A 199      -3.841  50.143  58.047  1.00  0.00          N   \nATOM   1513  N   ARG A 200      -1.612  43.817  56.936  1.00  0.00          N   \nATOM   1514  CA  ARG A 200      -1.353  42.734  55.991  1.00  0.00          C   \nATOM   1515  C   ARG A 200      -1.987  41.470  56.564  1.00  0.00          C   \nATOM   1516  O   ARG A 200      -2.731  40.753  55.884  1.00  0.00          O   \nATOM   1517  CB  ARG A 200       0.155  42.525  55.807  1.00  0.00          C   \nATOM   1518  CG  ARG A 200       0.500  41.570  54.680  1.00  0.00          C   \nATOM   1519  CD  ARG A 200       2.013  41.385  54.566  1.00  0.00          C   \nATOM   1520  NE  ARG A 200       2.340  40.253  53.702  1.00  0.00          N   \nATOM   1521  CZ  ARG A 200       2.384  40.304  52.373  1.00  0.00          C   \nATOM   1522  NH1 ARG A 200       2.130  41.443  51.744  1.00  0.00          N   \nATOM   1523  NH2 ARG A 200       2.668  39.207  51.675  1.00  0.00          N   \nATOM   1524  N   VAL A 201      -1.689  41.204  57.829  1.00  0.00          N   \nATOM   1525  CA  VAL A 201      -2.230  40.035  58.514  1.00  0.00          C   \nATOM   1526  C   VAL A 201      -3.754  40.076  58.434  1.00  0.00          C   \nATOM   1527  O   VAL A 201      -4.413  39.077  58.107  1.00  0.00          O   \nATOM   1528  CB  VAL A 201      -1.806  40.030  59.998  1.00  0.00          C   \nATOM   1529  CG1 VAL A 201      -2.555  38.940  60.741  1.00  0.00          C   \nATOM   1530  CG2 VAL A 201      -0.296  39.827  60.097  1.00  0.00          C   \nATOM   1531  N   GLU A 202      -4.293  41.252  58.727  1.00  0.00          N   \nATOM   1532  CA  GLU A 202      -5.727  41.488  58.696  1.00  0.00          C   \nATOM   1533  C   GLU A 202      -6.312  41.160  57.322  1.00  0.00          C   \nATOM   1534  O   GLU A 202      -7.333  40.466  57.223  1.00  0.00          O   \nATOM   1535  CB  GLU A 202      -6.020  42.946  59.050  1.00  0.00          C   \nATOM   1536  CG  GLU A 202      -7.498  43.292  59.037  1.00  0.00          C   \nATOM   1537  CD  GLU A 202      -7.771  44.669  59.593  1.00  0.00          C   \nATOM   1538  OE1 GLU A 202      -7.329  45.669  58.982  1.00  0.00          O   \nATOM   1539  OE2 GLU A 202      -8.425  44.750  60.654  1.00  0.00          O   \nATOM   1540  N   PHE A 203      -5.669  41.651  56.263  1.00  0.00          N   \nATOM   1541  CA  PHE A 203      -6.159  41.376  54.920  1.00  0.00          C   \nATOM   1542  C   PHE A 203      -6.095  39.890  54.631  1.00  0.00          C   \nATOM   1543  O   PHE A 203      -7.030  39.316  54.078  1.00  0.00          O   \nATOM   1544  CB  PHE A 203      -5.341  42.114  53.861  1.00  0.00          C   \nATOM   1545  CG  PHE A 203      -5.815  41.855  52.455  1.00  0.00          C   \nATOM   1546  CD1 PHE A 203      -7.063  42.309  52.036  1.00  0.00          C   \nATOM   1547  CD2 PHE A 203      -5.026  41.144  51.558  1.00  0.00          C   \nATOM   1548  CE1 PHE A 203      -7.519  42.052  50.752  1.00  0.00          C   \nATOM   1549  CE2 PHE A 203      -5.475  40.882  50.270  1.00  0.00          C   \nATOM   1550  CZ  PHE A 203      -6.722  41.340  49.866  1.00  0.00          C   \nATOM   1551  N   LEU A 204      -4.985  39.265  55.007  1.00  0.00          N   \nATOM   1552  CA  LEU A 204      -4.813  37.839  54.763  1.00  0.00          C   \nATOM   1553  C   LEU A 204      -5.843  36.997  55.506  1.00  0.00          C   \nATOM   1554  O   LEU A 204      -6.424  36.067  54.937  1.00  0.00          O   \nATOM   1555  CB  LEU A 204      -3.392  37.403  55.151  1.00  0.00          C   \nATOM   1556  CG  LEU A 204      -2.286  37.814  54.165  1.00  0.00          C   \nATOM   1557  CD1 LEU A 204      -0.925  37.640  54.818  1.00  0.00          C   \nATOM   1558  CD2 LEU A 204      -2.384  36.976  52.886  1.00  0.00          C   \nATOM   1559  N   VAL A 205      -6.064  37.323  56.776  1.00  0.00          N   \nATOM   1560  CA  VAL A 205      -7.021  36.577  57.588  1.00  0.00          C   \nATOM   1561  C   VAL A 205      -8.447  36.753  57.073  1.00  0.00          C   \nATOM   1562  O   VAL A 205      -9.177  35.780  56.938  1.00  0.00          O   \nATOM   1563  CB  VAL A 205      -6.964  37.008  59.077  1.00  0.00          C   \nATOM   1564  CG1 VAL A 205      -8.028  36.249  59.884  1.00  0.00          C   \nATOM   1565  CG2 VAL A 205      -5.586  36.726  59.637  1.00  0.00          C   \nATOM   1566  N   ASN A 206      -8.838  37.988  56.782  1.00  0.00          N   \nATOM   1567  CA  ASN A 206     -10.176  38.234  56.269  1.00  0.00          C   \nATOM   1568  C   ASN A 206     -10.378  37.558  54.923  1.00  0.00          C   \nATOM   1569  O   ASN A 206     -11.444  37.001  54.652  1.00  0.00          O   \nATOM   1570  CB  ASN A 206     -10.445  39.736  56.138  1.00  0.00          C   \nATOM   1571  CG  ASN A 206     -10.642  40.399  57.479  1.00  0.00          C   \nATOM   1572  OD1 ASN A 206     -11.040  39.745  58.433  1.00  0.00          O   \nATOM   1573  ND2 ASN A 206     -10.382  41.703  57.558  1.00  0.00          N   \nATOM   1574  N   THR A 207      -9.358  37.604  54.073  1.00  0.00          N   \nATOM   1575  CA  THR A 207      -9.465  36.978  52.763  1.00  0.00          C   \nATOM   1576  C   THR A 207      -9.595  35.478  52.950  1.00  0.00          C   \nATOM   1577  O   THR A 207     -10.419  34.822  52.313  1.00  0.00          O   \nATOM   1578  CB  THR A 207      -8.224  37.256  51.895  1.00  0.00          C   \nATOM   1579  OG1 THR A 207      -8.146  38.659  51.609  1.00  0.00          O   \nATOM   1580  CG2 THR A 207      -8.296  36.468  50.592  1.00  0.00          C   \nATOM   1581  N   TRP A 208      -8.770  34.935  53.829  1.00  0.00          N   \nATOM   1582  CA  TRP A 208      -8.800  33.514  54.093  1.00  0.00          C   \nATOM   1583  C   TRP A 208     -10.172  33.086  54.614  1.00  0.00          C   \nATOM   1584  O   TRP A 208     -10.676  32.026  54.240  1.00  0.00          O   \nATOM   1585  CB  TRP A 208      -7.704  33.171  55.097  1.00  0.00          C   \nATOM   1586  CG  TRP A 208      -7.522  31.721  55.318  1.00  0.00          C   \nATOM   1587  CD1 TRP A 208      -8.064  30.973  56.316  1.00  0.00          C   \nATOM   1588  CD2 TRP A 208      -6.732  30.832  54.525  1.00  0.00          C   \nATOM   1589  NE1 TRP A 208      -7.657  29.669  56.202  1.00  0.00          N   \nATOM   1590  CE2 TRP A 208      -6.840  29.552  55.113  1.00  0.00          C   \nATOM   1591  CE3 TRP A 208      -5.946  30.992  53.376  1.00  0.00          C   \nATOM   1592  CZ2 TRP A 208      -6.184  28.431  54.587  1.00  0.00          C   \nATOM   1593  CZ3 TRP A 208      -5.291  29.870  52.851  1.00  0.00          C   \nATOM   1594  CH2 TRP A 208      -5.417  28.608  53.461  1.00  0.00          C   \nATOM   1595  N   LYS A 209     -10.768  33.926  55.458  1.00  0.00          N   \nATOM   1596  CA  LYS A 209     -12.085  33.656  56.041  1.00  0.00          C   \nATOM   1597  C   LYS A 209     -13.240  33.881  55.061  1.00  0.00          C   \nATOM   1598  O   LYS A 209     -14.354  33.418  55.288  1.00  0.00          O   \nATOM   1599  CB  LYS A 209     -12.330  34.548  57.261  1.00  0.00          C   \nATOM   1600  CG  LYS A 209     -11.497  34.239  58.494  1.00  0.00          C   \nATOM   1601  CD  LYS A 209     -11.903  35.200  59.595  1.00  0.00          C   \nATOM   1602  CE  LYS A 209     -11.113  34.996  60.866  1.00  0.00          C   \nATOM   1603  NZ  LYS A 209     -11.504  36.028  61.872  1.00  0.00          N   \nATOM   1604  N   SER A 210     -12.977  34.595  53.976  1.00  0.00          N   \nATOM   1605  CA  SER A 210     -14.024  34.873  53.003  1.00  0.00          C   \nATOM   1606  C   SER A 210     -14.289  33.712  52.055  1.00  0.00          C   \nATOM   1607  O   SER A 210     -15.218  33.776  51.250  1.00  0.00          O   \nATOM   1608  CB  SER A 210     -13.667  36.114  52.188  1.00  0.00          C   \nATOM   1609  OG  SER A 210     -12.593  35.828  51.308  1.00  0.00          O   \nATOM   1610  N   LYS A 211     -13.480  32.658  52.134  1.00  0.00          N   \nATOM   1611  CA  LYS A 211     -13.669  31.501  51.260  1.00  0.00          C   \nATOM   1612  C   LYS A 211     -14.363  30.392  52.047  1.00  0.00          C   \nATOM   1613  O   LYS A 211     -14.181  30.286  53.259  1.00  0.00          O   \nATOM   1614  CB  LYS A 211     -12.324  30.968  50.739  1.00  0.00          C   \nATOM   1615  CG  LYS A 211     -11.316  32.027  50.293  1.00  0.00          C   \nATOM   1616  CD  LYS A 211     -11.853  32.930  49.198  1.00  0.00          C   \nATOM   1617  CE  LYS A 211     -10.811  33.980  48.807  1.00  0.00          C   \nATOM   1618  NZ  LYS A 211     -11.339  34.971  47.821  1.00  0.00          N   \nATOM   1619  N   LYS A 212     -15.147  29.565  51.358  1.00  0.00          N   \nATOM   1620  CA  LYS A 212     -15.846  28.465  52.016  1.00  0.00          C   \nATOM   1621  C   LYS A 212     -14.817  27.411  52.407  1.00  0.00          C   \nATOM   1622  O   LYS A 212     -14.832  26.896  53.524  1.00  0.00          O   \nATOM   1623  CB  LYS A 212     -16.899  27.865  51.077  1.00  0.00          C   \nATOM   1624  CG  LYS A 212     -17.945  28.870  50.610  1.00  0.00          C   \nATOM   1625  CD  LYS A 212     -19.083  28.193  49.856  1.00  0.00          C   \nATOM   1626  CE  LYS A 212     -20.092  29.206  49.337  1.00  0.00          C   \nATOM   1627  NZ  LYS A 212     -19.538  30.027  48.222  1.00  0.00          N   \nATOM   1628  N   ASN A 213     -13.931  27.090  51.472  1.00  0.00          N   \nATOM   1629  CA  ASN A 213     -12.859  26.135  51.707  1.00  0.00          C   \nATOM   1630  C   ASN A 213     -11.588  26.790  51.164  1.00  0.00          C   \nATOM   1631  O   ASN A 213     -11.194  26.567  50.016  1.00  0.00          O   \nATOM   1632  CB  ASN A 213     -13.125  24.810  50.984  1.00  0.00          C   \nATOM   1633  CG  ASN A 213     -14.464  24.201  51.357  1.00  0.00          C   \nATOM   1634  OD1 ASN A 213     -14.843  24.181  52.529  1.00  0.00          O   \nATOM   1635  ND2 ASN A 213     -15.185  23.690  50.359  1.00  0.00          N   \nATOM   1636  N   PRO A 214     -10.927  27.607  51.995  1.00  0.00          N   \nATOM   1637  CA  PRO A 214      -9.704  28.301  51.588  1.00  0.00          C   \nATOM   1638  C   PRO A 214      -8.520  27.431  51.216  1.00  0.00          C   \nATOM   1639  O   PRO A 214      -8.278  26.372  51.797  1.00  0.00          O   \nATOM   1640  CB  PRO A 214      -9.403  29.205  52.785  1.00  0.00          C   \nATOM   1641  CG  PRO A 214      -9.896  28.394  53.938  1.00  0.00          C   \nATOM   1642  CD  PRO A 214     -11.230  27.874  53.414  1.00  0.00          C   \nATOM   1643  N   MET A 215      -7.792  27.904  50.213  1.00  0.00          N   \nATOM   1644  CA  MET A 215      -6.585  27.266  49.718  1.00  0.00          C   \nATOM   1645  C   MET A 215      -5.774  28.460  49.256  1.00  0.00          C   \nATOM   1646  O   MET A 215      -6.328  29.429  48.736  1.00  0.00          O   \nATOM   1647  CB  MET A 215      -6.879  26.336  48.536  1.00  0.00          C   \nATOM   1648  CG  MET A 215      -5.648  25.626  47.951  1.00  0.00          C   \nATOM   1649  SD  MET A 215      -4.547  26.679  46.902  1.00  0.00          S   \nATOM   1650  CE  MET A 215      -5.199  26.317  45.251  1.00  0.00          C   \nATOM   1651  N   GLY A 216      -4.470  28.402  49.472  1.00  0.00          N   \nATOM   1652  CA  GLY A 216      -3.623  29.499  49.070  1.00  0.00          C   \nATOM   1653  C   GLY A 216      -2.234  29.012  48.782  1.00  0.00          C   \nATOM   1654  O   GLY A 216      -1.849  27.910  49.186  1.00  0.00          O   \nATOM   1655  N   PHE A 217      -1.483  29.835  48.060  1.00  0.00          N   \nATOM   1656  CA  PHE A 217      -0.119  29.506  47.732  1.00  0.00          C   \nATOM   1657  C   PHE A 217       0.651  30.777  47.429  1.00  0.00          C   \nATOM   1658  O   PHE A 217       0.074  31.826  47.115  1.00  0.00          O   \nATOM   1659  CB  PHE A 217      -0.055  28.568  46.519  1.00  0.00          C   \nATOM   1660  CG  PHE A 217      -0.691  29.126  45.279  1.00  0.00          C   \nATOM   1661  CD1 PHE A 217      -2.062  28.979  45.057  1.00  0.00          C   \nATOM   1662  CD2 PHE A 217       0.075  29.808  44.334  1.00  0.00          C   \nATOM   1663  CE1 PHE A 217      -2.668  29.506  43.910  1.00  0.00          C   \nATOM   1664  CE2 PHE A 217      -0.514  30.342  43.179  1.00  0.00          C   \nATOM   1665  CZ  PHE A 217      -1.890  30.192  42.966  1.00  0.00          C   \nATOM   1666  N   SER A 218       1.961  30.689  47.564  1.00  0.00          N   \nATOM   1667  CA  SER A 218       2.818  31.810  47.237  1.00  0.00          C   \nATOM   1668  C   SER A 218       3.412  31.329  45.927  1.00  0.00          C   \nATOM   1669  O   SER A 218       3.569  30.124  45.737  1.00  0.00          O   \nATOM   1670  CB  SER A 218       3.919  31.982  48.291  1.00  0.00          C   \nATOM   1671  OG  SER A 218       4.680  30.797  48.430  1.00  0.00          O   \nATOM   1672  N   TYR A 219       3.690  32.243  45.009  1.00  0.00          N   \nATOM   1673  CA  TYR A 219       4.285  31.860  43.729  1.00  0.00          C   \nATOM   1674  C   TYR A 219       5.623  32.575  43.572  1.00  0.00          C   \nATOM   1675  O   TYR A 219       5.673  33.791  43.441  1.00  0.00          O   \nATOM   1676  CB  TYR A 219       3.370  32.240  42.562  1.00  0.00          C   \nATOM   1677  CG  TYR A 219       3.891  31.771  41.222  1.00  0.00          C   \nATOM   1678  CD1 TYR A 219       3.570  30.501  40.733  1.00  0.00          C   \nATOM   1679  CD2 TYR A 219       4.741  32.575  40.460  1.00  0.00          C   \nATOM   1680  CE1 TYR A 219       4.079  30.040  39.516  1.00  0.00          C   \nATOM   1681  CE2 TYR A 219       5.271  32.119  39.237  1.00  0.00          C   \nATOM   1682  CZ  TYR A 219       4.935  30.851  38.771  1.00  0.00          C   \nATOM   1683  OH  TYR A 219       5.462  30.386  37.574  1.00  0.00          O   \nATOM   1684  N   ASP A 220       6.704  31.812  43.604  1.00  0.00          N   \nATOM   1685  CA  ASP A 220       8.028  32.385  43.460  1.00  0.00          C   \nATOM   1686  C   ASP A 220       8.486  32.338  42.009  1.00  0.00          C   \nATOM   1687  O   ASP A 220       8.741  31.266  41.462  1.00  0.00          O   \nATOM   1688  CB  ASP A 220       9.019  31.629  44.347  1.00  0.00          C   \nATOM   1689  CG  ASP A 220      10.456  32.018  44.075  1.00  0.00          C   \nATOM   1690  OD1 ASP A 220      10.752  33.236  44.041  1.00  0.00          O   \nATOM   1691  OD2 ASP A 220      11.286  31.101  43.900  1.00  0.00          O   \nATOM   1692  N   THR A 221       8.572  33.502  41.378  1.00  0.00          N   \nATOM   1693  CA  THR A 221       9.026  33.556  39.994  1.00  0.00          C   \nATOM   1694  C   THR A 221      10.536  33.465  40.095  1.00  0.00          C   \nATOM   1695  O   THR A 221      11.131  34.048  40.996  1.00  0.00          O   \nATOM   1696  CB  THR A 221       8.659  34.897  39.300  1.00  0.00          C   \nATOM   1697  OG1 THR A 221       7.235  35.040  39.212  1.00  0.00          O   \nATOM   1698  CG2 THR A 221       9.254  34.946  37.908  1.00  0.00          C   \nATOM   1699  N   ARG A 222      11.157  32.722  39.191  1.00  0.00          N   \nATOM   1700  CA  ARG A 222      12.610  32.592  39.221  1.00  0.00          C   \nATOM   1701  C   ARG A 222      13.265  33.840  38.610  1.00  0.00          C   \nATOM   1702  O   ARG A 222      12.978  34.197  37.465  1.00  0.00          O   \nATOM   1703  CB  ARG A 222      13.026  31.332  38.463  1.00  0.00          C   \nATOM   1704  CG  ARG A 222      14.509  31.136  38.368  1.00  0.00          C   \nATOM   1705  CD  ARG A 222      14.897  31.022  36.916  1.00  0.00          C   \nATOM   1706  NE  ARG A 222      14.294  29.853  36.284  1.00  0.00          N   \nATOM   1707  CZ  ARG A 222      14.078  29.738  34.976  1.00  0.00          C   \nATOM   1708  NH1 ARG A 222      14.410  30.728  34.153  1.00  0.00          N   \nATOM   1709  NH2 ARG A 222      13.540  28.628  34.486  1.00  0.00          N   \nATOM   1710  N   CYS A 223      14.128  34.505  39.383  1.00  0.00          N   \nATOM   1711  CA  CYS A 223      14.813  35.719  38.914  1.00  0.00          C   \nATOM   1712  C   CYS A 223      13.808  36.605  38.171  1.00  0.00          C   \nATOM   1713  O   CYS A 223      13.943  36.851  36.973  1.00  0.00          O   \nATOM   1714  CB  CYS A 223      15.966  35.333  37.979  1.00  0.00          C   \nATOM   1715  SG  CYS A 223      17.228  34.308  38.800  1.00  0.00          S   \nATOM   1716  N   PHE A 224      12.800  37.082  38.887  1.00  0.00          N   \nATOM   1717  CA  PHE A 224      11.754  37.897  38.281  1.00  0.00          C   \nATOM   1718  C   PHE A 224      12.254  39.037  37.396  1.00  0.00          C   \nATOM   1719  O   PHE A 224      11.780  39.193  36.272  1.00  0.00          O   \nATOM   1720  CB  PHE A 224      10.812  38.447  39.362  1.00  0.00          C   \nATOM   1721  CG  PHE A 224       9.549  39.088  38.810  1.00  0.00          C   \nATOM   1722  CD1 PHE A 224       9.563  40.401  38.339  1.00  0.00          C   \nATOM   1723  CD2 PHE A 224       8.340  38.379  38.783  1.00  0.00          C   \nATOM   1724  CE1 PHE A 224       8.395  41.005  37.855  1.00  0.00          C   \nATOM   1725  CE2 PHE A 224       7.165  38.970  38.302  1.00  0.00          C   \nATOM   1726  CZ  PHE A 224       7.192  40.290  37.836  1.00  0.00          C   \nATOM   1727  N   ASP A 225      13.205  39.830  37.887  1.00  0.00          N   \nATOM   1728  CA  ASP A 225      13.700  40.947  37.088  1.00  0.00          C   \nATOM   1729  C   ASP A 225      14.152  40.523  35.702  1.00  0.00          C   \nATOM   1730  O   ASP A 225      13.911  41.226  34.723  1.00  0.00          O   \nATOM   1731  CB  ASP A 225      14.858  41.650  37.793  1.00  0.00          C   \nATOM   1732  CG  ASP A 225      14.404  42.470  38.982  1.00  0.00          C   \nATOM   1733  OD1 ASP A 225      13.178  42.589  39.222  1.00  0.00          O   \nATOM   1734  OD2 ASP A 225      15.288  43.007  39.672  1.00  0.00          O   \nATOM   1735  N   SER A 226      14.813  39.371  35.626  1.00  0.00          N   \nATOM   1736  CA  SER A 226      15.308  38.843  34.356  1.00  0.00          C   \nATOM   1737  C   SER A 226      14.207  38.332  33.444  1.00  0.00          C   \nATOM   1738  O   SER A 226      14.410  38.231  32.232  1.00  0.00          O   \nATOM   1739  CB  SER A 226      16.310  37.711  34.600  1.00  0.00          C   \nATOM   1740  OG  SER A 226      17.533  38.225  35.095  1.00  0.00          O   \nATOM   1741  N   THR A 227      13.049  38.014  34.013  1.00  0.00          N   \nATOM   1742  CA  THR A 227      11.933  37.509  33.209  1.00  0.00          C   \nATOM   1743  C   THR A 227      11.132  38.640  32.576  1.00  0.00          C   \nATOM   1744  O   THR A 227      10.353  38.415  31.643  1.00  0.00          O   \nATOM   1745  CB  THR A 227      10.962  36.630  34.053  1.00  0.00          C   \nATOM   1746  OG1 THR A 227      10.222  37.455  34.969  1.00  0.00          O   \nATOM   1747  CG2 THR A 227      11.742  35.556  34.841  1.00  0.00          C   \nATOM   1748  N   VAL A 228      11.328  39.862  33.072  1.00  0.00          N   \nATOM   1749  CA  VAL A 228      10.605  41.023  32.536  1.00  0.00          C   \nATOM   1750  C   VAL A 228      11.164  41.350  31.161  1.00  0.00          C   \nATOM   1751  O   VAL A 228      12.346  41.660  31.016  1.00  0.00          O   \nATOM   1752  CB  VAL A 228      10.739  42.249  33.481  1.00  0.00          C   \nATOM   1753  CG1 VAL A 228      10.017  43.490  32.894  1.00  0.00          C   \nATOM   1754  CG2 VAL A 228      10.150  41.893  34.850  1.00  0.00          C   \nATOM   1755  N   THR A 229      10.286  41.298  30.162  1.00  0.00          N   \nATOM   1756  CA  THR A 229      10.629  41.526  28.758  1.00  0.00          C   \nATOM   1757  C   THR A 229      10.390  42.962  28.284  1.00  0.00          C   \nATOM   1758  O   THR A 229       9.770  43.755  28.985  1.00  0.00          O   \nATOM   1759  CB  THR A 229       9.769  40.643  27.867  1.00  0.00          C   \nATOM   1760  OG1 THR A 229       8.403  41.076  27.983  1.00  0.00          O   \nATOM   1761  CG2 THR A 229       9.881  39.145  28.292  1.00  0.00          C   \nATOM   1762  N   GLU A 230      10.848  43.275  27.068  1.00  0.00          N   \nATOM   1763  CA  GLU A 230      10.628  44.613  26.508  1.00  0.00          C   \nATOM   1764  C   GLU A 230       9.116  44.857  26.431  1.00  0.00          C   \nATOM   1765  O   GLU A 230       8.645  45.948  26.708  1.00  0.00          O   \nATOM   1766  CB  GLU A 230      11.224  44.748  25.094  1.00  0.00          C   \nATOM   1767  CG  GLU A 230      11.022  46.158  24.500  1.00  0.00          C   \nATOM   1768  CD  GLU A 230      11.557  46.307  23.083  1.00  0.00          C   \nATOM   1769  OE1 GLU A 230      12.623  45.725  22.788  1.00  0.00          O   \nATOM   1770  OE2 GLU A 230      10.923  47.025  22.269  1.00  0.00          O   \nATOM   1771  N   ASN A 231       8.360  43.836  26.036  1.00  0.00          N   \nATOM   1772  CA  ASN A 231       6.907  43.953  25.957  1.00  0.00          C   \nATOM   1773  C   ASN A 231       6.342  44.371  27.309  1.00  0.00          C   \nATOM   1774  O   ASN A 231       5.530  45.300  27.404  1.00  0.00          O   \nATOM   1775  CB  ASN A 231       6.261  42.614  25.584  1.00  0.00          C   \nATOM   1776  CG  ASN A 231       4.759  42.609  25.859  1.00  0.00          C   \nATOM   1777  OD1 ASN A 231       3.964  43.061  25.026  1.00  0.00          O   \nATOM   1778  ND2 ASN A 231       4.364  42.133  27.059  1.00  0.00          N   \nATOM   1779  N   ASP A 232       6.762  43.660  28.352  1.00  0.00          N   \nATOM   1780  CA  ASP A 232       6.308  43.952  29.704  1.00  0.00          C   \nATOM   1781  C   ASP A 232       6.556  45.427  30.065  1.00  0.00          C   \nATOM   1782  O   ASP A 232       5.688  46.105  30.627  1.00  0.00          O   \nATOM   1783  CB  ASP A 232       7.042  43.068  30.722  1.00  0.00          C   \nATOM   1784  CG  ASP A 232       6.685  41.600  30.600  1.00  0.00          C   \nATOM   1785  OD1 ASP A 232       5.658  41.285  29.958  1.00  0.00          O   \nATOM   1786  OD2 ASP A 232       7.431  40.762  31.165  1.00  0.00          O   \nATOM   1787  N   ILE A 233       7.748  45.910  29.743  1.00  0.00          N   \nATOM   1788  CA  ILE A 233       8.110  47.278  30.083  1.00  0.00          C   \nATOM   1789  C   ILE A 233       7.348  48.321  29.267  1.00  0.00          C   \nATOM   1790  O   ILE A 233       7.082  49.411  29.761  1.00  0.00          O   \nATOM   1791  CB  ILE A 233       9.643  47.448  30.002  1.00  0.00          C   \nATOM   1792  CG1 ILE A 233      10.296  46.525  31.053  1.00  0.00          C   \nATOM   1793  CG2 ILE A 233      10.050  48.887  30.281  1.00  0.00          C   \nATOM   1794  CD1 ILE A 233      11.793  46.234  30.798  1.00  0.00          C   \nATOM   1795  N   ARG A 234       6.972  47.985  28.035  1.00  0.00          N   \nATOM   1796  CA  ARG A 234       6.184  48.914  27.228  1.00  0.00          C   \nATOM   1797  C   ARG A 234       4.733  48.849  27.702  1.00  0.00          C   \nATOM   1798  O   ARG A 234       4.020  49.844  27.671  1.00  0.00          O   \nATOM   1799  CB  ARG A 234       6.272  48.574  25.734  1.00  0.00          C   \nATOM   1800  CG  ARG A 234       7.656  48.817  25.145  1.00  0.00          C   \nATOM   1801  CD  ARG A 234       7.679  48.537  23.645  1.00  0.00          C   \nATOM   1802  NE  ARG A 234       8.999  48.768  23.069  1.00  0.00          N   \nATOM   1803  CZ  ARG A 234       9.517  49.965  22.830  1.00  0.00          C   \nATOM   1804  NH1 ARG A 234       8.824  51.061  23.116  1.00  0.00          N   \nATOM   1805  NH2 ARG A 234      10.733  50.062  22.301  1.00  0.00          N   \nATOM   1806  N   VAL A 235       4.285  47.687  28.149  1.00  0.00          N   \nATOM   1807  CA  VAL A 235       2.914  47.598  28.649  1.00  0.00          C   \nATOM   1808  C   VAL A 235       2.824  48.414  29.952  1.00  0.00          C   \nATOM   1809  O   VAL A 235       1.822  49.066  30.214  1.00  0.00          O   \nATOM   1810  CB  VAL A 235       2.498  46.134  28.902  1.00  0.00          C   \nATOM   1811  CG1 VAL A 235       1.252  46.088  29.774  1.00  0.00          C   \nATOM   1812  CG2 VAL A 235       2.239  45.439  27.577  1.00  0.00          C   \nATOM   1813  N   GLU A 236       3.872  48.369  30.769  1.00  0.00          N   \nATOM   1814  CA  GLU A 236       3.891  49.161  31.987  1.00  0.00          C   \nATOM   1815  C   GLU A 236       3.765  50.652  31.602  1.00  0.00          C   \nATOM   1816  O   GLU A 236       3.001  51.397  32.207  1.00  0.00          O   \nATOM   1817  CB  GLU A 236       5.202  48.944  32.737  1.00  0.00          C   \nATOM   1818  CG  GLU A 236       5.283  47.645  33.519  1.00  0.00          C   \nATOM   1819  CD  GLU A 236       6.714  47.218  33.798  1.00  0.00          C   \nATOM   1820  OE1 GLU A 236       7.621  48.080  33.772  1.00  0.00          O   \nATOM   1821  OE2 GLU A 236       6.927  46.009  34.052  1.00  0.00          O   \nATOM   1822  N   GLU A 237       4.513  51.089  30.595  1.00  0.00          N   \nATOM   1823  CA  GLU A 237       4.421  52.486  30.188  1.00  0.00          C   \nATOM   1824  C   GLU A 237       3.012  52.819  29.680  1.00  0.00          C   \nATOM   1825  O   GLU A 237       2.487  53.908  29.941  1.00  0.00          O   \nATOM   1826  CB  GLU A 237       5.427  52.803  29.081  1.00  0.00          C   \nATOM   1827  CG  GLU A 237       5.697  54.312  28.956  1.00  0.00          C   \nATOM   1828  CD  GLU A 237       5.727  54.795  27.526  1.00  0.00          C   \nATOM   1829  OE1 GLU A 237       6.257  54.058  26.671  1.00  0.00          O   \nATOM   1830  OE2 GLU A 237       5.241  55.916  27.253  1.00  0.00          O   \nATOM   1831  N   SER A 238       2.395  51.888  28.954  1.00  0.00          N   \nATOM   1832  CA  SER A 238       1.059  52.155  28.437  1.00  0.00          C   \nATOM   1833  C   SER A 238       0.112  52.379  29.619  1.00  0.00          C   \nATOM   1834  O   SER A 238      -0.862  53.136  29.512  1.00  0.00          O   \nATOM   1835  CB  SER A 238       0.557  51.008  27.532  1.00  0.00          C   \nATOM   1836  OG  SER A 238       0.308  49.818  28.263  1.00  0.00          O   \nATOM   1837  N   ILE A 239       0.401  51.734  30.748  1.00  0.00          N   \nATOM   1838  CA  ILE A 239      -0.430  51.914  31.937  1.00  0.00          C   \nATOM   1839  C   ILE A 239      -0.155  53.305  32.516  1.00  0.00          C   \nATOM   1840  O   ILE A 239      -1.090  54.080  32.736  1.00  0.00          O   \nATOM   1841  CB  ILE A 239      -0.151  50.828  33.002  1.00  0.00          C   \nATOM   1842  CG1 ILE A 239      -0.538  49.457  32.439  1.00  0.00          C   \nATOM   1843  CG2 ILE A 239      -0.963  51.111  34.276  1.00  0.00          C   \nATOM   1844  CD1 ILE A 239      -0.162  48.301  33.325  1.00  0.00          C   \nATOM   1845  N   TYR A 240       1.117  53.632  32.749  1.00  0.00          N   \nATOM   1846  CA  TYR A 240       1.451  54.959  33.285  1.00  0.00          C   \nATOM   1847  C   TYR A 240       0.820  56.064  32.434  1.00  0.00          C   \nATOM   1848  O   TYR A 240       0.257  57.019  32.968  1.00  0.00          O   \nATOM   1849  CB  TYR A 240       2.960  55.214  33.315  1.00  0.00          C   \nATOM   1850  CG  TYR A 240       3.801  54.179  34.024  1.00  0.00          C   \nATOM   1851  CD1 TYR A 240       3.360  53.548  35.190  1.00  0.00          C   \nATOM   1852  CD2 TYR A 240       5.068  53.861  33.542  1.00  0.00          C   \nATOM   1853  CE1 TYR A 240       4.177  52.618  35.851  1.00  0.00          C   \nATOM   1854  CE2 TYR A 240       5.884  52.947  34.188  1.00  0.00          C   \nATOM   1855  CZ  TYR A 240       5.437  52.325  35.335  1.00  0.00          C   \nATOM   1856  OH  TYR A 240       6.244  51.375  35.921  1.00  0.00          O   \nATOM   1857  N   GLN A 241       0.920  55.935  31.114  1.00  0.00          N   \nATOM   1858  CA  GLN A 241       0.368  56.943  30.210  1.00  0.00          C   \nATOM   1859  C   GLN A 241      -1.156  57.072  30.279  1.00  0.00          C   \nATOM   1860  O   GLN A 241      -1.715  58.026  29.743  1.00  0.00          O   \nATOM   1861  CB  GLN A 241       0.817  56.671  28.761  1.00  0.00          C   \nATOM   1862  CG  GLN A 241       2.339  56.763  28.537  1.00  0.00          C   \nATOM   1863  CD  GLN A 241       2.882  58.199  28.576  1.00  0.00          C   \nATOM   1864  OE1 GLN A 241       2.140  59.165  28.807  1.00  0.00          O   \nATOM   1865  NE2 GLN A 241       4.183  58.339  28.353  1.00  0.00          N   \nATOM   1866  N   CYS A 242      -1.837  56.129  30.935  1.00  0.00          N   \nATOM   1867  CA  CYS A 242      -3.289  56.241  31.059  1.00  0.00          C   \nATOM   1868  C   CYS A 242      -3.640  57.349  32.036  1.00  0.00          C   \nATOM   1869  O   CYS A 242      -4.723  57.926  31.958  1.00  0.00          O   \nATOM   1870  CB  CYS A 242      -3.914  54.933  31.548  1.00  0.00          C   \nATOM   1871  SG  CYS A 242      -4.017  53.676  30.266  1.00  0.00          S   \nATOM   1872  N   CYS A 243      -2.728  57.641  32.960  1.00  0.00          N   \nATOM   1873  CA  CYS A 243      -2.969  58.678  33.959  1.00  0.00          C   \nATOM   1874  C   CYS A 243      -3.146  60.065  33.339  1.00  0.00          C   \nATOM   1875  O   CYS A 243      -2.740  60.307  32.200  1.00  0.00          O   \nATOM   1876  CB  CYS A 243      -1.806  58.745  34.966  1.00  0.00          C   \nATOM   1877  SG  CYS A 243      -1.464  57.199  35.864  1.00  0.00          S   \nATOM   1878  N   ASP A 244      -3.769  60.961  34.097  1.00  0.00          N   \nATOM   1879  CA  ASP A 244      -3.956  62.354  33.684  1.00  0.00          C   \nATOM   1880  C   ASP A 244      -2.576  62.908  34.047  1.00  0.00          C   \nATOM   1881  O   ASP A 244      -2.191  62.910  35.218  1.00  0.00          O   \nATOM   1882  CB  ASP A 244      -5.041  62.999  34.556  1.00  0.00          C   \nATOM   1883  CG  ASP A 244      -5.233  64.490  34.285  1.00  0.00          C   \nATOM   1884  OD1 ASP A 244      -4.268  65.194  33.921  1.00  0.00          O   \nATOM   1885  OD2 ASP A 244      -6.372  64.969  34.473  1.00  0.00          O   \nATOM   1886  N   LEU A 245      -1.823  63.363  33.060  1.00  0.00          N   \nATOM   1887  CA  LEU A 245      -0.471  63.845  33.323  1.00  0.00          C   \nATOM   1888  C   LEU A 245      -0.126  65.113  32.562  1.00  0.00          C   \nATOM   1889  O   LEU A 245      -0.685  65.386  31.510  1.00  0.00          O   \nATOM   1890  CB  LEU A 245       0.539  62.779  32.898  1.00  0.00          C   \nATOM   1891  CG  LEU A 245       0.450  61.352  33.448  1.00  0.00          C   \nATOM   1892  CD1 LEU A 245       1.206  60.403  32.531  1.00  0.00          C   \nATOM   1893  CD2 LEU A 245       1.023  61.303  34.831  1.00  0.00          C   \nATOM   1894  N   ALA A 246       0.836  65.859  33.090  1.00  0.00          N   \nATOM   1895  CA  ALA A 246       1.318  67.076  32.446  1.00  0.00          C   \nATOM   1896  C   ALA A 246       2.030  66.624  31.174  1.00  0.00          C   \nATOM   1897  O   ALA A 246       2.636  65.551  31.144  1.00  0.00          O   \nATOM   1898  CB  ALA A 246       2.301  67.793  33.362  1.00  0.00          C   \nATOM   1899  N   PRO A 247       1.961  67.426  30.103  1.00  0.00          N   \nATOM   1900  CA  PRO A 247       2.624  67.049  28.850  1.00  0.00          C   \nATOM   1901  C   PRO A 247       4.111  66.711  29.011  1.00  0.00          C   \nATOM   1902  O   PRO A 247       4.623  65.803  28.371  1.00  0.00          O   \nATOM   1903  CB  PRO A 247       2.405  68.274  27.966  1.00  0.00          C   \nATOM   1904  CG  PRO A 247       1.066  68.763  28.412  1.00  0.00          C   \nATOM   1905  CD  PRO A 247       1.155  68.649  29.927  1.00  0.00          C   \nATOM   1906  N   GLU A 248       4.794  67.445  29.875  1.00  0.00          N   \nATOM   1907  CA  GLU A 248       6.217  67.241  30.099  1.00  0.00          C   \nATOM   1908  C   GLU A 248       6.447  65.905  30.806  1.00  0.00          C   \nATOM   1909  O   GLU A 248       7.462  65.241  30.596  1.00  0.00          O   \nATOM   1910  CB  GLU A 248       6.761  68.407  30.929  1.00  0.00          C   \nATOM   1911  CG  GLU A 248       8.227  68.729  30.719  1.00  0.00          C   \nATOM   1912  CD  GLU A 248       8.615  70.109  31.248  1.00  0.00          C   \nATOM   1913  OE1 GLU A 248       9.810  70.329  31.531  1.00  0.00          O   \nATOM   1914  OE2 GLU A 248       7.731  70.982  31.369  1.00  0.00          O   \nATOM   1915  N   ALA A 249       5.490  65.509  31.639  1.00  0.00          N   \nATOM   1916  CA  ALA A 249       5.596  64.249  32.360  1.00  0.00          C   \nATOM   1917  C   ALA A 249       5.471  63.076  31.393  1.00  0.00          C   \nATOM   1918  O   ALA A 249       6.205  62.093  31.509  1.00  0.00          O   \nATOM   1919  CB  ALA A 249       4.519  64.163  33.440  1.00  0.00          C   \nATOM   1920  N   ARG A 250       4.547  63.183  30.440  1.00  0.00          N   \nATOM   1921  CA  ARG A 250       4.345  62.126  29.449  1.00  0.00          C   \nATOM   1922  C   ARG A 250       5.600  61.905  28.625  1.00  0.00          C   \nATOM   1923  O   ARG A 250       6.019  60.765  28.394  1.00  0.00          O   \nATOM   1924  CB  ARG A 250       3.198  62.473  28.493  1.00  0.00          C   \nATOM   1925  CG  ARG A 250       1.831  62.529  29.149  1.00  0.00          C   \nATOM   1926  CD  ARG A 250       0.732  62.803  28.130  1.00  0.00          C   \nATOM   1927  NE  ARG A 250      -0.580  62.815  28.774  1.00  0.00          N   \nATOM   1928  CZ  ARG A 250      -1.168  61.743  29.302  1.00  0.00          C   \nATOM   1929  NH1 ARG A 250      -0.564  60.559  29.261  1.00  0.00          N   \nATOM   1930  NH2 ARG A 250      -2.357  61.858  29.881  1.00  0.00          N   \nATOM   1931  N   GLN A 251       6.189  63.005  28.169  1.00  0.00          N   \nATOM   1932  CA  GLN A 251       7.392  62.937  27.360  1.00  0.00          C   \nATOM   1933  C   GLN A 251       8.522  62.336  28.178  1.00  0.00          C   \nATOM   1934  O   GLN A 251       9.238  61.453  27.702  1.00  0.00          O   \nATOM   1935  CB  GLN A 251       7.792  64.336  26.872  1.00  0.00          C   \nATOM   1936  CG  GLN A 251       9.028  64.353  25.993  1.00  0.00          C   \nATOM   1937  CD  GLN A 251       8.765  63.767  24.622  1.00  0.00          C   \nATOM   1938  OE1 GLN A 251       7.702  63.205  24.370  1.00  0.00          O   \nATOM   1939  NE2 GLN A 251       9.731  63.895  23.728  1.00  0.00          N   \nATOM   1940  N   ALA A 252       8.688  62.810  29.409  1.00  0.00          N   \nATOM   1941  CA  ALA A 252       9.753  62.287  30.265  1.00  0.00          C   \nATOM   1942  C   ALA A 252       9.598  60.782  30.498  1.00  0.00          C   \nATOM   1943  O   ALA A 252      10.578  60.039  30.464  1.00  0.00          O   \nATOM   1944  CB  ALA A 252       9.766  63.011  31.596  1.00  0.00          C   \nATOM   1945  N   ILE A 253       8.364  60.337  30.722  1.00  0.00          N   \nATOM   1946  CA  ILE A 253       8.093  58.923  30.964  1.00  0.00          C   \nATOM   1947  C   ILE A 253       8.324  58.076  29.719  1.00  0.00          C   \nATOM   1948  O   ILE A 253       8.814  56.950  29.796  1.00  0.00          O   \nATOM   1949  CB  ILE A 253       6.658  58.740  31.475  1.00  0.00          C   \nATOM   1950  CG1 ILE A 253       6.566  59.274  32.908  1.00  0.00          C   \nATOM   1951  CG2 ILE A 253       6.257  57.273  31.419  1.00  0.00          C   \nATOM   1952  CD1 ILE A 253       5.151  59.488  33.373  1.00  0.00          C   \nATOM   1953  N   LYS A 254       7.960  58.612  28.567  1.00  0.00          N   \nATOM   1954  CA  LYS A 254       8.187  57.910  27.319  1.00  0.00          C   \nATOM   1955  C   LYS A 254       9.695  57.772  27.108  1.00  0.00          C   \nATOM   1956  O   LYS A 254      10.200  56.702  26.749  1.00  0.00          O   \nATOM   1957  CB  LYS A 254       7.587  58.712  26.162  1.00  0.00          C   \nATOM   1958  CG  LYS A 254       8.011  58.240  24.775  1.00  0.00          C   \nATOM   1959  CD  LYS A 254       7.259  59.018  23.705  1.00  0.00          C   \nATOM   1960  CE  LYS A 254       7.584  58.523  22.302  1.00  0.00          C   \nATOM   1961  NZ  LYS A 254       8.993  58.806  21.893  1.00  0.00          N   \nATOM   1962  N   SER A 255      10.409  58.870  27.335  1.00  0.00          N   \nATOM   1963  CA  SER A 255      11.855  58.895  27.145  1.00  0.00          C   \nATOM   1964  C   SER A 255      12.581  57.963  28.118  1.00  0.00          C   \nATOM   1965  O   SER A 255      13.455  57.201  27.706  1.00  0.00          O   \nATOM   1966  CB  SER A 255      12.374  60.334  27.285  1.00  0.00          C   \nATOM   1967  OG  SER A 255      13.747  60.420  26.952  1.00  0.00          O   \nATOM   1968  N   LEU A 256      12.208  58.006  29.396  1.00  0.00          N   \nATOM   1969  CA  LEU A 256      12.838  57.141  30.381  1.00  0.00          C   \nATOM   1970  C   LEU A 256      12.557  55.682  30.030  1.00  0.00          C   \nATOM   1971  O   LEU A 256      13.401  54.818  30.227  1.00  0.00          O   \nATOM   1972  CB  LEU A 256      12.325  57.459  31.790  1.00  0.00          C   \nATOM   1973  CG  LEU A 256      12.887  58.743  32.420  1.00  0.00          C   \nATOM   1974  CD1 LEU A 256      12.031  59.182  33.589  1.00  0.00          C   \nATOM   1975  CD2 LEU A 256      14.326  58.500  32.858  1.00  0.00          C   \nATOM   1976  N   THR A 257      11.373  55.403  29.501  1.00  0.00          N   \nATOM   1977  CA  THR A 257      11.056  54.023  29.134  1.00  0.00          C   \nATOM   1978  C   THR A 257      11.935  53.513  27.979  1.00  0.00          C   \nATOM   1979  O   THR A 257      12.536  52.447  28.081  1.00  0.00          O   \nATOM   1980  CB  THR A 257       9.568  53.864  28.737  1.00  0.00          C   \nATOM   1981  OG1 THR A 257       8.744  54.127  29.880  1.00  0.00          O   \nATOM   1982  CG2 THR A 257       9.288  52.424  28.246  1.00  0.00          C   \nATOM   1983  N   GLU A 258      12.012  54.273  26.890  1.00  0.00          N   \nATOM   1984  CA  GLU A 258      12.811  53.858  25.733  1.00  0.00          C   \nATOM   1985  C   GLU A 258      14.302  53.818  26.016  1.00  0.00          C   \nATOM   1986  O   GLU A 258      15.007  52.913  25.571  1.00  0.00          O   \nATOM   1987  CB  GLU A 258      12.624  54.817  24.549  1.00  0.00          C   \nATOM   1988  CG  GLU A 258      11.243  54.884  23.916  1.00  0.00          C   \nATOM   1989  CD  GLU A 258      10.809  53.576  23.263  1.00  0.00          C   \nATOM   1990  OE1 GLU A 258      11.657  52.820  22.735  1.00  0.00          O   \nATOM   1991  OE2 GLU A 258       9.592  53.319  23.267  1.00  0.00          O   \nATOM   1992  N   ARG A 259      14.775  54.812  26.753  1.00  0.00          N   \nATOM   1993  CA  ARG A 259      16.194  54.973  27.038  1.00  0.00          C   \nATOM   1994  C   ARG A 259      16.749  54.259  28.256  1.00  0.00          C   \nATOM   1995  O   ARG A 259      17.928  53.924  28.295  1.00  0.00          O   \nATOM   1996  CB  ARG A 259      16.507  56.472  27.159  1.00  0.00          C   \nATOM   1997  CG  ARG A 259      16.092  57.270  25.923  1.00  0.00          C   \nATOM   1998  CD  ARG A 259      16.373  58.741  26.097  1.00  0.00          C   \nATOM   1999  NE  ARG A 259      17.801  59.003  26.250  1.00  0.00          N   \nATOM   2000  CZ  ARG A 259      18.307  60.211  26.481  1.00  0.00          C   \nATOM   2001  NH1 ARG A 259      17.499  61.259  26.583  1.00  0.00          N   \nATOM   2002  NH2 ARG A 259      19.616  60.373  26.612  1.00  0.00          N   \nATOM   2003  N   LEU A 260      15.914  54.032  29.258  1.00  0.00          N   \nATOM   2004  CA  LEU A 260      16.407  53.395  30.470  1.00  0.00          C   \nATOM   2005  C   LEU A 260      15.678  52.128  30.926  1.00  0.00          C   \nATOM   2006  O   LEU A 260      16.298  51.090  31.133  1.00  0.00          O   \nATOM   2007  CB  LEU A 260      16.384  54.413  31.605  1.00  0.00          C   \nATOM   2008  CG  LEU A 260      16.689  53.907  33.016  1.00  0.00          C   \nATOM   2009  CD1 LEU A 260      18.139  53.479  33.093  1.00  0.00          C   \nATOM   2010  CD2 LEU A 260      16.401  55.004  34.038  1.00  0.00          C   \nATOM   2011  N   TYR A 261      14.366  52.223  31.099  1.00  0.00          N   \nATOM   2012  CA  TYR A 261      13.601  51.083  31.586  1.00  0.00          C   \nATOM   2013  C   TYR A 261      13.642  49.815  30.737  1.00  0.00          C   \nATOM   2014  O   TYR A 261      13.775  48.723  31.275  1.00  0.00          O   \nATOM   2015  CB  TYR A 261      12.159  51.515  31.858  1.00  0.00          C   \nATOM   2016  CG  TYR A 261      12.086  52.672  32.855  1.00  0.00          C   \nATOM   2017  CD1 TYR A 261      13.089  52.839  33.828  1.00  0.00          C   \nATOM   2018  CD2 TYR A 261      11.006  53.560  32.863  1.00  0.00          C   \nATOM   2019  CE1 TYR A 261      13.024  53.850  34.778  1.00  0.00          C   \nATOM   2020  CE2 TYR A 261      10.926  54.584  33.820  1.00  0.00          C   \nATOM   2021  CZ  TYR A 261      11.941  54.719  34.771  1.00  0.00          C   \nATOM   2022  OH  TYR A 261      11.889  55.724  35.704  1.00  0.00          O   \nATOM   2023  N   ILE A 262      13.554  49.957  29.421  1.00  0.00          N   \nATOM   2024  CA  ILE A 262      13.573  48.808  28.527  1.00  0.00          C   \nATOM   2025  C   ILE A 262      14.936  48.115  28.465  1.00  0.00          C   \nATOM   2026  O   ILE A 262      15.030  46.906  28.246  1.00  0.00          O   \nATOM   2027  CB  ILE A 262      13.142  49.244  27.104  1.00  0.00          C   \nATOM   2028  CG1 ILE A 262      11.657  49.632  27.113  1.00  0.00          C   \nATOM   2029  CG2 ILE A 262      13.420  48.138  26.097  1.00  0.00          C   \nATOM   2030  CD1 ILE A 262      11.122  49.965  25.733  1.00  0.00          C   \nATOM   2031  N   GLY A 263      16.001  48.874  28.659  1.00  0.00          N   \nATOM   2032  CA  GLY A 263      17.310  48.262  28.602  1.00  0.00          C   \nATOM   2033  C   GLY A 263      18.393  49.298  28.452  1.00  0.00          C   \nATOM   2034  O   GLY A 263      18.108  50.490  28.348  1.00  0.00          O   \nATOM   2035  N   GLY A 264      19.643  48.856  28.455  1.00  0.00          N   \nATOM   2036  CA  GLY A 264      20.721  49.807  28.306  1.00  0.00          C   \nATOM   2037  C   GLY A 264      22.078  49.212  28.590  1.00  0.00          C   \nATOM   2038  O   GLY A 264      22.170  48.100  29.108  1.00  0.00          O   \nATOM   2039  N   PRO A 265      23.154  49.934  28.251  1.00  0.00          N   \nATOM   2040  CA  PRO A 265      24.513  49.443  28.487  1.00  0.00          C   \nATOM   2041  C   PRO A 265      24.799  49.396  29.993  1.00  0.00          C   \nATOM   2042  O   PRO A 265      24.237  50.172  30.773  1.00  0.00          O   \nATOM   2043  CB  PRO A 265      25.379  50.444  27.712  1.00  0.00          C   \nATOM   2044  CG  PRO A 265      24.594  51.737  27.833  1.00  0.00          C   \nATOM   2045  CD  PRO A 265      23.160  51.283  27.645  1.00  0.00          C   \nATOM   2046  N   LEU A 266      25.653  48.459  30.385  1.00  0.00          N   \nATOM   2047  CA  LEU A 266      26.031  48.242  31.776  1.00  0.00          C   \nATOM   2048  C   LEU A 266      27.509  48.526  31.953  1.00  0.00          C   \nATOM   2049  O   LEU A 266      28.347  47.897  31.301  1.00  0.00          O   \nATOM   2050  CB  LEU A 266      25.789  46.780  32.154  1.00  0.00          C   \nATOM   2051  CG  LEU A 266      24.367  46.231  32.021  1.00  0.00          C   \nATOM   2052  CD1 LEU A 266      24.390  44.714  32.005  1.00  0.00          C   \nATOM   2053  CD2 LEU A 266      23.525  46.757  33.174  1.00  0.00          C   \nATOM   2054  N   THR A 267      27.825  49.443  32.862  1.00  0.00          N   \nATOM   2055  CA  THR A 267      29.205  49.815  33.141  1.00  0.00          C   \nATOM   2056  C   THR A 267      29.581  49.420  34.565  1.00  0.00          C   \nATOM   2057  O   THR A 267      28.780  49.597  35.491  1.00  0.00          O   \nATOM   2058  CB  THR A 267      29.373  51.327  32.986  1.00  0.00          C   \nATOM   2059  OG1 THR A 267      28.826  51.716  31.725  1.00  0.00          O   \nATOM   2060  CG2 THR A 267      30.848  51.727  33.036  1.00  0.00          C   \nATOM   2061  N   ASN A 268      30.784  48.871  34.747  1.00  0.00          N   \nATOM   2062  CA  ASN A 268      31.215  48.483  36.088  1.00  0.00          C   \nATOM   2063  C   ASN A 268      31.847  49.679  36.797  1.00  0.00          C   \nATOM   2064  O   ASN A 268      32.030  50.746  36.192  1.00  0.00          O   \nATOM   2065  CB  ASN A 268      32.193  47.287  36.047  1.00  0.00          C   \nATOM   2066  CG  ASN A 268      33.534  47.622  35.396  1.00  0.00          C   \nATOM   2067  OD1 ASN A 268      33.913  48.790  35.254  1.00  0.00          O   \nATOM   2068  ND2 ASN A 268      34.268  46.583  35.014  1.00  0.00          N   \nATOM   2069  N   SER A 269      32.192  49.503  38.070  1.00  0.00          N   \nATOM   2070  CA  SER A 269      32.765  50.581  38.862  1.00  0.00          C   \nATOM   2071  C   SER A 269      34.082  51.134  38.315  1.00  0.00          C   \nATOM   2072  O   SER A 269      34.514  52.213  38.711  1.00  0.00          O   \nATOM   2073  CB  SER A 269      32.981  50.110  40.302  1.00  0.00          C   \nATOM   2074  OG  SER A 269      34.017  49.147  40.353  1.00  0.00          O   \nATOM   2075  N   LYS A 270      34.726  50.393  37.420  1.00  0.00          N   \nATOM   2076  CA  LYS A 270      35.987  50.844  36.850  1.00  0.00          C   \nATOM   2077  C   LYS A 270      35.792  51.619  35.554  1.00  0.00          C   \nATOM   2078  O   LYS A 270      36.737  52.202  35.037  1.00  0.00          O   \nATOM   2079  CB  LYS A 270      36.917  49.656  36.599  1.00  0.00          C   \nATOM   2080  CG  LYS A 270      37.322  48.903  37.869  1.00  0.00          C   \nATOM   2081  CD  LYS A 270      38.590  48.075  37.672  1.00  0.00          C   \nATOM   2082  CE  LYS A 270      38.496  47.173  36.452  1.00  0.00          C   \nATOM   2083  NZ  LYS A 270      39.725  46.342  36.322  1.00  0.00          N   \nATOM   2084  N   GLY A 271      34.570  51.628  35.031  1.00  0.00          N   \nATOM   2085  CA  GLY A 271      34.317  52.350  33.798  1.00  0.00          C   \nATOM   2086  C   GLY A 271      34.340  51.490  32.545  1.00  0.00          C   \nATOM   2087  O   GLY A 271      34.297  52.011  31.438  1.00  0.00          O   \nATOM   2088  N   GLN A 272      34.418  50.171  32.706  1.00  0.00          N   \nATOM   2089  CA  GLN A 272      34.428  49.270  31.552  1.00  0.00          C   \nATOM   2090  C   GLN A 272      33.003  48.868  31.175  1.00  0.00          C   \nATOM   2091  O   GLN A 272      32.116  48.795  32.034  1.00  0.00          O   \nATOM   2092  CB  GLN A 272      35.220  47.995  31.868  1.00  0.00          C   \nATOM   2093  CG  GLN A 272      36.674  48.213  32.218  1.00  0.00          C   \nATOM   2094  CD  GLN A 272      37.351  46.934  32.662  1.00  0.00          C   \nATOM   2095  OE1 GLN A 272      36.943  46.310  33.647  1.00  0.00          O   \nATOM   2096  NE2 GLN A 272      38.395  46.535  31.940  1.00  0.00          N   \nATOM   2097  N   ASN A 273      32.786  48.603  29.890  1.00  0.00          N   \nATOM   2098  CA  ASN A 273      31.474  48.171  29.432  1.00  0.00          C   \nATOM   2099  C   ASN A 273      31.372  46.690  29.758  1.00  0.00          C   \nATOM   2100  O   ASN A 273      32.155  45.890  29.249  1.00  0.00          O   \nATOM   2101  CB  ASN A 273      31.325  48.346  27.922  1.00  0.00          C   \nATOM   2102  CG  ASN A 273      30.018  47.764  27.411  1.00  0.00          C   \nATOM   2103  OD1 ASN A 273      29.972  47.126  26.361  1.00  0.00          O   \nATOM   2104  ND2 ASN A 273      28.953  47.975  28.162  1.00  0.00          N   \nATOM   2105  N   CYS A 274      30.410  46.332  30.608  1.00  0.00          N   \nATOM   2106  CA  CYS A 274      30.205  44.947  31.016  1.00  0.00          C   \nATOM   2107  C   CYS A 274      29.265  44.202  30.102  1.00  0.00          C   \nATOM   2108  O   CYS A 274      29.295  42.976  30.040  1.00  0.00          O   \nATOM   2109  CB  CYS A 274      29.623  44.892  32.426  1.00  0.00          C   \nATOM   2110  SG  CYS A 274      30.833  45.132  33.685  1.00  0.00          S   \nATOM   2111  N   GLY A 275      28.407  44.947  29.415  1.00  0.00          N   \nATOM   2112  CA  GLY A 275      27.444  44.325  28.534  1.00  0.00          C   \nATOM   2113  C   GLY A 275      26.177  45.140  28.349  1.00  0.00          C   \nATOM   2114  O   GLY A 275      26.193  46.370  28.468  1.00  0.00          O   \nATOM   2115  N   TYR A 276      25.073  44.441  28.101  1.00  0.00          N   \nATOM   2116  CA  TYR A 276      23.796  45.080  27.833  1.00  0.00          C   \nATOM   2117  C   TYR A 276      22.635  44.388  28.525  1.00  0.00          C   \nATOM   2118  O   TYR A 276      22.579  43.153  28.586  1.00  0.00          O   \nATOM   2119  CB  TYR A 276      23.524  45.073  26.318  1.00  0.00          C   \nATOM   2120  CG  TYR A 276      22.616  46.196  25.871  1.00  0.00          C   \nATOM   2121  CD1 TYR A 276      23.129  47.475  25.639  1.00  0.00          C   \nATOM   2122  CD2 TYR A 276      21.242  45.999  25.718  1.00  0.00          C   \nATOM   2123  CE1 TYR A 276      22.298  48.532  25.266  1.00  0.00          C   \nATOM   2124  CE2 TYR A 276      20.395  47.061  25.341  1.00  0.00          C   \nATOM   2125  CZ  TYR A 276      20.942  48.321  25.120  1.00  0.00          C   \nATOM   2126  OH  TYR A 276      20.138  49.374  24.766  1.00  0.00          O   \nATOM   2127  N   ARG A 277      21.701  45.192  29.029  1.00  0.00          N   \nATOM   2128  CA  ARG A 277      20.505  44.694  29.723  1.00  0.00          C   \nATOM   2129  C   ARG A 277      19.265  44.867  28.855  1.00  0.00          C   \nATOM   2130  O   ARG A 277      19.100  45.904  28.205  1.00  0.00          O   \nATOM   2131  CB  ARG A 277      20.305  45.469  31.040  1.00  0.00          C   \nATOM   2132  CG  ARG A 277      18.942  45.300  31.734  1.00  0.00          C   \nATOM   2133  CD  ARG A 277      18.820  46.210  32.976  1.00  0.00          C   \nATOM   2134  NE  ARG A 277      19.036  47.629  32.652  1.00  0.00          N   \nATOM   2135  CZ  ARG A 277      18.101  48.451  32.183  1.00  0.00          C   \nATOM   2136  NH1 ARG A 277      16.857  48.027  31.989  1.00  0.00          N   \nATOM   2137  NH2 ARG A 277      18.422  49.693  31.858  1.00  0.00          N   \nATOM   2138  N   ARG A 278      18.407  43.846  28.824  1.00  0.00          N   \nATOM   2139  CA  ARG A 278      17.154  43.929  28.075  1.00  0.00          C   \nATOM   2140  C   ARG A 278      16.025  43.448  28.982  1.00  0.00          C   \nATOM   2141  O   ARG A 278      15.003  42.941  28.501  1.00  0.00          O   \nATOM   2142  CB  ARG A 278      17.189  43.060  26.808  1.00  0.00          C   \nATOM   2143  CG  ARG A 278      18.302  43.433  25.836  1.00  0.00          C   \nATOM   2144  CD  ARG A 278      18.177  42.670  24.535  1.00  0.00          C   \nATOM   2145  NE  ARG A 278      19.444  42.678  23.810  1.00  0.00          N   \nATOM   2146  CZ  ARG A 278      20.551  42.053  24.210  1.00  0.00          C   \nATOM   2147  NH1 ARG A 278      21.651  42.141  23.473  1.00  0.00          N   \nATOM   2148  NH2 ARG A 278      20.563  41.329  25.328  1.00  0.00          N   \nATOM   2149  N   CYS A 279      16.214  43.601  30.289  1.00  0.00          N   \nATOM   2150  CA  CYS A 279      15.206  43.180  31.264  1.00  0.00          C   \nATOM   2151  C   CYS A 279      15.062  44.279  32.298  1.00  0.00          C   \nATOM   2152  O   CYS A 279      15.603  45.366  32.105  1.00  0.00          O   \nATOM   2153  CB  CYS A 279      15.623  41.852  31.934  1.00  0.00          C   \nATOM   2154  SG  CYS A 279      17.173  41.896  32.874  1.00  0.00          S   \nATOM   2155  N   ARG A 280      14.325  44.025  33.383  1.00  0.00          N   \nATOM   2156  CA  ARG A 280      14.145  45.032  34.444  1.00  0.00          C   \nATOM   2157  C   ARG A 280      15.455  45.463  35.120  1.00  0.00          C   \nATOM   2158  O   ARG A 280      16.286  44.619  35.472  1.00  0.00          O   \nATOM   2159  CB  ARG A 280      13.207  44.501  35.551  1.00  0.00          C   \nATOM   2160  CG  ARG A 280      13.090  45.423  36.791  1.00  0.00          C   \nATOM   2161  CD  ARG A 280      12.097  46.564  36.561  1.00  0.00          C   \nATOM   2162  NE  ARG A 280      10.755  45.988  36.520  1.00  0.00          N   \nATOM   2163  CZ  ARG A 280       9.771  46.381  35.725  1.00  0.00          C   \nATOM   2164  NH1 ARG A 280       9.933  47.396  34.873  1.00  0.00          N   \nATOM   2165  NH2 ARG A 280       8.637  45.702  35.741  1.00  0.00          N   \nATOM   2166  N   ALA A 281      15.641  46.777  35.285  1.00  0.00          N   \nATOM   2167  CA  ALA A 281      16.803  47.305  35.997  1.00  0.00          C   \nATOM   2168  C   ALA A 281      16.369  47.260  37.462  1.00  0.00          C   \nATOM   2169  O   ALA A 281      15.276  47.721  37.813  1.00  0.00          O   \nATOM   2170  CB  ALA A 281      17.097  48.756  35.586  1.00  0.00          C   \nATOM   2171  N   SER A 282      17.210  46.711  38.320  1.00  0.00          N   \nATOM   2172  CA  SER A 282      16.854  46.591  39.728  1.00  0.00          C   \nATOM   2173  C   SER A 282      16.936  47.877  40.526  1.00  0.00          C   \nATOM   2174  O   SER A 282      16.354  47.976  41.602  1.00  0.00          O   \nATOM   2175  CB  SER A 282      17.731  45.539  40.402  1.00  0.00          C   \nATOM   2176  OG  SER A 282      19.098  45.914  40.357  1.00  0.00          O   \nATOM   2177  N   GLY A 283      17.643  48.863  39.994  1.00  0.00          N   \nATOM   2178  CA  GLY A 283      17.807  50.103  40.723  1.00  0.00          C   \nATOM   2179  C   GLY A 283      17.234  51.364  40.120  1.00  0.00          C   \nATOM   2180  O   GLY A 283      17.930  52.378  40.059  1.00  0.00          O   \nATOM   2181  N   VAL A 284      15.990  51.306  39.656  1.00  0.00          N   \nATOM   2182  CA  VAL A 284      15.332  52.489  39.109  1.00  0.00          C   \nATOM   2183  C   VAL A 284      14.081  52.767  39.951  1.00  0.00          C   \nATOM   2184  O   VAL A 284      13.653  51.923  40.741  1.00  0.00          O   \nATOM   2185  CB  VAL A 284      14.967  52.335  37.587  1.00  0.00          C   \nATOM   2186  CG1 VAL A 284      16.248  52.399  36.759  1.00  0.00          C   \nATOM   2187  CG2 VAL A 284      14.196  51.013  37.320  1.00  0.00          C   \nATOM   2188  N   LEU A 285      13.494  53.943  39.791  1.00  0.00          N   \nATOM   2189  CA  LEU A 285      12.330  54.308  40.594  1.00  0.00          C   \nATOM   2190  C   LEU A 285      11.112  53.403  40.400  1.00  0.00          C   \nATOM   2191  O   LEU A 285      10.378  53.124  41.351  1.00  0.00          O   \nATOM   2192  CB  LEU A 285      11.927  55.757  40.319  1.00  0.00          C   \nATOM   2193  CG  LEU A 285      10.714  56.232  41.131  1.00  0.00          C   \nATOM   2194  CD1 LEU A 285      10.928  56.005  42.620  1.00  0.00          C   \nATOM   2195  CD2 LEU A 285      10.490  57.706  40.844  1.00  0.00          C   \nATOM   2196  N   THR A 286      10.909  52.961  39.163  1.00  0.00          N   \nATOM   2197  CA  THR A 286       9.784  52.103  38.797  1.00  0.00          C   \nATOM   2198  C   THR A 286      10.013  50.605  39.010  1.00  0.00          C   \nATOM   2199  O   THR A 286       9.185  49.800  38.584  1.00  0.00          O   \nATOM   2200  CB  THR A 286       9.435  52.283  37.312  1.00  0.00          C   \nATOM   2201  OG1 THR A 286      10.640  52.140  36.548  1.00  0.00          O   \nATOM   2202  CG2 THR A 286       8.791  53.655  37.047  1.00  0.00          C   \nATOM   2203  N   THR A 287      11.119  50.208  39.634  1.00  0.00          N   \nATOM   2204  CA  THR A 287      11.330  48.769  39.832  1.00  0.00          C   \nATOM   2205  C   THR A 287      10.209  48.103  40.647  1.00  0.00          C   \nATOM   2206  O   THR A 287       9.683  47.070  40.258  1.00  0.00          O   \nATOM   2207  CB  THR A 287      12.667  48.477  40.525  1.00  0.00          C   \nATOM   2208  OG1 THR A 287      13.734  49.028  39.751  1.00  0.00          O   \nATOM   2209  CG2 THR A 287      12.885  46.948  40.641  1.00  0.00          C   \nATOM   2210  N   SER A 288       9.845  48.687  41.781  1.00  0.00          N   \nATOM   2211  CA  SER A 288       8.794  48.113  42.610  1.00  0.00          C   \nATOM   2212  C   SER A 288       7.401  48.205  41.964  1.00  0.00          C   \nATOM   2213  O   SER A 288       6.672  47.205  41.875  1.00  0.00          O   \nATOM   2214  CB  SER A 288       8.796  48.796  43.976  1.00  0.00          C   \nATOM   2215  OG  SER A 288       7.762  48.258  44.764  1.00  0.00          O   \nATOM   2216  N   CYS A 289       7.033  49.398  41.505  1.00  0.00          N   \nATOM   2217  CA  CYS A 289       5.744  49.589  40.860  1.00  0.00          C   \nATOM   2218  C   CYS A 289       5.645  48.738  39.594  1.00  0.00          C   \nATOM   2219  O   CYS A 289       4.652  48.046  39.386  1.00  0.00          O   \nATOM   2220  CB  CYS A 289       5.540  51.070  40.514  1.00  0.00          C   \nATOM   2221  SG  CYS A 289       3.929  51.407  39.761  1.00  0.00          S   \nATOM   2222  N   GLY A 290       6.668  48.782  38.743  1.00  0.00          N   \nATOM   2223  CA  GLY A 290       6.636  47.982  37.531  1.00  0.00          C   \nATOM   2224  C   GLY A 290       6.514  46.484  37.834  1.00  0.00          C   \nATOM   2225  O   GLY A 290       5.695  45.795  37.237  1.00  0.00          O   \nATOM   2226  N   ASN A 291       7.327  45.970  38.749  1.00  0.00          N   \nATOM   2227  CA  ASN A 291       7.234  44.555  39.092  1.00  0.00          C   \nATOM   2228  C   ASN A 291       5.862  44.178  39.670  1.00  0.00          C   \nATOM   2229  O   ASN A 291       5.303  43.121  39.349  1.00  0.00          O   \nATOM   2230  CB  ASN A 291       8.331  44.176  40.082  1.00  0.00          C   \nATOM   2231  CG  ASN A 291       9.671  44.013  39.419  1.00  0.00          C   \nATOM   2232  OD1 ASN A 291       9.771  44.008  38.187  1.00  0.00          O   \nATOM   2233  ND2 ASN A 291      10.722  43.867  40.230  1.00  0.00          N   \nATOM   2234  N   THR A 292       5.318  45.035  40.524  1.00  0.00          N   \nATOM   2235  CA  THR A 292       4.018  44.764  41.120  1.00  0.00          C   \nATOM   2236  C   THR A 292       2.941  44.723  40.029  1.00  0.00          C   \nATOM   2237  O   THR A 292       2.139  43.783  39.976  1.00  0.00          O   \nATOM   2238  CB  THR A 292       3.633  45.835  42.173  1.00  0.00          C   \nATOM   2239  OG1 THR A 292       4.635  45.900  43.203  1.00  0.00          O   \nATOM   2240  CG2 THR A 292       2.300  45.483  42.814  1.00  0.00          C   \nATOM   2241  N   LEU A 293       2.903  45.731  39.166  1.00  0.00          N   \nATOM   2242  CA  LEU A 293       1.901  45.746  38.093  1.00  0.00          C   \nATOM   2243  C   LEU A 293       2.020  44.510  37.193  1.00  0.00          C   \nATOM   2244  O   LEU A 293       1.030  43.837  36.892  1.00  0.00          O   \nATOM   2245  CB  LEU A 293       2.049  47.013  37.237  1.00  0.00          C   \nATOM   2246  CG  LEU A 293       1.630  48.338  37.886  1.00  0.00          C   \nATOM   2247  CD1 LEU A 293       2.019  49.527  36.976  1.00  0.00          C   \nATOM   2248  CD2 LEU A 293       0.121  48.313  38.131  1.00  0.00          C   \nATOM   2249  N   THR A 294       3.242  44.218  36.769  1.00  0.00          N   \nATOM   2250  CA  THR A 294       3.502  43.086  35.896  1.00  0.00          C   \nATOM   2251  C   THR A 294       3.191  41.743  36.567  1.00  0.00          C   \nATOM   2252  O   THR A 294       2.595  40.858  35.960  1.00  0.00          O   \nATOM   2253  CB  THR A 294       4.962  43.133  35.420  1.00  0.00          C   \nATOM   2254  OG1 THR A 294       5.153  44.333  34.651  1.00  0.00          O   \nATOM   2255  CG2 THR A 294       5.316  41.889  34.564  1.00  0.00          C   \nATOM   2256  N   CYS A 295       3.570  41.594  37.825  1.00  0.00          N   \nATOM   2257  CA  CYS A 295       3.305  40.349  38.529  1.00  0.00          C   \nATOM   2258  C   CYS A 295       1.799  40.167  38.687  1.00  0.00          C   \nATOM   2259  O   CYS A 295       1.266  39.062  38.512  1.00  0.00          O   \nATOM   2260  CB  CYS A 295       3.972  40.367  39.904  1.00  0.00          C   \nATOM   2261  SG  CYS A 295       3.818  38.802  40.800  1.00  0.00          S   \nATOM   2262  N   TYR A 296       1.123  41.262  39.015  1.00  0.00          N   \nATOM   2263  CA  TYR A 296      -0.324  41.259  39.197  1.00  0.00          C   \nATOM   2264  C   TYR A 296      -1.043  40.971  37.874  1.00  0.00          C   \nATOM   2265  O   TYR A 296      -2.015  40.208  37.835  1.00  0.00          O   \nATOM   2266  CB  TYR A 296      -0.777  42.612  39.749  1.00  0.00          C   \nATOM   2267  CG  TYR A 296      -2.276  42.772  39.831  1.00  0.00          C   \nATOM   2268  CD1 TYR A 296      -3.016  43.224  38.737  1.00  0.00          C   \nATOM   2269  CD2 TYR A 296      -2.953  42.477  41.006  1.00  0.00          C   \nATOM   2270  CE1 TYR A 296      -4.398  43.382  38.821  1.00  0.00          C   \nATOM   2271  CE2 TYR A 296      -4.334  42.629  41.102  1.00  0.00          C   \nATOM   2272  CZ  TYR A 296      -5.053  43.081  40.012  1.00  0.00          C   \nATOM   2273  OH  TYR A 296      -6.418  43.229  40.132  1.00  0.00          O   \nATOM   2274  N   LEU A 297      -0.568  41.586  36.795  1.00  0.00          N   \nATOM   2275  CA  LEU A 297      -1.168  41.371  35.489  1.00  0.00          C   \nATOM   2276  C   LEU A 297      -1.057  39.892  35.087  1.00  0.00          C   \nATOM   2277  O   LEU A 297      -2.057  39.248  34.752  1.00  0.00          O   \nATOM   2278  CB  LEU A 297      -0.481  42.255  34.443  1.00  0.00          C   \nATOM   2279  CG  LEU A 297      -0.724  41.945  32.954  1.00  0.00          C   \nATOM   2280  CD1 LEU A 297      -2.217  41.944  32.641  1.00  0.00          C   \nATOM   2281  CD2 LEU A 297      -0.012  42.976  32.099  1.00  0.00          C   \nATOM   2282  N   LYS A 298       0.155  39.353  35.119  1.00  0.00          N   \nATOM   2283  CA  LYS A 298       0.359  37.950  34.739  1.00  0.00          C   \nATOM   2284  C   LYS A 298      -0.398  36.966  35.628  1.00  0.00          C   \nATOM   2285  O   LYS A 298      -1.029  36.033  35.134  1.00  0.00          O   \nATOM   2286  CB  LYS A 298       1.855  37.607  34.737  1.00  0.00          C   \nATOM   2287  CG  LYS A 298       2.613  38.360  33.652  1.00  0.00          C   \nATOM   2288  CD  LYS A 298       4.126  38.119  33.683  1.00  0.00          C   \nATOM   2289  CE  LYS A 298       4.813  38.926  32.561  1.00  0.00          C   \nATOM   2290  NZ  LYS A 298       6.279  38.693  32.496  1.00  0.00          N   \nATOM   2291  N   ALA A 299      -0.333  37.177  36.939  1.00  0.00          N   \nATOM   2292  CA  ALA A 299      -1.021  36.298  37.883  1.00  0.00          C   \nATOM   2293  C   ALA A 299      -2.534  36.367  37.713  1.00  0.00          C   \nATOM   2294  O   ALA A 299      -3.202  35.334  37.724  1.00  0.00          O   \nATOM   2295  CB  ALA A 299      -0.633  36.651  39.318  1.00  0.00          C   \nATOM   2296  N   SER A 300      -3.081  37.575  37.555  1.00  0.00          N   \nATOM   2297  CA  SER A 300      -4.525  37.725  37.369  1.00  0.00          C   \nATOM   2298  C   SER A 300      -4.970  36.957  36.130  1.00  0.00          C   \nATOM   2299  O   SER A 300      -5.954  36.216  36.169  1.00  0.00          O   \nATOM   2300  CB  SER A 300      -4.917  39.200  37.216  1.00  0.00          C   \nATOM   2301  OG  SER A 300      -4.704  39.911  38.421  1.00  0.00          O   \nATOM   2302  N   ALA A 301      -4.253  37.142  35.027  1.00  0.00          N   \nATOM   2303  CA  ALA A 301      -4.574  36.436  33.794  1.00  0.00          C   \nATOM   2304  C   ALA A 301      -4.377  34.923  33.986  1.00  0.00          C   \nATOM   2305  O   ALA A 301      -5.193  34.120  33.521  1.00  0.00          O   \nATOM   2306  CB  ALA A 301      -3.689  36.947  32.654  1.00  0.00          C   \nATOM   2307  N   ALA A 302      -3.305  34.529  34.672  1.00  0.00          N   \nATOM   2308  CA  ALA A 302      -3.054  33.104  34.899  1.00  0.00          C   \nATOM   2309  C   ALA A 302      -4.171  32.447  35.730  1.00  0.00          C   \nATOM   2310  O   ALA A 302      -4.499  31.270  35.527  1.00  0.00          O   \nATOM   2311  CB  ALA A 302      -1.705  32.912  35.597  1.00  0.00          C   \nATOM   2312  N   CYS A 303      -4.736  33.199  36.676  1.00  0.00          N   \nATOM   2313  CA  CYS A 303      -5.811  32.678  37.522  1.00  0.00          C   \nATOM   2314  C   CYS A 303      -7.019  32.317  36.655  1.00  0.00          C   \nATOM   2315  O   CYS A 303      -7.678  31.299  36.884  1.00  0.00          O   \nATOM   2316  CB  CYS A 303      -6.220  33.712  38.577  1.00  0.00          C   \nATOM   2317  SG  CYS A 303      -7.962  33.578  39.122  1.00  0.00          S   \nATOM   2318  N   ARG A 304      -7.302  33.162  35.665  1.00  0.00          N   \nATOM   2319  CA  ARG A 304      -8.411  32.932  34.741  1.00  0.00          C   \nATOM   2320  C   ARG A 304      -8.088  31.733  33.862  1.00  0.00          C   \nATOM   2321  O   ARG A 304      -8.917  30.848  33.671  1.00  0.00          O   \nATOM   2322  CB  ARG A 304      -8.637  34.166  33.867  1.00  0.00          C   \nATOM   2323  CG  ARG A 304      -8.862  35.421  34.679  1.00  0.00          C   \nATOM   2324  CD  ARG A 304      -9.287  36.579  33.818  1.00  0.00          C   \nATOM   2325  NE  ARG A 304      -9.653  37.735  34.640  1.00  0.00          N   \nATOM   2326  CZ  ARG A 304     -10.221  38.838  34.161  1.00  0.00          C   \nATOM   2327  NH1 ARG A 304     -10.490  38.939  32.870  1.00  0.00          N   \nATOM   2328  NH2 ARG A 304     -10.522  39.841  34.971  1.00  0.00          N   \nATOM   2329  N   ALA A 305      -6.876  31.706  33.321  1.00  0.00          N   \nATOM   2330  CA  ALA A 305      -6.464  30.588  32.488  1.00  0.00          C   \nATOM   2331  C   ALA A 305      -6.591  29.259  33.251  1.00  0.00          C   \nATOM   2332  O   ALA A 305      -7.065  28.265  32.696  1.00  0.00          O   \nATOM   2333  CB  ALA A 305      -5.024  30.786  32.025  1.00  0.00          C   \nATOM   2334  N   ALA A 306      -6.177  29.240  34.520  1.00  0.00          N   \nATOM   2335  CA  ALA A 306      -6.229  28.016  35.327  1.00  0.00          C   \nATOM   2336  C   ALA A 306      -7.596  27.717  35.938  1.00  0.00          C   \nATOM   2337  O   ALA A 306      -7.778  26.701  36.611  1.00  0.00          O   \nATOM   2338  CB  ALA A 306      -5.163  28.064  36.434  1.00  0.00          C   \nATOM   2339  N   LYS A 307      -8.552  28.607  35.720  1.00  0.00          N   \nATOM   2340  CA  LYS A 307      -9.897  28.411  36.236  1.00  0.00          C   \nATOM   2341  C   LYS A 307      -9.954  28.350  37.759  1.00  0.00          C   \nATOM   2342  O   LYS A 307     -10.750  27.601  38.336  1.00  0.00          O   \nATOM   2343  CB  LYS A 307     -10.494  27.134  35.647  1.00  0.00          C   \nATOM   2344  CG  LYS A 307     -10.636  27.168  34.140  1.00  0.00          C   \nATOM   2345  CD  LYS A 307     -10.672  25.758  33.576  1.00  0.00          C   \nATOM   2346  CE  LYS A 307      -9.334  25.040  33.804  1.00  0.00          C   \nATOM   2347  NZ  LYS A 307      -9.405  23.585  33.476  1.00  0.00          N   \nATOM   2348  N   LEU A 308      -9.103  29.135  38.409  1.00  0.00          N   \nATOM   2349  CA  LEU A 308      -9.098  29.186  39.859  1.00  0.00          C   \nATOM   2350  C   LEU A 308     -10.385  29.860  40.305  1.00  0.00          C   \nATOM   2351  O   LEU A 308     -10.784  30.883  39.749  1.00  0.00          O   \nATOM   2352  CB  LEU A 308      -7.887  29.983  40.362  1.00  0.00          C   \nATOM   2353  CG  LEU A 308      -6.626  29.199  40.739  1.00  0.00          C   \nATOM   2354  CD1 LEU A 308      -6.308  28.143  39.694  1.00  0.00          C   \nATOM   2355  CD2 LEU A 308      -5.467  30.176  40.906  1.00  0.00          C   \nATOM   2356  N   GLN A 309     -11.032  29.285  41.310  1.00  0.00          N   \nATOM   2357  CA  GLN A 309     -12.274  29.835  41.840  1.00  0.00          C   \nATOM   2358  C   GLN A 309     -12.016  30.999  42.803  1.00  0.00          C   \nATOM   2359  O   GLN A 309     -11.299  30.856  43.791  1.00  0.00          O   \nATOM   2360  CB  GLN A 309     -13.049  28.727  42.560  1.00  0.00          C   \nATOM   2361  CG  GLN A 309     -14.235  28.155  41.794  1.00  0.00          C   \nATOM   2362  CD  GLN A 309     -15.542  28.855  42.142  1.00  0.00          C   \nATOM   2363  OE1 GLN A 309     -16.161  29.511  41.297  1.00  0.00          O   \nATOM   2364  NE2 GLN A 309     -15.968  28.718  43.397  1.00  0.00          N   \nATOM   2365  N   ASP A 310     -12.608  32.147  42.501  1.00  0.00          N   \nATOM   2366  CA  ASP A 310     -12.478  33.348  43.319  1.00  0.00          C   \nATOM   2367  C   ASP A 310     -11.062  33.612  43.851  1.00  0.00          C   \nATOM   2368  O   ASP A 310     -10.855  33.777  45.056  1.00  0.00          O   \nATOM   2369  CB  ASP A 310     -13.478  33.297  44.482  1.00  0.00          C   \nATOM   2370  CG  ASP A 310     -13.470  34.571  45.314  1.00  0.00          C   \nATOM   2371  OD1 ASP A 310     -13.340  35.666  44.724  1.00  0.00          O   \nATOM   2372  OD2 ASP A 310     -13.605  34.484  46.556  1.00  0.00          O   \nATOM   2373  N   CYS A 311     -10.091  33.668  42.943  1.00  0.00          N   \nATOM   2374  CA  CYS A 311      -8.714  33.923  43.330  1.00  0.00          C   \nATOM   2375  C   CYS A 311      -8.536  35.356  43.819  1.00  0.00          C   \nATOM   2376  O   CYS A 311      -8.888  36.314  43.122  1.00  0.00          O   \nATOM   2377  CB  CYS A 311      -7.776  33.689  42.143  1.00  0.00          C   \nATOM   2378  SG  CYS A 311      -8.092  34.806  40.734  1.00  0.00          S   \nATOM   2379  N   THR A 312      -8.024  35.511  45.033  1.00  0.00          N   \nATOM   2380  CA  THR A 312      -7.756  36.843  45.563  1.00  0.00          C   \nATOM   2381  C   THR A 312      -6.241  36.897  45.649  1.00  0.00          C   \nATOM   2382  O   THR A 312      -5.600  35.925  46.054  1.00  0.00          O   \nATOM   2383  CB  THR A 312      -8.325  37.055  46.969  1.00  0.00          C   \nATOM   2384  OG1 THR A 312      -9.753  36.978  46.927  1.00  0.00          O   \nATOM   2385  CG2 THR A 312      -7.906  38.430  47.508  1.00  0.00          C   \nATOM   2386  N   MET A 313      -5.653  38.019  45.267  1.00  0.00          N   \nATOM   2387  CA  MET A 313      -4.207  38.094  45.319  1.00  0.00          C   \nATOM   2388  C   MET A 313      -3.626  39.184  46.181  1.00  0.00          C   \nATOM   2389  O   MET A 313      -4.273  40.197  46.458  1.00  0.00          O   \nATOM   2390  CB  MET A 313      -3.655  38.197  43.904  1.00  0.00          C   \nATOM   2391  CG  MET A 313      -3.955  36.940  43.124  1.00  0.00          C   \nATOM   2392  SD  MET A 313      -3.504  37.039  41.439  1.00  0.00          S   \nATOM   2393  CE  MET A 313      -4.300  38.628  41.015  1.00  0.00          C   \nATOM   2394  N   LEU A 314      -2.399  38.940  46.629  1.00  0.00          N   \nATOM   2395  CA  LEU A 314      -1.661  39.902  47.439  1.00  0.00          C   \nATOM   2396  C   LEU A 314      -0.304  39.958  46.756  1.00  0.00          C   \nATOM   2397  O   LEU A 314       0.424  38.969  46.713  1.00  0.00          O   \nATOM   2398  CB  LEU A 314      -1.542  39.437  48.895  1.00  0.00          C   \nATOM   2399  CG  LEU A 314      -0.852  40.454  49.816  1.00  0.00          C   \nATOM   2400  CD1 LEU A 314      -1.546  41.820  49.711  1.00  0.00          C   \nATOM   2401  CD2 LEU A 314      -0.885  39.944  51.256  1.00  0.00          C   \nATOM   2402  N   VAL A 315       0.006  41.125  46.206  1.00  0.00          N   \nATOM   2403  CA  VAL A 315       1.224  41.330  45.448  1.00  0.00          C   \nATOM   2404  C   VAL A 315       2.079  42.479  45.979  1.00  0.00          C   \nATOM   2405  O   VAL A 315       1.578  43.571  46.238  1.00  0.00          O   \nATOM   2406  CB  VAL A 315       0.872  41.630  43.958  1.00  0.00          C   \nATOM   2407  CG1 VAL A 315       2.131  41.624  43.100  1.00  0.00          C   \nATOM   2408  CG2 VAL A 315      -0.135  40.606  43.433  1.00  0.00          C   \nATOM   2409  N   ASN A 316       3.368  42.206  46.134  1.00  0.00          N   \nATOM   2410  CA  ASN A 316       4.360  43.180  46.607  1.00  0.00          C   \nATOM   2411  C   ASN A 316       5.552  42.999  45.663  1.00  0.00          C   \nATOM   2412  O   ASN A 316       6.350  42.078  45.836  1.00  0.00          O   \nATOM   2413  CB  ASN A 316       4.808  42.857  48.045  1.00  0.00          C   \nATOM   2414  CG  ASN A 316       3.717  43.097  49.079  1.00  0.00          C   \nATOM   2415  OD1 ASN A 316       2.702  42.401  49.110  1.00  0.00          O   \nATOM   2416  ND2 ASN A 316       3.931  44.091  49.938  1.00  0.00          N   \nATOM   2417  N   GLY A 317       5.680  43.857  44.662  1.00  0.00          N   \nATOM   2418  CA  GLY A 317       6.777  43.677  43.726  1.00  0.00          C   \nATOM   2419  C   GLY A 317       6.679  42.293  43.086  1.00  0.00          C   \nATOM   2420  O   GLY A 317       5.627  41.905  42.583  1.00  0.00          O   \nATOM   2421  N   ASP A 318       7.758  41.527  43.114  1.00  0.00          N   \nATOM   2422  CA  ASP A 318       7.725  40.194  42.514  1.00  0.00          C   \nATOM   2423  C   ASP A 318       7.158  39.157  43.483  1.00  0.00          C   \nATOM   2424  O   ASP A 318       7.018  37.985  43.131  1.00  0.00          O   \nATOM   2425  CB  ASP A 318       9.135  39.778  42.073  1.00  0.00          C   \nATOM   2426  CG  ASP A 318      10.057  39.471  43.247  1.00  0.00          C   \nATOM   2427  OD1 ASP A 318       9.969  40.154  44.289  1.00  0.00          O   \nATOM   2428  OD2 ASP A 318      10.887  38.550  43.121  1.00  0.00          O   \nATOM   2429  N   ASP A 319       6.828  39.586  44.697  1.00  0.00          N   \nATOM   2430  CA  ASP A 319       6.305  38.654  45.686  1.00  0.00          C   \nATOM   2431  C   ASP A 319       4.810  38.479  45.461  1.00  0.00          C   \nATOM   2432  O   ASP A 319       4.055  39.454  45.433  1.00  0.00          O   \nATOM   2433  CB  ASP A 319       6.606  39.165  47.096  1.00  0.00          C   \nATOM   2434  CG  ASP A 319       6.631  38.052  48.118  1.00  0.00          C   \nATOM   2435  OD1 ASP A 319       5.548  37.603  48.560  1.00  0.00          O   \nATOM   2436  OD2 ASP A 319       7.743  37.612  48.467  1.00  0.00          O   \nATOM   2437  N   LEU A 320       4.389  37.229  45.303  1.00  0.00          N   \nATOM   2438  CA  LEU A 320       2.995  36.931  45.018  1.00  0.00          C   \nATOM   2439  C   LEU A 320       2.403  35.821  45.866  1.00  0.00          C   \nATOM   2440  O   LEU A 320       3.023  34.777  46.073  1.00  0.00          O   \nATOM   2441  CB  LEU A 320       2.855  36.541  43.542  1.00  0.00          C   \nATOM   2442  CG  LEU A 320       1.549  35.869  43.107  1.00  0.00          C   \nATOM   2443  CD1 LEU A 320       0.393  36.846  43.260  1.00  0.00          C   \nATOM   2444  CD2 LEU A 320       1.676  35.392  41.657  1.00  0.00          C   \nATOM   2445  N   VAL A 321       1.194  36.054  46.350  1.00  0.00          N   \nATOM   2446  CA  VAL A 321       0.500  35.057  47.134  1.00  0.00          C   \nATOM   2447  C   VAL A 321      -0.953  35.069  46.670  1.00  0.00          C   \nATOM   2448  O   VAL A 321      -1.555  36.130  46.453  1.00  0.00          O   \nATOM   2449  CB  VAL A 321       0.623  35.337  48.655  1.00  0.00          C   \nATOM   2450  CG1 VAL A 321      -0.084  36.610  49.008  1.00  0.00          C   \nATOM   2451  CG2 VAL A 321       0.072  34.176  49.451  1.00  0.00          C   \nATOM   2452  N   VAL A 322      -1.494  33.872  46.474  1.00  0.00          N   \nATOM   2453  CA  VAL A 322      -2.861  33.716  46.016  1.00  0.00          C   \nATOM   2454  C   VAL A 322      -3.696  32.888  46.990  1.00  0.00          C   \nATOM   2455  O   VAL A 322      -3.258  31.834  47.460  1.00  0.00          O   \nATOM   2456  CB  VAL A 322      -2.902  33.036  44.628  1.00  0.00          C   \nATOM   2457  CG1 VAL A 322      -4.357  32.797  44.205  1.00  0.00          C   \nATOM   2458  CG2 VAL A 322      -2.182  33.909  43.595  1.00  0.00          C   \nATOM   2459  N   ILE A 323      -4.888  33.391  47.298  1.00  0.00          N   \nATOM   2460  CA  ILE A 323      -5.823  32.696  48.171  1.00  0.00          C   \nATOM   2461  C   ILE A 323      -7.137  32.515  47.384  1.00  0.00          C   \nATOM   2462  O   ILE A 323      -7.744  33.490  46.926  1.00  0.00          O   \nATOM   2463  CB  ILE A 323      -6.081  33.499  49.467  1.00  0.00          C   \nATOM   2464  CG1 ILE A 323      -4.762  33.680  50.230  1.00  0.00          C   \nATOM   2465  CG2 ILE A 323      -7.088  32.753  50.365  1.00  0.00          C   \nATOM   2466  CD1 ILE A 323      -4.901  34.476  51.505  1.00  0.00          C   \nATOM   2467  N   CYS A 324      -7.571  31.272  47.214  1.00  0.00          N   \nATOM   2468  CA  CYS A 324      -8.795  31.024  46.454  1.00  0.00          C   \nATOM   2469  C   CYS A 324      -9.748  30.019  47.098  1.00  0.00          C   \nATOM   2470  O   CYS A 324      -9.568  29.620  48.252  1.00  0.00          O   \nATOM   2471  CB  CYS A 324      -8.438  30.552  45.038  1.00  0.00          C   \nATOM   2472  SG  CYS A 324      -7.420  29.040  44.959  1.00  0.00          S   \nATOM   2473  N   GLU A 325     -10.761  29.617  46.333  1.00  0.00          N   \nATOM   2474  CA  GLU A 325     -11.757  28.649  46.789  1.00  0.00          C   \nATOM   2475  C   GLU A 325     -11.232  27.276  46.366  1.00  0.00          C   \nATOM   2476  O   GLU A 325     -10.953  27.052  45.191  1.00  0.00          O   \nATOM   2477  CB  GLU A 325     -13.107  28.954  46.122  1.00  0.00          C   \nATOM   2478  CG  GLU A 325     -14.303  28.319  46.781  1.00  0.00          C   \nATOM   2479  CD  GLU A 325     -14.487  28.753  48.225  1.00  0.00          C   \nATOM   2480  OE1 GLU A 325     -14.976  29.878  48.489  1.00  0.00          O   \nATOM   2481  OE2 GLU A 325     -14.127  27.955  49.104  1.00  0.00          O   \nATOM   2482  N   SER A 326     -11.075  26.364  47.320  1.00  0.00          N   \nATOM   2483  CA  SER A 326     -10.550  25.036  47.000  1.00  0.00          C   \nATOM   2484  C   SER A 326     -11.553  24.168  46.247  1.00  0.00          C   \nATOM   2485  O   SER A 326     -12.759  24.396  46.316  1.00  0.00          O   \nATOM   2486  CB  SER A 326     -10.126  24.302  48.271  1.00  0.00          C   \nATOM   2487  OG  SER A 326      -9.801  22.952  47.973  1.00  0.00          O   \nATOM   2488  N   ALA A 327     -11.041  23.170  45.535  1.00  0.00          N   \nATOM   2489  CA  ALA A 327     -11.887  22.254  44.778  1.00  0.00          C   \nATOM   2490  C   ALA A 327     -11.428  20.833  45.053  1.00  0.00          C   \nATOM   2491  O   ALA A 327     -11.683  19.921  44.272  1.00  0.00          O   \nATOM   2492  CB  ALA A 327     -11.786  22.554  43.293  1.00  0.00          C   \nATOM   2493  N   GLY A 328     -10.751  20.653  46.179  1.00  0.00          N   \nATOM   2494  CA  GLY A 328     -10.240  19.346  46.525  1.00  0.00          C   \nATOM   2495  C   GLY A 328      -8.746  19.401  46.308  1.00  0.00          C   \nATOM   2496  O   GLY A 328      -8.273  20.130  45.448  1.00  0.00          O   \nATOM   2497  N   THR A 329      -7.994  18.640  47.087  1.00  0.00          N   \nATOM   2498  CA  THR A 329      -6.539  18.643  46.971  1.00  0.00          C   \nATOM   2499  C   THR A 329      -6.059  18.269  45.556  1.00  0.00          C   \nATOM   2500  O   THR A 329      -5.091  18.847  45.037  1.00  0.00          O   \nATOM   2501  CB  THR A 329      -5.930  17.676  48.014  1.00  0.00          C   \nATOM   2502  OG1 THR A 329      -4.496  17.702  47.932  1.00  0.00          O   \nATOM   2503  CG2 THR A 329      -6.424  16.263  47.755  1.00  0.00          C   \nATOM   2504  N   GLN A 330      -6.737  17.307  44.934  1.00  0.00          N   \nATOM   2505  CA  GLN A 330      -6.376  16.865  43.590  1.00  0.00          C   \nATOM   2506  C   GLN A 330      -6.600  17.940  42.540  1.00  0.00          C   \nATOM   2507  O   GLN A 330      -5.731  18.206  41.710  1.00  0.00          O   \nATOM   2508  CB  GLN A 330      -7.178  15.616  43.211  1.00  0.00          C   \nATOM   2509  CG  GLN A 330      -6.614  14.318  43.748  1.00  0.00          C   \nATOM   2510  CD  GLN A 330      -5.242  14.013  43.178  1.00  0.00          C   \nATOM   2511  OE1 GLN A 330      -4.293  14.779  43.368  1.00  0.00          O   \nATOM   2512  NE2 GLN A 330      -5.130  12.890  42.472  1.00  0.00          N   \nATOM   2513  N   GLU A 331      -7.782  18.543  42.576  1.00  0.00          N   \nATOM   2514  CA  GLU A 331      -8.132  19.590  41.633  1.00  0.00          C   \nATOM   2515  C   GLU A 331      -7.224  20.800  41.842  1.00  0.00          C   \nATOM   2516  O   GLU A 331      -6.895  21.531  40.902  1.00  0.00          O   \nATOM   2517  CB  GLU A 331      -9.595  19.992  41.827  1.00  0.00          C   \nATOM   2518  CG  GLU A 331     -10.081  21.039  40.846  1.00  0.00          C   \nATOM   2519  CD  GLU A 331      -9.867  20.615  39.408  1.00  0.00          C   \nATOM   2520  OE1 GLU A 331     -10.411  19.558  39.018  1.00  0.00          O   \nATOM   2521  OE2 GLU A 331      -9.156  21.337  38.674  1.00  0.00          O   \nATOM   2522  N   ASP A 332      -6.816  21.000  43.088  1.00  0.00          N   \nATOM   2523  CA  ASP A 332      -5.949  22.111  43.428  1.00  0.00          C   \nATOM   2524  C   ASP A 332      -4.583  21.940  42.792  1.00  0.00          C   \nATOM   2525  O   ASP A 332      -4.046  22.884  42.213  1.00  0.00          O   \nATOM   2526  CB  ASP A 332      -5.827  22.242  44.949  1.00  0.00          C   \nATOM   2527  CG  ASP A 332      -7.103  22.756  45.587  1.00  0.00          C   \nATOM   2528  OD1 ASP A 332      -7.898  23.420  44.884  1.00  0.00          O   \nATOM   2529  OD2 ASP A 332      -7.308  22.513  46.794  1.00  0.00          O   \nATOM   2530  N   ALA A 333      -4.032  20.731  42.883  1.00  0.00          N   \nATOM   2531  CA  ALA A 333      -2.721  20.445  42.313  1.00  0.00          C   \nATOM   2532  C   ALA A 333      -2.737  20.654  40.807  1.00  0.00          C   \nATOM   2533  O   ALA A 333      -1.776  21.167  40.225  1.00  0.00          O   \nATOM   2534  CB  ALA A 333      -2.303  19.014  42.636  1.00  0.00          C   \nATOM   2535  N   ALA A 334      -3.837  20.256  40.181  1.00  0.00          N   \nATOM   2536  CA  ALA A 334      -3.986  20.396  38.740  1.00  0.00          C   \nATOM   2537  C   ALA A 334      -4.046  21.864  38.328  1.00  0.00          C   \nATOM   2538  O   ALA A 334      -3.340  22.286  37.421  1.00  0.00          O   \nATOM   2539  CB  ALA A 334      -5.248  19.675  38.274  1.00  0.00          C   \nATOM   2540  N   SER A 335      -4.898  22.632  38.993  1.00  0.00          N   \nATOM   2541  CA  SER A 335      -5.056  24.051  38.682  1.00  0.00          C   \nATOM   2542  C   SER A 335      -3.730  24.793  38.793  1.00  0.00          C   \nATOM   2543  O   SER A 335      -3.279  25.449  37.848  1.00  0.00          O   \nATOM   2544  CB  SER A 335      -6.069  24.673  39.637  1.00  0.00          C   \nATOM   2545  OG  SER A 335      -7.266  23.917  39.645  1.00  0.00          O   \nATOM   2546  N   LEU A 336      -3.107  24.665  39.959  1.00  0.00          N   \nATOM   2547  CA  LEU A 336      -1.838  25.311  40.233  1.00  0.00          C   \nATOM   2548  C   LEU A 336      -0.818  24.966  39.158  1.00  0.00          C   \nATOM   2549  O   LEU A 336       0.022  25.783  38.797  1.00  0.00          O   \nATOM   2550  CB  LEU A 336      -1.354  24.893  41.624  1.00  0.00          C   \nATOM   2551  CG  LEU A 336       0.050  25.251  42.090  1.00  0.00          C   \nATOM   2552  CD1 LEU A 336       0.105  25.230  43.614  1.00  0.00          C   \nATOM   2553  CD2 LEU A 336       1.045  24.258  41.499  1.00  0.00          C   \nATOM   2554  N   ARG A 337      -0.907  23.746  38.642  1.00  0.00          N   \nATOM   2555  CA  ARG A 337      -0.007  23.297  37.591  1.00  0.00          C   \nATOM   2556  C   ARG A 337      -0.308  24.054  36.303  1.00  0.00          C   \nATOM   2557  O   ARG A 337       0.591  24.414  35.564  1.00  0.00          O   \nATOM   2558  CB  ARG A 337      -0.174  21.792  37.378  1.00  0.00          C   \nATOM   2559  CG  ARG A 337       0.815  21.172  36.406  1.00  0.00          C   \nATOM   2560  CD  ARG A 337       0.716  19.651  36.458  1.00  0.00          C   \nATOM   2561  NE  ARG A 337       1.514  19.003  35.419  1.00  0.00          N   \nATOM   2562  CZ  ARG A 337       1.260  19.092  34.115  1.00  0.00          C   \nATOM   2563  NH1 ARG A 337       0.225  19.805  33.689  1.00  0.00          N   \nATOM   2564  NH2 ARG A 337       2.036  18.465  33.239  1.00  0.00          N   \nATOM   2565  N   VAL A 338      -1.582  24.288  36.036  1.00  0.00          N   \nATOM   2566  CA  VAL A 338      -1.980  25.018  34.846  1.00  0.00          C   \nATOM   2567  C   VAL A 338      -1.640  26.504  35.060  1.00  0.00          C   \nATOM   2568  O   VAL A 338      -1.196  27.195  34.142  1.00  0.00          O   \nATOM   2569  CB  VAL A 338      -3.506  24.836  34.585  1.00  0.00          C   \nATOM   2570  CG1 VAL A 338      -3.937  25.680  33.397  1.00  0.00          C   \nATOM   2571  CG2 VAL A 338      -3.826  23.350  34.295  1.00  0.00          C   \nATOM   2572  N   PHE A 339      -1.860  26.976  36.283  1.00  0.00          N   \nATOM   2573  CA  PHE A 339      -1.566  28.356  36.652  1.00  0.00          C   \nATOM   2574  C   PHE A 339      -0.088  28.620  36.370  1.00  0.00          C   \nATOM   2575  O   PHE A 339       0.276  29.654  35.830  1.00  0.00          O   \nATOM   2576  CB  PHE A 339      -1.836  28.578  38.148  1.00  0.00          C   \nATOM   2577  CG  PHE A 339      -1.503  29.964  38.622  1.00  0.00          C   \nATOM   2578  CD1 PHE A 339      -2.448  30.974  38.569  1.00  0.00          C   \nATOM   2579  CD2 PHE A 339      -0.239  30.254  39.123  1.00  0.00          C   \nATOM   2580  CE1 PHE A 339      -2.148  32.263  39.007  1.00  0.00          C   \nATOM   2581  CE2 PHE A 339       0.078  31.545  39.564  1.00  0.00          C   \nATOM   2582  CZ  PHE A 339      -0.878  32.546  39.509  1.00  0.00          C   \nATOM   2583  N   THR A 340       0.752  27.665  36.756  1.00  0.00          N   \nATOM   2584  CA  THR A 340       2.192  27.758  36.558  1.00  0.00          C   \nATOM   2585  C   THR A 340       2.552  27.722  35.076  1.00  0.00          C   \nATOM   2586  O   THR A 340       3.450  28.444  34.623  1.00  0.00          O   \nATOM   2587  CB  THR A 340       2.898  26.608  37.297  1.00  0.00          C   \nATOM   2588  OG1 THR A 340       2.637  26.732  38.698  1.00  0.00          O   \nATOM   2589  CG2 THR A 340       4.396  26.632  37.064  1.00  0.00          C   \nATOM   2590  N   GLU A 341       1.848  26.885  34.316  1.00  0.00          N   \nATOM   2591  CA  GLU A 341       2.101  26.778  32.881  1.00  0.00          C   \nATOM   2592  C   GLU A 341       1.784  28.117  32.208  1.00  0.00          C   \nATOM   2593  O   GLU A 341       2.496  28.538  31.304  1.00  0.00          O   \nATOM   2594  CB  GLU A 341       1.244  25.653  32.254  1.00  0.00          C   \nATOM   2595  CG  GLU A 341       1.516  24.272  32.861  1.00  0.00          C   \nATOM   2596  CD  GLU A 341       0.711  23.143  32.218  1.00  0.00          C   \nATOM   2597  OE1 GLU A 341      -0.467  23.360  31.855  1.00  0.00          O   \nATOM   2598  OE2 GLU A 341       1.257  22.024  32.093  1.00  0.00          O   \nATOM   2599  N   ALA A 342       0.715  28.768  32.658  1.00  0.00          N   \nATOM   2600  CA  ALA A 342       0.297  30.063  32.118  1.00  0.00          C   \nATOM   2601  C   ALA A 342       1.339  31.142  32.448  1.00  0.00          C   \nATOM   2602  O   ALA A 342       1.780  31.890  31.562  1.00  0.00          O   \nATOM   2603  CB  ALA A 342      -1.063  30.452  32.700  1.00  0.00          C   \nATOM   2604  N   MET A 343       1.735  31.217  33.722  1.00  0.00          N   \nATOM   2605  CA  MET A 343       2.742  32.191  34.143  1.00  0.00          C   \nATOM   2606  C   MET A 343       4.004  31.992  33.315  1.00  0.00          C   \nATOM   2607  O   MET A 343       4.685  32.958  32.958  1.00  0.00          O   \nATOM   2608  CB  MET A 343       3.104  32.004  35.625  1.00  0.00          C   \nATOM   2609  CG  MET A 343       2.030  32.397  36.597  1.00  0.00          C   \nATOM   2610  SD  MET A 343       1.671  34.184  36.518  1.00  0.00          S   \nATOM   2611  CE  MET A 343       2.912  34.833  37.714  1.00  0.00          C   \nATOM   2612  N   THR A 344       4.334  30.729  33.028  1.00  0.00          N   \nATOM   2613  CA  THR A 344       5.529  30.425  32.253  1.00  0.00          C   \nATOM   2614  C   THR A 344       5.373  30.970  30.834  1.00  0.00          C   \nATOM   2615  O   THR A 344       6.297  31.582  30.296  1.00  0.00          O   \nATOM   2616  CB  THR A 344       5.817  28.897  32.233  1.00  0.00          C   \nATOM   2617  OG1 THR A 344       6.127  28.457  33.565  1.00  0.00          O   \nATOM   2618  CG2 THR A 344       7.002  28.579  31.322  1.00  0.00          C   \nATOM   2619  N   ARG A 345       4.196  30.779  30.242  1.00  0.00          N   \nATOM   2620  CA  ARG A 345       3.952  31.296  28.902  1.00  0.00          C   \nATOM   2621  C   ARG A 345       4.039  32.824  28.908  1.00  0.00          C   \nATOM   2622  O   ARG A 345       4.362  33.436  27.895  1.00  0.00          O   \nATOM   2623  CB  ARG A 345       2.576  30.857  28.384  1.00  0.00          C   \nATOM   2624  CG  ARG A 345       2.491  29.375  28.031  1.00  0.00          C   \nATOM   2625  CD  ARG A 345       1.239  29.058  27.228  1.00  0.00          C   \nATOM   2626  NE  ARG A 345       0.010  29.300  27.977  1.00  0.00          N   \nATOM   2627  CZ  ARG A 345      -0.589  28.407  28.764  1.00  0.00          C   \nATOM   2628  NH1 ARG A 345      -0.073  27.193  28.918  1.00  0.00          N   \nATOM   2629  NH2 ARG A 345      -1.720  28.724  29.391  1.00  0.00          N   \nATOM   2630  N   TYR A 346       3.761  33.430  30.056  1.00  0.00          N   \nATOM   2631  CA  TYR A 346       3.811  34.887  30.182  1.00  0.00          C   \nATOM   2632  C   TYR A 346       5.217  35.355  30.533  1.00  0.00          C   \nATOM   2633  O   TYR A 346       5.447  36.546  30.758  1.00  0.00          O   \nATOM   2634  CB  TYR A 346       2.865  35.369  31.282  1.00  0.00          C   \nATOM   2635  CG  TYR A 346       1.400  35.103  31.038  1.00  0.00          C   \nATOM   2636  CD1 TYR A 346       0.870  35.082  29.736  1.00  0.00          C   \nATOM   2637  CD2 TYR A 346       0.522  34.965  32.106  1.00  0.00          C   \nATOM   2638  CE1 TYR A 346      -0.501  34.936  29.524  1.00  0.00          C   \nATOM   2639  CE2 TYR A 346      -0.828  34.824  31.908  1.00  0.00          C   \nATOM   2640  CZ  TYR A 346      -1.342  34.814  30.619  1.00  0.00          C   \nATOM   2641  OH  TYR A 346      -2.714  34.726  30.442  1.00  0.00          O   \nATOM   2642  N   SER A 347       6.150  34.413  30.569  1.00  0.00          N   \nATOM   2643  CA  SER A 347       7.527  34.704  30.935  1.00  0.00          C   \nATOM   2644  C   SER A 347       7.642  35.030  32.416  1.00  0.00          C   \nATOM   2645  O   SER A 347       8.206  36.065  32.791  1.00  0.00          O   \nATOM   2646  CB  SER A 347       8.094  35.876  30.145  1.00  0.00          C   \nATOM   2647  OG  SER A 347       9.431  36.106  30.543  1.00  0.00          O   \nATOM   2648  N   ALA A 348       7.065  34.167  33.244  1.00  0.00          N   \nATOM   2649  CA  ALA A 348       7.154  34.297  34.696  1.00  0.00          C   \nATOM   2650  C   ALA A 348       7.276  32.877  35.251  1.00  0.00          C   \nATOM   2651  O   ALA A 348       6.489  32.453  36.101  1.00  0.00          O   \nATOM   2652  CB  ALA A 348       5.929  34.990  35.267  1.00  0.00          C   \nATOM   2653  N   PRO A 349       8.264  32.113  34.751  1.00  0.00          N   \nATOM   2654  CA  PRO A 349       8.504  30.737  35.187  1.00  0.00          C   \nATOM   2655  C   PRO A 349       8.834  30.689  36.683  1.00  0.00          C   \nATOM   2656  O   PRO A 349       9.436  31.613  37.227  1.00  0.00          O   \nATOM   2657  CB  PRO A 349       9.682  30.311  34.325  1.00  0.00          C   \nATOM   2658  CG  PRO A 349      10.446  31.561  34.211  1.00  0.00          C   \nATOM   2659  CD  PRO A 349       9.368  32.570  33.892  1.00  0.00          C   \nATOM   2660  N   PRO A 350       8.450  29.602  37.359  1.00  0.00          N   \nATOM   2661  CA  PRO A 350       8.698  29.432  38.793  1.00  0.00          C   \nATOM   2662  C   PRO A 350      10.101  28.987  39.168  1.00  0.00          C   \nATOM   2663  O   PRO A 350      10.795  28.340  38.380  1.00  0.00          O   \nATOM   2664  CB  PRO A 350       7.672  28.382  39.185  1.00  0.00          C   \nATOM   2665  CG  PRO A 350       7.697  27.482  37.984  1.00  0.00          C   \nATOM   2666  CD  PRO A 350       7.659  28.474  36.832  1.00  0.00          C   \nATOM   2667  N   GLY A 351      10.511  29.351  40.382  1.00  0.00          N   \nATOM   2668  CA  GLY A 351      11.798  28.926  40.887  1.00  0.00          C   \nATOM   2669  C   GLY A 351      11.410  27.630  41.572  1.00  0.00          C   \nATOM   2670  O   GLY A 351      11.607  26.544  41.034  1.00  0.00          O   \nATOM   2671  N   ASP A 352      10.828  27.758  42.759  1.00  0.00          N   \nATOM   2672  CA  ASP A 352      10.340  26.610  43.508  1.00  0.00          C   \nATOM   2673  C   ASP A 352       8.909  26.397  43.027  1.00  0.00          C   \nATOM   2674  O   ASP A 352       8.111  27.329  43.031  1.00  0.00          O   \nATOM   2675  CB  ASP A 352      10.317  26.913  45.007  1.00  0.00          C   \nATOM   2676  CG  ASP A 352      11.699  27.114  45.576  1.00  0.00          C   \nATOM   2677  OD1 ASP A 352      12.482  26.140  45.559  1.00  0.00          O   \nATOM   2678  OD2 ASP A 352      12.003  28.240  46.034  1.00  0.00          O   \nATOM   2679  N   PRO A 353       8.570  25.175  42.591  1.00  0.00          N   \nATOM   2680  CA  PRO A 353       7.203  24.926  42.124  1.00  0.00          C   \nATOM   2681  C   PRO A 353       6.215  25.303  43.235  1.00  0.00          C   \nATOM   2682  O   PRO A 353       6.471  25.062  44.413  1.00  0.00          O   \nATOM   2683  CB  PRO A 353       7.204  23.423  41.836  1.00  0.00          C   \nATOM   2684  CG  PRO A 353       8.637  23.142  41.484  1.00  0.00          C   \nATOM   2685  CD  PRO A 353       9.392  23.955  42.505  1.00  0.00          C   \nATOM   2686  N   PRO A 354       5.083  25.917  42.875  1.00  0.00          N   \nATOM   2687  CA  PRO A 354       4.096  26.305  43.887  1.00  0.00          C   \nATOM   2688  C   PRO A 354       3.342  25.110  44.463  1.00  0.00          C   \nATOM   2689  O   PRO A 354       3.077  24.139  43.752  1.00  0.00          O   \nATOM   2690  CB  PRO A 354       3.178  27.245  43.120  1.00  0.00          C   \nATOM   2691  CG  PRO A 354       3.206  26.658  41.719  1.00  0.00          C   \nATOM   2692  CD  PRO A 354       4.667  26.344  41.525  1.00  0.00          C   \nATOM   2693  N   GLN A 355       3.011  25.171  45.748  1.00  0.00          N   \nATOM   2694  CA  GLN A 355       2.265  24.082  46.371  1.00  0.00          C   \nATOM   2695  C   GLN A 355       1.036  24.623  47.069  1.00  0.00          C   \nATOM   2696  O   GLN A 355       1.060  25.695  47.672  1.00  0.00          O   \nATOM   2697  CB  GLN A 355       3.124  23.297  47.376  1.00  0.00          C   \nATOM   2698  CG  GLN A 355       3.430  24.016  48.694  1.00  0.00          C   \nATOM   2699  CD  GLN A 355       3.968  23.069  49.786  1.00  0.00          C   \nATOM   2700  OE1 GLN A 355       3.256  22.171  50.259  1.00  0.00          O   \nATOM   2701  NE2 GLN A 355       5.225  23.273  50.185  1.00  0.00          N   \nATOM   2702  N   PRO A 356      -0.074  23.890  46.983  1.00  0.00          N   \nATOM   2703  CA  PRO A 356      -1.268  24.396  47.653  1.00  0.00          C   \nATOM   2704  C   PRO A 356      -1.152  24.215  49.169  1.00  0.00          C   \nATOM   2705  O   PRO A 356      -0.599  23.222  49.641  1.00  0.00          O   \nATOM   2706  CB  PRO A 356      -2.391  23.570  47.015  1.00  0.00          C   \nATOM   2707  CG  PRO A 356      -1.741  22.274  46.728  1.00  0.00          C   \nATOM   2708  CD  PRO A 356      -0.352  22.653  46.233  1.00  0.00          C   \nATOM   2709  N   GLU A 357      -1.638  25.198  49.917  1.00  0.00          N   \nATOM   2710  CA  GLU A 357      -1.619  25.150  51.377  1.00  0.00          C   \nATOM   2711  C   GLU A 357      -3.039  25.299  51.906  1.00  0.00          C   \nATOM   2712  O   GLU A 357      -3.843  26.034  51.347  1.00  0.00          O   \nATOM   2713  CB  GLU A 357      -0.736  26.264  51.949  1.00  0.00          C   \nATOM   2714  CG  GLU A 357       0.742  26.067  51.707  1.00  0.00          C   \nATOM   2715  CD  GLU A 357       1.293  24.834  52.405  1.00  0.00          C   \nATOM   2716  OE1 GLU A 357       0.813  24.497  53.514  1.00  0.00          O   \nATOM   2717  OE2 GLU A 357       2.223  24.212  51.851  1.00  0.00          O   \nATOM   2718  N   TYR A 358      -3.341  24.592  52.988  1.00  0.00          N   \nATOM   2719  CA  TYR A 358      -4.668  24.628  53.577  1.00  0.00          C   \nATOM   2720  C   TYR A 358      -4.634  25.230  54.980  1.00  0.00          C   \nATOM   2721  O   TYR A 358      -5.659  25.317  55.663  1.00  0.00          O   \nATOM   2722  CB  TYR A 358      -5.235  23.202  53.583  1.00  0.00          C   \nATOM   2723  CG  TYR A 358      -5.372  22.651  52.178  1.00  0.00          C   \nATOM   2724  CD1 TYR A 358      -6.435  23.035  51.365  1.00  0.00          C   \nATOM   2725  CD2 TYR A 358      -4.379  21.857  51.619  1.00  0.00          C   \nATOM   2726  CE1 TYR A 358      -6.498  22.652  50.023  1.00  0.00          C   \nATOM   2727  CE2 TYR A 358      -4.433  21.469  50.277  1.00  0.00          C   \nATOM   2728  CZ  TYR A 358      -5.492  21.876  49.488  1.00  0.00          C   \nATOM   2729  OH  TYR A 358      -5.533  21.538  48.155  1.00  0.00          O   \nATOM   2730  N   ASP A 359      -3.442  25.662  55.382  1.00  0.00          N   \nATOM   2731  CA  ASP A 359      -3.196  26.276  56.687  1.00  0.00          C   \nATOM   2732  C   ASP A 359      -2.577  27.650  56.403  1.00  0.00          C   \nATOM   2733  O   ASP A 359      -1.451  27.729  55.933  1.00  0.00          O   \nATOM   2734  CB  ASP A 359      -2.197  25.416  57.470  1.00  0.00          C   \nATOM   2735  CG  ASP A 359      -2.017  25.876  58.908  1.00  0.00          C   \nATOM   2736  OD1 ASP A 359      -2.270  27.070  59.202  1.00  0.00          O   \nATOM   2737  OD2 ASP A 359      -1.603  25.038  59.742  1.00  0.00          O   \nATOM   2738  N   LEU A 360      -3.309  28.725  56.673  1.00  0.00          N   \nATOM   2739  CA  LEU A 360      -2.794  30.069  56.415  1.00  0.00          C   \nATOM   2740  C   LEU A 360      -1.407  30.288  57.029  1.00  0.00          C   \nATOM   2741  O   LEU A 360      -0.561  30.955  56.438  1.00  0.00          O   \nATOM   2742  CB  LEU A 360      -3.774  31.124  56.946  1.00  0.00          C   \nATOM   2743  CG  LEU A 360      -3.350  32.605  56.883  1.00  0.00          C   \nATOM   2744  CD1 LEU A 360      -3.134  33.037  55.435  1.00  0.00          C   \nATOM   2745  CD2 LEU A 360      -4.415  33.472  57.531  1.00  0.00          C   \nATOM   2746  N   GLU A 361      -1.174  29.705  58.202  1.00  0.00          N   \nATOM   2747  CA  GLU A 361       0.096  29.851  58.909  1.00  0.00          C   \nATOM   2748  C   GLU A 361       1.273  29.247  58.153  1.00  0.00          C   \nATOM   2749  O   GLU A 361       2.435  29.586  58.409  1.00  0.00          O   \nATOM   2750  CB  GLU A 361      -0.014  29.220  60.306  1.00  0.00          C   \nATOM   2751  CG  GLU A 361      -0.941  29.987  61.270  1.00  0.00          C   \nATOM   2752  CD  GLU A 361      -1.029  29.360  62.668  1.00  0.00          C   \nATOM   2753  OE1 GLU A 361       0.029  29.049  63.261  1.00  0.00          O   \nATOM   2754  OE2 GLU A 361      -2.161  29.191  63.177  1.00  0.00          O   \nATOM   2755  N   LEU A 362       0.973  28.359  57.211  1.00  0.00          N   \nATOM   2756  CA  LEU A 362       2.020  27.706  56.439  1.00  0.00          C   \nATOM   2757  C   LEU A 362       2.340  28.410  55.134  1.00  0.00          C   \nATOM   2758  O   LEU A 362       3.141  27.920  54.342  1.00  0.00          O   \nATOM   2759  CB  LEU A 362       1.635  26.254  56.151  1.00  0.00          C   \nATOM   2760  CG  LEU A 362       1.399  25.375  57.386  1.00  0.00          C   \nATOM   2761  CD1 LEU A 362       1.304  23.906  56.950  1.00  0.00          C   \nATOM   2762  CD2 LEU A 362       2.535  25.569  58.387  1.00  0.00          C   \nATOM   2763  N   ILE A 363       1.718  29.557  54.902  1.00  0.00          N   \nATOM   2764  CA  ILE A 363       1.975  30.282  53.669  1.00  0.00          C   \nATOM   2765  C   ILE A 363       2.939  31.428  53.908  1.00  0.00          C   \nATOM   2766  O   ILE A 363       2.668  32.320  54.722  1.00  0.00          O   \nATOM   2767  CB  ILE A 363       0.690  30.856  53.068  1.00  0.00          C   \nATOM   2768  CG1 ILE A 363      -0.274  29.710  52.705  1.00  0.00          C   \nATOM   2769  CG2 ILE A 363       1.044  31.708  51.864  1.00  0.00          C   \nATOM   2770  CD1 ILE A 363      -1.584  30.169  52.118  1.00  0.00          C   \nATOM   2771  N   THR A 364       4.056  31.389  53.186  1.00  0.00          N   \nATOM   2772  CA  THR A 364       5.089  32.402  53.289  1.00  0.00          C   \nATOM   2773  C   THR A 364       5.058  33.335  52.078  1.00  0.00          C   \nATOM   2774  O   THR A 364       5.206  32.890  50.939  1.00  0.00          O   \nATOM   2775  CB  THR A 364       6.483  31.752  53.384  1.00  0.00          C   \nATOM   2776  OG1 THR A 364       6.601  31.025  54.619  1.00  0.00          O   \nATOM   2777  CG2 THR A 364       7.569  32.825  53.315  1.00  0.00          C   \nATOM   2778  N   SER A 365       4.850  34.624  52.339  1.00  0.00          N   \nATOM   2779  CA  SER A 365       4.810  35.659  51.306  1.00  0.00          C   \nATOM   2780  C   SER A 365       5.554  36.868  51.903  1.00  0.00          C   \nATOM   2781  O   SER A 365       5.359  37.208  53.072  1.00  0.00          O   \nATOM   2782  CB  SER A 365       3.364  36.020  50.972  1.00  0.00          C   \nATOM   2783  OG  SER A 365       2.681  36.510  52.116  1.00  0.00          O   \nATOM   2784  N   CYS A 366       6.397  37.509  51.098  1.00  0.00          N   \nATOM   2785  CA  CYS A 366       7.233  38.610  51.577  1.00  0.00          C   \nATOM   2786  C   CYS A 366       8.081  38.063  52.716  1.00  0.00          C   \nATOM   2787  O   CYS A 366       8.258  38.711  53.745  1.00  0.00          O   \nATOM   2788  CB  CYS A 366       6.399  39.790  52.072  1.00  0.00          C   \nATOM   2789  SG  CYS A 366       5.988  40.955  50.770  1.00  0.00          S   \nATOM   2790  N   SER A 367       8.571  36.839  52.527  1.00  0.00          N   \nATOM   2791  CA  SER A 367       9.411  36.171  53.512  1.00  0.00          C   \nATOM   2792  C   SER A 367       8.759  36.109  54.890  1.00  0.00          C   \nATOM   2793  O   SER A 367       9.443  35.871  55.885  1.00  0.00          O   \nATOM   2794  CB  SER A 367      10.756  36.900  53.636  1.00  0.00          C   \nATOM   2795  OG  SER A 367      11.435  36.932  52.393  1.00  0.00          O   \nATOM   2796  N   SER A 368       7.444  36.301  54.949  1.00  0.00          N   \nATOM   2797  CA  SER A 368       6.744  36.305  56.230  1.00  0.00          C   \nATOM   2798  C   SER A 368       5.520  35.412  56.224  1.00  0.00          C   \nATOM   2799  O   SER A 368       5.091  34.932  55.177  1.00  0.00          O   \nATOM   2800  CB  SER A 368       6.292  37.726  56.568  1.00  0.00          C   \nATOM   2801  OG  SER A 368       7.335  38.666  56.374  1.00  0.00          O   \nATOM   2802  N   ASN A 369       4.944  35.214  57.403  1.00  0.00          N   \nATOM   2803  CA  ASN A 369       3.741  34.400  57.522  1.00  0.00          C   \nATOM   2804  C   ASN A 369       2.946  34.827  58.743  1.00  0.00          C   \nATOM   2805  O   ASN A 369       3.489  35.407  59.694  1.00  0.00          O   \nATOM   2806  CB  ASN A 369       4.086  32.908  57.650  1.00  0.00          C   \nATOM   2807  CG  ASN A 369       4.762  32.571  58.977  1.00  0.00          C   \nATOM   2808  OD1 ASN A 369       5.972  32.740  59.132  1.00  0.00          O   \nATOM   2809  ND2 ASN A 369       3.975  32.092  59.942  1.00  0.00          N   \nATOM   2810  N   VAL A 370       1.653  34.526  58.709  1.00  0.00          N   \nATOM   2811  CA  VAL A 370       0.759  34.850  59.804  1.00  0.00          C   \nATOM   2812  C   VAL A 370       0.884  33.779  60.874  1.00  0.00          C   \nATOM   2813  O   VAL A 370       1.017  32.593  60.576  1.00  0.00          O   \nATOM   2814  CB  VAL A 370      -0.726  34.886  59.347  1.00  0.00          C   \nATOM   2815  CG1 VAL A 370      -1.624  35.194  60.541  1.00  0.00          C   \nATOM   2816  CG2 VAL A 370      -0.934  35.934  58.264  1.00  0.00          C   \nATOM   2817  N   SER A 371       0.872  34.203  62.128  1.00  0.00          N   \nATOM   2818  CA  SER A 371       0.918  33.259  63.224  1.00  0.00          C   \nATOM   2819  C   SER A 371      -0.013  33.814  64.283  1.00  0.00          C   \nATOM   2820  O   SER A 371      -0.529  34.925  64.144  1.00  0.00          O   \nATOM   2821  CB  SER A 371       2.328  33.115  63.789  1.00  0.00          C   \nATOM   2822  OG  SER A 371       2.428  31.899  64.519  1.00  0.00          O   \nATOM   2823  N   VAL A 372      -0.229  33.046  65.343  1.00  0.00          N   \nATOM   2824  CA  VAL A 372      -1.124  33.484  66.398  1.00  0.00          C   \nATOM   2825  C   VAL A 372      -0.499  33.353  67.778  1.00  0.00          C   \nATOM   2826  O   VAL A 372       0.301  32.453  68.034  1.00  0.00          O   \nATOM   2827  CB  VAL A 372      -2.457  32.681  66.358  1.00  0.00          C   \nATOM   2828  CG1 VAL A 372      -2.178  31.193  66.498  1.00  0.00          C   \nATOM   2829  CG2 VAL A 372      -3.387  33.153  67.465  1.00  0.00          C   \nATOM   2830  N   ALA A 373      -0.862  34.290  68.645  1.00  0.00          N   \nATOM   2831  CA  ALA A 373      -0.400  34.323  70.030  1.00  0.00          C   \nATOM   2832  C   ALA A 373      -1.586  34.799  70.867  1.00  0.00          C   \nATOM   2833  O   ALA A 373      -2.710  34.882  70.368  1.00  0.00          O   \nATOM   2834  CB  ALA A 373       0.776  35.291  70.178  1.00  0.00          C   \nATOM   2835  N   HIS A 374      -1.343  35.114  72.130  1.00  0.00          N   \nATOM   2836  CA  HIS A 374      -2.414  35.576  73.007  1.00  0.00          C   \nATOM   2837  C   HIS A 374      -2.014  36.862  73.699  1.00  0.00          C   \nATOM   2838  O   HIS A 374      -0.864  37.006  74.115  1.00  0.00          O   \nATOM   2839  CB  HIS A 374      -2.713  34.519  74.057  1.00  0.00          C   \nATOM   2840  CG  HIS A 374      -3.179  33.223  73.486  1.00  0.00          C   \nATOM   2841  ND1 HIS A 374      -4.476  33.020  73.065  1.00  0.00          N   \nATOM   2842  CD2 HIS A 374      -2.516  32.072  73.223  1.00  0.00          C   \nATOM   2843  CE1 HIS A 374      -4.590  31.803  72.569  1.00  0.00          C   \nATOM   2844  NE2 HIS A 374      -3.415  31.206  72.652  1.00  0.00          N   \nATOM   2845  N   ASP A 375      -2.956  37.799  73.820  1.00  0.00          N   \nATOM   2846  CA  ASP A 375      -2.665  39.061  74.489  1.00  0.00          C   \nATOM   2847  C   ASP A 375      -2.833  38.911  75.998  1.00  0.00          C   \nATOM   2848  O   ASP A 375      -3.026  37.805  76.510  1.00  0.00          O   \nATOM   2849  CB  ASP A 375      -3.564  40.195  73.979  1.00  0.00          C   \nATOM   2850  CG  ASP A 375      -5.053  39.883  74.107  1.00  0.00          C   \nATOM   2851  OD1 ASP A 375      -5.457  39.139  75.025  1.00  0.00          O   \nATOM   2852  OD2 ASP A 375      -5.830  40.401  73.282  1.00  0.00          O   \nATOM   2853  N   ALA A 376      -2.758  40.028  76.706  1.00  0.00          N   \nATOM   2854  CA  ALA A 376      -2.877  40.014  78.156  1.00  0.00          C   \nATOM   2855  C   ALA A 376      -4.232  39.515  78.660  1.00  0.00          C   \nATOM   2856  O   ALA A 376      -4.383  39.246  79.847  1.00  0.00          O   \nATOM   2857  CB  ALA A 376      -2.593  41.402  78.702  1.00  0.00          C   \nATOM   2858  N   SER A 377      -5.206  39.387  77.762  1.00  0.00          N   \nATOM   2859  CA  SER A 377      -6.542  38.923  78.129  1.00  0.00          C   \nATOM   2860  C   SER A 377      -6.725  37.443  77.847  1.00  0.00          C   \nATOM   2861  O   SER A 377      -7.706  36.837  78.282  1.00  0.00          O   \nATOM   2862  CB  SER A 377      -7.603  39.703  77.356  1.00  0.00          C   \nATOM   2863  OG  SER A 377      -7.451  41.098  77.556  1.00  0.00          O   \nATOM   2864  N   GLY A 378      -5.788  36.861  77.110  1.00  0.00          N   \nATOM   2865  CA  GLY A 378      -5.887  35.449  76.792  1.00  0.00          C   \nATOM   2866  C   GLY A 378      -6.501  35.261  75.419  1.00  0.00          C   \nATOM   2867  O   GLY A 378      -6.635  34.139  74.933  1.00  0.00          O   \nATOM   2868  N   LYS A 379      -6.874  36.376  74.798  1.00  0.00          N   \nATOM   2869  CA  LYS A 379      -7.477  36.371  73.466  1.00  0.00          C   \nATOM   2870  C   LYS A 379      -6.468  36.036  72.368  1.00  0.00          C   \nATOM   2871  O   LYS A 379      -5.316  36.461  72.413  1.00  0.00          O   \nATOM   2872  CB  LYS A 379      -8.097  37.745  73.167  1.00  0.00          C   \nATOM   2873  CG  LYS A 379      -8.594  37.905  71.731  1.00  0.00          C   \nATOM   2874  CD  LYS A 379      -9.312  39.234  71.502  1.00  0.00          C   \nATOM   2875  CE  LYS A 379      -8.380  40.417  71.708  1.00  0.00          C   \nATOM   2876  NZ  LYS A 379      -9.032  41.728  71.413  1.00  0.00          N   \nATOM   2877  N   ARG A 380      -6.909  35.273  71.378  1.00  0.00          N   \nATOM   2878  CA  ARG A 380      -6.046  34.929  70.262  1.00  0.00          C   \nATOM   2879  C   ARG A 380      -5.836  36.188  69.430  1.00  0.00          C   \nATOM   2880  O   ARG A 380      -6.787  36.921  69.156  1.00  0.00          O   \nATOM   2881  CB  ARG A 380      -6.692  33.848  69.401  1.00  0.00          C   \nATOM   2882  CG  ARG A 380      -6.717  32.474  70.041  1.00  0.00          C   \nATOM   2883  CD  ARG A 380      -7.741  31.592  69.363  1.00  0.00          C   \nATOM   2884  NE  ARG A 380      -7.440  31.358  67.956  1.00  0.00          N   \nATOM   2885  CZ  ARG A 380      -6.449  30.588  67.518  1.00  0.00          C   \nATOM   2886  NH1 ARG A 380      -5.645  29.971  68.379  1.00  0.00          N   \nATOM   2887  NH2 ARG A 380      -6.277  30.414  66.215  1.00  0.00          N   \nATOM   2888  N   VAL A 381      -4.584  36.446  69.058  1.00  0.00          N   \nATOM   2889  CA  VAL A 381      -4.230  37.600  68.240  1.00  0.00          C   \nATOM   2890  C   VAL A 381      -3.292  37.149  67.118  1.00  0.00          C   \nATOM   2891  O   VAL A 381      -2.335  36.404  67.354  1.00  0.00          O   \nATOM   2892  CB  VAL A 381      -3.531  38.703  69.085  1.00  0.00          C   \nATOM   2893  CG1 VAL A 381      -2.569  38.062  70.065  1.00  0.00          C   \nATOM   2894  CG2 VAL A 381      -2.772  39.675  68.177  1.00  0.00          C   \nATOM   2895  N   TYR A 382      -3.583  37.592  65.899  1.00  0.00          N   \nATOM   2896  CA  TYR A 382      -2.765  37.251  64.738  1.00  0.00          C   \nATOM   2897  C   TYR A 382      -1.732  38.338  64.538  1.00  0.00          C   \nATOM   2898  O   TYR A 382      -2.004  39.516  64.794  1.00  0.00          O   \nATOM   2899  CB  TYR A 382      -3.633  37.152  63.482  1.00  0.00          C   \nATOM   2900  CG  TYR A 382      -4.575  35.981  63.498  1.00  0.00          C   \nATOM   2901  CD1 TYR A 382      -4.103  34.685  63.323  1.00  0.00          C   \nATOM   2902  CD2 TYR A 382      -5.934  36.165  63.727  1.00  0.00          C   \nATOM   2903  CE1 TYR A 382      -4.966  33.592  63.377  1.00  0.00          C   \nATOM   2904  CE2 TYR A 382      -6.808  35.085  63.787  1.00  0.00          C   \nATOM   2905  CZ  TYR A 382      -6.317  33.799  63.612  1.00  0.00          C   \nATOM   2906  OH  TYR A 382      -7.174  32.722  63.683  1.00  0.00          O   \nATOM   2907  N   TYR A 383      -0.555  37.949  64.056  1.00  0.00          N   \nATOM   2908  CA  TYR A 383       0.517  38.913  63.834  1.00  0.00          C   \nATOM   2909  C   TYR A 383       1.488  38.371  62.794  1.00  0.00          C   \nATOM   2910  O   TYR A 383       1.517  37.175  62.523  1.00  0.00          O   \nATOM   2911  CB  TYR A 383       1.265  39.181  65.146  1.00  0.00          C   \nATOM   2912  CG  TYR A 383       2.045  37.983  65.625  1.00  0.00          C   \nATOM   2913  CD1 TYR A 383       1.395  36.868  66.175  1.00  0.00          C   \nATOM   2914  CD2 TYR A 383       3.430  37.923  65.457  1.00  0.00          C   \nATOM   2915  CE1 TYR A 383       2.115  35.726  66.536  1.00  0.00          C   \nATOM   2916  CE2 TYR A 383       4.151  36.786  65.813  1.00  0.00          C   \nATOM   2917  CZ  TYR A 383       3.496  35.698  66.346  1.00  0.00          C   \nATOM   2918  OH  TYR A 383       4.222  34.578  66.674  1.00  0.00          O   \nATOM   2919  N   LEU A 384       2.294  39.253  62.221  1.00  0.00          N   \nATOM   2920  CA  LEU A 384       3.243  38.833  61.206  1.00  0.00          C   \nATOM   2921  C   LEU A 384       4.594  38.466  61.822  1.00  0.00          C   \nATOM   2922  O   LEU A 384       5.116  39.185  62.675  1.00  0.00          O   \nATOM   2923  CB  LEU A 384       3.434  39.946  60.180  1.00  0.00          C   \nATOM   2924  CG  LEU A 384       4.017  39.510  58.837  1.00  0.00          C   \nATOM   2925  CD1 LEU A 384       2.956  38.722  58.091  1.00  0.00          C   \nATOM   2926  CD2 LEU A 384       4.424  40.722  58.012  1.00  0.00          C   \nATOM   2927  N   THR A 385       5.146  37.339  61.387  1.00  0.00          N   \nATOM   2928  CA  THR A 385       6.438  36.883  61.868  1.00  0.00          C   \nATOM   2929  C   THR A 385       7.188  36.314  60.671  1.00  0.00          C   \nATOM   2930  O   THR A 385       6.697  36.356  59.534  1.00  0.00          O   \nATOM   2931  CB  THR A 385       6.305  35.777  62.930  1.00  0.00          C   \nATOM   2932  OG1 THR A 385       7.612  35.385  63.388  1.00  0.00          O   \nATOM   2933  CG2 THR A 385       5.607  34.561  62.337  1.00  0.00          C   \nATOM   2934  N   ARG A 386       8.382  35.799  60.926  1.00  0.00          N   \nATOM   2935  CA  ARG A 386       9.181  35.207  59.875  1.00  0.00          C   \nATOM   2936  C   ARG A 386      10.297  34.387  60.481  1.00  0.00          C   \nATOM   2937  O   ARG A 386      10.559  34.466  61.691  1.00  0.00          O   \nATOM   2938  CB  ARG A 386       9.788  36.287  58.977  1.00  0.00          C   \nATOM   2939  CG  ARG A 386      10.731  37.271  59.687  1.00  0.00          C   \nATOM   2940  CD  ARG A 386      11.627  37.948  58.663  1.00  0.00          C   \nATOM   2941  NE  ARG A 386      12.553  36.983  58.070  1.00  0.00          N   \nATOM   2942  CZ  ARG A 386      13.163  37.139  56.898  1.00  0.00          C   \nATOM   2943  NH1 ARG A 386      12.953  38.231  56.168  1.00  0.00          N   \nATOM   2944  NH2 ARG A 386      13.996  36.206  56.456  1.00  0.00          N   \nATOM   2945  N   ASP A 387      10.936  33.577  59.645  1.00  0.00          N   \nATOM   2946  CA  ASP A 387      12.064  32.788  60.111  1.00  0.00          C   \nATOM   2947  C   ASP A 387      13.053  33.883  60.537  1.00  0.00          C   \nATOM   2948  O   ASP A 387      13.295  34.827  59.784  1.00  0.00          O   \nATOM   2949  CB  ASP A 387      12.642  31.968  58.966  1.00  0.00          C   \nATOM   2950  CG  ASP A 387      13.804  31.121  59.405  1.00  0.00          C   \nATOM   2951  OD1 ASP A 387      14.837  31.694  59.795  1.00  0.00          O   \nATOM   2952  OD2 ASP A 387      13.679  29.880  59.371  1.00  0.00          O   \nATOM   2953  N   PRO A 388      13.636  33.777  61.742  1.00  0.00          N   \nATOM   2954  CA  PRO A 388      14.577  34.813  62.198  1.00  0.00          C   \nATOM   2955  C   PRO A 388      16.022  34.669  61.758  1.00  0.00          C   \nATOM   2956  O   PRO A 388      16.865  35.501  62.120  1.00  0.00          O   \nATOM   2957  CB  PRO A 388      14.439  34.748  63.710  1.00  0.00          C   \nATOM   2958  CG  PRO A 388      14.250  33.256  63.955  1.00  0.00          C   \nATOM   2959  CD  PRO A 388      13.395  32.762  62.787  1.00  0.00          C   \nATOM   2960  N   THR A 389      16.305  33.643  60.961  1.00  0.00          N   \nATOM   2961  CA  THR A 389      17.672  33.381  60.537  1.00  0.00          C   \nATOM   2962  C   THR A 389      18.392  34.563  59.910  1.00  0.00          C   \nATOM   2963  O   THR A 389      19.460  34.945  60.380  1.00  0.00          O   \nATOM   2964  CB  THR A 389      17.753  32.195  59.558  1.00  0.00          C   \nATOM   2965  OG1 THR A 389      17.241  31.010  60.184  1.00  0.00          O   \nATOM   2966  CG2 THR A 389      19.176  31.951  59.164  1.00  0.00          C   \nATOM   2967  N   THR A 390      17.813  35.137  58.859  1.00  0.00          N   \nATOM   2968  CA  THR A 390      18.449  36.250  58.190  1.00  0.00          C   \nATOM   2969  C   THR A 390      18.489  37.477  59.085  1.00  0.00          C   \nATOM   2970  O   THR A 390      19.497  38.165  59.129  1.00  0.00          O   \nATOM   2971  CB  THR A 390      17.768  36.566  56.849  1.00  0.00          C   \nATOM   2972  OG1 THR A 390      17.881  35.425  55.991  1.00  0.00          O   \nATOM   2973  CG2 THR A 390      18.449  37.748  56.170  1.00  0.00          C   \nATOM   2974  N   PRO A 391      17.384  37.781  59.790  1.00  0.00          N   \nATOM   2975  CA  PRO A 391      17.399  38.948  60.677  1.00  0.00          C   \nATOM   2976  C   PRO A 391      18.515  38.807  61.736  1.00  0.00          C   \nATOM   2977  O   PRO A 391      19.190  39.787  62.079  1.00  0.00          O   \nATOM   2978  CB  PRO A 391      16.001  38.917  61.303  1.00  0.00          C   \nATOM   2979  CG  PRO A 391      15.152  38.441  60.167  1.00  0.00          C   \nATOM   2980  CD  PRO A 391      15.999  37.333  59.534  1.00  0.00          C   \nATOM   2981  N   LEU A 392      18.727  37.591  62.244  1.00  0.00          N   \nATOM   2982  CA  LEU A 392      19.762  37.391  63.262  1.00  0.00          C   \nATOM   2983  C   LEU A 392      21.173  37.416  62.669  1.00  0.00          C   \nATOM   2984  O   LEU A 392      22.108  37.906  63.297  1.00  0.00          O   \nATOM   2985  CB  LEU A 392      19.515  36.091  64.047  1.00  0.00          C   \nATOM   2986  CG  LEU A 392      18.241  36.139  64.904  1.00  0.00          C   \nATOM   2987  CD1 LEU A 392      18.057  34.833  65.604  1.00  0.00          C   \nATOM   2988  CD2 LEU A 392      18.308  37.292  65.908  1.00  0.00          C   \nATOM   2989  N   ALA A 393      21.325  36.891  61.461  1.00  0.00          N   \nATOM   2990  CA  ALA A 393      22.619  36.911  60.793  1.00  0.00          C   \nATOM   2991  C   ALA A 393      23.012  38.371  60.562  1.00  0.00          C   \nATOM   2992  O   ALA A 393      24.167  38.767  60.751  1.00  0.00          O   \nATOM   2993  CB  ALA A 393      22.523  36.197  59.457  1.00  0.00          C   \nATOM   2994  N   ARG A 394      22.044  39.175  60.134  1.00  0.00          N   \nATOM   2995  CA  ARG A 394      22.322  40.582  59.872  1.00  0.00          C   \nATOM   2996  C   ARG A 394      22.599  41.346  61.155  1.00  0.00          C   \nATOM   2997  O   ARG A 394      23.455  42.237  61.188  1.00  0.00          O   \nATOM   2998  CB  ARG A 394      21.164  41.187  59.081  1.00  0.00          C   \nATOM   2999  CG  ARG A 394      21.101  40.520  57.725  1.00  0.00          C   \nATOM   3000  CD  ARG A 394      20.060  41.047  56.773  1.00  0.00          C   \nATOM   3001  NE  ARG A 394      20.303  40.441  55.466  1.00  0.00          N   \nATOM   3002  CZ  ARG A 394      19.600  40.687  54.367  1.00  0.00          C   \nATOM   3003  NH1 ARG A 394      18.582  41.538  54.396  1.00  0.00          N   \nATOM   3004  NH2 ARG A 394      19.930  40.088  53.235  1.00  0.00          N   \nATOM   3005  N   ALA A 395      21.902  40.986  62.227  1.00  0.00          N   \nATOM   3006  CA  ALA A 395      22.138  41.665  63.497  1.00  0.00          C   \nATOM   3007  C   ALA A 395      23.558  41.326  63.970  1.00  0.00          C   \nATOM   3008  O   ALA A 395      24.240  42.165  64.571  1.00  0.00          O   \nATOM   3009  CB  ALA A 395      21.113  41.227  64.535  1.00  0.00          C   \nATOM   3010  N   ALA A 396      24.003  40.097  63.707  1.00  0.00          N   \nATOM   3011  CA  ALA A 396      25.355  39.713  64.113  1.00  0.00          C   \nATOM   3012  C   ALA A 396      26.339  40.642  63.416  1.00  0.00          C   \nATOM   3013  O   ALA A 396      27.254  41.172  64.039  1.00  0.00          O   \nATOM   3014  CB  ALA A 396      25.641  38.252  63.739  1.00  0.00          C   \nATOM   3015  N   TRP A 397      26.140  40.849  62.115  1.00  0.00          N   \nATOM   3016  CA  TRP A 397      27.014  41.724  61.336  1.00  0.00          C   \nATOM   3017  C   TRP A 397      26.966  43.153  61.873  1.00  0.00          C   \nATOM   3018  O   TRP A 397      27.993  43.820  62.015  1.00  0.00          O   \nATOM   3019  CB  TRP A 397      26.588  41.709  59.859  1.00  0.00          C   \nATOM   3020  CG  TRP A 397      27.631  42.261  58.942  1.00  0.00          C   \nATOM   3021  CD1 TRP A 397      27.951  43.583  58.750  1.00  0.00          C   \nATOM   3022  CD2 TRP A 397      28.537  41.504  58.132  1.00  0.00          C   \nATOM   3023  NE1 TRP A 397      29.005  43.685  57.871  1.00  0.00          N   \nATOM   3024  CE2 TRP A 397      29.384  42.427  57.478  1.00  0.00          C   \nATOM   3025  CE3 TRP A 397      28.717  40.135  57.896  1.00  0.00          C   \nATOM   3026  CZ2 TRP A 397      30.402  42.021  56.601  1.00  0.00          C   \nATOM   3027  CZ3 TRP A 397      29.728  39.732  57.025  1.00  0.00          C   \nATOM   3028  CH2 TRP A 397      30.557  40.677  56.389  1.00  0.00          C   \nATOM   3029  N   GLU A 398      25.763  43.626  62.168  1.00  0.00          N   \nATOM   3030  CA  GLU A 398      25.592  44.976  62.686  1.00  0.00          C   \nATOM   3031  C   GLU A 398      26.118  45.135  64.113  1.00  0.00          C   \nATOM   3032  O   GLU A 398      26.254  46.251  64.612  1.00  0.00          O   \nATOM   3033  CB  GLU A 398      24.116  45.382  62.578  1.00  0.00          C   \nATOM   3034  CG  GLU A 398      23.722  45.704  61.116  1.00  0.00          C   \nATOM   3035  CD  GLU A 398      22.222  45.640  60.824  1.00  0.00          C   \nATOM   3036  OE1 GLU A 398      21.413  45.507  61.760  1.00  0.00          O   \nATOM   3037  OE2 GLU A 398      21.848  45.725  59.635  1.00  0.00          O   \nATOM   3038  N   THR A 399      26.437  44.019  64.760  1.00  0.00          N   \nATOM   3039  CA  THR A 399      26.972  44.060  66.118  1.00  0.00          C   \nATOM   3040  C   THR A 399      28.447  44.484  66.087  1.00  0.00          C   \nATOM   3041  O   THR A 399      28.948  45.097  67.027  1.00  0.00          O   \nATOM   3042  CB  THR A 399      26.850  42.672  66.798  1.00  0.00          C   \nATOM   3043  OG1 THR A 399      25.461  42.347  66.967  1.00  0.00          O   \nATOM   3044  CG2 THR A 399      27.552  42.660  68.164  1.00  0.00          C   \nATOM   3045  N   ALA A 400      29.137  44.162  64.996  1.00  0.00          N   \nATOM   3046  CA  ALA A 400      30.556  44.497  64.857  1.00  0.00          C   \nATOM   3047  C   ALA A 400      30.821  45.602  63.837  1.00  0.00          C   \nATOM   3048  O   ALA A 400      31.914  46.167  63.807  1.00  0.00          O   \nATOM   3049  CB  ALA A 400      31.354  43.245  64.452  1.00  0.00          C   \nATOM   3050  N   ARG A 401      29.827  45.901  63.001  1.00  0.00          N   \nATOM   3051  CA  ARG A 401      29.981  46.902  61.954  1.00  0.00          C   \nATOM   3052  C   ARG A 401      28.787  47.826  61.784  1.00  0.00          C   \nATOM   3053  O   ARG A 401      27.640  47.385  61.794  1.00  0.00          O   \nATOM   3054  CB  ARG A 401      30.232  46.199  60.622  1.00  0.00          C   \nATOM   3055  CG  ARG A 401      31.552  45.507  60.515  1.00  0.00          C   \nATOM   3056  CD  ARG A 401      32.585  46.404  59.849  1.00  0.00          C   \nATOM   3057  NE  ARG A 401      33.789  45.648  59.526  1.00  0.00          N   \nATOM   3058  CZ  ARG A 401      34.691  45.277  60.422  1.00  0.00          C   \nATOM   3059  NH1 ARG A 401      34.528  45.602  61.699  1.00  0.00          N   \nATOM   3060  NH2 ARG A 401      35.744  44.569  60.043  1.00  0.00          N   \nATOM   3061  N   HIS A 402      29.061  49.113  61.620  1.00  0.00          N   \nATOM   3062  CA  HIS A 402      28.001  50.079  61.390  1.00  0.00          C   \nATOM   3063  C   HIS A 402      27.759  50.037  59.889  1.00  0.00          C   \nATOM   3064  O   HIS A 402      28.700  50.094  59.104  1.00  0.00          O   \nATOM   3065  CB  HIS A 402      28.439  51.480  61.825  1.00  0.00          C   \nATOM   3066  CG  HIS A 402      28.630  51.610  63.304  1.00  0.00          C   \nATOM   3067  ND1 HIS A 402      29.822  52.002  63.873  1.00  0.00          N   \nATOM   3068  CD2 HIS A 402      27.783  51.362  64.333  1.00  0.00          C   \nATOM   3069  CE1 HIS A 402      29.704  51.988  65.189  1.00  0.00          C   \nATOM   3070  NE2 HIS A 402      28.478  51.603  65.494  1.00  0.00          N   \nATOM   3071  N   THR A 403      26.500  49.913  59.491  1.00  0.00          N   \nATOM   3072  CA  THR A 403      26.156  49.836  58.080  1.00  0.00          C   \nATOM   3073  C   THR A 403      25.167  50.940  57.727  1.00  0.00          C   \nATOM   3074  O   THR A 403      24.585  51.552  58.611  1.00  0.00          O   \nATOM   3075  CB  THR A 403      25.534  48.455  57.747  1.00  0.00          C   \nATOM   3076  OG1 THR A 403      24.293  48.302  58.443  1.00  0.00          O   \nATOM   3077  CG2 THR A 403      26.469  47.339  58.182  1.00  0.00          C   \nATOM   3078  N   PRO A 404      24.972  51.211  56.426  1.00  0.00          N   \nATOM   3079  CA  PRO A 404      24.048  52.248  55.950  1.00  0.00          C   \nATOM   3080  C   PRO A 404      22.612  52.004  56.408  1.00  0.00          C   \nATOM   3081  O   PRO A 404      21.915  52.925  56.833  1.00  0.00          O   \nATOM   3082  CB  PRO A 404      24.174  52.150  54.431  1.00  0.00          C   \nATOM   3083  CG  PRO A 404      25.564  51.639  54.230  1.00  0.00          C   \nATOM   3084  CD  PRO A 404      25.652  50.568  55.288  1.00  0.00          C   \nATOM   3085  N   VAL A 405      22.184  50.752  56.316  1.00  0.00          N   \nATOM   3086  CA  VAL A 405      20.837  50.346  56.693  1.00  0.00          C   \nATOM   3087  C   VAL A 405      20.929  49.462  57.927  1.00  0.00          C   \nATOM   3088  O   VAL A 405      21.551  48.401  57.898  1.00  0.00          O   \nATOM   3089  CB  VAL A 405      20.170  49.534  55.558  1.00  0.00          C   \nATOM   3090  CG1 VAL A 405      18.749  49.154  55.936  1.00  0.00          C   \nATOM   3091  CG2 VAL A 405      20.194  50.336  54.266  1.00  0.00          C   \nATOM   3092  N   ASN A 406      20.307  49.906  59.008  1.00  0.00          N   \nATOM   3093  CA  ASN A 406      20.315  49.166  60.251  1.00  0.00          C   \nATOM   3094  C   ASN A 406      19.153  48.179  60.228  1.00  0.00          C   \nATOM   3095  O   ASN A 406      18.056  48.500  60.670  1.00  0.00          O   \nATOM   3096  CB  ASN A 406      20.174  50.152  61.400  1.00  0.00          C   \nATOM   3097  CG  ASN A 406      21.428  50.987  61.597  1.00  0.00          C   \nATOM   3098  OD1 ASN A 406      22.416  50.509  62.162  1.00  0.00          O   \nATOM   3099  ND2 ASN A 406      21.405  52.231  61.112  1.00  0.00          N   \nATOM   3100  N   SER A 407      19.393  46.977  59.716  1.00  0.00          N   \nATOM   3101  CA  SER A 407      18.313  45.996  59.637  1.00  0.00          C   \nATOM   3102  C   SER A 407      17.734  45.640  61.004  1.00  0.00          C   \nATOM   3103  O   SER A 407      16.540  45.372  61.118  1.00  0.00          O   \nATOM   3104  CB  SER A 407      18.790  44.714  58.937  1.00  0.00          C   \nATOM   3105  OG  SER A 407      19.664  43.996  59.786  1.00  0.00          O   \nATOM   3106  N   TRP A 408      18.550  45.641  62.054  1.00  0.00          N   \nATOM   3107  CA  TRP A 408      17.998  45.288  63.358  1.00  0.00          C   \nATOM   3108  C   TRP A 408      16.840  46.204  63.762  1.00  0.00          C   \nATOM   3109  O   TRP A 408      15.855  45.747  64.340  1.00  0.00          O   \nATOM   3110  CB  TRP A 408      19.075  45.315  64.463  1.00  0.00          C   \nATOM   3111  CG  TRP A 408      19.666  46.683  64.783  1.00  0.00          C   \nATOM   3112  CD1 TRP A 408      20.795  47.246  64.242  1.00  0.00          C   \nATOM   3113  CD2 TRP A 408      19.151  47.643  65.721  1.00  0.00          C   \nATOM   3114  NE1 TRP A 408      21.013  48.496  64.793  1.00  0.00          N   \nATOM   3115  CE2 TRP A 408      20.021  48.758  65.698  1.00  0.00          C   \nATOM   3116  CE3 TRP A 408      18.046  47.664  66.580  1.00  0.00          C   \nATOM   3117  CZ2 TRP A 408      19.808  49.884  66.500  1.00  0.00          C   \nATOM   3118  CZ3 TRP A 408      17.841  48.786  67.376  1.00  0.00          C   \nATOM   3119  CH2 TRP A 408      18.721  49.877  67.328  1.00  0.00          C   \nATOM   3120  N   LEU A 409      16.971  47.496  63.461  1.00  0.00          N   \nATOM   3121  CA  LEU A 409      15.953  48.478  63.829  1.00  0.00          C   \nATOM   3122  C   LEU A 409      14.706  48.315  62.981  1.00  0.00          C   \nATOM   3123  O   LEU A 409      13.584  48.392  63.489  1.00  0.00          O   \nATOM   3124  CB  LEU A 409      16.499  49.898  63.670  1.00  0.00          C   \nATOM   3125  CG  LEU A 409      15.520  51.029  63.982  1.00  0.00          C   \nATOM   3126  CD1 LEU A 409      14.854  50.800  65.332  1.00  0.00          C   \nATOM   3127  CD2 LEU A 409      16.268  52.340  63.960  1.00  0.00          C   \nATOM   3128  N   GLY A 410      14.916  48.097  61.685  1.00  0.00          N   \nATOM   3129  CA  GLY A 410      13.802  47.889  60.787  1.00  0.00          C   \nATOM   3130  C   GLY A 410      13.063  46.626  61.189  1.00  0.00          C   \nATOM   3131  O   GLY A 410      11.832  46.607  61.164  1.00  0.00          O   \nATOM   3132  N   ASN A 411      13.793  45.566  61.556  1.00  0.00          N   \nATOM   3133  CA  ASN A 411      13.142  44.317  61.961  1.00  0.00          C   \nATOM   3134  C   ASN A 411      12.384  44.434  63.272  1.00  0.00          C   \nATOM   3135  O   ASN A 411      11.350  43.776  63.470  1.00  0.00          O   \nATOM   3136  CB  ASN A 411      14.151  43.160  62.026  1.00  0.00          C   \nATOM   3137  CG  ASN A 411      14.465  42.619  60.656  1.00  0.00          C   \nATOM   3138  OD1 ASN A 411      13.556  42.444  59.854  1.00  0.00          O   \nATOM   3139  ND2 ASN A 411      15.736  42.361  60.371  1.00  0.00          N   \nATOM   3140  N   ILE A 412      12.880  45.272  64.174  1.00  0.00          N   \nATOM   3141  CA  ILE A 412      12.174  45.470  65.436  1.00  0.00          C   \nATOM   3142  C   ILE A 412      10.850  46.180  65.133  1.00  0.00          C   \nATOM   3143  O   ILE A 412       9.799  45.847  65.685  1.00  0.00          O   \nATOM   3144  CB  ILE A 412      13.010  46.317  66.421  1.00  0.00          C   \nATOM   3145  CG1 ILE A 412      14.084  45.420  67.059  1.00  0.00          C   \nATOM   3146  CG2 ILE A 412      12.095  46.965  67.463  1.00  0.00          C   \nATOM   3147  CD1 ILE A 412      14.962  46.103  68.072  1.00  0.00          C   \nATOM   3148  N   ILE A 413      10.907  47.154  64.235  1.00  0.00          N   \nATOM   3149  CA  ILE A 413       9.721  47.898  63.873  1.00  0.00          C   \nATOM   3150  C   ILE A 413       8.685  47.017  63.192  1.00  0.00          C   \nATOM   3151  O   ILE A 413       7.522  47.020  63.568  1.00  0.00          O   \nATOM   3152  CB  ILE A 413      10.080  49.071  62.940  1.00  0.00          C   \nATOM   3153  CG1 ILE A 413      10.815  50.145  63.742  1.00  0.00          C   \nATOM   3154  CG2 ILE A 413       8.825  49.659  62.316  1.00  0.00          C   \nATOM   3155  CD1 ILE A 413      11.418  51.242  62.914  1.00  0.00          C   \nATOM   3156  N   MET A 414       9.118  46.247  62.202  1.00  0.00          N   \nATOM   3157  CA  MET A 414       8.203  45.411  61.445  1.00  0.00          C   \nATOM   3158  C   MET A 414       7.781  44.121  62.133  1.00  0.00          C   \nATOM   3159  O   MET A 414       6.678  43.625  61.905  1.00  0.00          O   \nATOM   3160  CB  MET A 414       8.818  45.112  60.074  1.00  0.00          C   \nATOM   3161  CG  MET A 414       9.108  46.373  59.242  1.00  0.00          C   \nATOM   3162  SD  MET A 414       7.595  47.269  58.668  1.00  0.00          S   \nATOM   3163  CE  MET A 414       7.138  46.220  57.285  1.00  0.00          C   \nATOM   3164  N   TYR A 415       8.637  43.586  62.990  1.00  0.00          N   \nATOM   3165  CA  TYR A 415       8.321  42.341  63.689  1.00  0.00          C   \nATOM   3166  C   TYR A 415       8.266  42.497  65.199  1.00  0.00          C   \nATOM   3167  O   TYR A 415       8.572  41.567  65.939  1.00  0.00          O   \nATOM   3168  CB  TYR A 415       9.347  41.276  63.288  1.00  0.00          C   \nATOM   3169  CG  TYR A 415       9.263  40.944  61.806  1.00  0.00          C   \nATOM   3170  CD1 TYR A 415       8.204  40.185  61.307  1.00  0.00          C   \nATOM   3171  CD2 TYR A 415      10.211  41.436  60.901  1.00  0.00          C   \nATOM   3172  CE1 TYR A 415       8.081  39.923  59.945  1.00  0.00          C   \nATOM   3173  CE2 TYR A 415      10.104  41.173  59.524  1.00  0.00          C   \nATOM   3174  CZ  TYR A 415       9.030  40.415  59.058  1.00  0.00          C   \nATOM   3175  OH  TYR A 415       8.899  40.151  57.708  1.00  0.00          O   \nATOM   3176  N   ALA A 416       7.853  43.677  65.651  1.00  0.00          N   \nATOM   3177  CA  ALA A 416       7.763  43.980  67.081  1.00  0.00          C   \nATOM   3178  C   ALA A 416       7.042  42.965  67.965  1.00  0.00          C   \nATOM   3179  O   ALA A 416       7.486  42.708  69.072  1.00  0.00          O   \nATOM   3180  CB  ALA A 416       7.134  45.358  67.274  1.00  0.00          C   \nATOM   3181  N   PRO A 417       5.916  42.387  67.505  1.00  0.00          N   \nATOM   3182  CA  PRO A 417       5.166  41.407  68.303  1.00  0.00          C   \nATOM   3183  C   PRO A 417       5.810  40.013  68.426  1.00  0.00          C   \nATOM   3184  O   PRO A 417       5.413  39.213  69.275  1.00  0.00          O   \nATOM   3185  CB  PRO A 417       3.815  41.308  67.570  1.00  0.00          C   \nATOM   3186  CG  PRO A 417       3.745  42.530  66.733  1.00  0.00          C   \nATOM   3187  CD  PRO A 417       5.174  42.716  66.277  1.00  0.00          C   \nATOM   3188  N   THR A 418       6.801  39.727  67.589  1.00  0.00          N   \nATOM   3189  CA  THR A 418       7.418  38.399  67.587  1.00  0.00          C   \nATOM   3190  C   THR A 418       8.257  38.005  68.781  1.00  0.00          C   \nATOM   3191  O   THR A 418       8.879  38.848  69.439  1.00  0.00          O   \nATOM   3192  CB  THR A 418       8.277  38.188  66.319  1.00  0.00          C   \nATOM   3193  OG1 THR A 418       9.495  38.943  66.428  1.00  0.00          O   \nATOM   3194  CG2 THR A 418       7.497  38.663  65.078  1.00  0.00          C   \nATOM   3195  N   LEU A 419       8.282  36.703  69.048  1.00  0.00          N   \nATOM   3196  CA  LEU A 419       9.062  36.177  70.161  1.00  0.00          C   \nATOM   3197  C   LEU A 419      10.541  36.535  69.993  1.00  0.00          C   \nATOM   3198  O   LEU A 419      11.182  37.027  70.929  1.00  0.00          O   \nATOM   3199  CB  LEU A 419       8.887  34.652  70.239  1.00  0.00          C   \nATOM   3200  CG  LEU A 419       9.813  33.836  71.140  1.00  0.00          C   \nATOM   3201  CD1 LEU A 419       9.612  34.213  72.603  1.00  0.00          C   \nATOM   3202  CD2 LEU A 419       9.522  32.340  70.912  1.00  0.00          C   \nATOM   3203  N   TRP A 420      11.073  36.306  68.789  1.00  0.00          N   \nATOM   3204  CA  TRP A 420      12.482  36.569  68.508  1.00  0.00          C   \nATOM   3205  C   TRP A 420      12.891  38.044  68.474  1.00  0.00          C   \nATOM   3206  O   TRP A 420      13.981  38.396  68.934  1.00  0.00          O   \nATOM   3207  CB  TRP A 420      12.910  35.876  67.201  1.00  0.00          C   \nATOM   3208  CG  TRP A 420      12.051  36.192  66.025  1.00  0.00          C   \nATOM   3209  CD1 TRP A 420      10.864  35.594  65.684  1.00  0.00          C   \nATOM   3210  CD2 TRP A 420      12.282  37.221  65.050  1.00  0.00          C   \nATOM   3211  NE1 TRP A 420      10.336  36.203  64.560  1.00  0.00          N   \nATOM   3212  CE2 TRP A 420      11.175  37.203  64.162  1.00  0.00          C   \nATOM   3213  CE3 TRP A 420      13.302  38.170  64.862  1.00  0.00          C   \nATOM   3214  CZ2 TRP A 420      11.083  38.089  63.067  1.00  0.00          C   \nATOM   3215  CZ3 TRP A 420      13.209  39.048  63.778  1.00  0.00          C   \nATOM   3216  CH2 TRP A 420      12.094  39.005  62.896  1.00  0.00          C   \nATOM   3217  N   ALA A 421      12.046  38.914  67.927  1.00  0.00          N   \nATOM   3218  CA  ALA A 421      12.421  40.327  67.891  1.00  0.00          C   \nATOM   3219  C   ALA A 421      12.472  40.899  69.312  1.00  0.00          C   \nATOM   3220  O   ALA A 421      13.362  41.683  69.652  1.00  0.00          O   \nATOM   3221  CB  ALA A 421      11.424  41.140  67.023  1.00  0.00          C   \nATOM   3222  N   ARG A 422      11.518  40.497  70.144  1.00  0.00          N   \nATOM   3223  CA  ARG A 422      11.452  40.999  71.520  1.00  0.00          C   \nATOM   3224  C   ARG A 422      12.514  40.413  72.441  1.00  0.00          C   \nATOM   3225  O   ARG A 422      13.200  41.140  73.158  1.00  0.00          O   \nATOM   3226  CB  ARG A 422      10.075  40.710  72.107  1.00  0.00          C   \nATOM   3227  CG  ARG A 422       8.936  41.273  71.292  1.00  0.00          C   \nATOM   3228  CD  ARG A 422       7.597  40.690  71.756  1.00  0.00          C   \nATOM   3229  NE  ARG A 422       7.207  41.167  73.083  1.00  0.00          N   \nATOM   3230  CZ  ARG A 422       6.253  40.611  73.825  1.00  0.00          C   \nATOM   3231  NH1 ARG A 422       5.593  39.545  73.379  1.00  0.00          N   \nATOM   3232  NH2 ARG A 422       5.928  41.146  75.000  1.00  0.00          N   \nATOM   3233  N   MET A 423      12.643  39.091  72.428  1.00  0.00          N   \nATOM   3234  CA  MET A 423      13.623  38.440  73.283  1.00  0.00          C   \nATOM   3235  C   MET A 423      15.054  38.804  72.942  1.00  0.00          C   \nATOM   3236  O   MET A 423      15.853  39.068  73.833  1.00  0.00          O   \nATOM   3237  CB  MET A 423      13.490  36.908  73.194  1.00  0.00          C   \nATOM   3238  CG  MET A 423      14.502  36.126  74.030  1.00  0.00          C   \nATOM   3239  SD  MET A 423      14.122  34.328  74.025  1.00  0.00          S   \nATOM   3240  CE  MET A 423      14.592  33.920  72.360  1.00  0.00          C   \nATOM   3241  N   ILE A 424      15.363  38.830  71.650  1.00  0.00          N   \nATOM   3242  CA  ILE A 424      16.729  39.048  71.200  1.00  0.00          C   \nATOM   3243  C   ILE A 424      17.153  40.406  70.660  1.00  0.00          C   \nATOM   3244  O   ILE A 424      18.060  41.032  71.216  1.00  0.00          O   \nATOM   3245  CB  ILE A 424      17.096  37.960  70.147  1.00  0.00          C   \nATOM   3246  CG1 ILE A 424      16.845  36.571  70.745  1.00  0.00          C   \nATOM   3247  CG2 ILE A 424      18.557  38.094  69.722  1.00  0.00          C   \nATOM   3248  CD1 ILE A 424      16.915  35.436  69.746  1.00  0.00          C   \nATOM   3249  N   LEU A 425      16.528  40.861  69.578  1.00  0.00          N   \nATOM   3250  CA  LEU A 425      16.937  42.133  68.989  1.00  0.00          C   \nATOM   3251  C   LEU A 425      16.788  43.315  69.924  1.00  0.00          C   \nATOM   3252  O   LEU A 425      17.686  44.149  70.021  1.00  0.00          O   \nATOM   3253  CB  LEU A 425      16.161  42.398  67.701  1.00  0.00          C   \nATOM   3254  CG  LEU A 425      16.376  41.380  66.576  1.00  0.00          C   \nATOM   3255  CD1 LEU A 425      15.516  41.778  65.361  1.00  0.00          C   \nATOM   3256  CD2 LEU A 425      17.856  41.333  66.207  1.00  0.00          C   \nATOM   3257  N   MET A 426      15.642  43.408  70.589  1.00  0.00          N   \nATOM   3258  CA  MET A 426      15.403  44.507  71.511  1.00  0.00          C   \nATOM   3259  C   MET A 426      16.419  44.472  72.657  1.00  0.00          C   \nATOM   3260  O   MET A 426      17.079  45.466  72.940  1.00  0.00          O   \nATOM   3261  CB  MET A 426      13.981  44.434  72.068  1.00  0.00          C   \nATOM   3262  CG  MET A 426      12.912  44.992  71.115  1.00  0.00          C   \nATOM   3263  SD  MET A 426      11.285  44.915  71.905  1.00  0.00          S   \nATOM   3264  CE  MET A 426      10.201  45.166  70.449  1.00  0.00          C   \nATOM   3265  N   THR A 427      16.565  43.316  73.290  1.00  0.00          N   \nATOM   3266  CA  THR A 427      17.488  43.184  74.407  1.00  0.00          C   \nATOM   3267  C   THR A 427      18.925  43.490  73.998  1.00  0.00          C   \nATOM   3268  O   THR A 427      19.603  44.319  74.611  1.00  0.00          O   \nATOM   3269  CB  THR A 427      17.421  41.779  74.979  1.00  0.00          C   \nATOM   3270  OG1 THR A 427      16.050  41.445  75.201  1.00  0.00          O   \nATOM   3271  CG2 THR A 427      18.198  41.684  76.305  1.00  0.00          C   \nATOM   3272  N   HIS A 428      19.366  42.836  72.934  1.00  0.00          N   \nATOM   3273  CA  HIS A 428      20.722  42.992  72.441  1.00  0.00          C   \nATOM   3274  C   HIS A 428      21.100  44.421  72.042  1.00  0.00          C   \nATOM   3275  O   HIS A 428      22.109  44.966  72.494  1.00  0.00          O   \nATOM   3276  CB  HIS A 428      20.923  42.057  71.261  1.00  0.00          C   \nATOM   3277  CG  HIS A 428      22.317  42.043  70.731  1.00  0.00          C   \nATOM   3278  ND1 HIS A 428      23.378  41.521  71.442  1.00  0.00          N   \nATOM   3279  CD2 HIS A 428      22.823  42.449  69.542  1.00  0.00          C   \nATOM   3280  CE1 HIS A 428      24.474  41.601  70.708  1.00  0.00          C   \nATOM   3281  NE2 HIS A 428      24.164  42.160  69.550  1.00  0.00          N   \nATOM   3282  N   PHE A 429      20.297  45.040  71.200  1.00  0.00          N   \nATOM   3283  CA  PHE A 429      20.639  46.379  70.762  1.00  0.00          C   \nATOM   3284  C   PHE A 429      20.407  47.487  71.786  1.00  0.00          C   \nATOM   3285  O   PHE A 429      21.173  48.460  71.836  1.00  0.00          O   \nATOM   3286  CB  PHE A 429      19.957  46.652  69.416  1.00  0.00          C   \nATOM   3287  CG  PHE A 429      20.636  45.947  68.274  1.00  0.00          C   \nATOM   3288  CD1 PHE A 429      21.850  46.415  67.780  1.00  0.00          C   \nATOM   3289  CD2 PHE A 429      20.149  44.728  67.801  1.00  0.00          C   \nATOM   3290  CE1 PHE A 429      22.576  45.677  66.830  1.00  0.00          C   \nATOM   3291  CE2 PHE A 429      20.870  43.982  66.858  1.00  0.00          C   \nATOM   3292  CZ  PHE A 429      22.081  44.454  66.378  1.00  0.00          C   \nATOM   3293  N   PHE A 430      19.378  47.361  72.620  1.00  0.00          N   \nATOM   3294  CA  PHE A 430      19.190  48.395  73.621  1.00  0.00          C   \nATOM   3295  C   PHE A 430      20.332  48.319  74.624  1.00  0.00          C   \nATOM   3296  O   PHE A 430      20.784  49.338  75.130  1.00  0.00          O   \nATOM   3297  CB  PHE A 430      17.835  48.281  74.314  1.00  0.00          C   \nATOM   3298  CG  PHE A 430      16.774  49.104  73.647  1.00  0.00          C   \nATOM   3299  CD1 PHE A 430      16.074  48.609  72.557  1.00  0.00          C   \nATOM   3300  CD2 PHE A 430      16.539  50.415  74.054  1.00  0.00          C   \nATOM   3301  CE1 PHE A 430      15.159  49.408  71.873  1.00  0.00          C   \nATOM   3302  CE2 PHE A 430      15.628  51.221  73.381  1.00  0.00          C   \nATOM   3303  CZ  PHE A 430      14.934  50.713  72.287  1.00  0.00          C   \nATOM   3304  N   SER A 431      20.813  47.105  74.878  1.00  0.00          N   \nATOM   3305  CA  SER A 431      21.921  46.918  75.792  1.00  0.00          C   \nATOM   3306  C   SER A 431      23.129  47.663  75.229  1.00  0.00          C   \nATOM   3307  O   SER A 431      23.803  48.403  75.941  1.00  0.00          O   \nATOM   3308  CB  SER A 431      22.227  45.433  75.930  1.00  0.00          C   \nATOM   3309  OG  SER A 431      23.401  45.232  76.680  1.00  0.00          O   \nATOM   3310  N   ILE A 432      23.389  47.492  73.936  1.00  0.00          N   \nATOM   3311  CA  ILE A 432      24.509  48.179  73.308  1.00  0.00          C   \nATOM   3312  C   ILE A 432      24.287  49.704  73.316  1.00  0.00          C   \nATOM   3313  O   ILE A 432      25.188  50.457  73.672  1.00  0.00          O   \nATOM   3314  CB  ILE A 432      24.715  47.704  71.854  1.00  0.00          C   \nATOM   3315  CG1 ILE A 432      25.133  46.231  71.840  1.00  0.00          C   \nATOM   3316  CG2 ILE A 432      25.796  48.545  71.175  1.00  0.00          C   \nATOM   3317  CD1 ILE A 432      25.090  45.614  70.441  1.00  0.00          C   \nATOM   3318  N   LEU A 433      23.099  50.158  72.921  1.00  0.00          N   \nATOM   3319  CA  LEU A 433      22.814  51.593  72.915  1.00  0.00          C   \nATOM   3320  C   LEU A 433      23.056  52.225  74.287  1.00  0.00          C   \nATOM   3321  O   LEU A 433      23.614  53.316  74.379  1.00  0.00          O   \nATOM   3322  CB  LEU A 433      21.374  51.861  72.480  1.00  0.00          C   \nATOM   3323  CG  LEU A 433      21.008  51.545  71.033  1.00  0.00          C   \nATOM   3324  CD1 LEU A 433      19.536  51.903  70.815  1.00  0.00          C   \nATOM   3325  CD2 LEU A 433      21.908  52.309  70.075  1.00  0.00          C   \nATOM   3326  N   LEU A 434      22.629  51.541  75.343  1.00  0.00          N   \nATOM   3327  CA  LEU A 434      22.830  52.032  76.700  1.00  0.00          C   \nATOM   3328  C   LEU A 434      24.324  52.189  76.979  1.00  0.00          C   \nATOM   3329  O   LEU A 434      24.793  53.268  77.345  1.00  0.00          O   \nATOM   3330  CB  LEU A 434      22.239  51.049  77.726  1.00  0.00          C   \nATOM   3331  CG  LEU A 434      20.767  51.180  78.140  1.00  0.00          C   \nATOM   3332  CD1 LEU A 434      20.315  49.910  78.850  1.00  0.00          C   \nATOM   3333  CD2 LEU A 434      20.586  52.384  79.047  1.00  0.00          C   \nATOM   3334  N   ALA A 435      25.062  51.099  76.793  1.00  0.00          N   \nATOM   3335  CA  ALA A 435      26.498  51.081  77.037  1.00  0.00          C   \nATOM   3336  C   ALA A 435      27.257  52.171  76.284  1.00  0.00          C   \nATOM   3337  O   ALA A 435      28.231  52.727  76.791  1.00  0.00          O   \nATOM   3338  CB  ALA A 435      27.062  49.703  76.682  1.00  0.00          C   \nATOM   3339  N   GLN A 436      26.816  52.472  75.068  1.00  0.00          N   \nATOM   3340  CA  GLN A 436      27.468  53.491  74.256  1.00  0.00          C   \nATOM   3341  C   GLN A 436      26.827  54.869  74.431  1.00  0.00          C   \nATOM   3342  O   GLN A 436      27.272  55.848  73.834  1.00  0.00          O   \nATOM   3343  CB  GLN A 436      27.402  53.089  72.779  1.00  0.00          C   \nATOM   3344  CG  GLN A 436      28.086  51.772  72.439  1.00  0.00          C   \nATOM   3345  CD  GLN A 436      29.563  51.778  72.786  1.00  0.00          C   \nATOM   3346  OE1 GLN A 436      30.291  52.699  72.424  1.00  0.00          O   \nATOM   3347  NE2 GLN A 436      30.011  50.746  73.489  1.00  0.00          N   \nATOM   3348  N   GLU A 437      25.790  54.949  75.255  1.00  0.00          N   \nATOM   3349  CA  GLU A 437      25.082  56.207  75.454  1.00  0.00          C   \nATOM   3350  C   GLU A 437      24.706  56.808  74.103  1.00  0.00          C   \nATOM   3351  O   GLU A 437      25.069  57.943  73.788  1.00  0.00          O   \nATOM   3352  CB  GLU A 437      25.939  57.201  76.250  1.00  0.00          C   \nATOM   3353  CG  GLU A 437      25.873  56.995  77.761  1.00  0.00          C   \nATOM   3354  CD  GLU A 437      26.740  57.979  78.534  1.00  0.00          C   \nATOM   3355  OE1 GLU A 437      26.551  59.206  78.382  1.00  0.00          O   \nATOM   3356  OE2 GLU A 437      27.613  57.519  79.299  1.00  0.00          O   \nATOM   3357  N   GLN A 438      23.986  56.027  73.303  1.00  0.00          N   \nATOM   3358  CA  GLN A 438      23.542  56.470  71.988  1.00  0.00          C   \nATOM   3359  C   GLN A 438      22.056  56.206  71.792  1.00  0.00          C   \nATOM   3360  O   GLN A 438      21.599  56.002  70.670  1.00  0.00          O   \nATOM   3361  CB  GLN A 438      24.331  55.766  70.891  1.00  0.00          C   \nATOM   3362  CG  GLN A 438      25.820  55.963  71.019  1.00  0.00          C   \nATOM   3363  CD  GLN A 438      26.557  55.588  69.761  1.00  0.00          C   \nATOM   3364  OE1 GLN A 438      26.241  54.583  69.121  1.00  0.00          O   \nATOM   3365  NE2 GLN A 438      27.555  56.389  69.399  1.00  0.00          N   \nATOM   3366  N   LEU A 439      21.310  56.210  72.890  1.00  0.00          N   \nATOM   3367  CA  LEU A 439      19.871  55.988  72.841  1.00  0.00          C   \nATOM   3368  C   LEU A 439      19.135  56.973  71.937  1.00  0.00          C   \nATOM   3369  O   LEU A 439      18.250  56.583  71.178  1.00  0.00          O   \nATOM   3370  CB  LEU A 439      19.278  56.074  74.252  1.00  0.00          C   \nATOM   3371  CG  LEU A 439      19.030  54.787  75.047  1.00  0.00          C   \nATOM   3372  CD1 LEU A 439      19.862  53.668  74.498  1.00  0.00          C   \nATOM   3373  CD2 LEU A 439      19.338  55.023  76.524  1.00  0.00          C   \nATOM   3374  N   GLU A 440      19.503  58.248  72.005  1.00  0.00          N   \nATOM   3375  CA  GLU A 440      18.821  59.262  71.211  1.00  0.00          C   \nATOM   3376  C   GLU A 440      19.400  59.580  69.840  1.00  0.00          C   \nATOM   3377  O   GLU A 440      18.973  60.531  69.191  1.00  0.00          O   \nATOM   3378  CB  GLU A 440      18.690  60.546  72.032  1.00  0.00          C   \nATOM   3379  CG  GLU A 440      19.931  60.936  72.798  1.00  0.00          C   \nATOM   3380  CD  GLU A 440      19.629  61.929  73.904  1.00  0.00          C   \nATOM   3381  OE1 GLU A 440      20.572  62.324  74.624  1.00  0.00          O   \nATOM   3382  OE2 GLU A 440      18.449  62.316  74.056  1.00  0.00          O   \nATOM   3383  N   LYS A 441      20.357  58.777  69.391  1.00  0.00          N   \nATOM   3384  CA  LYS A 441      20.962  58.987  68.085  1.00  0.00          C   \nATOM   3385  C   LYS A 441      20.068  58.359  67.012  1.00  0.00          C   \nATOM   3386  O   LYS A 441      19.798  57.160  67.051  1.00  0.00          O   \nATOM   3387  CB  LYS A 441      22.352  58.349  68.054  1.00  0.00          C   \nATOM   3388  CG  LYS A 441      23.087  58.492  66.727  1.00  0.00          C   \nATOM   3389  CD  LYS A 441      24.353  57.645  66.719  1.00  0.00          C   \nATOM   3390  CE  LYS A 441      25.060  57.690  65.371  1.00  0.00          C   \nATOM   3391  NZ  LYS A 441      26.261  56.800  65.368  1.00  0.00          N   \nATOM   3392  N   ALA A 442      19.613  59.173  66.062  1.00  0.00          N   \nATOM   3393  CA  ALA A 442      18.750  58.695  64.981  1.00  0.00          C   \nATOM   3394  C   ALA A 442      19.529  57.796  64.039  1.00  0.00          C   \nATOM   3395  O   ALA A 442      20.642  58.128  63.635  1.00  0.00          O   \nATOM   3396  CB  ALA A 442      18.171  59.871  64.207  1.00  0.00          C   \nATOM   3397  N   LEU A 443      18.933  56.663  63.683  1.00  0.00          N   \nATOM   3398  CA  LEU A 443      19.573  55.698  62.795  1.00  0.00          C   \nATOM   3399  C   LEU A 443      18.732  55.450  61.541  1.00  0.00          C   \nATOM   3400  O   LEU A 443      17.500  55.467  61.596  1.00  0.00          O   \nATOM   3401  CB  LEU A 443      19.780  54.374  63.539  1.00  0.00          C   \nATOM   3402  CG  LEU A 443      20.674  54.397  64.784  1.00  0.00          C   \nATOM   3403  CD1 LEU A 443      20.455  53.128  65.590  1.00  0.00          C   \nATOM   3404  CD2 LEU A 443      22.137  54.516  64.374  1.00  0.00          C   \nATOM   3405  N   ASP A 444      19.394  55.213  60.415  1.00  0.00          N   \nATOM   3406  CA  ASP A 444      18.677  54.950  59.175  1.00  0.00          C   \nATOM   3407  C   ASP A 444      18.335  53.474  59.017  1.00  0.00          C   \nATOM   3408  O   ASP A 444      19.125  52.602  59.390  1.00  0.00          O   \nATOM   3409  CB  ASP A 444      19.510  55.385  57.961  1.00  0.00          C   \nATOM   3410  CG  ASP A 444      19.822  56.869  57.965  1.00  0.00          C   \nATOM   3411  OD1 ASP A 444      18.990  57.657  58.468  1.00  0.00          O   \nATOM   3412  OD2 ASP A 444      20.892  57.250  57.449  1.00  0.00          O   \nATOM   3413  N   CYS A 445      17.152  53.208  58.468  1.00  0.00          N   \nATOM   3414  CA  CYS A 445      16.697  51.848  58.195  1.00  0.00          C   \nATOM   3415  C   CYS A 445      15.732  51.942  57.023  1.00  0.00          C   \nATOM   3416  O   CYS A 445      15.335  53.043  56.641  1.00  0.00          O   \nATOM   3417  CB  CYS A 445      15.993  51.239  59.417  1.00  0.00          C   \nATOM   3418  SG  CYS A 445      14.508  52.111  59.970  1.00  0.00          S   \nATOM   3419  N   GLN A 446      15.356  50.803  56.446  1.00  0.00          N   \nATOM   3420  CA  GLN A 446      14.433  50.821  55.317  1.00  0.00          C   \nATOM   3421  C   GLN A 446      13.144  50.070  55.594  1.00  0.00          C   \nATOM   3422  O   GLN A 446      13.142  49.026  56.248  1.00  0.00          O   \nATOM   3423  CB  GLN A 446      15.094  50.238  54.061  1.00  0.00          C   \nATOM   3424  CG  GLN A 446      16.343  50.968  53.600  1.00  0.00          C   \nATOM   3425  CD  GLN A 446      16.913  50.385  52.313  1.00  0.00          C   \nATOM   3426  OE1 GLN A 446      17.018  49.164  52.161  1.00  0.00          O   \nATOM   3427  NE2 GLN A 446      17.290  51.258  51.382  1.00  0.00          N   \nATOM   3428  N   ILE A 447      12.046  50.617  55.087  1.00  0.00          N   \nATOM   3429  CA  ILE A 447      10.725  50.016  55.242  1.00  0.00          C   \nATOM   3430  C   ILE A 447      10.134  50.007  53.844  1.00  0.00          C   \nATOM   3431  O   ILE A 447       9.850  51.065  53.279  1.00  0.00          O   \nATOM   3432  CB  ILE A 447       9.813  50.856  56.186  1.00  0.00          C   \nATOM   3433  CG1 ILE A 447      10.383  50.841  57.610  1.00  0.00          C   \nATOM   3434  CG2 ILE A 447       8.382  50.307  56.179  1.00  0.00          C   \nATOM   3435  CD1 ILE A 447       9.554  51.632  58.606  1.00  0.00          C   \nATOM   3436  N   TYR A 448       9.964  48.812  53.288  1.00  0.00          N   \nATOM   3437  CA  TYR A 448       9.435  48.647  51.938  1.00  0.00          C   \nATOM   3438  C   TYR A 448      10.336  49.371  50.947  1.00  0.00          C   \nATOM   3439  O   TYR A 448       9.862  49.954  49.968  1.00  0.00          O   \nATOM   3440  CB  TYR A 448       7.998  49.182  51.825  1.00  0.00          C   \nATOM   3441  CG  TYR A 448       6.945  48.355  52.533  1.00  0.00          C   \nATOM   3442  CD1 TYR A 448       7.219  47.057  52.974  1.00  0.00          C   \nATOM   3443  CD2 TYR A 448       5.660  48.870  52.748  1.00  0.00          C   \nATOM   3444  CE1 TYR A 448       6.239  46.291  53.616  1.00  0.00          C   \nATOM   3445  CE2 TYR A 448       4.673  48.117  53.382  1.00  0.00          C   \nATOM   3446  CZ  TYR A 448       4.966  46.829  53.816  1.00  0.00          C   \nATOM   3447  OH  TYR A 448       3.989  46.089  54.451  1.00  0.00          O   \nATOM   3448  N   GLY A 449      11.638  49.350  51.226  1.00  0.00          N   \nATOM   3449  CA  GLY A 449      12.597  49.970  50.332  1.00  0.00          C   \nATOM   3450  C   GLY A 449      12.914  51.431  50.577  1.00  0.00          C   \nATOM   3451  O   GLY A 449      13.971  51.900  50.166  1.00  0.00          O   \nATOM   3452  N   ALA A 450      12.006  52.154  51.225  1.00  0.00          N   \nATOM   3453  CA  ALA A 450      12.236  53.570  51.510  1.00  0.00          C   \nATOM   3454  C   ALA A 450      13.081  53.724  52.776  1.00  0.00          C   \nATOM   3455  O   ALA A 450      12.895  52.988  53.753  1.00  0.00          O   \nATOM   3456  CB  ALA A 450      10.909  54.296  51.684  1.00  0.00          C   \nATOM   3457  N   CYS A 451      13.999  54.686  52.751  1.00  0.00          N   \nATOM   3458  CA  CYS A 451      14.879  54.938  53.883  1.00  0.00          C   \nATOM   3459  C   CYS A 451      14.267  55.956  54.832  1.00  0.00          C   \nATOM   3460  O   CYS A 451      13.743  56.990  54.408  1.00  0.00          O   \nATOM   3461  CB  CYS A 451      16.251  55.434  53.398  1.00  0.00          C   \nATOM   3462  SG  CYS A 451      17.501  55.617  54.720  1.00  0.00          S   \nATOM   3463  N   TYR A 452      14.345  55.644  56.122  1.00  0.00          N   \nATOM   3464  CA  TYR A 452      13.816  56.486  57.185  1.00  0.00          C   \nATOM   3465  C   TYR A 452      14.889  56.725  58.252  1.00  0.00          C   \nATOM   3466  O   TYR A 452      15.758  55.883  58.480  1.00  0.00          O   \nATOM   3467  CB  TYR A 452      12.615  55.795  57.840  1.00  0.00          C   \nATOM   3468  CG  TYR A 452      11.401  55.657  56.945  1.00  0.00          C   \nATOM   3469  CD1 TYR A 452      10.378  56.607  56.974  1.00  0.00          C   \nATOM   3470  CD2 TYR A 452      11.281  54.585  56.063  1.00  0.00          C   \nATOM   3471  CE1 TYR A 452       9.261  56.490  56.143  1.00  0.00          C   \nATOM   3472  CE2 TYR A 452      10.173  54.455  55.229  1.00  0.00          C   \nATOM   3473  CZ  TYR A 452       9.167  55.409  55.272  1.00  0.00          C   \nATOM   3474  OH  TYR A 452       8.076  55.290  54.438  1.00  0.00          O   \nATOM   3475  N   SER A 453      14.827  57.884  58.895  1.00  0.00          N   \nATOM   3476  CA  SER A 453      15.756  58.214  59.969  1.00  0.00          C   \nATOM   3477  C   SER A 453      14.905  58.025  61.214  1.00  0.00          C   \nATOM   3478  O   SER A 453      13.869  58.684  61.364  1.00  0.00          O   \nATOM   3479  CB  SER A 453      16.220  59.665  59.867  1.00  0.00          C   \nATOM   3480  OG  SER A 453      17.084  59.982  60.944  1.00  0.00          O   \nATOM   3481  N   ILE A 454      15.324  57.123  62.098  1.00  0.00          N   \nATOM   3482  CA  ILE A 454      14.541  56.838  63.293  1.00  0.00          C   \nATOM   3483  C   ILE A 454      15.337  56.798  64.592  1.00  0.00          C   \nATOM   3484  O   ILE A 454      16.422  56.214  64.654  1.00  0.00          O   \nATOM   3485  CB  ILE A 454      13.799  55.492  63.125  1.00  0.00          C   \nATOM   3486  CG1 ILE A 454      12.801  55.607  61.966  1.00  0.00          C   \nATOM   3487  CG2 ILE A 454      13.106  55.095  64.432  1.00  0.00          C   \nATOM   3488  CD1 ILE A 454      12.118  54.322  61.638  1.00  0.00          C   \nATOM   3489  N   GLU A 455      14.773  57.413  65.627  1.00  0.00          N   \nATOM   3490  CA  GLU A 455      15.396  57.456  66.944  1.00  0.00          C   \nATOM   3491  C   GLU A 455      14.852  56.290  67.760  1.00  0.00          C   \nATOM   3492  O   GLU A 455      13.644  56.184  67.988  1.00  0.00          O   \nATOM   3493  CB  GLU A 455      15.079  58.785  67.644  1.00  0.00          C   \nATOM   3494  CG  GLU A 455      15.539  60.015  66.865  1.00  0.00          C   \nATOM   3495  CD  GLU A 455      15.322  61.307  67.620  1.00  0.00          C   \nATOM   3496  OE1 GLU A 455      14.193  61.535  68.103  1.00  0.00          O   \nATOM   3497  OE2 GLU A 455      16.281  62.104  67.722  1.00  0.00          O   \nATOM   3498  N   PRO A 456      15.743  55.387  68.196  1.00  0.00          N   \nATOM   3499  CA  PRO A 456      15.395  54.204  68.990  1.00  0.00          C   \nATOM   3500  C   PRO A 456      14.430  54.464  70.149  1.00  0.00          C   \nATOM   3501  O   PRO A 456      13.567  53.636  70.446  1.00  0.00          O   \nATOM   3502  CB  PRO A 456      16.756  53.715  69.464  1.00  0.00          C   \nATOM   3503  CG  PRO A 456      17.613  54.009  68.278  1.00  0.00          C   \nATOM   3504  CD  PRO A 456      17.189  55.413  67.911  1.00  0.00          C   \nATOM   3505  N   LEU A 457      14.580  55.617  70.793  1.00  0.00          N   \nATOM   3506  CA  LEU A 457      13.736  55.986  71.921  1.00  0.00          C   \nATOM   3507  C   LEU A 457      12.272  56.144  71.541  1.00  0.00          C   \nATOM   3508  O   LEU A 457      11.402  56.124  72.409  1.00  0.00          O   \nATOM   3509  CB  LEU A 457      14.255  57.273  72.571  1.00  0.00          C   \nATOM   3510  CG  LEU A 457      15.507  57.130  73.444  1.00  0.00          C   \nATOM   3511  CD1 LEU A 457      15.893  58.491  73.997  1.00  0.00          C   \nATOM   3512  CD2 LEU A 457      15.243  56.153  74.582  1.00  0.00          C   \nATOM   3513  N   ASP A 458      11.997  56.295  70.248  1.00  0.00          N   \nATOM   3514  CA  ASP A 458      10.617  56.430  69.787  1.00  0.00          C   \nATOM   3515  C   ASP A 458       9.990  55.086  69.457  1.00  0.00          C   \nATOM   3516  O   ASP A 458       8.837  55.033  69.029  1.00  0.00          O   \nATOM   3517  CB  ASP A 458      10.536  57.310  68.538  1.00  0.00          C   \nATOM   3518  CG  ASP A 458      10.840  58.758  68.829  1.00  0.00          C   \nATOM   3519  OD1 ASP A 458      10.460  59.227  69.926  1.00  0.00          O   \nATOM   3520  OD2 ASP A 458      11.443  59.426  67.960  1.00  0.00          O   \nATOM   3521  N   LEU A 459      10.736  54.002  69.657  1.00  0.00          N   \nATOM   3522  CA  LEU A 459      10.222  52.677  69.338  1.00  0.00          C   \nATOM   3523  C   LEU A 459       8.903  52.296  69.991  1.00  0.00          C   \nATOM   3524  O   LEU A 459       8.016  51.777  69.323  1.00  0.00          O   \nATOM   3525  CB  LEU A 459      11.288  51.609  69.621  1.00  0.00          C   \nATOM   3526  CG  LEU A 459      12.303  51.469  68.480  1.00  0.00          C   \nATOM   3527  CD1 LEU A 459      13.441  50.511  68.852  1.00  0.00          C   \nATOM   3528  CD2 LEU A 459      11.554  50.978  67.236  1.00  0.00          C   \nATOM   3529  N   PRO A 460       8.745  52.550  71.299  1.00  0.00          N   \nATOM   3530  CA  PRO A 460       7.491  52.205  71.974  1.00  0.00          C   \nATOM   3531  C   PRO A 460       6.280  52.835  71.281  1.00  0.00          C   \nATOM   3532  O   PRO A 460       5.261  52.179  71.073  1.00  0.00          O   \nATOM   3533  CB  PRO A 460       7.688  52.767  73.378  1.00  0.00          C   \nATOM   3534  CG  PRO A 460       9.172  52.689  73.569  1.00  0.00          C   \nATOM   3535  CD  PRO A 460       9.697  53.161  72.243  1.00  0.00          C   \nATOM   3536  N   GLN A 461       6.408  54.115  70.945  1.00  0.00          N   \nATOM   3537  CA  GLN A 461       5.355  54.878  70.276  1.00  0.00          C   \nATOM   3538  C   GLN A 461       5.074  54.305  68.895  1.00  0.00          C   \nATOM   3539  O   GLN A 461       3.924  54.076  68.528  1.00  0.00          O   \nATOM   3540  CB  GLN A 461       5.783  56.341  70.122  1.00  0.00          C   \nATOM   3541  CG  GLN A 461       6.101  57.034  71.428  1.00  0.00          C   \nATOM   3542  CD  GLN A 461       6.897  58.310  71.223  1.00  0.00          C   \nATOM   3543  OE1 GLN A 461       6.475  59.209  70.490  1.00  0.00          O   \nATOM   3544  NE2 GLN A 461       8.059  58.397  71.868  1.00  0.00          N   \nATOM   3545  N   ILE A 462       6.139  54.092  68.131  1.00  0.00          N   \nATOM   3546  CA  ILE A 462       6.028  53.540  66.791  1.00  0.00          C   \nATOM   3547  C   ILE A 462       5.346  52.170  66.830  1.00  0.00          C   \nATOM   3548  O   ILE A 462       4.417  51.893  66.060  1.00  0.00          O   \nATOM   3549  CB  ILE A 462       7.427  53.410  66.156  1.00  0.00          C   \nATOM   3550  CG1 ILE A 462       8.059  54.795  66.043  1.00  0.00          C   \nATOM   3551  CG2 ILE A 462       7.331  52.744  64.789  1.00  0.00          C   \nATOM   3552  CD1 ILE A 462       9.480  54.790  65.478  1.00  0.00          C   \nATOM   3553  N   ILE A 463       5.798  51.320  67.746  1.00  0.00          N   \nATOM   3554  CA  ILE A 463       5.228  49.987  67.868  1.00  0.00          C   \nATOM   3555  C   ILE A 463       3.749  50.036  68.245  1.00  0.00          C   \nATOM   3556  O   ILE A 463       2.946  49.299  67.676  1.00  0.00          O   \nATOM   3557  CB  ILE A 463       6.002  49.127  68.907  1.00  0.00          C   \nATOM   3558  CG1 ILE A 463       7.402  48.809  68.381  1.00  0.00          C   \nATOM   3559  CG2 ILE A 463       5.255  47.824  69.175  1.00  0.00          C   \nATOM   3560  CD1 ILE A 463       8.308  48.110  69.410  1.00  0.00          C   \nATOM   3561  N   GLU A 464       3.386  50.893  69.201  1.00  0.00          N   \nATOM   3562  CA  GLU A 464       1.984  50.998  69.613  1.00  0.00          C   \nATOM   3563  C   GLU A 464       1.151  51.417  68.410  1.00  0.00          C   \nATOM   3564  O   GLU A 464       0.137  50.797  68.093  1.00  0.00          O   \nATOM   3565  CB  GLU A 464       1.804  52.038  70.728  1.00  0.00          C   \nATOM   3566  CG  GLU A 464       0.382  52.074  71.309  1.00  0.00          C   \nATOM   3567  CD  GLU A 464       0.150  53.245  72.260  1.00  0.00          C   \nATOM   3568  OE1 GLU A 464       0.900  53.369  73.249  1.00  0.00          O   \nATOM   3569  OE2 GLU A 464      -0.785  54.043  72.022  1.00  0.00          O   \nATOM   3570  N   ARG A 465       1.597  52.471  67.742  1.00  0.00          N   \nATOM   3571  CA  ARG A 465       0.910  52.986  66.569  1.00  0.00          C   \nATOM   3572  C   ARG A 465       0.699  51.930  65.482  1.00  0.00          C   \nATOM   3573  O   ARG A 465      -0.414  51.751  64.992  1.00  0.00          O   \nATOM   3574  CB  ARG A 465       1.698  54.160  65.988  1.00  0.00          C   \nATOM   3575  CG  ARG A 465       1.641  55.423  66.839  1.00  0.00          C   \nATOM   3576  CD  ARG A 465       0.279  56.081  66.749  1.00  0.00          C   \nATOM   3577  NE  ARG A 465      -0.022  56.480  65.377  1.00  0.00          N   \nATOM   3578  CZ  ARG A 465       0.627  57.431  64.712  1.00  0.00          C   \nATOM   3579  NH1 ARG A 465       1.621  58.096  65.290  1.00  0.00          N   \nATOM   3580  NH2 ARG A 465       0.289  57.715  63.462  1.00  0.00          N   \nATOM   3581  N   LEU A 466       1.769  51.232  65.115  1.00  0.00          N   \nATOM   3582  CA  LEU A 466       1.708  50.217  64.071  1.00  0.00          C   \nATOM   3583  C   LEU A 466       1.104  48.877  64.477  1.00  0.00          C   \nATOM   3584  O   LEU A 466       0.393  48.255  63.695  1.00  0.00          O   \nATOM   3585  CB  LEU A 466       3.110  49.938  63.519  1.00  0.00          C   \nATOM   3586  CG  LEU A 466       4.002  51.029  62.943  1.00  0.00          C   \nATOM   3587  CD1 LEU A 466       5.214  50.345  62.316  1.00  0.00          C   \nATOM   3588  CD2 LEU A 466       3.269  51.846  61.901  1.00  0.00          C   \nATOM   3589  N   HIS A 467       1.384  48.427  65.693  1.00  0.00          N   \nATOM   3590  CA  HIS A 467       0.903  47.120  66.119  1.00  0.00          C   \nATOM   3591  C   HIS A 467      -0.128  47.094  67.230  1.00  0.00          C   \nATOM   3592  O   HIS A 467      -0.757  46.060  67.471  1.00  0.00          O   \nATOM   3593  CB  HIS A 467       2.098  46.265  66.540  1.00  0.00          C   \nATOM   3594  CG  HIS A 467       3.112  46.077  65.459  1.00  0.00          C   \nATOM   3595  ND1 HIS A 467       3.000  45.096  64.500  1.00  0.00          N   \nATOM   3596  CD2 HIS A 467       4.267  46.729  65.197  1.00  0.00          C   \nATOM   3597  CE1 HIS A 467       4.048  45.146  63.695  1.00  0.00          C   \nATOM   3598  NE2 HIS A 467       4.833  46.127  64.096  1.00  0.00          N   \nATOM   3599  N   GLY A 468      -0.304  48.216  67.911  1.00  0.00          N   \nATOM   3600  CA  GLY A 468      -1.251  48.245  69.008  1.00  0.00          C   \nATOM   3601  C   GLY A 468      -0.560  47.771  70.278  1.00  0.00          C   \nATOM   3602  O   GLY A 468       0.502  47.143  70.220  1.00  0.00          O   \nATOM   3603  N   LEU A 469      -1.163  48.070  71.425  1.00  0.00          N   \nATOM   3604  CA  LEU A 469      -0.615  47.697  72.718  1.00  0.00          C   \nATOM   3605  C   LEU A 469      -0.299  46.225  72.904  1.00  0.00          C   \nATOM   3606  O   LEU A 469       0.588  45.875  73.683  1.00  0.00          O   \nATOM   3607  CB  LEU A 469      -1.570  48.118  73.829  1.00  0.00          C   \nATOM   3608  CG  LEU A 469      -1.395  49.513  74.412  1.00  0.00          C   \nATOM   3609  CD1 LEU A 469      -2.409  49.693  75.544  1.00  0.00          C   \nATOM   3610  CD2 LEU A 469       0.015  49.691  74.938  1.00  0.00          C   \nATOM   3611  N   SER A 470      -1.021  45.360  72.205  1.00  0.00          N   \nATOM   3612  CA  SER A 470      -0.817  43.925  72.352  1.00  0.00          C   \nATOM   3613  C   SER A 470       0.584  43.456  71.987  1.00  0.00          C   \nATOM   3614  O   SER A 470       0.996  42.362  72.383  1.00  0.00          O   \nATOM   3615  CB  SER A 470      -1.850  43.159  71.525  1.00  0.00          C   \nATOM   3616  OG  SER A 470      -1.788  43.551  70.173  1.00  0.00          O   \nATOM   3617  N   ALA A 471       1.310  44.276  71.230  1.00  0.00          N   \nATOM   3618  CA  ALA A 471       2.667  43.930  70.819  1.00  0.00          C   \nATOM   3619  C   ALA A 471       3.563  43.762  72.033  1.00  0.00          C   \nATOM   3620  O   ALA A 471       4.570  43.042  71.986  1.00  0.00          O   \nATOM   3621  CB  ALA A 471       3.247  45.013  69.899  1.00  0.00          C   \nATOM   3622  N   PHE A 472       3.184  44.416  73.125  1.00  0.00          N   \nATOM   3623  CA  PHE A 472       3.973  44.362  74.347  1.00  0.00          C   \nATOM   3624  C   PHE A 472       3.507  43.314  75.327  1.00  0.00          C   \nATOM   3625  O   PHE A 472       4.083  43.183  76.401  1.00  0.00          O   \nATOM   3626  CB  PHE A 472       3.951  45.718  75.042  1.00  0.00          C   \nATOM   3627  CG  PHE A 472       4.293  46.855  74.142  1.00  0.00          C   \nATOM   3628  CD1 PHE A 472       5.523  46.905  73.508  1.00  0.00          C   \nATOM   3629  CD2 PHE A 472       3.383  47.890  73.935  1.00  0.00          C   \nATOM   3630  CE1 PHE A 472       5.851  47.970  72.677  1.00  0.00          C   \nATOM   3631  CE2 PHE A 472       3.703  48.957  73.109  1.00  0.00          C   \nATOM   3632  CZ  PHE A 472       4.946  48.997  72.479  1.00  0.00          C   \nATOM   3633  N   SER A 473       2.468  42.566  74.982  1.00  0.00          N   \nATOM   3634  CA  SER A 473       1.989  41.552  75.907  1.00  0.00          C   \nATOM   3635  C   SER A 473       1.694  40.205  75.267  1.00  0.00          C   \nATOM   3636  O   SER A 473       1.047  39.356  75.883  1.00  0.00          O   \nATOM   3637  CB  SER A 473       0.739  42.049  76.636  1.00  0.00          C   \nATOM   3638  OG  SER A 473      -0.359  42.088  75.750  1.00  0.00          O   \nATOM   3639  N   LEU A 474       2.159  39.988  74.043  1.00  0.00          N   \nATOM   3640  CA  LEU A 474       1.906  38.699  73.404  1.00  0.00          C   \nATOM   3641  C   LEU A 474       2.648  37.595  74.135  1.00  0.00          C   \nATOM   3642  O   LEU A 474       3.762  37.799  74.624  1.00  0.00          O   \nATOM   3643  CB  LEU A 474       2.336  38.711  71.930  1.00  0.00          C   \nATOM   3644  CG  LEU A 474       1.519  39.584  70.974  1.00  0.00          C   \nATOM   3645  CD1 LEU A 474       1.866  39.192  69.541  1.00  0.00          C   \nATOM   3646  CD2 LEU A 474       0.019  39.394  71.224  1.00  0.00          C   \nATOM   3647  N   HIS A 475       2.025  36.423  74.216  1.00  0.00          N   \nATOM   3648  CA  HIS A 475       2.634  35.276  74.880  1.00  0.00          C   \nATOM   3649  C   HIS A 475       1.907  34.033  74.389  1.00  0.00          C   \nATOM   3650  O   HIS A 475       0.937  34.136  73.634  1.00  0.00          O   \nATOM   3651  CB  HIS A 475       2.509  35.415  76.408  1.00  0.00          C   \nATOM   3652  CG  HIS A 475       1.094  35.488  76.885  1.00  0.00          C   \nATOM   3653  ND1 HIS A 475       0.369  34.369  77.232  1.00  0.00          N   \nATOM   3654  CD2 HIS A 475       0.242  36.537  76.985  1.00  0.00          C   \nATOM   3655  CE1 HIS A 475      -0.870  34.725  77.523  1.00  0.00          C   \nATOM   3656  NE2 HIS A 475      -0.972  36.035  77.382  1.00  0.00          N   \nATOM   3657  N   SER A 476       2.366  32.864  74.819  1.00  0.00          N   \nATOM   3658  CA  SER A 476       1.772  31.608  74.378  1.00  0.00          C   \nATOM   3659  C   SER A 476       1.773  31.570  72.853  1.00  0.00          C   \nATOM   3660  O   SER A 476       0.725  31.395  72.215  1.00  0.00          O   \nATOM   3661  CB  SER A 476       0.335  31.450  74.900  1.00  0.00          C   \nATOM   3662  OG  SER A 476       0.322  31.217  76.297  1.00  0.00          O   \nATOM   3663  N   TYR A 477       2.957  31.751  72.277  1.00  0.00          N   \nATOM   3664  CA  TYR A 477       3.122  31.698  70.835  1.00  0.00          C   \nATOM   3665  C   TYR A 477       2.811  30.277  70.377  1.00  0.00          C   \nATOM   3666  O   TYR A 477       2.704  29.368  71.196  1.00  0.00          O   \nATOM   3667  CB  TYR A 477       4.558  32.073  70.456  1.00  0.00          C   \nATOM   3668  CG  TYR A 477       4.885  33.532  70.694  1.00  0.00          C   \nATOM   3669  CD1 TYR A 477       5.449  33.964  71.898  1.00  0.00          C   \nATOM   3670  CD2 TYR A 477       4.614  34.484  69.714  1.00  0.00          C   \nATOM   3671  CE1 TYR A 477       5.738  35.331  72.112  1.00  0.00          C   \nATOM   3672  CE2 TYR A 477       4.891  35.840  69.915  1.00  0.00          C   \nATOM   3673  CZ  TYR A 477       5.453  36.254  71.112  1.00  0.00          C   \nATOM   3674  OH  TYR A 477       5.719  37.591  71.297  1.00  0.00          O   \nATOM   3675  N   SER A 478       2.687  30.071  69.073  1.00  0.00          N   \nATOM   3676  CA  SER A 478       2.357  28.746  68.557  1.00  0.00          C   \nATOM   3677  C   SER A 478       3.504  27.745  68.615  1.00  0.00          C   \nATOM   3678  O   SER A 478       4.684  28.111  68.669  1.00  0.00          O   \nATOM   3679  CB  SER A 478       1.876  28.852  67.109  1.00  0.00          C   \nATOM   3680  OG  SER A 478       2.962  29.172  66.251  1.00  0.00          O   \nATOM   3681  N   PRO A 479       3.162  26.450  68.613  1.00  0.00          N   \nATOM   3682  CA  PRO A 479       4.140  25.363  68.654  1.00  0.00          C   \nATOM   3683  C   PRO A 479       5.070  25.459  67.457  1.00  0.00          C   \nATOM   3684  O   PRO A 479       6.283  25.282  67.590  1.00  0.00          O   \nATOM   3685  CB  PRO A 479       3.269  24.112  68.582  1.00  0.00          C   \nATOM   3686  CG  PRO A 479       2.050  24.534  69.314  1.00  0.00          C   \nATOM   3687  CD  PRO A 479       1.793  25.929  68.791  1.00  0.00          C   \nATOM   3688  N   GLY A 480       4.488  25.736  66.288  1.00  0.00          N   \nATOM   3689  CA  GLY A 480       5.276  25.847  65.073  1.00  0.00          C   \nATOM   3690  C   GLY A 480       6.236  27.025  65.110  1.00  0.00          C   \nATOM   3691  O   GLY A 480       7.371  26.929  64.647  1.00  0.00          O   \nATOM   3692  N   GLU A 481       5.772  28.144  65.652  1.00  0.00          N   \nATOM   3693  CA  GLU A 481       6.594  29.347  65.755  1.00  0.00          C   \nATOM   3694  C   GLU A 481       7.711  29.103  66.775  1.00  0.00          C   \nATOM   3695  O   GLU A 481       8.885  29.358  66.500  1.00  0.00          O   \nATOM   3696  CB  GLU A 481       5.717  30.536  66.173  1.00  0.00          C   \nATOM   3697  CG  GLU A 481       6.463  31.864  66.321  1.00  0.00          C   \nATOM   3698  CD  GLU A 481       7.168  32.306  65.049  1.00  0.00          C   \nATOM   3699  OE1 GLU A 481       6.970  31.662  63.991  1.00  0.00          O   \nATOM   3700  OE2 GLU A 481       7.924  33.307  65.115  1.00  0.00          O   \nATOM   3701  N   ILE A 482       7.348  28.606  67.950  1.00  0.00          N   \nATOM   3702  CA  ILE A 482       8.335  28.310  68.982  1.00  0.00          C   \nATOM   3703  C   ILE A 482       9.392  27.330  68.444  1.00  0.00          C   \nATOM   3704  O   ILE A 482      10.587  27.518  68.661  1.00  0.00          O   \nATOM   3705  CB  ILE A 482       7.651  27.707  70.240  1.00  0.00          C   \nATOM   3706  CG1 ILE A 482       6.826  28.791  70.945  1.00  0.00          C   \nATOM   3707  CG2 ILE A 482       8.696  27.132  71.197  1.00  0.00          C   \nATOM   3708  CD1 ILE A 482       6.060  28.310  72.174  1.00  0.00          C   \nATOM   3709  N   ASN A 483       8.956  26.298  67.728  1.00  0.00          N   \nATOM   3710  CA  ASN A 483       9.890  25.319  67.174  1.00  0.00          C   \nATOM   3711  C   ASN A 483      10.880  25.939  66.199  1.00  0.00          C   \nATOM   3712  O   ASN A 483      12.065  25.609  66.218  1.00  0.00          O   \nATOM   3713  CB  ASN A 483       9.142  24.184  66.457  1.00  0.00          C   \nATOM   3714  CG  ASN A 483       8.598  23.144  67.417  1.00  0.00          C   \nATOM   3715  OD1 ASN A 483       8.925  23.151  68.609  1.00  0.00          O   \nATOM   3716  ND2 ASN A 483       7.771  22.234  66.900  1.00  0.00          N   \nATOM   3717  N   ARG A 484      10.388  26.825  65.337  1.00  0.00          N   \nATOM   3718  CA  ARG A 484      11.244  27.469  64.356  1.00  0.00          C   \nATOM   3719  C   ARG A 484      12.274  28.356  65.040  1.00  0.00          C   \nATOM   3720  O   ARG A 484      13.443  28.364  64.662  1.00  0.00          O   \nATOM   3721  CB  ARG A 484      10.421  28.309  63.371  1.00  0.00          C   \nATOM   3722  CG  ARG A 484      11.274  28.910  62.249  1.00  0.00          C   \nATOM   3723  CD  ARG A 484      10.424  29.449  61.123  1.00  0.00          C   \nATOM   3724  NE  ARG A 484       9.532  30.511  61.576  1.00  0.00          N   \nATOM   3725  CZ  ARG A 484       8.605  31.079  60.809  1.00  0.00          C   \nATOM   3726  NH1 ARG A 484       8.442  30.689  59.547  1.00  0.00          N   \nATOM   3727  NH2 ARG A 484       7.839  32.042  61.301  1.00  0.00          N   \nATOM   3728  N   VAL A 485      11.836  29.115  66.038  1.00  0.00          N   \nATOM   3729  CA  VAL A 485      12.751  29.976  66.766  1.00  0.00          C   \nATOM   3730  C   VAL A 485      13.816  29.098  67.435  1.00  0.00          C   \nATOM   3731  O   VAL A 485      15.008  29.367  67.328  1.00  0.00          O   \nATOM   3732  CB  VAL A 485      12.002  30.794  67.832  1.00  0.00          C   \nATOM   3733  CG1 VAL A 485      12.995  31.507  68.738  1.00  0.00          C   \nATOM   3734  CG2 VAL A 485      11.073  31.803  67.147  1.00  0.00          C   \nATOM   3735  N   ALA A 486      13.375  28.033  68.101  1.00  0.00          N   \nATOM   3736  CA  ALA A 486      14.292  27.115  68.777  1.00  0.00          C   \nATOM   3737  C   ALA A 486      15.314  26.532  67.815  1.00  0.00          C   \nATOM   3738  O   ALA A 486      16.509  26.511  68.116  1.00  0.00          O   \nATOM   3739  CB  ALA A 486      13.503  25.984  69.456  1.00  0.00          C   \nATOM   3740  N   SER A 487      14.850  26.069  66.656  1.00  0.00          N   \nATOM   3741  CA  SER A 487      15.740  25.489  65.646  1.00  0.00          C   \nATOM   3742  C   SER A 487      16.750  26.519  65.174  1.00  0.00          C   \nATOM   3743  O   SER A 487      17.941  26.220  65.031  1.00  0.00          O   \nATOM   3744  CB  SER A 487      14.952  24.997  64.428  1.00  0.00          C   \nATOM   3745  OG  SER A 487      14.007  24.016  64.803  1.00  0.00          O   \nATOM   3746  N   CYS A 488      16.272  27.733  64.917  1.00  0.00          N   \nATOM   3747  CA  CYS A 488      17.169  28.791  64.457  1.00  0.00          C   \nATOM   3748  C   CYS A 488      18.240  29.106  65.509  1.00  0.00          C   \nATOM   3749  O   CYS A 488      19.418  29.238  65.178  1.00  0.00          O   \nATOM   3750  CB  CYS A 488      16.370  30.057  64.118  1.00  0.00          C   \nATOM   3751  SG  CYS A 488      17.416  31.489  63.793  1.00  0.00          S   \nATOM   3752  N   LEU A 489      17.844  29.201  66.776  1.00  0.00          N   \nATOM   3753  CA  LEU A 489      18.806  29.518  67.825  1.00  0.00          C   \nATOM   3754  C   LEU A 489      19.899  28.460  67.961  1.00  0.00          C   \nATOM   3755  O   LEU A 489      21.060  28.793  68.199  1.00  0.00          O   \nATOM   3756  CB  LEU A 489      18.099  29.735  69.167  1.00  0.00          C   \nATOM   3757  CG  LEU A 489      17.005  30.816  69.216  1.00  0.00          C   \nATOM   3758  CD1 LEU A 489      16.853  31.294  70.648  1.00  0.00          C   \nATOM   3759  CD2 LEU A 489      17.352  31.994  68.312  1.00  0.00          C   \nATOM   3760  N   ARG A 490      19.537  27.191  67.807  1.00  0.00          N   \nATOM   3761  CA  ARG A 490      20.527  26.122  67.899  1.00  0.00          C   \nATOM   3762  C   ARG A 490      21.501  26.208  66.729  1.00  0.00          C   \nATOM   3763  O   ARG A 490      22.713  26.083  66.907  1.00  0.00          O   \nATOM   3764  CB  ARG A 490      19.854  24.746  67.880  1.00  0.00          C   \nATOM   3765  CG  ARG A 490      19.077  24.393  69.123  1.00  0.00          C   \nATOM   3766  CD  ARG A 490      18.702  22.916  69.088  1.00  0.00          C   \nATOM   3767  NE  ARG A 490      17.853  22.514  70.207  1.00  0.00          N   \nATOM   3768  CZ  ARG A 490      18.221  22.549  71.485  1.00  0.00          C   \nATOM   3769  NH1 ARG A 490      19.434  22.974  71.822  1.00  0.00          N   \nATOM   3770  NH2 ARG A 490      17.375  22.154  72.429  1.00  0.00          N   \nATOM   3771  N   LYS A 491      20.954  26.423  65.536  1.00  0.00          N   \nATOM   3772  CA  LYS A 491      21.729  26.515  64.310  1.00  0.00          C   \nATOM   3773  C   LYS A 491      22.814  27.585  64.375  1.00  0.00          C   \nATOM   3774  O   LYS A 491      23.982  27.319  64.068  1.00  0.00          O   \nATOM   3775  CB  LYS A 491      20.777  26.790  63.138  1.00  0.00          C   \nATOM   3776  CG  LYS A 491      21.445  27.158  61.821  1.00  0.00          C   \nATOM   3777  CD  LYS A 491      20.422  27.255  60.684  1.00  0.00          C   \nATOM   3778  CE  LYS A 491      19.474  28.447  60.851  1.00  0.00          C   \nATOM   3779  NZ  LYS A 491      18.467  28.553  59.740  1.00  0.00          N   \nATOM   3780  N   LEU A 492      22.418  28.791  64.776  1.00  0.00          N   \nATOM   3781  CA  LEU A 492      23.324  29.926  64.884  1.00  0.00          C   \nATOM   3782  C   LEU A 492      24.095  29.964  66.212  1.00  0.00          C   \nATOM   3783  O   LEU A 492      25.072  30.703  66.338  1.00  0.00          O   \nATOM   3784  CB  LEU A 492      22.544  31.235  64.732  1.00  0.00          C   \nATOM   3785  CG  LEU A 492      21.868  31.588  63.406  1.00  0.00          C   \nATOM   3786  CD1 LEU A 492      21.179  32.941  63.527  1.00  0.00          C   \nATOM   3787  CD2 LEU A 492      22.907  31.634  62.295  1.00  0.00          C   \nATOM   3788  N   GLY A 493      23.646  29.181  67.196  1.00  0.00          N   \nATOM   3789  CA  GLY A 493      24.308  29.155  68.493  1.00  0.00          C   \nATOM   3790  C   GLY A 493      24.019  30.379  69.347  1.00  0.00          C   \nATOM   3791  O   GLY A 493      24.925  30.985  69.913  1.00  0.00          O   \nATOM   3792  N   VAL A 494      22.743  30.731  69.446  1.00  0.00          N   \nATOM   3793  CA  VAL A 494      22.298  31.894  70.210  1.00  0.00          C   \nATOM   3794  C   VAL A 494      21.951  31.468  71.628  1.00  0.00          C   \nATOM   3795  O   VAL A 494      21.323  30.430  71.829  1.00  0.00          O   \nATOM   3796  CB  VAL A 494      21.018  32.512  69.566  1.00  0.00          C   \nATOM   3797  CG1 VAL A 494      20.493  33.650  70.420  1.00  0.00          C   \nATOM   3798  CG2 VAL A 494      21.315  32.985  68.138  1.00  0.00          C   \nATOM   3799  N   PRO A 495      22.341  32.271  72.630  1.00  0.00          N   \nATOM   3800  CA  PRO A 495      22.056  31.952  74.033  1.00  0.00          C   \nATOM   3801  C   PRO A 495      20.572  31.668  74.279  1.00  0.00          C   \nATOM   3802  O   PRO A 495      19.704  32.190  73.584  1.00  0.00          O   \nATOM   3803  CB  PRO A 495      22.522  33.198  74.770  1.00  0.00          C   \nATOM   3804  CG  PRO A 495      23.638  33.686  73.919  1.00  0.00          C   \nATOM   3805  CD  PRO A 495      23.085  33.535  72.526  1.00  0.00          C   \nATOM   3806  N   PRO A 496      20.267  30.843  75.291  1.00  0.00          N   \nATOM   3807  CA  PRO A 496      18.877  30.500  75.609  1.00  0.00          C   \nATOM   3808  C   PRO A 496      18.079  31.609  76.311  1.00  0.00          C   \nATOM   3809  O   PRO A 496      18.612  32.653  76.696  1.00  0.00          O   \nATOM   3810  CB  PRO A 496      19.028  29.232  76.451  1.00  0.00          C   \nATOM   3811  CG  PRO A 496      20.299  29.524  77.220  1.00  0.00          C   \nATOM   3812  CD  PRO A 496      21.207  30.077  76.136  1.00  0.00          C   \nATOM   3813  N   LEU A 497      16.785  31.364  76.456  1.00  0.00          N   \nATOM   3814  CA  LEU A 497      15.864  32.312  77.072  1.00  0.00          C   \nATOM   3815  C   LEU A 497      16.333  32.858  78.421  1.00  0.00          C   \nATOM   3816  O   LEU A 497      16.233  34.064  78.678  1.00  0.00          O   \nATOM   3817  CB  LEU A 497      14.504  31.643  77.225  1.00  0.00          C   \nATOM   3818  CG  LEU A 497      13.228  32.432  77.520  1.00  0.00          C   \nATOM   3819  CD1 LEU A 497      12.586  31.814  78.740  1.00  0.00          C   \nATOM   3820  CD2 LEU A 497      13.489  33.912  77.713  1.00  0.00          C   \nATOM   3821  N   ARG A 498      16.853  31.986  79.284  1.00  0.00          N   \nATOM   3822  CA  ARG A 498      17.304  32.435  80.598  1.00  0.00          C   \nATOM   3823  C   ARG A 498      18.317  33.562  80.500  1.00  0.00          C   \nATOM   3824  O   ARG A 498      18.295  34.492  81.307  1.00  0.00          O   \nATOM   3825  CB  ARG A 498      17.883  31.273  81.424  1.00  0.00          C   \nATOM   3826  CG  ARG A 498      18.889  30.394  80.706  1.00  0.00          C   \nATOM   3827  CD  ARG A 498      19.434  29.314  81.649  1.00  0.00          C   \nATOM   3828  NE  ARG A 498      20.229  28.299  80.952  1.00  0.00          N   \nATOM   3829  CZ  ARG A 498      19.735  27.420  80.083  1.00  0.00          C   \nATOM   3830  NH1 ARG A 498      20.538  26.536  79.499  1.00  0.00          N   \nATOM   3831  NH2 ARG A 498      18.438  27.418  79.796  1.00  0.00          N   \nATOM   3832  N   VAL A 499      19.194  33.503  79.504  1.00  0.00          N   \nATOM   3833  CA  VAL A 499      20.188  34.561  79.348  1.00  0.00          C   \nATOM   3834  C   VAL A 499      19.543  35.870  78.886  1.00  0.00          C   \nATOM   3835  O   VAL A 499      19.800  36.918  79.460  1.00  0.00          O   \nATOM   3836  CB  VAL A 499      21.314  34.142  78.363  1.00  0.00          C   \nATOM   3837  CG1 VAL A 499      22.314  35.290  78.170  1.00  0.00          C   \nATOM   3838  CG2 VAL A 499      22.039  32.916  78.913  1.00  0.00          C   \nATOM   3839  N   TRP A 500      18.676  35.814  77.881  1.00  0.00          N   \nATOM   3840  CA  TRP A 500      18.028  37.031  77.391  1.00  0.00          C   \nATOM   3841  C   TRP A 500      17.120  37.690  78.426  1.00  0.00          C   \nATOM   3842  O   TRP A 500      17.025  38.921  78.499  1.00  0.00          O   \nATOM   3843  CB  TRP A 500      17.237  36.730  76.106  1.00  0.00          C   \nATOM   3844  CG  TRP A 500      18.143  36.370  74.957  1.00  0.00          C   \nATOM   3845  CD1 TRP A 500      18.317  35.133  74.398  1.00  0.00          C   \nATOM   3846  CD2 TRP A 500      19.113  37.229  74.342  1.00  0.00          C   \nATOM   3847  NE1 TRP A 500      19.347  35.167  73.484  1.00  0.00          N   \nATOM   3848  CE2 TRP A 500      19.855  36.441  73.433  1.00  0.00          C   \nATOM   3849  CE3 TRP A 500      19.436  38.589  74.483  1.00  0.00          C   \nATOM   3850  CZ2 TRP A 500      20.906  36.967  72.661  1.00  0.00          C   \nATOM   3851  CZ3 TRP A 500      20.478  39.115  73.721  1.00  0.00          C   \nATOM   3852  CH2 TRP A 500      21.205  38.299  72.818  1.00  0.00          C   \nATOM   3853  N   ARG A 501      16.445  36.875  79.229  1.00  0.00          N   \nATOM   3854  CA  ARG A 501      15.558  37.421  80.255  1.00  0.00          C   \nATOM   3855  C   ARG A 501      16.408  38.178  81.276  1.00  0.00          C   \nATOM   3856  O   ARG A 501      16.072  39.291  81.683  1.00  0.00          O   \nATOM   3857  CB  ARG A 501      14.770  36.289  80.930  1.00  0.00          C   \nATOM   3858  CG  ARG A 501      13.790  36.736  82.014  1.00  0.00          C   \nATOM   3859  CD  ARG A 501      12.895  35.562  82.427  1.00  0.00          C   \nATOM   3860  NE  ARG A 501      13.702  34.374  82.703  1.00  0.00          N   \nATOM   3861  CZ  ARG A 501      13.351  33.131  82.379  1.00  0.00          C   \nATOM   3862  NH1 ARG A 501      12.198  32.900  81.763  1.00  0.00          N   \nATOM   3863  NH2 ARG A 501      14.161  32.118  82.663  1.00  0.00          N   \nATOM   3864  N   HIS A 502      17.524  37.579  81.671  1.00  0.00          N   \nATOM   3865  CA  HIS A 502      18.413  38.218  82.631  1.00  0.00          C   \nATOM   3866  C   HIS A 502      18.908  39.543  82.064  1.00  0.00          C   \nATOM   3867  O   HIS A 502      18.864  40.579  82.730  1.00  0.00          O   \nATOM   3868  CB  HIS A 502      19.603  37.315  82.915  1.00  0.00          C   \nATOM   3869  CG  HIS A 502      20.333  37.663  84.172  1.00  0.00          C   \nATOM   3870  ND1 HIS A 502      20.254  36.895  85.311  1.00  0.00          N   \nATOM   3871  CD2 HIS A 502      21.139  38.709  84.473  1.00  0.00          C   \nATOM   3872  CE1 HIS A 502      20.981  37.452  86.264  1.00  0.00          C   \nATOM   3873  NE2 HIS A 502      21.528  38.552  85.783  1.00  0.00          N   \nATOM   3874  N   ARG A 503      19.376  39.511  80.821  1.00  0.00          N   \nATOM   3875  CA  ARG A 503      19.868  40.714  80.169  1.00  0.00          C   \nATOM   3876  C   ARG A 503      18.773  41.780  80.021  1.00  0.00          C   \nATOM   3877  O   ARG A 503      19.035  42.984  80.162  1.00  0.00          O   \nATOM   3878  CB  ARG A 503      20.418  40.353  78.793  1.00  0.00          C   \nATOM   3879  CG  ARG A 503      21.550  39.357  78.846  1.00  0.00          C   \nATOM   3880  CD  ARG A 503      22.114  39.117  77.473  1.00  0.00          C   \nATOM   3881  NE  ARG A 503      23.310  38.286  77.535  1.00  0.00          N   \nATOM   3882  CZ  ARG A 503      23.895  37.758  76.471  1.00  0.00          C   \nATOM   3883  NH1 ARG A 503      24.981  37.006  76.616  1.00  0.00          N   \nATOM   3884  NH2 ARG A 503      23.383  37.975  75.261  1.00  0.00          N   \nATOM   3885  N   ALA A 504      17.549  41.336  79.737  1.00  0.00          N   \nATOM   3886  CA  ALA A 504      16.433  42.257  79.561  1.00  0.00          C   \nATOM   3887  C   ALA A 504      16.101  42.953  80.880  1.00  0.00          C   \nATOM   3888  O   ALA A 504      15.848  44.161  80.908  1.00  0.00          O   \nATOM   3889  CB  ALA A 504      15.208  41.497  79.023  1.00  0.00          C   \nATOM   3890  N   ARG A 505      16.110  42.197  81.973  1.00  0.00          N   \nATOM   3891  CA  ARG A 505      15.824  42.776  83.285  1.00  0.00          C   \nATOM   3892  C   ARG A 505      16.832  43.878  83.556  1.00  0.00          C   \nATOM   3893  O   ARG A 505      16.472  44.979  83.977  1.00  0.00          O   \nATOM   3894  CB  ARG A 505      15.894  41.697  84.368  1.00  0.00          C   \nATOM   3895  CG  ARG A 505      14.760  40.704  84.255  1.00  0.00          C   \nATOM   3896  CD  ARG A 505      14.766  39.663  85.366  1.00  0.00          C   \nATOM   3897  NE  ARG A 505      13.618  38.772  85.223  1.00  0.00          N   \nATOM   3898  CZ  ARG A 505      13.264  37.838  86.101  1.00  0.00          C   \nATOM   3899  NH1 ARG A 505      13.973  37.658  87.211  1.00  0.00          N   \nATOM   3900  NH2 ARG A 505      12.190  37.088  85.871  1.00  0.00          N   \nATOM   3901  N   SER A 506      18.100  43.581  83.286  1.00  0.00          N   \nATOM   3902  CA  SER A 506      19.173  44.551  83.473  1.00  0.00          C   \nATOM   3903  C   SER A 506      18.931  45.770  82.602  1.00  0.00          C   \nATOM   3904  O   SER A 506      18.951  46.903  83.079  1.00  0.00          O   \nATOM   3905  CB  SER A 506      20.513  43.938  83.088  1.00  0.00          C   \nATOM   3906  OG  SER A 506      21.504  44.940  83.019  1.00  0.00          O   \nATOM   3907  N   VAL A 507      18.731  45.528  81.312  1.00  0.00          N   \nATOM   3908  CA  VAL A 507      18.474  46.600  80.354  1.00  0.00          C   \nATOM   3909  C   VAL A 507      17.281  47.459  80.781  1.00  0.00          C   \nATOM   3910  O   VAL A 507      17.335  48.695  80.753  1.00  0.00          O   \nATOM   3911  CB  VAL A 507      18.185  46.013  78.950  1.00  0.00          C   \nATOM   3912  CG1 VAL A 507      17.660  47.097  78.024  1.00  0.00          C   \nATOM   3913  CG2 VAL A 507      19.452  45.382  78.387  1.00  0.00          C   \nATOM   3914  N   ARG A 508      16.203  46.793  81.171  1.00  0.00          N   \nATOM   3915  CA  ARG A 508      14.987  47.469  81.610  1.00  0.00          C   \nATOM   3916  C   ARG A 508      15.291  48.382  82.797  1.00  0.00          C   \nATOM   3917  O   ARG A 508      14.958  49.568  82.781  1.00  0.00          O   \nATOM   3918  CB  ARG A 508      13.932  46.425  82.003  1.00  0.00          C   \nATOM   3919  CG  ARG A 508      12.642  46.986  82.614  1.00  0.00          C   \nATOM   3920  CD  ARG A 508      11.603  45.881  82.820  1.00  0.00          C   \nATOM   3921  NE  ARG A 508      10.386  46.359  83.489  1.00  0.00          N   \nATOM   3922  CZ  ARG A 508      10.313  46.671  84.783  1.00  0.00          C   \nATOM   3923  NH1 ARG A 508      11.386  46.553  85.556  1.00  0.00          N   \nATOM   3924  NH2 ARG A 508       9.175  47.104  85.311  1.00  0.00          N   \nATOM   3925  N   ALA A 509      15.924  47.822  83.825  1.00  0.00          N   \nATOM   3926  CA  ALA A 509      16.264  48.587  85.022  1.00  0.00          C   \nATOM   3927  C   ALA A 509      17.057  49.837  84.659  1.00  0.00          C   \nATOM   3928  O   ALA A 509      16.786  50.927  85.165  1.00  0.00          O   \nATOM   3929  CB  ALA A 509      17.059  47.716  85.988  1.00  0.00          C   \nATOM   3930  N   ARG A 510      18.033  49.686  83.773  1.00  0.00          N   \nATOM   3931  CA  ARG A 510      18.829  50.831  83.364  1.00  0.00          C   \nATOM   3932  C   ARG A 510      17.989  51.891  82.651  1.00  0.00          C   \nATOM   3933  O   ARG A 510      18.161  53.092  82.881  1.00  0.00          O   \nATOM   3934  CB  ARG A 510      19.975  50.386  82.445  1.00  0.00          C   \nATOM   3935  CG  ARG A 510      21.262  50.069  83.181  1.00  0.00          C   \nATOM   3936  CD  ARG A 510      21.595  48.585  83.186  1.00  0.00          C   \nATOM   3937  NE  ARG A 510      22.102  48.079  81.910  1.00  0.00          N   \nATOM   3938  CZ  ARG A 510      23.090  48.635  81.210  1.00  0.00          C   \nATOM   3939  NH1 ARG A 510      23.689  49.732  81.642  1.00  0.00          N   \nATOM   3940  NH2 ARG A 510      23.510  48.070  80.089  1.00  0.00          N   \nATOM   3941  N   LEU A 511      17.081  51.448  81.784  1.00  0.00          N   \nATOM   3942  CA  LEU A 511      16.245  52.377  81.033  1.00  0.00          C   \nATOM   3943  C   LEU A 511      15.276  53.142  81.932  1.00  0.00          C   \nATOM   3944  O   LEU A 511      15.081  54.344  81.765  1.00  0.00          O   \nATOM   3945  CB  LEU A 511      15.472  51.626  79.940  1.00  0.00          C   \nATOM   3946  CG  LEU A 511      16.312  51.019  78.812  1.00  0.00          C   \nATOM   3947  CD1 LEU A 511      15.450  50.064  77.969  1.00  0.00          C   \nATOM   3948  CD2 LEU A 511      16.890  52.140  77.955  1.00  0.00          C   \nATOM   3949  N   LEU A 512      14.673  52.439  82.879  1.00  0.00          N   \nATOM   3950  CA  LEU A 512      13.740  53.058  83.803  1.00  0.00          C   \nATOM   3951  C   LEU A 512      14.441  54.183  84.559  1.00  0.00          C   \nATOM   3952  O   LEU A 512      13.875  55.254  84.770  1.00  0.00          O   \nATOM   3953  CB  LEU A 512      13.213  52.021  84.794  1.00  0.00          C   \nATOM   3954  CG  LEU A 512      12.174  51.022  84.284  1.00  0.00          C   \nATOM   3955  CD1 LEU A 512      11.949  49.929  85.330  1.00  0.00          C   \nATOM   3956  CD2 LEU A 512      10.873  51.752  83.983  1.00  0.00          C   \nATOM   3957  N   SER A 513      15.685  53.939  84.955  1.00  0.00          N   \nATOM   3958  CA  SER A 513      16.456  54.931  85.694  1.00  0.00          C   \nATOM   3959  C   SER A 513      16.737  56.173  84.868  1.00  0.00          C   \nATOM   3960  O   SER A 513      16.858  57.270  85.413  1.00  0.00          O   \nATOM   3961  CB  SER A 513      17.772  54.316  86.173  1.00  0.00          C   \nATOM   3962  OG  SER A 513      17.554  53.321  87.162  1.00  0.00          O   \nATOM   3963  N   GLN A 514      16.828  56.006  83.552  1.00  0.00          N   \nATOM   3964  CA  GLN A 514      17.116  57.117  82.655  1.00  0.00          C   \nATOM   3965  C   GLN A 514      15.959  58.119  82.578  1.00  0.00          C   \nATOM   3966  O   GLN A 514      16.160  59.285  82.251  1.00  0.00          O   \nATOM   3967  CB  GLN A 514      17.509  56.578  81.265  1.00  0.00          C   \nATOM   3968  CG  GLN A 514      18.537  57.412  80.532  1.00  0.00          C   \nATOM   3969  CD  GLN A 514      19.799  56.633  80.268  1.00  0.00          C   \nATOM   3970  OE1 GLN A 514      20.575  56.959  79.364  1.00  0.00          O   \nATOM   3971  NE2 GLN A 514      20.016  55.588  81.060  1.00  0.00          N   \nATOM   3972  N   GLY A 515      14.756  57.645  82.880  1.00  0.00          N   \nATOM   3973  CA  GLY A 515      13.598  58.518  82.848  1.00  0.00          C   \nATOM   3974  C   GLY A 515      13.146  58.870  81.444  1.00  0.00          C   \nATOM   3975  O   GLY A 515      13.776  58.462  80.462  1.00  0.00          O   \nATOM   3976  N   GLY A 516      12.056  59.626  81.342  1.00  0.00          N   \nATOM   3977  CA  GLY A 516      11.553  60.023  80.038  1.00  0.00          C   \nATOM   3978  C   GLY A 516      11.294  58.858  79.100  1.00  0.00          C   \nATOM   3979  O   GLY A 516      10.875  57.784  79.528  1.00  0.00          O   \nATOM   3980  N   ARG A 517      11.537  59.072  77.812  1.00  0.00          N   \nATOM   3981  CA  ARG A 517      11.320  58.037  76.806  1.00  0.00          C   \nATOM   3982  C   ARG A 517      12.069  56.733  77.092  1.00  0.00          C   \nATOM   3983  O   ARG A 517      11.551  55.642  76.834  1.00  0.00          O   \nATOM   3984  CB  ARG A 517      11.726  58.546  75.417  1.00  0.00          C   \nATOM   3985  CG  ARG A 517      10.726  59.484  74.759  1.00  0.00          C   \nATOM   3986  CD  ARG A 517      11.258  60.894  74.694  1.00  0.00          C   \nATOM   3987  NE  ARG A 517      12.494  61.014  73.915  1.00  0.00          N   \nATOM   3988  CZ  ARG A 517      12.600  60.726  72.622  1.00  0.00          C   \nATOM   3989  NH1 ARG A 517      11.541  60.287  71.954  1.00  0.00          N   \nATOM   3990  NH2 ARG A 517      13.753  60.909  71.987  1.00  0.00          N   \nATOM   3991  N   ALA A 518      13.292  56.840  77.605  1.00  0.00          N   \nATOM   3992  CA  ALA A 518      14.073  55.644  77.903  1.00  0.00          C   \nATOM   3993  C   ALA A 518      13.303  54.803  78.909  1.00  0.00          C   \nATOM   3994  O   ALA A 518      13.282  53.567  78.827  1.00  0.00          O   \nATOM   3995  CB  ALA A 518      15.438  56.026  78.459  1.00  0.00          C   \nATOM   3996  N   ALA A 519      12.654  55.488  79.850  1.00  0.00          N   \nATOM   3997  CA  ALA A 519      11.870  54.819  80.885  1.00  0.00          C   \nATOM   3998  C   ALA A 519      10.712  54.081  80.233  1.00  0.00          C   \nATOM   3999  O   ALA A 519      10.376  52.955  80.610  1.00  0.00          O   \nATOM   4000  CB  ALA A 519      11.346  55.846  81.886  1.00  0.00          C   \nATOM   4001  N   THR A 520      10.101  54.731  79.250  1.00  0.00          N   \nATOM   4002  CA  THR A 520       8.991  54.130  78.527  1.00  0.00          C   \nATOM   4003  C   THR A 520       9.494  52.892  77.784  1.00  0.00          C   \nATOM   4004  O   THR A 520       8.845  51.850  77.781  1.00  0.00          O   \nATOM   4005  CB  THR A 520       8.381  55.129  77.533  1.00  0.00          C   \nATOM   4006  OG1 THR A 520       7.915  56.274  78.259  1.00  0.00          O   \nATOM   4007  CG2 THR A 520       7.199  54.497  76.779  1.00  0.00          C   \nATOM   4008  N   CYS A 521      10.658  53.008  77.165  1.00  0.00          N   \nATOM   4009  CA  CYS A 521      11.228  51.877  76.455  1.00  0.00          C   \nATOM   4010  C   CYS A 521      11.350  50.697  77.413  1.00  0.00          C   \nATOM   4011  O   CYS A 521      10.930  49.584  77.098  1.00  0.00          O   \nATOM   4012  CB  CYS A 521      12.603  52.246  75.886  1.00  0.00          C   \nATOM   4013  SG  CYS A 521      12.538  53.242  74.363  1.00  0.00          S   \nATOM   4014  N   GLY A 522      11.896  50.953  78.600  1.00  0.00          N   \nATOM   4015  CA  GLY A 522      12.075  49.889  79.572  1.00  0.00          C   \nATOM   4016  C   GLY A 522      10.768  49.238  79.956  1.00  0.00          C   \nATOM   4017  O   GLY A 522      10.633  48.006  80.000  1.00  0.00          O   \nATOM   4018  N   LYS A 523       9.794  50.098  80.223  1.00  0.00          N   \nATOM   4019  CA  LYS A 523       8.458  49.710  80.636  1.00  0.00          C   \nATOM   4020  C   LYS A 523       7.704  48.818  79.632  1.00  0.00          C   \nATOM   4021  O   LYS A 523       7.245  47.722  79.977  1.00  0.00          O   \nATOM   4022  CB  LYS A 523       7.676  51.003  80.926  1.00  0.00          C   \nATOM   4023  CG  LYS A 523       6.367  50.863  81.683  1.00  0.00          C   \nATOM   4024  CD  LYS A 523       5.981  52.229  82.273  1.00  0.00          C   \nATOM   4025  CE  LYS A 523       4.586  52.242  82.895  1.00  0.00          C   \nATOM   4026  NZ  LYS A 523       3.489  52.316  81.881  1.00  0.00          N   \nATOM   4027  N   TYR A 524       7.588  49.274  78.391  1.00  0.00          N   \nATOM   4028  CA  TYR A 524       6.848  48.522  77.378  1.00  0.00          C   \nATOM   4029  C   TYR A 524       7.623  47.429  76.658  1.00  0.00          C   \nATOM   4030  O   TYR A 524       7.131  46.314  76.496  1.00  0.00          O   \nATOM   4031  CB  TYR A 524       6.277  49.480  76.320  1.00  0.00          C   \nATOM   4032  CG  TYR A 524       5.269  50.475  76.869  1.00  0.00          C   \nATOM   4033  CD1 TYR A 524       5.667  51.503  77.716  1.00  0.00          C   \nATOM   4034  CD2 TYR A 524       3.913  50.350  76.578  1.00  0.00          C   \nATOM   4035  CE1 TYR A 524       4.741  52.382  78.268  1.00  0.00          C   \nATOM   4036  CE2 TYR A 524       2.976  51.222  77.126  1.00  0.00          C   \nATOM   4037  CZ  TYR A 524       3.398  52.234  77.972  1.00  0.00          C   \nATOM   4038  OH  TYR A 524       2.480  53.081  78.541  1.00  0.00          O   \nATOM   4039  N   LEU A 525       8.837  47.751  76.224  1.00  0.00          N   \nATOM   4040  CA  LEU A 525       9.633  46.797  75.469  1.00  0.00          C   \nATOM   4041  C   LEU A 525      10.042  45.554  76.216  1.00  0.00          C   \nATOM   4042  O   LEU A 525      10.173  44.487  75.617  1.00  0.00          O   \nATOM   4043  CB  LEU A 525      10.889  47.467  74.909  1.00  0.00          C   \nATOM   4044  CG  LEU A 525      10.687  48.672  73.995  1.00  0.00          C   \nATOM   4045  CD1 LEU A 525      12.042  49.173  73.508  1.00  0.00          C   \nATOM   4046  CD2 LEU A 525       9.796  48.287  72.815  1.00  0.00          C   \nATOM   4047  N   PHE A 526      10.222  45.676  77.525  1.00  0.00          N   \nATOM   4048  CA  PHE A 526      10.689  44.547  78.309  1.00  0.00          C   \nATOM   4049  C   PHE A 526       9.775  44.053  79.437  1.00  0.00          C   \nATOM   4050  O   PHE A 526      10.225  43.358  80.353  1.00  0.00          O   \nATOM   4051  CB  PHE A 526      12.076  44.903  78.840  1.00  0.00          C   \nATOM   4052  CG  PHE A 526      13.056  45.266  77.754  1.00  0.00          C   \nATOM   4053  CD1 PHE A 526      13.605  44.280  76.940  1.00  0.00          C   \nATOM   4054  CD2 PHE A 526      13.430  46.594  77.544  1.00  0.00          C   \nATOM   4055  CE1 PHE A 526      14.524  44.604  75.928  1.00  0.00          C   \nATOM   4056  CE2 PHE A 526      14.343  46.939  76.540  1.00  0.00          C   \nATOM   4057  CZ  PHE A 526      14.893  45.938  75.727  1.00  0.00          C   \nATOM   4058  N   ASN A 527       8.489  44.390  79.352  1.00  0.00          N   \nATOM   4059  CA  ASN A 527       7.523  43.964  80.364  1.00  0.00          C   \nATOM   4060  C   ASN A 527       7.477  42.433  80.395  1.00  0.00          C   \nATOM   4061  O   ASN A 527       7.148  41.841  81.418  1.00  0.00          O   \nATOM   4062  CB  ASN A 527       6.133  44.506  80.033  1.00  0.00          C   \nATOM   4063  CG  ASN A 527       5.181  44.456  81.216  1.00  0.00          C   \nATOM   4064  OD1 ASN A 527       3.966  44.482  81.032  1.00  0.00          O   \nATOM   4065  ND2 ASN A 527       5.722  44.409  82.431  1.00  0.00          N   \nATOM   4066  N   TRP A 528       7.806  41.800  79.270  1.00  0.00          N   \nATOM   4067  CA  TRP A 528       7.812  40.347  79.189  1.00  0.00          C   \nATOM   4068  C   TRP A 528       8.868  39.697  80.073  1.00  0.00          C   \nATOM   4069  O   TRP A 528       8.756  38.518  80.405  1.00  0.00          O   \nATOM   4070  CB  TRP A 528       8.069  39.884  77.752  1.00  0.00          C   \nATOM   4071  CG  TRP A 528       9.371  40.385  77.159  1.00  0.00          C   \nATOM   4072  CD1 TRP A 528       9.546  41.501  76.387  1.00  0.00          C   \nATOM   4073  CD2 TRP A 528      10.673  39.792  77.303  1.00  0.00          C   \nATOM   4074  NE1 TRP A 528      10.872  41.636  76.038  1.00  0.00          N   \nATOM   4075  CE2 TRP A 528      11.586  40.598  76.582  1.00  0.00          C   \nATOM   4076  CE3 TRP A 528      11.158  38.652  77.960  1.00  0.00          C   \nATOM   4077  CZ2 TRP A 528      12.957  40.309  76.508  1.00  0.00          C   \nATOM   4078  CZ3 TRP A 528      12.526  38.361  77.885  1.00  0.00          C   \nATOM   4079  CH2 TRP A 528      13.405  39.188  77.159  1.00  0.00          C   \nATOM   4080  N   ALA A 529       9.903  40.451  80.435  1.00  0.00          N   \nATOM   4081  CA  ALA A 529      10.993  39.912  81.252  1.00  0.00          C   \nATOM   4082  C   ALA A 529      10.746  39.827  82.760  1.00  0.00          C   \nATOM   4083  O   ALA A 529      11.387  39.031  83.445  1.00  0.00          O   \nATOM   4084  CB  ALA A 529      12.274  40.717  80.992  1.00  0.00          C   \nATOM   4085  N   VAL A 530       9.842  40.647  83.287  1.00  0.00          N   \nATOM   4086  CA  VAL A 530       9.557  40.630  84.722  1.00  0.00          C   \nATOM   4087  C   VAL A 530       8.410  39.680  85.065  1.00  0.00          C   \nATOM   4088  O   VAL A 530       7.540  39.425  84.234  1.00  0.00          O   \nATOM   4089  CB  VAL A 530       9.200  42.031  85.226  1.00  0.00          C   \nATOM   4090  CG1 VAL A 530      10.344  42.980  84.954  1.00  0.00          C   \nATOM   4091  CG2 VAL A 530       7.933  42.511  84.561  1.00  0.00          C   \nATOM   4092  N   LYS A 531       8.396  39.148  86.285  1.00  0.00          N   \nATOM   4093  CA  LYS A 531       7.313  38.234  86.639  1.00  0.00          C   \nATOM   4094  C   LYS A 531       6.035  38.964  87.000  1.00  0.00          C   \nATOM   4095  O   LYS A 531       4.945  38.489  86.691  1.00  0.00          O   \nATOM   4096  CB  LYS A 531       7.723  37.275  87.758  1.00  0.00          C   \nATOM   4097  CG  LYS A 531       8.279  37.890  89.012  1.00  0.00          C   \nATOM   4098  CD  LYS A 531       8.683  36.758  89.942  1.00  0.00          C   \nATOM   4099  CE  LYS A 531       9.308  37.250  91.229  1.00  0.00          C   \nATOM   4100  NZ  LYS A 531       9.705  36.090  92.078  1.00  0.00          N   \nATOM   4101  N   THR A 532       6.162  40.120  87.638  1.00  0.00          N   \nATOM   4102  CA  THR A 532       4.986  40.911  87.985  1.00  0.00          C   \nATOM   4103  C   THR A 532       4.850  41.994  86.919  1.00  0.00          C   \nATOM   4104  O   THR A 532       5.431  43.071  87.043  1.00  0.00          O   \nATOM   4105  CB  THR A 532       5.131  41.582  89.372  1.00  0.00          C   \nATOM   4106  OG1 THR A 532       5.345  40.578  90.374  1.00  0.00          O   \nATOM   4107  CG2 THR A 532       3.873  42.384  89.707  1.00  0.00          C   \nATOM   4108  N   LYS A 533       4.090  41.699  85.869  1.00  0.00          N   \nATOM   4109  CA  LYS A 533       3.898  42.636  84.760  1.00  0.00          C   \nATOM   4110  C   LYS A 533       3.295  43.988  85.124  1.00  0.00          C   \nATOM   4111  O   LYS A 533       2.487  44.093  86.046  1.00  0.00          O   \nATOM   4112  CB  LYS A 533       3.023  41.999  83.678  1.00  0.00          C   \nATOM   4113  CG  LYS A 533       3.765  41.141  82.677  1.00  0.00          C   \nATOM   4114  CD  LYS A 533       4.367  39.904  83.310  1.00  0.00          C   \nATOM   4115  CE  LYS A 533       5.043  39.048  82.253  1.00  0.00          C   \nATOM   4116  NZ  LYS A 533       5.665  37.841  82.841  1.00  0.00          N   \nATOM   4117  N   LEU A 534       3.683  45.019  84.377  1.00  0.00          N   \nATOM   4118  CA  LEU A 534       3.158  46.362  84.589  1.00  0.00          C   \nATOM   4119  C   LEU A 534       1.853  46.549  83.820  1.00  0.00          C   \nATOM   4120  O   LEU A 534       1.523  45.762  82.931  1.00  0.00          O   \nATOM   4121  CB  LEU A 534       4.167  47.424  84.142  1.00  0.00          C   \nATOM   4122  CG  LEU A 534       4.958  48.107  85.259  1.00  0.00          C   \nATOM   4123  CD1 LEU A 534       5.737  47.062  86.042  1.00  0.00          C   \nATOM   4124  CD2 LEU A 534       5.889  49.155  84.666  1.00  0.00          C   \nATOM   4125  N   LYS A 535       1.127  47.605  84.181  1.00  0.00          N   \nATOM   4126  CA  LYS A 535      -0.156  47.958  83.578  1.00  0.00          C   \nATOM   4127  C   LYS A 535      -0.096  48.076  82.059  1.00  0.00          C   \nATOM   4128  O   LYS A 535      -0.810  47.370  81.345  1.00  0.00          O   \nATOM   4129  CB  LYS A 535      -0.651  49.282  84.175  1.00  0.00          C   \nATOM   4130  CG  LYS A 535       0.373  50.419  84.075  1.00  0.00          C   \nATOM   4131  CD  LYS A 535       0.011  51.622  84.946  1.00  0.00          C   \nATOM   4132  CE  LYS A 535      -1.251  52.326  84.466  1.00  0.00          C   \nATOM   4133  NZ  LYS A 535      -1.537  53.554  85.266  1.00  0.00          N   \nATOM   4134  N   LEU A 536       0.765  48.964  81.572  1.00  0.00          N   \nATOM   4135  CA  LEU A 536       0.904  49.194  80.140  1.00  0.00          C   \nATOM   4136  C   LEU A 536      -0.374  49.813  79.592  1.00  0.00          C   \nATOM   4137  O   LEU A 536      -1.418  49.166  79.518  1.00  0.00          O   \nATOM   4138  CB  LEU A 536       1.220  47.887  79.404  1.00  0.00          C   \nATOM   4139  CG  LEU A 536       2.707  47.530  79.315  1.00  0.00          C   \nATOM   4140  CD1 LEU A 536       3.306  47.420  80.707  1.00  0.00          C   \nATOM   4141  CD2 LEU A 536       2.865  46.227  78.558  1.00  0.00          C   \nATOM   4142  N   THR A 537      -0.271  51.078  79.209  1.00  0.00          N   \nATOM   4143  CA  THR A 537      -1.399  51.824  78.680  1.00  0.00          C   \nATOM   4144  C   THR A 537      -0.904  52.694  77.534  1.00  0.00          C   \nATOM   4145  O   THR A 537       0.297  52.810  77.311  1.00  0.00          O   \nATOM   4146  CB  THR A 537      -2.012  52.727  79.775  1.00  0.00          C   \nATOM   4147  OG1 THR A 537      -1.011  53.626  80.277  1.00  0.00          O   \nATOM   4148  CG2 THR A 537      -2.542  51.880  80.926  1.00  0.00          C   \nATOM   4149  N   PRO A 538      -1.823  53.315  76.785  1.00  0.00          N   \nATOM   4150  CA  PRO A 538      -1.428  54.173  75.664  1.00  0.00          C   \nATOM   4151  C   PRO A 538      -0.295  55.149  75.992  1.00  0.00          C   \nATOM   4152  O   PRO A 538       0.134  55.252  77.139  1.00  0.00          O   \nATOM   4153  CB  PRO A 538      -2.732  54.871  75.306  1.00  0.00          C   \nATOM   4154  CG  PRO A 538      -3.726  53.764  75.515  1.00  0.00          C   \nATOM   4155  CD  PRO A 538      -3.288  53.163  76.838  1.00  0.00          C   \nATOM   4156  N   ILE A 539       0.181  55.865  74.977  1.00  0.00          N   \nATOM   4157  CA  ILE A 539       1.274  56.817  75.155  1.00  0.00          C   \nATOM   4158  C   ILE A 539       1.027  58.131  74.403  1.00  0.00          C   \nATOM   4159  O   ILE A 539       0.782  58.130  73.196  1.00  0.00          O   \nATOM   4160  CB  ILE A 539       2.607  56.207  74.670  1.00  0.00          C   \nATOM   4161  CG1 ILE A 539       2.863  54.883  75.395  1.00  0.00          C   \nATOM   4162  CG2 ILE A 539       3.747  57.187  74.907  1.00  0.00          C   \nATOM   4163  CD1 ILE A 539       4.077  54.132  74.891  1.00  0.00          C   \nATOM   4164  N   PRO A 540       1.102  59.270  75.117  1.00  0.00          N   \nATOM   4165  CA  PRO A 540       0.894  60.623  74.585  1.00  0.00          C   \nATOM   4166  C   PRO A 540       1.644  60.983  73.299  1.00  0.00          C   \nATOM   4167  O   PRO A 540       1.023  61.207  72.258  1.00  0.00          O   \nATOM   4168  CB  PRO A 540       1.302  61.514  75.756  1.00  0.00          C   \nATOM   4169  CG  PRO A 540       0.867  60.707  76.937  1.00  0.00          C   \nATOM   4170  CD  PRO A 540       1.362  59.324  76.569  1.00  0.00          C   \nATOM   4171  N   ALA A 541       2.971  61.048  73.377  1.00  0.00          N   \nATOM   4172  CA  ALA A 541       3.802  61.404  72.225  1.00  0.00          C   \nATOM   4173  C   ALA A 541       3.510  60.597  70.960  1.00  0.00          C   \nATOM   4174  O   ALA A 541       3.810  61.047  69.852  1.00  0.00          O   \nATOM   4175  CB  ALA A 541       5.277  61.272  72.590  1.00  0.00          C   \nATOM   4176  N   ALA A 542       2.927  59.413  71.126  1.00  0.00          N   \nATOM   4177  CA  ALA A 542       2.606  58.540  69.999  1.00  0.00          C   \nATOM   4178  C   ALA A 542       1.934  59.267  68.840  1.00  0.00          C   \nATOM   4179  O   ALA A 542       2.449  59.275  67.721  1.00  0.00          O   \nATOM   4180  CB  ALA A 542       1.720  57.389  70.469  1.00  0.00          C   \nATOM   4181  N   SER A 543       0.784  59.874  69.117  1.00  0.00          N   \nATOM   4182  CA  SER A 543       0.023  60.595  68.101  1.00  0.00          C   \nATOM   4183  C   SER A 543       0.831  61.683  67.394  1.00  0.00          C   \nATOM   4184  O   SER A 543       0.475  62.108  66.294  1.00  0.00          O   \nATOM   4185  CB  SER A 543      -1.228  61.215  68.728  1.00  0.00          C   \nATOM   4186  OG  SER A 543      -0.881  62.146  69.738  1.00  0.00          O   \nATOM   4187  N   GLN A 544       1.913  62.134  68.025  1.00  0.00          N   \nATOM   4188  CA  GLN A 544       2.761  63.171  67.441  1.00  0.00          C   \nATOM   4189  C   GLN A 544       3.493  62.688  66.197  1.00  0.00          C   \nATOM   4190  O   GLN A 544       3.636  63.433  65.227  1.00  0.00          O   \nATOM   4191  CB  GLN A 544       3.785  63.673  68.463  1.00  0.00          C   \nATOM   4192  CG  GLN A 544       3.230  64.653  69.484  1.00  0.00          C   \nATOM   4193  CD  GLN A 544       4.324  65.313  70.310  1.00  0.00          C   \nATOM   4194  OE1 GLN A 544       4.056  66.202  71.122  1.00  0.00          O   \nATOM   4195  NE2 GLN A 544       5.565  64.879  70.107  1.00  0.00          N   \nATOM   4196  N   LEU A 545       3.963  61.445  66.228  1.00  0.00          N   \nATOM   4197  CA  LEU A 545       4.677  60.868  65.093  1.00  0.00          C   \nATOM   4198  C   LEU A 545       3.817  60.880  63.827  1.00  0.00          C   \nATOM   4199  O   LEU A 545       2.698  60.359  63.811  1.00  0.00          O   \nATOM   4200  CB  LEU A 545       5.104  59.433  65.418  1.00  0.00          C   \nATOM   4201  CG  LEU A 545       6.196  59.247  66.475  1.00  0.00          C   \nATOM   4202  CD1 LEU A 545       6.225  57.801  66.927  1.00  0.00          C   \nATOM   4203  CD2 LEU A 545       7.547  59.664  65.902  1.00  0.00          C   \nATOM   4204  N   ASP A 546       4.348  61.484  62.769  1.00  0.00          N   \nATOM   4205  CA  ASP A 546       3.646  61.565  61.493  1.00  0.00          C   \nATOM   4206  C   ASP A 546       4.009  60.344  60.657  1.00  0.00          C   \nATOM   4207  O   ASP A 546       4.957  60.375  59.870  1.00  0.00          O   \nATOM   4208  CB  ASP A 546       4.045  62.847  60.753  1.00  0.00          C   \nATOM   4209  CG  ASP A 546       3.354  62.987  59.411  1.00  0.00          C   \nATOM   4210  OD1 ASP A 546       3.543  62.105  58.548  1.00  0.00          O   \nATOM   4211  OD2 ASP A 546       2.623  63.981  59.215  1.00  0.00          O   \nATOM   4212  N   LEU A 547       3.249  59.269  60.836  1.00  0.00          N   \nATOM   4213  CA  LEU A 547       3.497  58.026  60.117  1.00  0.00          C   \nATOM   4214  C   LEU A 547       2.845  57.958  58.741  1.00  0.00          C   \nATOM   4215  O   LEU A 547       2.680  56.871  58.183  1.00  0.00          O   \nATOM   4216  CB  LEU A 547       3.045  56.836  60.968  1.00  0.00          C   \nATOM   4217  CG  LEU A 547       4.044  56.287  61.997  1.00  0.00          C   \nATOM   4218  CD1 LEU A 547       4.790  57.414  62.697  1.00  0.00          C   \nATOM   4219  CD2 LEU A 547       3.291  55.432  63.000  1.00  0.00          C   \nATOM   4220  N   SER A 548       2.478  59.112  58.191  1.00  0.00          N   \nATOM   4221  CA  SER A 548       1.868  59.143  56.866  1.00  0.00          C   \nATOM   4222  C   SER A 548       2.987  58.985  55.834  1.00  0.00          C   \nATOM   4223  O   SER A 548       3.969  59.729  55.851  1.00  0.00          O   \nATOM   4224  CB  SER A 548       1.112  60.461  56.641  1.00  0.00          C   \nATOM   4225  OG  SER A 548       1.993  61.567  56.547  1.00  0.00          O   \nATOM   4226  N   GLY A 549       2.841  58.001  54.952  1.00  0.00          N   \nATOM   4227  CA  GLY A 549       3.854  57.749  53.941  1.00  0.00          C   \nATOM   4228  C   GLY A 549       4.643  56.482  54.224  1.00  0.00          C   \nATOM   4229  O   GLY A 549       5.341  55.963  53.354  1.00  0.00          O   \nATOM   4230  N   TRP A 550       4.531  55.978  55.447  1.00  0.00          N   \nATOM   4231  CA  TRP A 550       5.239  54.767  55.847  1.00  0.00          C   \nATOM   4232  C   TRP A 550       4.823  53.518  55.070  1.00  0.00          C   \nATOM   4233  O   TRP A 550       5.671  52.765  54.574  1.00  0.00          O   \nATOM   4234  CB  TRP A 550       5.031  54.524  57.341  1.00  0.00          C   \nATOM   4235  CG  TRP A 550       6.023  55.227  58.192  1.00  0.00          C   \nATOM   4236  CD1 TRP A 550       6.432  56.524  58.076  1.00  0.00          C   \nATOM   4237  CD2 TRP A 550       6.732  54.676  59.305  1.00  0.00          C   \nATOM   4238  NE1 TRP A 550       7.359  56.815  59.048  1.00  0.00          N   \nATOM   4239  CE2 TRP A 550       7.565  55.697  59.816  1.00  0.00          C   \nATOM   4240  CE3 TRP A 550       6.752  53.415  59.917  1.00  0.00          C   \nATOM   4241  CZ2 TRP A 550       8.403  55.500  60.921  1.00  0.00          C   \nATOM   4242  CZ3 TRP A 550       7.585  53.219  61.016  1.00  0.00          C   \nATOM   4243  CH2 TRP A 550       8.403  54.259  61.502  1.00  0.00          C   \nATOM   4244  N   PHE A 551       3.517  53.298  54.979  1.00  0.00          N   \nATOM   4245  CA  PHE A 551       2.997  52.137  54.281  1.00  0.00          C   \nATOM   4246  C   PHE A 551       2.034  52.551  53.176  1.00  0.00          C   \nATOM   4247  O   PHE A 551       0.836  52.247  53.216  1.00  0.00          O   \nATOM   4248  CB  PHE A 551       2.310  51.216  55.283  1.00  0.00          C   \nATOM   4249  CG  PHE A 551       3.206  50.779  56.405  1.00  0.00          C   \nATOM   4250  CD1 PHE A 551       4.210  49.844  56.185  1.00  0.00          C   \nATOM   4251  CD2 PHE A 551       3.069  51.329  57.678  1.00  0.00          C   \nATOM   4252  CE1 PHE A 551       5.062  49.454  57.213  1.00  0.00          C   \nATOM   4253  CE2 PHE A 551       3.915  50.947  58.714  1.00  0.00          C   \nATOM   4254  CZ  PHE A 551       4.916  50.008  58.478  1.00  0.00          C   \nATOM   4255  N   VAL A 552       2.576  53.246  52.184  1.00  0.00          N   \nATOM   4256  CA  VAL A 552       1.791  53.716  51.055  1.00  0.00          C   \nATOM   4257  C   VAL A 552       2.201  52.981  49.785  1.00  0.00          C   \nATOM   4258  O   VAL A 552       1.360  52.485  49.042  1.00  0.00          O   \nATOM   4259  CB  VAL A 552       2.001  55.240  50.827  1.00  0.00          C   \nATOM   4260  CG1 VAL A 552       1.371  55.662  49.514  1.00  0.00          C   \nATOM   4261  CG2 VAL A 552       1.416  56.029  51.991  1.00  0.00          C   \nATOM   4262  N   ALA A 553       3.506  52.914  49.545  1.00  0.00          N   \nATOM   4263  CA  ALA A 553       4.018  52.268  48.349  1.00  0.00          C   \nATOM   4264  C   ALA A 553       5.382  51.620  48.568  1.00  0.00          C   \nATOM   4265  O   ALA A 553       6.072  51.909  49.548  1.00  0.00          O   \nATOM   4266  CB  ALA A 553       4.110  53.296  47.224  1.00  0.00          C   \nATOM   4267  N   GLY A 554       5.757  50.731  47.650  1.00  0.00          N   \nATOM   4268  CA  GLY A 554       7.054  50.083  47.723  1.00  0.00          C   \nATOM   4269  C   GLY A 554       8.049  50.901  46.909  1.00  0.00          C   \nATOM   4270  O   GLY A 554       7.679  51.505  45.901  1.00  0.00          O   \nATOM   4271  N   TYR A 555       9.305  50.938  47.337  1.00  0.00          N   \nATOM   4272  CA  TYR A 555      10.314  51.717  46.624  1.00  0.00          C   \nATOM   4273  C   TYR A 555      11.672  51.013  46.557  1.00  0.00          C   \nATOM   4274  O   TYR A 555      12.704  51.670  46.363  1.00  0.00          O   \nATOM   4275  CB  TYR A 555      10.499  53.065  47.310  1.00  0.00          C   \nATOM   4276  CG  TYR A 555       9.242  53.886  47.412  1.00  0.00          C   \nATOM   4277  CD1 TYR A 555       8.772  54.614  46.324  1.00  0.00          C   \nATOM   4278  CD2 TYR A 555       8.532  53.955  48.615  1.00  0.00          C   \nATOM   4279  CE1 TYR A 555       7.627  55.401  46.433  1.00  0.00          C   \nATOM   4280  CE2 TYR A 555       7.396  54.729  48.735  1.00  0.00          C   \nATOM   4281  CZ  TYR A 555       6.948  55.453  47.642  1.00  0.00          C   \nATOM   4282  OH  TYR A 555       5.832  56.243  47.772  1.00  0.00          O   \nATOM   4283  N   SER A 556      11.679  49.691  46.719  1.00  0.00          N   \nATOM   4284  CA  SER A 556      12.929  48.940  46.680  1.00  0.00          C   \nATOM   4285  C   SER A 556      13.758  49.333  45.460  1.00  0.00          C   \nATOM   4286  O   SER A 556      13.260  49.330  44.331  1.00  0.00          O   \nATOM   4287  CB  SER A 556      12.645  47.440  46.646  1.00  0.00          C   \nATOM   4288  OG  SER A 556      13.857  46.717  46.746  1.00  0.00          O   \nATOM   4289  N   GLY A 557      15.022  49.668  45.692  1.00  0.00          N   \nATOM   4290  CA  GLY A 557      15.894  50.075  44.606  1.00  0.00          C   \nATOM   4291  C   GLY A 557      15.539  51.449  44.049  1.00  0.00          C   \nATOM   4292  O   GLY A 557      16.215  51.940  43.136  1.00  0.00          O   \nATOM   4293  N   GLY A 558      14.513  52.083  44.623  1.00  0.00          N   \nATOM   4294  CA  GLY A 558      14.046  53.377  44.142  1.00  0.00          C   \nATOM   4295  C   GLY A 558      14.755  54.627  44.634  1.00  0.00          C   \nATOM   4296  O   GLY A 558      14.358  55.746  44.296  1.00  0.00          O   \nATOM   4297  N   ASP A 559      15.789  54.450  45.447  1.00  0.00          N   \nATOM   4298  CA  ASP A 559      16.543  55.587  45.953  1.00  0.00          C   \nATOM   4299  C   ASP A 559      15.634  56.625  46.615  1.00  0.00          C   \nATOM   4300  O   ASP A 559      15.762  57.823  46.353  1.00  0.00          O   \nATOM   4301  CB  ASP A 559      17.330  56.225  44.796  1.00  0.00          C   \nATOM   4302  CG  ASP A 559      18.351  57.247  45.269  1.00  0.00          C   \nATOM   4303  OD1 ASP A 559      19.076  56.967  46.265  1.00  0.00          O   \nATOM   4304  OD2 ASP A 559      18.435  58.324  44.632  1.00  0.00          O   \nATOM   4305  N   ILE A 560      14.728  56.163  47.479  1.00  0.00          N   \nATOM   4306  CA  ILE A 560      13.791  57.046  48.176  1.00  0.00          C   \nATOM   4307  C   ILE A 560      14.113  57.202  49.668  1.00  0.00          C   \nATOM   4308  O   ILE A 560      14.351  56.216  50.373  1.00  0.00          O   \nATOM   4309  CB  ILE A 560      12.334  56.529  48.043  1.00  0.00          C   \nATOM   4310  CG1 ILE A 560      11.942  56.431  46.564  1.00  0.00          C   \nATOM   4311  CG2 ILE A 560      11.382  57.436  48.820  1.00  0.00          C   \nATOM   4312  CD1 ILE A 560      12.036  57.741  45.794  1.00  0.00          C   \nATOM   4313  N   TYR A 561      14.085  58.447  50.136  1.00  0.00          N   \nATOM   4314  CA  TYR A 561      14.377  58.788  51.528  1.00  0.00          C   \nATOM   4315  C   TYR A 561      13.297  59.691  52.136  1.00  0.00          C   \nATOM   4316  O   TYR A 561      12.778  60.580  51.469  1.00  0.00          O   \nATOM   4317  CB  TYR A 561      15.723  59.518  51.600  1.00  0.00          C   \nATOM   4318  CG  TYR A 561      16.094  60.033  52.978  1.00  0.00          C   \nATOM   4319  CD1 TYR A 561      16.775  59.222  53.891  1.00  0.00          C   \nATOM   4320  CD2 TYR A 561      15.766  61.332  53.369  1.00  0.00          C   \nATOM   4321  CE1 TYR A 561      17.126  59.697  55.162  1.00  0.00          C   \nATOM   4322  CE2 TYR A 561      16.106  61.818  54.639  1.00  0.00          C   \nATOM   4323  CZ  TYR A 561      16.787  60.996  55.527  1.00  0.00          C   \nATOM   4324  OH  TYR A 561      17.138  61.468  56.774  1.00  0.00          O   \nATOM   4325  N   HIS A 562      12.971  59.458  53.404  1.00  0.00          N   \nATOM   4326  CA  HIS A 562      11.988  60.268  54.122  1.00  0.00          C   \nATOM   4327  C   HIS A 562      12.562  60.661  55.485  1.00  0.00          C   \nATOM   4328  O   HIS A 562      13.171  59.833  56.163  1.00  0.00          O   \nATOM   4329  CB  HIS A 562      10.687  59.494  54.348  1.00  0.00          C   \nATOM   4330  CG  HIS A 562       9.902  59.230  53.100  1.00  0.00          C   \nATOM   4331  ND1 HIS A 562       9.952  58.027  52.428  1.00  0.00          N   \nATOM   4332  CD2 HIS A 562       9.029  60.008  52.417  1.00  0.00          C   \nATOM   4333  CE1 HIS A 562       9.140  58.074  51.386  1.00  0.00          C   \nATOM   4334  NE2 HIS A 562       8.568  59.264  51.356  1.00  0.00          N   \nATOM   4335  N   SER A 563      12.351  61.918  55.877  1.00  0.00          N   \nATOM   4336  CA  SER A 563      12.819  62.476  57.157  1.00  0.00          C   \nATOM   4337  C   SER A 563      13.796  61.603  57.946  1.00  0.00          C   \nATOM   4338  O   SER A 563      14.962  62.031  58.088  1.00  0.00          O   \nATOM   4339  CB  SER A 563      11.624  62.811  58.062  1.00  0.00          C   \nATOM   4340  OG  SER A 563      11.038  61.636  58.605  1.00  0.00          O   \nATOM   4341  H   SER A   1      14.865  37.645  21.864  1.00  0.00          H   \nATOM   4342  H   SER A   1      14.701  36.607  23.156  1.00  0.00          H   \nATOM   4343  H   SER A   1      16.183  36.791  22.419  1.00  0.00          H   \nATOM   4344  H   SER A   1      13.908  40.808  24.965  1.00  0.00          H   \nATOM   4345  H   MET A   2      18.142  38.276  24.091  1.00  0.00          H   \nATOM   4346  H   SER A   3      18.084  37.264  28.559  1.00  0.00          H   \nATOM   4347  H   SER A   3      18.516  36.926  31.803  1.00  0.00          H   \nATOM   4348  H   TYR A   4      20.386  38.522  29.032  1.00  0.00          H   \nATOM   4349  H   TYR A   4      19.795  42.983  35.352  1.00  0.00          H   \nATOM   4350  H   THR A   5      23.482  41.428  28.003  1.00  0.00          H   \nATOM   4351  H   THR A   5      24.307  42.588  24.714  1.00  0.00          H   \nATOM   4352  H   TRP A   6      26.786  38.888  27.373  1.00  0.00          H   \nATOM   4353  H   TRP A   6      24.579  37.432  31.679  1.00  0.00          H   \nATOM   4354  H   THR A   7      30.017  41.814  28.783  1.00  0.00          H   \nATOM   4355  H   THR A   7      32.397  44.282  28.511  1.00  0.00          H   \nATOM   4356  H   GLY A   8      31.650  40.554  29.571  1.00  0.00          H   \nATOM   4357  H   ALA A   9      33.417  42.045  29.955  1.00  0.00          H   \nATOM   4358  H   LEU A  10      35.525  42.501  34.029  1.00  0.00          H   \nATOM   4359  H   ILE A  11      32.259  41.482  37.121  1.00  0.00          H   \nATOM   4360  H   THR A  12      32.760  45.444  38.354  1.00  0.00          H   \nATOM   4361  H   THR A  12      35.010  47.784  38.549  1.00  0.00          H   \nATOM   4362  H   CYS A  14      32.622  48.117  45.021  1.00  0.00          H   \nATOM   4363  H   CYS A  14      31.743  53.189  43.872  1.00  0.00          H   \nATOM   4364  H   ALA A  15      34.889  48.289  46.024  1.00  0.00          H   \nATOM   4365  H   ALA A  16      37.227  48.638  49.603  1.00  0.00          H   \nATOM   4366  H   GLU A  17      36.443  44.097  49.807  1.00  0.00          H   \nATOM   4367  H   GLU A  18      33.336  43.230  52.794  1.00  0.00          H   \nATOM   4368  H   SER A  19      34.599  38.983  53.574  1.00  0.00          H   \nATOM   4369  H   SER A  19      34.586  35.796  51.936  1.00  0.00          H   \nATOM   4370  H   LYS A  20      34.340  37.706  55.147  1.00  0.00          H   \nATOM   4371  H   LYS A  20      35.029  32.748  52.761  1.00  0.00          H   \nATOM   4372  H   LYS A  20      34.161  34.159  52.934  1.00  0.00          H   \nATOM   4373  H   LYS A  20      34.274  33.089  54.206  1.00  0.00          H   \nATOM   4374  H   LEU A  21      31.818  36.057  58.599  1.00  0.00          H   \nATOM   4375  H   ILE A  23      35.109  39.912  63.553  1.00  0.00          H   \nATOM   4376  H   ASN A  24      32.820  40.812  67.247  1.00  0.00          H   \nATOM   4377  H   ASN A  24      32.320  45.653  67.822  1.00  0.00          H   \nATOM   4378  H   ASN A  24      30.780  45.086  68.285  1.00  0.00          H   \nATOM   4379  H   ALA A  25      34.379  43.313  70.413  1.00  0.00          H   \nATOM   4380  H   LEU A  26      31.774  42.541  70.531  1.00  0.00          H   \nATOM   4381  H   SER A  27      31.021  40.643  69.517  1.00  0.00          H   \nATOM   4382  H   SER A  27      31.794  39.653  66.490  1.00  0.00          H   \nATOM   4383  H   ASN A  28      30.862  37.793  70.661  1.00  0.00          H   \nATOM   4384  H   ASN A  28      33.861  34.997  70.731  1.00  0.00          H   \nATOM   4385  H   ASN A  28      33.330  36.091  71.927  1.00  0.00          H   \nATOM   4386  H   SER A  29      28.672  38.231  72.363  1.00  0.00          H   \nATOM   4387  H   SER A  29      26.792  40.855  73.436  1.00  0.00          H   \nATOM   4388  H   LEU A  30      26.531  37.937  70.817  1.00  0.00          H   \nATOM   4389  H   LEU A  31      26.409  36.017  68.995  1.00  0.00          H   \nATOM   4390  H   ARG A  32      26.462  31.636  68.613  1.00  0.00          H   \nATOM   4391  H   ARG A  32      27.732  30.664  74.060  1.00  0.00          H   \nATOM   4392  H   ARG A  32      30.913  30.959  72.631  1.00  0.00          H   \nATOM   4393  H   ARG A  32      31.561  31.817  73.954  1.00  0.00          H   \nATOM   4394  H   ARG A  32      30.229  32.276  75.802  1.00  0.00          H   \nATOM   4395  H   ARG A  32      28.591  31.802  75.807  1.00  0.00          H   \nATOM   4396  H   HIS A  33      27.209  30.579  66.701  1.00  0.00          H   \nATOM   4397  H   HIS A  34      30.423  31.472  65.071  1.00  0.00          H   \nATOM   4398  H   ASN A  35      31.083  30.521  62.429  1.00  0.00          H   \nATOM   4399  H   ASN A  35      33.285  28.042  62.009  1.00  0.00          H   \nATOM   4400  H   ASN A  35      34.710  28.966  62.157  1.00  0.00          H   \nATOM   4401  H   MET A  36      28.688  30.842  61.133  1.00  0.00          H   \nATOM   4402  H   VAL A  37      28.202  32.691  59.961  1.00  0.00          H   \nATOM   4403  H   TYR A  38      26.737  35.165  56.425  1.00  0.00          H   \nATOM   4404  H   TYR A  38      24.300  33.242  50.529  1.00  0.00          H   \nATOM   4405  H   ALA A  39      29.651  37.400  54.064  1.00  0.00          H   \nATOM   4406  H   THR A  40      26.785  38.998  51.067  1.00  0.00          H   \nATOM   4407  H   THR A  40      25.189  39.494  48.095  1.00  0.00          H   \nATOM   4408  H   THR A  41      29.780  38.165  47.664  1.00  0.00          H   \nATOM   4409  H   THR A  41      32.679  38.747  46.541  1.00  0.00          H   \nATOM   4410  H   SER A  42      31.778  41.579  45.704  1.00  0.00          H   \nATOM   4411  H   SER A  42      33.986  42.879  43.483  1.00  0.00          H   \nATOM   4412  H   ARG A  43      33.019  39.147  45.140  1.00  0.00          H   \nATOM   4413  H   ARG A  43      35.480  36.679  48.023  1.00  0.00          H   \nATOM   4414  H   ARG A  43      35.785  35.462  49.934  1.00  0.00          H   \nATOM   4415  H   ARG A  43      36.983  34.249  49.984  1.00  0.00          H   \nATOM   4416  H   ARG A  43      38.084  34.775  46.725  1.00  0.00          H   \nATOM   4417  H   ARG A  43      38.300  33.856  48.145  1.00  0.00          H   \nATOM   4418  H   SER A  44      31.107  37.296  44.319  1.00  0.00          H   \nATOM   4419  H   SER A  44      28.282  35.891  45.736  1.00  0.00          H   \nATOM   4420  H   ALA A  45      30.581  37.870  42.052  1.00  0.00          H   \nATOM   4421  H   GLY A  46      30.753  36.524  39.576  1.00  0.00          H   \nATOM   4422  H   LEU A  47      29.314  34.254  39.821  1.00  0.00          H   \nATOM   4423  H   ARG A  48      26.865  35.258  39.976  1.00  0.00          H   \nATOM   4424  H   ARG A  48      23.078  36.700  42.429  1.00  0.00          H   \nATOM   4425  H   ARG A  48      20.226  35.518  40.819  1.00  0.00          H   \nATOM   4426  H   ARG A  48      19.605  34.739  42.203  1.00  0.00          H   \nATOM   4427  H   ARG A  48      20.763  34.829  44.194  1.00  0.00          H   \nATOM   4428  H   ARG A  48      22.250  35.660  44.279  1.00  0.00          H   \nATOM   4429  H   GLN A  49      26.365  36.411  37.713  1.00  0.00          H   \nATOM   4430  H   GLN A  49      30.484  38.568  35.895  1.00  0.00          H   \nATOM   4431  H   GLN A  49      29.398  37.881  37.016  1.00  0.00          H   \nATOM   4432  H   LYS A  50      26.141  34.146  36.130  1.00  0.00          H   \nATOM   4433  H   LYS A  50      30.984  31.902  33.485  1.00  0.00          H   \nATOM   4434  H   LYS A  50      30.407  32.356  34.980  1.00  0.00          H   \nATOM   4435  H   LYS A  50      29.493  32.635  33.616  1.00  0.00          H   \nATOM   4436  H   LYS A  51      23.779  33.123  37.025  1.00  0.00          H   \nATOM   4437  H   LYS A  51      23.915  29.280  42.064  1.00  0.00          H   \nATOM   4438  H   LYS A  51      23.216  29.235  40.553  1.00  0.00          H   \nATOM   4439  H   LYS A  51      22.260  29.396  41.908  1.00  0.00          H   \nATOM   4440  H   VAL A  52      21.948  35.003  36.355  1.00  0.00          H   \nATOM   4441  H   THR A  53      21.635  34.552  33.703  1.00  0.00          H   \nATOM   4442  H   THR A  53      24.237  36.344  31.829  1.00  0.00          H   \nATOM   4443  H   PHE A  54      19.020  35.410  30.387  1.00  0.00          H   \nATOM   4444  H   ASP A  55      17.372  32.451  27.601  1.00  0.00          H   \nATOM   4445  H   ARG A  56      14.390  35.688  26.216  1.00  0.00          H   \nATOM   4446  H   ARG A  56      15.133  37.801  27.466  1.00  0.00          H   \nATOM   4447  H   ARG A  56      13.761  40.567  30.013  1.00  0.00          H   \nATOM   4448  H   ARG A  56      13.757  38.931  30.497  1.00  0.00          H   \nATOM   4449  H   ARG A  56      15.057  39.943  26.841  1.00  0.00          H   \nATOM   4450  H   ARG A  56      14.445  41.133  27.897  1.00  0.00          H   \nATOM   4451  H   LEU A  57      10.476  33.784  27.112  1.00  0.00          H   \nATOM   4452  H   GLN A  58       7.588  35.082  24.114  1.00  0.00          H   \nATOM   4453  H   GLN A  58       5.507  39.650  29.108  1.00  0.00          H   \nATOM   4454  H   GLN A  58       5.385  37.972  29.386  1.00  0.00          H   \nATOM   4455  H   VAL A  59       3.955  33.982  26.031  1.00  0.00          H   \nATOM   4456  H   LEU A  60       1.402  35.975  23.674  1.00  0.00          H   \nATOM   4457  H   ASP A  61      -1.922  35.510  26.457  1.00  0.00          H   \nATOM   4458  H   ASP A  62      -6.154  35.140  25.024  1.00  0.00          H   \nATOM   4459  H   HIS A  63      -6.601  35.500  27.716  1.00  0.00          H   \nATOM   4460  H   TYR A  64      -4.639  36.928  28.582  1.00  0.00          H   \nATOM   4461  H   TYR A  64       1.780  42.798  28.452  1.00  0.00          H   \nATOM   4462  H   ARG A  65      -4.854  39.358  27.218  1.00  0.00          H   \nATOM   4463  H   ARG A  65      -5.929  40.075  22.900  1.00  0.00          H   \nATOM   4464  H   ARG A  65      -2.471  38.104  23.090  1.00  0.00          H   \nATOM   4465  H   ARG A  65      -2.504  39.774  23.434  1.00  0.00          H   \nATOM   4466  H   ARG A  65      -5.883  37.837  22.552  1.00  0.00          H   \nATOM   4467  H   ARG A  65      -4.413  36.984  22.685  1.00  0.00          H   \nATOM   4468  H   ASP A  66      -7.238  40.084  28.280  1.00  0.00          H   \nATOM   4469  H   VAL A  67      -6.882  40.628  30.902  1.00  0.00          H   \nATOM   4470  H   LEU A  68      -5.178  42.615  30.678  1.00  0.00          H   \nATOM   4471  H   LYS A  69      -6.731  44.594  29.557  1.00  0.00          H   \nATOM   4472  H   LYS A  69     -12.747  48.072  24.980  1.00  0.00          H   \nATOM   4473  H   LYS A  69     -12.832  46.519  25.578  1.00  0.00          H   \nATOM   4474  H   LYS A  69     -11.584  46.954  24.565  1.00  0.00          H   \nATOM   4475  H   GLU A  70      -8.369  45.173  31.691  1.00  0.00          H   \nATOM   4476  H   MET A  71      -6.668  46.189  33.501  1.00  0.00          H   \nATOM   4477  H   LYS A  72      -5.908  48.513  32.251  1.00  0.00          H   \nATOM   4478  H   LYS A  72      -2.202  49.723  26.100  1.00  0.00          H   \nATOM   4479  H   LYS A  72      -3.654  50.532  26.197  1.00  0.00          H   \nATOM   4480  H   LYS A  72      -3.620  48.870  26.285  1.00  0.00          H   \nATOM   4481  H   ALA A  73      -8.249  49.899  32.338  1.00  0.00          H   \nATOM   4482  H   LYS A  74      -8.440  50.528  34.963  1.00  0.00          H   \nATOM   4483  H   LYS A  74     -11.424  46.528  38.133  1.00  0.00          H   \nATOM   4484  H   LYS A  74      -9.959  46.520  37.341  1.00  0.00          H   \nATOM   4485  H   LYS A  74     -11.007  47.800  37.143  1.00  0.00          H   \nATOM   4486  H   ALA A  75      -6.290  51.969  35.228  1.00  0.00          H   \nATOM   4487  H   SER A  76      -7.112  54.234  33.949  1.00  0.00          H   \nATOM   4488  H   SER A  76      -8.238  55.018  30.793  1.00  0.00          H   \nATOM   4489  H   THR A  77      -8.024  55.796  35.989  1.00  0.00          H   \nATOM   4490  H   THR A  77      -8.318  56.525  39.130  1.00  0.00          H   \nATOM   4491  H   VAL A  78      -6.370  57.199  36.838  1.00  0.00          H   \nATOM   4492  H   LYS A  79      -4.895  60.672  39.440  1.00  0.00          H   \nATOM   4493  H   LYS A  79      -5.274  69.079  37.546  1.00  0.00          H   \nATOM   4494  H   LYS A  79      -6.217  68.281  38.664  1.00  0.00          H   \nATOM   4495  H   LYS A  79      -6.486  68.056  37.035  1.00  0.00          H   \nATOM   4496  H   ALA A  80      -1.761  62.424  37.237  1.00  0.00          H   \nATOM   4497  H   LYS A  81       1.068  63.831  40.413  1.00  0.00          H   \nATOM   4498  H   LYS A  81       1.896  66.035  46.012  1.00  0.00          H   \nATOM   4499  H   LYS A  81       1.193  65.415  44.635  1.00  0.00          H   \nATOM   4500  H   LYS A  81       0.753  66.923  45.189  1.00  0.00          H   \nATOM   4501  H   LEU A  82       3.100  67.067  38.059  1.00  0.00          H   \nATOM   4502  H   LEU A  83       7.038  65.554  39.558  1.00  0.00          H   \nATOM   4503  H   SER A  84       8.229  69.016  42.391  1.00  0.00          H   \nATOM   4504  H   SER A  84       9.497  71.824  43.971  1.00  0.00          H   \nATOM   4505  H   VAL A  85      11.855  70.983  40.329  1.00  0.00          H   \nATOM   4506  H   GLU A  86      12.924  71.525  42.925  1.00  0.00          H   \nATOM   4507  H   GLU A  87      11.194  69.943  44.515  1.00  0.00          H   \nATOM   4508  H   ALA A  88      11.793  67.449  43.642  1.00  0.00          H   \nATOM   4509  H   CYS A  89      14.358  67.389  44.408  1.00  0.00          H   \nATOM   4510  H   CYS A  89      17.824  69.668  44.015  1.00  0.00          H   \nATOM   4511  H   LYS A  90      14.106  67.214  47.208  1.00  0.00          H   \nATOM   4512  H   LYS A  90      10.817  72.359  49.753  1.00  0.00          H   \nATOM   4513  H   LYS A  90      10.357  71.128  50.776  1.00  0.00          H   \nATOM   4514  H   LYS A  90      10.178  70.953  49.129  1.00  0.00          H   \nATOM   4515  H   LEU A  91      13.684  64.487  47.381  1.00  0.00          H   \nATOM   4516  H   THR A  92      15.902  63.380  47.867  1.00  0.00          H   \nATOM   4517  H   THR A  92      18.188  63.342  45.141  1.00  0.00          H   \nATOM   4518  H   HIS A  95      22.186  59.080  51.881  1.00  0.00          H   \nATOM   4519  H   SER A  96      22.560  59.376  49.390  1.00  0.00          H   \nATOM   4520  H   SER A  96      20.594  58.503  45.499  1.00  0.00          H   \nATOM   4521  H   ALA A  97      23.772  56.472  45.979  1.00  0.00          H   \nATOM   4522  H   LYS A  98      27.727  58.623  45.068  1.00  0.00          H   \nATOM   4523  H   LYS A  98      34.314  59.743  43.057  1.00  0.00          H   \nATOM   4524  H   LYS A  98      33.942  60.461  44.513  1.00  0.00          H   \nATOM   4525  H   LYS A  98      33.600  61.247  43.085  1.00  0.00          H   \nATOM   4526  H   SER A  99      26.728  62.293  42.436  1.00  0.00          H   \nATOM   4527  H   SER A  99      25.916  64.003  38.436  1.00  0.00          H   \nATOM   4528  H   LYS A 100      27.482  60.893  38.086  1.00  0.00          H   \nATOM   4529  H   LYS A 100      29.112  54.281  35.545  1.00  0.00          H   \nATOM   4530  H   LYS A 100      29.960  55.341  34.579  1.00  0.00          H   \nATOM   4531  H   LYS A 100      28.300  55.445  34.672  1.00  0.00          H   \nATOM   4532  H   PHE A 101      28.147  63.157  38.001  1.00  0.00          H   \nATOM   4533  H   GLY A 102      30.465  64.514  39.140  1.00  0.00          H   \nATOM   4534  H   TYR A 103      28.815  65.184  41.163  1.00  0.00          H   \nATOM   4535  H   TYR A 103      23.418  65.605  37.932  1.00  0.00          H   \nATOM   4536  H   GLY A 104      27.360  66.133  45.210  1.00  0.00          H   \nATOM   4537  H   ALA A 105      25.452  62.570  46.967  1.00  0.00          H   \nATOM   4538  H   LYS A 106      25.292  64.467  48.841  1.00  0.00          H   \nATOM   4539  H   LYS A 106      22.695  63.921  52.207  1.00  0.00          H   \nATOM   4540  H   LYS A 106      23.197  65.462  51.826  1.00  0.00          H   \nATOM   4541  H   LYS A 106      22.978  65.027  53.419  1.00  0.00          H   \nATOM   4542  H   ASP A 107      25.501  66.849  47.676  1.00  0.00          H   \nATOM   4543  H   VAL A 108      23.172  66.845  46.511  1.00  0.00          H   \nATOM   4544  H   ARG A 109      21.222  66.824  48.222  1.00  0.00          H   \nATOM   4545  H   ARG A 109      19.687  65.537  53.305  1.00  0.00          H   \nATOM   4546  H   ARG A 109      17.591  64.066  50.939  1.00  0.00          H   \nATOM   4547  H   ARG A 109      16.173  64.352  51.842  1.00  0.00          H   \nATOM   4548  H   ARG A 109      16.298  65.395  53.878  1.00  0.00          H   \nATOM   4549  H   ARG A 109      17.806  65.909  54.486  1.00  0.00          H   \nATOM   4550  H   ASN A 110      21.584  69.338  48.885  1.00  0.00          H   \nATOM   4551  H   ASN A 110      24.856  70.572  50.400  1.00  0.00          H   \nATOM   4552  H   ASN A 110      24.642  69.994  51.990  1.00  0.00          H   \nATOM   4553  H   LEU A 111      20.093  70.591  47.355  1.00  0.00          H   \nATOM   4554  H   SER A 112      22.234  71.666  46.149  1.00  0.00          H   \nATOM   4555  H   SER A 112      25.569  70.998  44.450  1.00  0.00          H   \nATOM   4556  H   SER A 113      24.074  75.372  44.072  1.00  0.00          H   \nATOM   4557  H   SER A 113      25.907  77.391  41.668  1.00  0.00          H   \nATOM   4558  H   LYS A 114      25.588  73.879  42.218  1.00  0.00          H   \nATOM   4559  H   LYS A 114      30.979  70.359  37.947  1.00  0.00          H   \nATOM   4560  H   LYS A 114      30.891  71.757  38.848  1.00  0.00          H   \nATOM   4561  H   LYS A 114      29.524  71.153  38.112  1.00  0.00          H   \nATOM   4562  H   ALA A 115      24.601  71.297  42.210  1.00  0.00          H   \nATOM   4563  H   VAL A 116      21.859  71.713  41.700  1.00  0.00          H   \nATOM   4564  H   ASN A 117      22.256  72.799  39.152  1.00  0.00          H   \nATOM   4565  H   ASN A 117      23.546  76.697  38.466  1.00  0.00          H   \nATOM   4566  H   ASN A 117      24.369  75.216  38.660  1.00  0.00          H   \nATOM   4567  H   HIS A 118      23.184  70.751  37.620  1.00  0.00          H   \nATOM   4568  H   ILE A 119      21.141  69.002  37.763  1.00  0.00          H   \nATOM   4569  H   HIS A 120      19.265  70.386  36.313  1.00  0.00          H   \nATOM   4570  H   SER A 121      20.253  70.059  33.842  1.00  0.00          H   \nATOM   4571  H   SER A 121      23.259  69.153  32.493  1.00  0.00          H   \nATOM   4572  H   VAL A 122      20.032  67.656  33.332  1.00  0.00          H   \nATOM   4573  H   TRP A 123      17.420  67.206  33.253  1.00  0.00          H   \nATOM   4574  H   TRP A 123      10.661  68.859  31.923  1.00  0.00          H   \nATOM   4575  H   LYS A 124      16.700  68.721  31.041  1.00  0.00          H   \nATOM   4576  H   LYS A 124      18.088  74.447  25.252  1.00  0.00          H   \nATOM   4577  H   LYS A 124      19.202  73.789  26.301  1.00  0.00          H   \nATOM   4578  H   LYS A 124      17.892  74.628  26.896  1.00  0.00          H   \nATOM   4579  H   ASP A 125      17.962  67.230  29.097  1.00  0.00          H   \nATOM   4580  H   LEU A 126      16.348  65.056  29.219  1.00  0.00          H   \nATOM   4581  H   LEU A 127      14.028  65.913  28.109  1.00  0.00          H   \nATOM   4582  H   GLU A 128      14.774  66.267  25.625  1.00  0.00          H   \nATOM   4583  H   ASP A 129      15.435  64.164  24.588  1.00  0.00          H   \nATOM   4584  H   THR A 130      14.540  61.024  21.871  1.00  0.00          H   \nATOM   4585  H   THR A 130      12.471  59.120  21.135  1.00  0.00          H   \nATOM   4586  H   VAL A 131      16.052  59.133  22.398  1.00  0.00          H   \nATOM   4587  H   THR A 132      17.987  58.534  22.716  1.00  0.00          H   \nATOM   4588  H   THR A 132      19.464  59.451  21.894  1.00  0.00          H   \nATOM   4589  H   ILE A 134      19.685  54.677  28.198  1.00  0.00          H   \nATOM   4590  H   ASP A 135      23.436  56.365  30.075  1.00  0.00          H   \nATOM   4591  H   THR A 136      24.775  52.433  32.130  1.00  0.00          H   \nATOM   4592  H   THR A 136      22.545  49.576  33.256  1.00  0.00          H   \nATOM   4593  H   THR A 137      24.452  52.174  36.475  1.00  0.00          H   \nATOM   4594  H   THR A 137      26.351  53.706  36.954  1.00  0.00          H   \nATOM   4595  H   ILE A 138      27.428  48.865  36.593  1.00  0.00          H   \nATOM   4596  H   MET A 139      25.605  46.722  39.912  1.00  0.00          H   \nATOM   4597  H   ALA A 140      28.581  44.768  42.488  1.00  0.00          H   \nATOM   4598  H   LYS A 141      25.072  42.881  44.434  1.00  0.00          H   \nATOM   4599  H   LYS A 141      18.581  46.021  45.231  1.00  0.00          H   \nATOM   4600  H   LYS A 141      19.200  45.811  46.763  1.00  0.00          H   \nATOM   4601  H   LYS A 141      18.704  44.491  45.878  1.00  0.00          H   \nATOM   4602  H   ASN A 142      26.022  44.740  48.576  1.00  0.00          H   \nATOM   4603  H   ASN A 142      30.370  43.783  47.968  1.00  0.00          H   \nATOM   4604  H   ASN A 142      29.449  42.608  48.792  1.00  0.00          H   \nATOM   4605  H   GLU A 143      25.711  40.985  50.220  1.00  0.00          H   \nATOM   4606  H   VAL A 144      23.476  41.010  54.039  1.00  0.00          H   \nATOM   4607  H   PHE A 145      24.691  36.718  54.904  1.00  0.00          H   \nATOM   4608  H   CYS A 146      21.777  33.583  55.916  1.00  0.00          H   \nATOM   4609  H   CYS A 146      21.763  30.220  59.894  1.00  0.00          H   \nATOM   4610  H   VAL A 147      24.993  30.652  56.832  1.00  0.00          H   \nATOM   4611  H   GLN A 148      24.052  28.272  53.025  1.00  0.00          H   \nATOM   4612  H   GLN A 148      22.657  22.506  49.801  1.00  0.00          H   \nATOM   4613  H   GLN A 148      21.469  23.723  49.919  1.00  0.00          H   \nATOM   4614  H   ARG A 154      24.579  28.664  50.283  1.00  0.00          H   \nATOM   4615  H   ARG A 154      25.972  28.545  51.188  1.00  0.00          H   \nATOM   4616  H   ARG A 154      26.012  28.230  49.553  1.00  0.00          H   \nATOM   4617  H   ARG A 154      28.889  32.536  50.303  1.00  0.00          H   \nATOM   4618  H   ARG A 154      32.508  33.058  51.894  1.00  0.00          H   \nATOM   4619  H   ARG A 154      31.694  31.576  52.118  1.00  0.00          H   \nATOM   4620  H   ARG A 154      29.978  34.517  50.005  1.00  0.00          H   \nATOM   4621  H   ARG A 154      31.524  34.744  50.686  1.00  0.00          H   \nATOM   4622  H   LYS A 155      24.377  32.260  49.572  1.00  0.00          H   \nATOM   4623  H   LYS A 155      18.480  32.378  50.373  1.00  0.00          H   \nATOM   4624  H   LYS A 155      18.426  33.501  49.144  1.00  0.00          H   \nATOM   4625  H   LYS A 155      18.802  33.980  50.694  1.00  0.00          H   \nATOM   4626  H   ALA A 157      26.911  36.862  44.855  1.00  0.00          H   \nATOM   4627  H   ARG A 158      23.380  39.819  45.539  1.00  0.00          H   \nATOM   4628  H   ARG A 158      19.999  41.298  46.518  1.00  0.00          H   \nATOM   4629  H   ARG A 158      16.659  40.533  45.929  1.00  0.00          H   \nATOM   4630  H   ARG A 158      16.063  41.793  46.910  1.00  0.00          H   \nATOM   4631  H   ARG A 158      19.207  42.973  47.824  1.00  0.00          H   \nATOM   4632  H   ARG A 158      17.525  43.194  47.998  1.00  0.00          H   \nATOM   4633  H   LEU A 159      21.768  40.975  41.428  1.00  0.00          H   \nATOM   4634  H   ILE A 160      23.687  44.977  41.450  1.00  0.00          H   \nATOM   4635  H   VAL A 161      20.485  47.375  39.592  1.00  0.00          H   \nATOM   4636  H   PHE A 162      22.722  50.607  37.930  1.00  0.00          H   \nATOM   4637  H   ASP A 164      22.685  57.387  35.715  1.00  0.00          H   \nATOM   4638  H   LEU A 165      23.812  59.560  39.646  1.00  0.00          H   \nATOM   4639  H   GLY A 166      22.620  62.038  38.435  1.00  0.00          H   \nATOM   4640  H   VAL A 167      20.770  61.015  36.762  1.00  0.00          H   \nATOM   4641  H   ARG A 168      19.230  59.800  38.456  1.00  0.00          H   \nATOM   4642  H   ARG A 168      19.772  57.670  43.225  1.00  0.00          H   \nATOM   4643  H   ARG A 168      21.702  56.743  40.471  1.00  0.00          H   \nATOM   4644  H   ARG A 168      23.161  57.290  41.163  1.00  0.00          H   \nATOM   4645  H   ARG A 168      23.158  58.257  43.242  1.00  0.00          H   \nATOM   4646  H   ARG A 168      21.703  58.386  44.122  1.00  0.00          H   \nATOM   4647  H   VAL A 169      18.025  61.899  39.853  1.00  0.00          H   \nATOM   4648  H   CYS A 170      16.494  62.873  37.943  1.00  0.00          H   \nATOM   4649  H   CYS A 170      16.796  64.634  34.153  1.00  0.00          H   \nATOM   4650  H   GLU A 171      14.747  60.713  37.639  1.00  0.00          H   \nATOM   4651  H   LYS A 172      13.124  60.928  39.865  1.00  0.00          H   \nATOM   4652  H   LYS A 172      15.721  58.819  44.803  1.00  0.00          H   \nATOM   4653  H   LYS A 172      16.115  60.008  43.705  1.00  0.00          H   \nATOM   4654  H   LYS A 172      14.895  58.927  43.360  1.00  0.00          H   \nATOM   4655  H   MET A 173      11.845  63.310  39.484  1.00  0.00          H   \nATOM   4656  H   ALA A 174      10.239  62.754  37.242  1.00  0.00          H   \nATOM   4657  H   LEU A 175       8.852  60.819  38.227  1.00  0.00          H   \nATOM   4658  H   TYR A 176       8.011  60.803  40.332  1.00  0.00          H   \nATOM   4659  H   TYR A 176       7.807  58.855  48.805  1.00  0.00          H   \nATOM   4660  H   ASP A 177       5.476  61.309  41.325  1.00  0.00          H   \nATOM   4661  H   VAL A 178       4.055  59.644  39.994  1.00  0.00          H   \nATOM   4662  H   VAL A 179       4.934  56.959  40.572  1.00  0.00          H   \nATOM   4663  H   SER A 180       3.875  57.102  42.926  1.00  0.00          H   \nATOM   4664  H   SER A 180       5.260  58.699  45.965  1.00  0.00          H   \nATOM   4665  H   THR A 181       1.495  56.769  42.952  1.00  0.00          H   \nATOM   4666  H   THR A 181      -1.590  57.497  41.721  1.00  0.00          H   \nATOM   4667  H   LEU A 182       0.276  55.746  41.472  1.00  0.00          H   \nATOM   4668  H   GLN A 184      -1.565  52.106  44.207  1.00  0.00          H   \nATOM   4669  H   GLN A 184      -7.766  51.833  46.666  1.00  0.00          H   \nATOM   4670  H   GLN A 184      -6.751  50.563  47.180  1.00  0.00          H   \nATOM   4671  H   VAL A 185      -3.879  52.819  43.025  1.00  0.00          H   \nATOM   4672  H   VAL A 186      -4.122  51.206  40.897  1.00  0.00          H   \nATOM   4673  H   MET A 187      -4.233  48.781  41.936  1.00  0.00          H   \nATOM   4674  H   GLY A 188      -6.075  48.728  43.419  1.00  0.00          H   \nATOM   4675  H   SER A 189      -8.214  46.420  46.783  1.00  0.00          H   \nATOM   4676  H   SER A 189      -9.284  43.610  48.361  1.00  0.00          H   \nATOM   4677  H   SER A 190      -6.028  44.773  45.980  1.00  0.00          H   \nATOM   4678  H   SER A 190      -5.924  41.624  44.113  1.00  0.00          H   \nATOM   4679  H   TYR A 191      -3.687  45.939  46.362  1.00  0.00          H   \nATOM   4680  H   TYR A 191       4.040  49.864  45.416  1.00  0.00          H   \nATOM   4681  H   GLY A 192       0.479  44.653  47.260  1.00  0.00          H   \nATOM   4682  H   PHE A 193       1.977  46.880  48.408  1.00  0.00          H   \nATOM   4683  H   GLN A 194       0.154  48.200  50.227  1.00  0.00          H   \nATOM   4684  H   GLN A 194      -4.674  47.853  48.405  1.00  0.00          H   \nATOM   4685  H   GLN A 194      -3.145  47.255  48.864  1.00  0.00          H   \nATOM   4686  H   TYR A 195       0.227  47.733  52.424  1.00  0.00          H   \nATOM   4687  H   TYR A 195      -5.484  44.793  50.441  1.00  0.00          H   \nATOM   4688  H   SER A 196      -0.435  48.166  56.922  1.00  0.00          H   \nATOM   4689  H   SER A 196      -0.486  48.657  60.197  1.00  0.00          H   \nATOM   4690  H   GLY A 198       0.924  45.541  60.749  1.00  0.00          H   \nATOM   4691  H   GLN A 199      -1.299  46.039  59.221  1.00  0.00          H   \nATOM   4692  H   GLN A 199      -3.735  51.039  57.654  1.00  0.00          H   \nATOM   4693  H   GLN A 199      -4.607  49.951  58.635  1.00  0.00          H   \nATOM   4694  H   ARG A 200      -0.861  44.239  57.412  1.00  0.00          H   \nATOM   4695  H   ARG A 200       2.542  39.392  54.134  1.00  0.00          H   \nATOM   4696  H   ARG A 200       2.163  41.480  50.761  1.00  0.00          H   \nATOM   4697  H   ARG A 200       1.909  42.252  52.260  1.00  0.00          H   \nATOM   4698  H   ARG A 200       2.701  39.244  50.692  1.00  0.00          H   \nATOM   4699  H   ARG A 200       2.845  38.359  52.142  1.00  0.00          H   \nATOM   4700  H   VAL A 201      -1.087  41.811  58.318  1.00  0.00          H   \nATOM   4701  H   GLU A 202      -3.697  41.995  58.976  1.00  0.00          H   \nATOM   4702  H   PHE A 203      -4.862  42.200  56.389  1.00  0.00          H   \nATOM   4703  H   LEU A 204      -4.270  39.771  55.456  1.00  0.00          H   \nATOM   4704  H   VAL A 205      -5.575  38.079  57.175  1.00  0.00          H   \nATOM   4705  H   ASN A 206      -8.218  38.742  56.914  1.00  0.00          H   \nATOM   4706  H   ASN A 206     -10.063  42.190  56.764  1.00  0.00          H   \nATOM   4707  H   ASN A 206     -10.509  42.179  58.410  1.00  0.00          H   \nATOM   4708  H   THR A 207      -8.526  38.063  54.330  1.00  0.00          H   \nATOM   4709  H   THR A 207      -7.364  38.835  51.062  1.00  0.00          H   \nATOM   4710  H   TRP A 208      -8.127  35.505  54.310  1.00  0.00          H   \nATOM   4711  H   TRP A 208      -7.912  28.935  56.807  1.00  0.00          H   \nATOM   4712  H   LYS A 209     -10.306  34.761  55.700  1.00  0.00          H   \nATOM   4713  H   LYS A 209     -10.969  35.887  62.729  1.00  0.00          H   \nATOM   4714  H   LYS A 209     -12.500  35.944  62.076  1.00  0.00          H   \nATOM   4715  H   LYS A 209     -11.310  36.957  61.499  1.00  0.00          H   \nATOM   4716  H   SER A 210     -12.067  34.940  53.827  1.00  0.00          H   \nATOM   4717  H   SER A 210     -12.375  36.622  50.796  1.00  0.00          H   \nATOM   4718  H   LYS A 211     -12.745  32.657  52.789  1.00  0.00          H   \nATOM   4719  H   LYS A 211     -10.609  35.646  47.594  1.00  0.00          H   \nATOM   4720  H   LYS A 211     -12.140  35.458  48.224  1.00  0.00          H   \nATOM   4721  H   LYS A 211     -11.629  34.485  46.972  1.00  0.00          H   \nATOM   4722  H   LYS A 212     -15.257  29.698  50.389  1.00  0.00          H   \nATOM   4723  H   LYS A 212     -20.245  30.691  47.905  1.00  0.00          H   \nATOM   4724  H   LYS A 212     -19.274  29.416  47.449  1.00  0.00          H   \nATOM   4725  H   LYS A 212     -18.717  30.535  48.550  1.00  0.00          H   \nATOM   4726  H   ASN A 213     -14.002  27.514  50.586  1.00  0.00          H   \nATOM   4727  H   ASN A 213     -14.844  23.727  49.436  1.00  0.00          H   \nATOM   4728  H   ASN A 213     -16.058  23.275  50.545  1.00  0.00          H   \nATOM   4729  H   MET A 215      -8.087  28.737  49.779  1.00  0.00          H   \nATOM   4730  H   GLY A 216      -4.078  27.611  49.907  1.00  0.00          H   \nATOM   4731  H   PHE A 217      -1.865  30.685  47.743  1.00  0.00          H   \nATOM   4732  H   SER A 218       2.361  29.851  47.891  1.00  0.00          H   \nATOM   4733  H   SER A 218       5.368  30.931  49.101  1.00  0.00          H   \nATOM   4734  H   TYR A 219       3.494  33.191  45.190  1.00  0.00          H   \nATOM   4735  H   TYR A 219       6.037  30.930  37.073  1.00  0.00          H   \nATOM   4736  H   ASP A 220       6.608  30.840  43.728  1.00  0.00          H   \nATOM   4737  H   THR A 221       8.328  34.333  41.847  1.00  0.00          H   \nATOM   4738  H   THR A 221       7.021  35.880  38.777  1.00  0.00          H   \nATOM   4739  H   ARG A 222      10.637  32.256  38.497  1.00  0.00          H   \nATOM   4740  H   ARG A 222      14.030  29.101  36.862  1.00  0.00          H   \nATOM   4741  H   ARG A 222      14.249  30.639  33.186  1.00  0.00          H   \nATOM   4742  H   ARG A 222      14.817  31.550  34.511  1.00  0.00          H   \nATOM   4743  H   ARG A 222      13.381  28.544  33.518  1.00  0.00          H   \nATOM   4744  H   ARG A 222      13.300  27.889  35.090  1.00  0.00          H   \nATOM   4745  H   CYS A 223      14.311  34.173  40.292  1.00  0.00          H   \nATOM   4746  H   CYS A 223      18.214  34.006  37.913  1.00  0.00          H   \nATOM   4747  H   PHE A 224      12.757  36.881  39.850  1.00  0.00          H   \nATOM   4748  H   ASP A 225      13.573  39.663  38.785  1.00  0.00          H   \nATOM   4749  H   SER A 226      14.974  38.858  36.451  1.00  0.00          H   \nATOM   4750  H   SER A 226      18.154  37.495  35.243  1.00  0.00          H   \nATOM   4751  H   THR A 227      12.940  38.119  34.986  1.00  0.00          H   \nATOM   4752  H   THR A 227       9.732  38.130  34.473  1.00  0.00          H   \nATOM   4753  H   VAL A 228      11.968  39.988  33.810  1.00  0.00          H   \nATOM   4754  H   THR A 229       9.348  41.094  30.381  1.00  0.00          H   \nATOM   4755  H   THR A 229       7.839  40.522  27.420  1.00  0.00          H   \nATOM   4756  H   GLU A 230      11.338  42.601  26.543  1.00  0.00          H   \nATOM   4757  H   ASN A 231       8.789  42.984  25.792  1.00  0.00          H   \nATOM   4758  H   ASN A 231       3.407  42.107  27.288  1.00  0.00          H   \nATOM   4759  H   ASN A 231       5.033  41.811  27.706  1.00  0.00          H   \nATOM   4760  H   ASP A 232       7.394  42.920  28.206  1.00  0.00          H   \nATOM   4761  H   ILE A 233       8.394  45.338  29.269  1.00  0.00          H   \nATOM   4762  H   ARG A 234       7.226  47.108  27.666  1.00  0.00          H   \nATOM   4763  H   ARG A 234       9.544  47.980  22.842  1.00  0.00          H   \nATOM   4764  H   ARG A 234       9.211  51.949  22.938  1.00  0.00          H   \nATOM   4765  H   ARG A 234       7.923  50.986  23.506  1.00  0.00          H   \nATOM   4766  H   ARG A 234      11.244  49.248  22.090  1.00  0.00          H   \nATOM   4767  H   ARG A 234      11.122  50.948  22.120  1.00  0.00          H   \nATOM   4768  H   VAL A 235       4.867  46.893  28.145  1.00  0.00          H   \nATOM   4769  H   GLU A 236       4.640  47.795  30.547  1.00  0.00          H   \nATOM   4770  H   GLU A 237       5.124  50.475  30.128  1.00  0.00          H   \nATOM   4771  H   SER A 238       2.837  51.028  28.769  1.00  0.00          H   \nATOM   4772  H   SER A 238      -0.004  49.127  27.659  1.00  0.00          H   \nATOM   4773  H   ILE A 239       1.179  51.132  30.782  1.00  0.00          H   \nATOM   4774  H   TYR A 240       1.833  52.983  32.562  1.00  0.00          H   \nATOM   4775  H   TYR A 240       7.089  51.191  35.560  1.00  0.00          H   \nATOM   4776  H   GLN A 241       1.374  55.146  30.739  1.00  0.00          H   \nATOM   4777  H   GLN A 241       4.740  57.547  28.173  1.00  0.00          H   \nATOM   4778  H   GLN A 241       4.591  59.235  28.368  1.00  0.00          H   \nATOM   4779  H   CYS A 242      -1.364  55.363  31.333  1.00  0.00          H   \nATOM   4780  H   CYS A 242      -4.581  52.550  30.779  1.00  0.00          H   \nATOM   4781  H   CYS A 243      -1.875  57.149  32.970  1.00  0.00          H   \nATOM   4782  H   CYS A 243      -0.421  57.391  36.716  1.00  0.00          H   \nATOM   4783  H   ASP A 244      -4.119  60.677  34.972  1.00  0.00          H   \nATOM   4784  H   LEU A 245      -2.177  63.377  32.141  1.00  0.00          H   \nATOM   4785  H   ALA A 246       1.238  65.582  33.945  1.00  0.00          H   \nATOM   4786  H   GLU A 248       4.326  68.149  30.380  1.00  0.00          H   \nATOM   4787  H   ALA A 249       4.697  66.077  31.770  1.00  0.00          H   \nATOM   4788  H   ARG A 250       3.987  63.992  30.402  1.00  0.00          H   \nATOM   4789  H   ARG A 250      -1.061  63.673  28.821  1.00  0.00          H   \nATOM   4790  H   ARG A 250       0.318  60.471  28.832  1.00  0.00          H   \nATOM   4791  H   ARG A 250      -0.999  59.771  29.659  1.00  0.00          H   \nATOM   4792  H   ARG A 250      -2.806  62.733  29.913  1.00  0.00          H   \nATOM   4793  H   ARG A 250      -2.789  61.068  30.279  1.00  0.00          H   \nATOM   4794  H   GLN A 251       5.802  63.884  28.384  1.00  0.00          H   \nATOM   4795  H   GLN A 251       9.613  63.532  22.820  1.00  0.00          H   \nATOM   4796  H   GLN A 251      10.568  64.353  23.970  1.00  0.00          H   \nATOM   4797  H   ALA A 252       8.091  63.517  29.746  1.00  0.00          H   \nATOM   4798  H   ILE A 253       7.616  60.977  30.725  1.00  0.00          H   \nATOM   4799  H   LYS A 254       7.530  59.498  28.556  1.00  0.00          H   \nATOM   4800  H   LYS A 254       9.150  58.453  20.949  1.00  0.00          H   \nATOM   4801  H   LYS A 254       9.634  58.346  22.539  1.00  0.00          H   \nATOM   4802  H   LYS A 254       9.157  59.813  21.911  1.00  0.00          H   \nATOM   4803  H   SER A 255       9.951  59.687  27.639  1.00  0.00          H   \nATOM   4804  H   SER A 255      14.046  61.337  27.049  1.00  0.00          H   \nATOM   4805  H   LEU A 256      11.499  58.629  29.676  1.00  0.00          H   \nATOM   4806  H   THR A 257      10.713  56.119  29.357  1.00  0.00          H   \nATOM   4807  H   THR A 257       7.811  54.028  29.635  1.00  0.00          H   \nATOM   4808  H   GLU A 258      11.525  55.128  26.861  1.00  0.00          H   \nATOM   4809  H   ARG A 259      14.142  55.468  27.123  1.00  0.00          H   \nATOM   4810  H   ARG A 259      18.424  58.244  26.178  1.00  0.00          H   \nATOM   4811  H   ARG A 259      16.526  61.142  26.486  1.00  0.00          H   \nATOM   4812  H   ARG A 259      17.872  62.153  26.755  1.00  0.00          H   \nATOM   4813  H   ARG A 259      20.218  59.598  26.537  1.00  0.00          H   \nATOM   4814  H   ARG A 259      19.986  61.269  26.784  1.00  0.00          H   \nATOM   4815  H   LEU A 260      14.967  54.292  29.184  1.00  0.00          H   \nATOM   4816  H   TYR A 261      13.906  53.070  30.897  1.00  0.00          H   \nATOM   4817  H   TYR A 261      11.165  56.319  35.712  1.00  0.00          H   \nATOM   4818  H   ILE A 262      13.474  50.861  29.040  1.00  0.00          H   \nATOM   4819  H   GLY A 263      15.905  49.837  28.839  1.00  0.00          H   \nATOM   4820  H   GLY A 264      19.834  47.896  28.557  1.00  0.00          H   \nATOM   4821  H   LEU A 266      26.053  47.878  29.698  1.00  0.00          H   \nATOM   4822  H   THR A 267      27.102  49.885  33.363  1.00  0.00          H   \nATOM   4823  H   THR A 267      28.925  52.674  31.611  1.00  0.00          H   \nATOM   4824  H   ASN A 268      31.381  48.727  33.977  1.00  0.00          H   \nATOM   4825  H   ASN A 268      35.143  46.729  34.588  1.00  0.00          H   \nATOM   4826  H   ASN A 268      33.937  45.667  35.157  1.00  0.00          H   \nATOM   4827  H   SER A 269      32.056  48.621  38.487  1.00  0.00          H   \nATOM   4828  H   SER A 269      34.144  48.857  41.270  1.00  0.00          H   \nATOM   4829  H   LYS A 270      34.346  49.529  37.139  1.00  0.00          H   \nATOM   4830  H   LYS A 270      39.645  45.744  35.500  1.00  0.00          H   \nATOM   4831  H   LYS A 270      39.834  45.763  37.155  1.00  0.00          H   \nATOM   4832  H   LYS A 270      40.538  46.950  36.223  1.00  0.00          H   \nATOM   4833  H   GLY A 271      33.838  51.146  35.479  1.00  0.00          H   \nATOM   4834  H   GLN A 272      34.470  49.796  33.615  1.00  0.00          H   \nATOM   4835  H   GLN A 272      38.686  47.063  31.162  1.00  0.00          H   \nATOM   4836  H   GLN A 272      38.876  45.711  32.184  1.00  0.00          H   \nATOM   4837  H   ASN A 273      33.521  48.700  29.242  1.00  0.00          H   \nATOM   4838  H   ASN A 273      28.077  47.623  27.884  1.00  0.00          H   \nATOM   4839  H   ASN A 273      29.035  48.485  29.000  1.00  0.00          H   \nATOM   4840  H   CYS A 274      29.817  47.027  30.974  1.00  0.00          H   \nATOM   4841  H   CYS A 274      30.236  45.069  34.905  1.00  0.00          H   \nATOM   4842  H   GLY A 275      28.427  45.927  29.506  1.00  0.00          H   \nATOM   4843  H   TYR A 276      25.123  43.458  28.099  1.00  0.00          H   \nATOM   4844  H   TYR A 276      20.512  50.220  24.618  1.00  0.00          H   \nATOM   4845  H   ARG A 277      21.817  46.165  28.932  1.00  0.00          H   \nATOM   4846  H   ARG A 277      19.938  47.997  32.793  1.00  0.00          H   \nATOM   4847  H   ARG A 277      16.175  48.646  31.641  1.00  0.00          H   \nATOM   4848  H   ARG A 277      16.617  47.094  32.193  1.00  0.00          H   \nATOM   4849  H   ARG A 277      17.733  50.305  31.511  1.00  0.00          H   \nATOM   4850  H   ARG A 277      19.350  50.005  31.964  1.00  0.00          H   \nATOM   4851  H   ARG A 278      18.622  43.020  29.315  1.00  0.00          H   \nATOM   4852  H   ARG A 278      19.484  43.181  22.965  1.00  0.00          H   \nATOM   4853  H   ARG A 278      21.648  42.675  22.646  1.00  0.00          H   \nATOM   4854  H   ARG A 278      22.470  41.673  23.753  1.00  0.00          H   \nATOM   4855  H   ARG A 278      21.389  40.873  25.610  1.00  0.00          H   \nATOM   4856  H   ARG A 278      19.746  41.250  25.872  1.00  0.00          H   \nATOM   4857  H   CYS A 279      17.052  44.007  30.609  1.00  0.00          H   \nATOM   4858  H   CYS A 279      17.416  40.668  33.406  1.00  0.00          H   \nATOM   4859  H   ARG A 280      13.892  43.145  33.476  1.00  0.00          H   \nATOM   4860  H   ARG A 280      10.566  45.244  37.136  1.00  0.00          H   \nATOM   4861  H   ARG A 280       9.189  47.673  34.290  1.00  0.00          H   \nATOM   4862  H   ARG A 280      10.796  47.867  34.827  1.00  0.00          H   \nATOM   4863  H   ARG A 280       7.891  45.975  35.159  1.00  0.00          H   \nATOM   4864  H   ARG A 280       8.538  44.923  36.335  1.00  0.00          H   \nATOM   4865  H   ALA A 281      14.978  47.405  34.917  1.00  0.00          H   \nATOM   4866  H   SER A 282      18.083  46.381  38.006  1.00  0.00          H   \nATOM   4867  H   SER A 282      19.635  45.232  40.790  1.00  0.00          H   \nATOM   4868  H   GLY A 283      18.055  48.753  39.107  1.00  0.00          H   \nATOM   4869  H   VAL A 284      15.504  50.450  39.682  1.00  0.00          H   \nATOM   4870  H   LEU A 285      13.845  54.577  39.125  1.00  0.00          H   \nATOM   4871  H   THR A 286      11.546  53.224  38.460  1.00  0.00          H   \nATOM   4872  H   THR A 286      10.440  52.251  35.605  1.00  0.00          H   \nATOM   4873  H   THR A 287      11.780  50.863  39.955  1.00  0.00          H   \nATOM   4874  H   THR A 287      13.612  49.987  39.669  1.00  0.00          H   \nATOM   4875  H   SER A 288      10.290  49.518  42.065  1.00  0.00          H   \nATOM   4876  H   SER A 288       7.760  48.689  45.633  1.00  0.00          H   \nATOM   4877  H   CYS A 289       7.643  50.164  41.604  1.00  0.00          H   \nATOM   4878  H   CYS A 289       3.826  52.735  39.485  1.00  0.00          H   \nATOM   4879  H   GLY A 290       7.445  49.356  38.935  1.00  0.00          H   \nATOM   4880  H   ASN A 291       7.992  46.543  39.195  1.00  0.00          H   \nATOM   4881  H   ASN A 291      11.624  43.756  39.852  1.00  0.00          H   \nATOM   4882  H   ASN A 291      10.595  43.870  41.206  1.00  0.00          H   \nATOM   4883  H   THR A 292       5.798  45.862  40.757  1.00  0.00          H   \nATOM   4884  H   THR A 292       5.492  46.124  42.807  1.00  0.00          H   \nATOM   4885  H   LEU A 293       3.548  46.470  39.246  1.00  0.00          H   \nATOM   4886  H   THR A 294       3.995  44.785  37.054  1.00  0.00          H   \nATOM   4887  H   THR A 294       6.072  44.377  34.343  1.00  0.00          H   \nATOM   4888  H   CYS A 295       4.034  42.329  38.287  1.00  0.00          H   \nATOM   4889  H   CYS A 295       4.437  38.908  42.006  1.00  0.00          H   \nATOM   4890  H   TYR A 296       1.616  42.105  39.140  1.00  0.00          H   \nATOM   4891  H   TYR A 296      -6.922  43.534  39.403  1.00  0.00          H   \nATOM   4892  H   LEU A 297       0.200  42.195  36.882  1.00  0.00          H   \nATOM   4893  H   LYS A 298       0.924  39.899  35.400  1.00  0.00          H   \nATOM   4894  H   LYS A 298       6.678  39.249  31.740  1.00  0.00          H   \nATOM   4895  H   LYS A 298       6.459  37.704  32.322  1.00  0.00          H   \nATOM   4896  H   LYS A 298       6.706  38.966  33.381  1.00  0.00          H   \nATOM   4897  H   ALA A 299       0.188  37.939  37.280  1.00  0.00          H   \nATOM   4898  H   SER A 300      -2.506  38.374  37.564  1.00  0.00          H   \nATOM   4899  H   SER A 300      -4.959  40.839  38.298  1.00  0.00          H   \nATOM   4900  H   ALA A 301      -3.492  37.767  35.043  1.00  0.00          H   \nATOM   4901  H   ALA A 302      -2.678  35.199  35.028  1.00  0.00          H   \nATOM   4902  H   CYS A 303      -4.424  34.123  36.808  1.00  0.00          H   \nATOM   4903  H   ARG A 304      -6.747  33.967  35.552  1.00  0.00          H   \nATOM   4904  H   ARG A 304      -9.467  37.693  35.606  1.00  0.00          H   \nATOM   4905  H   ARG A 304     -10.269  38.197  32.262  1.00  0.00          H   \nATOM   4906  H   ARG A 304     -10.911  39.756  32.517  1.00  0.00          H   \nATOM   4907  H   ARG A 304     -10.942  40.655  34.611  1.00  0.00          H   \nATOM   4908  H   ARG A 304     -10.325  39.773  35.933  1.00  0.00          H   \nATOM   4909  H   ALA A 305      -6.251  32.449  33.485  1.00  0.00          H   \nATOM   4910  H   ALA A 306      -5.828  30.067  34.924  1.00  0.00          H   \nATOM   4911  H   LYS A 307      -8.348  29.417  35.199  1.00  0.00          H   \nATOM   4912  H   LYS A 307      -8.496  23.154  33.643  1.00  0.00          H   \nATOM   4913  H   LYS A 307      -9.661  23.470  32.495  1.00  0.00          H   \nATOM   4914  H   LYS A 307     -10.107  23.137  34.066  1.00  0.00          H   \nATOM   4915  H   LEU A 308      -8.468  29.689  37.901  1.00  0.00          H   \nATOM   4916  H   GLN A 309     -10.663  28.464  41.709  1.00  0.00          H   \nATOM   4917  H   GLN A 309     -16.807  29.148  43.682  1.00  0.00          H   \nATOM   4918  H   GLN A 309     -15.444  28.187  44.040  1.00  0.00          H   \nATOM   4919  H   ASP A 310     -13.161  32.191  41.687  1.00  0.00          H   \nATOM   4920  H   CYS A 311     -10.309  33.534  41.992  1.00  0.00          H   \nATOM   4921  H   THR A 312      -7.823  34.719  45.582  1.00  0.00          H   \nATOM   4922  H   THR A 312     -10.020  36.110  46.587  1.00  0.00          H   \nATOM   4923  H   MET A 313      -6.186  38.786  44.955  1.00  0.00          H   \nATOM   4924  H   LEU A 314      -1.974  38.080  46.406  1.00  0.00          H   \nATOM   4925  H   VAL A 315      -0.615  41.881  46.317  1.00  0.00          H   \nATOM   4926  H   ASN A 316       3.678  41.297  45.919  1.00  0.00          H   \nATOM   4927  H   ASN A 316       4.757  44.624  49.879  1.00  0.00          H   \nATOM   4928  H   ASN A 316       3.264  44.293  50.633  1.00  0.00          H   \nATOM   4929  H   GLY A 317       5.045  44.601  44.554  1.00  0.00          H   \nATOM   4930  H   ASP A 318       8.583  41.855  43.539  1.00  0.00          H   \nATOM   4931  H   ASP A 319       6.938  40.537  44.928  1.00  0.00          H   \nATOM   4932  H   LEU A 320       5.034  36.489  45.380  1.00  0.00          H   \nATOM   4933  H   VAL A 321       0.760  36.920  46.174  1.00  0.00          H   \nATOM   4934  H   VAL A 322      -0.952  33.068  46.644  1.00  0.00          H   \nATOM   4935  H   ILE A 323      -5.147  34.263  46.923  1.00  0.00          H   \nATOM   4936  H   CYS A 324      -7.069  30.518  47.599  1.00  0.00          H   \nATOM   4937  H   CYS A 324      -7.173  28.727  43.659  1.00  0.00          H   \nATOM   4938  H   GLU A 325     -10.842  29.986  45.424  1.00  0.00          H   \nATOM   4939  H   SER A 326     -11.314  26.584  48.249  1.00  0.00          H   \nATOM   4940  H   SER A 326      -9.534  22.497  48.787  1.00  0.00          H   \nATOM   4941  H   ALA A 327     -10.065  23.045  45.518  1.00  0.00          H   \nATOM   4942  H   GLY A 328     -10.595  21.415  46.783  1.00  0.00          H   \nATOM   4943  H   THR A 329      -8.424  18.061  47.757  1.00  0.00          H   \nATOM   4944  H   THR A 329      -4.179  18.604  48.097  1.00  0.00          H   \nATOM   4945  H   GLN A 330      -7.500  16.884  45.391  1.00  0.00          H   \nATOM   4946  H   GLN A 330      -4.263  12.641  42.078  1.00  0.00          H   \nATOM   4947  H   GLN A 330      -5.914  12.309  42.344  1.00  0.00          H   \nATOM   4948  H   GLU A 331      -8.438  18.274  43.259  1.00  0.00          H   \nATOM   4949  H   ASP A 332      -7.108  20.382  43.796  1.00  0.00          H   \nATOM   4950  H   ALA A 333      -4.522  20.013  43.346  1.00  0.00          H   \nATOM   4951  H   ALA A 334      -4.570  19.857  40.703  1.00  0.00          H   \nATOM   4952  H   SER A 335      -5.438  22.236  39.715  1.00  0.00          H   \nATOM   4953  H   SER A 335      -7.901  24.323  40.256  1.00  0.00          H   \nATOM   4954  H   LEU A 336      -3.521  24.113  40.661  1.00  0.00          H   \nATOM   4955  H   ARG A 337      -1.598  23.131  38.978  1.00  0.00          H   \nATOM   4956  H   ARG A 337       2.289  18.466  35.702  1.00  0.00          H   \nATOM   4957  H   ARG A 337       0.036  19.871  32.725  1.00  0.00          H   \nATOM   4958  H   ARG A 337      -0.354  20.268  34.337  1.00  0.00          H   \nATOM   4959  H   ARG A 337       1.845  18.533  32.276  1.00  0.00          H   \nATOM   4960  H   ARG A 337       2.802  17.932  33.553  1.00  0.00          H   \nATOM   4961  H   VAL A 338      -2.273  23.961  36.656  1.00  0.00          H   \nATOM   4962  H   PHE A 339      -2.235  26.372  36.964  1.00  0.00          H   \nATOM   4963  H   THR A 340       0.386  26.862  37.193  1.00  0.00          H   \nATOM   4964  H   THR A 340       1.679  26.717  38.851  1.00  0.00          H   \nATOM   4965  H   GLU A 341       1.147  26.330  34.727  1.00  0.00          H   \nATOM   4966  H   ALA A 342       0.184  28.364  33.382  1.00  0.00          H   \nATOM   4967  H   MET A 343       1.343  30.607  34.388  1.00  0.00          H   \nATOM   4968  H   THR A 344       3.761  29.994  33.345  1.00  0.00          H   \nATOM   4969  H   THR A 344       6.307  27.504  33.557  1.00  0.00          H   \nATOM   4970  H   ARG A 345       3.486  30.285  30.713  1.00  0.00          H   \nATOM   4971  H   ARG A 345      -0.407  30.188  27.895  1.00  0.00          H   \nATOM   4972  H   ARG A 345       0.763  26.951  28.458  1.00  0.00          H   \nATOM   4973  H   ARG A 345      -0.526  26.535  29.493  1.00  0.00          H   \nATOM   4974  H   ARG A 345      -2.108  29.622  29.282  1.00  0.00          H   \nATOM   4975  H   ARG A 345      -2.168  28.060  29.964  1.00  0.00          H   \nATOM   4976  H   TYR A 346       3.516  32.887  30.840  1.00  0.00          H   \nATOM   4977  H   TYR A 346      -3.075  34.718  29.577  1.00  0.00          H   \nATOM   4978  H   SER A 347       5.903  33.488  30.341  1.00  0.00          H   \nATOM   4979  H   SER A 347       9.794  36.852  30.041  1.00  0.00          H   \nATOM   4980  H   ALA A 348       6.559  33.412  32.865  1.00  0.00          H   \nATOM   4981  H   GLY A 351       9.931  29.916  40.942  1.00  0.00          H   \nATOM   4982  H   ASP A 352      10.724  28.658  43.144  1.00  0.00          H   \nATOM   4983  H   GLN A 355       3.270  25.955  46.283  1.00  0.00          H   \nATOM   4984  H   GLN A 355       5.757  23.998  49.784  1.00  0.00          H   \nATOM   4985  H   GLN A 355       5.620  22.699  50.880  1.00  0.00          H   \nATOM   4986  H   GLU A 357      -2.025  25.987  49.472  1.00  0.00          H   \nATOM   4987  H   TYR A 358      -2.648  24.029  53.402  1.00  0.00          H   \nATOM   4988  H   TYR A 358      -6.253  21.816  47.623  1.00  0.00          H   \nATOM   4989  H   ASP A 359      -2.681  25.563  54.765  1.00  0.00          H   \nATOM   4990  H   LEU A 360      -4.211  28.615  57.052  1.00  0.00          H   \nATOM   4991  H   GLU A 361      -1.883  29.156  58.608  1.00  0.00          H   \nATOM   4992  H   LEU A 362       0.030  28.140  57.033  1.00  0.00          H   \nATOM   4993  H   ILE A 363       1.084  29.917  55.563  1.00  0.00          H   \nATOM   4994  H   THR A 364       4.186  30.643  52.557  1.00  0.00          H   \nATOM   4995  H   THR A 364       5.911  30.345  54.661  1.00  0.00          H   \nATOM   4996  H   SER A 365       4.716  34.895  53.276  1.00  0.00          H   \nATOM   4997  H   SER A 365       1.767  36.731  51.877  1.00  0.00          H   \nATOM   4998  H   CYS A 366       6.460  37.235  50.154  1.00  0.00          H   \nATOM   4999  H   CYS A 366       5.248  41.972  51.288  1.00  0.00          H   \nATOM   5000  H   SER A 367       8.357  36.368  51.690  1.00  0.00          H   \nATOM   5001  H   SER A 367      12.280  37.397  52.498  1.00  0.00          H   \nATOM   5002  H   SER A 368       6.936  36.442  54.118  1.00  0.00          H   \nATOM   5003  H   SER A 368       7.015  39.554  56.597  1.00  0.00          H   \nATOM   5004  H   ASN A 369       5.335  35.623  58.209  1.00  0.00          H   \nATOM   5005  H   ASN A 369       3.012  31.972  59.777  1.00  0.00          H   \nATOM   5006  H   ASN A 369       4.357  31.859  60.819  1.00  0.00          H   \nATOM   5007  H   VAL A 370       1.290  34.069  57.916  1.00  0.00          H   \nATOM   5008  H   SER A 371       0.831  35.168  62.317  1.00  0.00          H   \nATOM   5009  H   SER A 371       3.325  31.810  64.877  1.00  0.00          H   \nATOM   5010  H   VAL A 372       0.221  32.173  65.414  1.00  0.00          H   \nATOM   5011  H   ALA A 373      -1.473  34.998  68.339  1.00  0.00          H   \nATOM   5012  H   HIS A 374      -0.427  35.035  72.482  1.00  0.00          H   \nATOM   5013  H   ASP A 375      -3.856  37.637  73.454  1.00  0.00          H   \nATOM   5014  H   ALA A 376      -2.619  40.885  76.242  1.00  0.00          H   \nATOM   5015  H   SER A 377      -5.021  39.612  76.822  1.00  0.00          H   \nATOM   5016  H   SER A 377      -8.134  41.573  77.057  1.00  0.00          H   \nATOM   5017  H   GLY A 378      -5.025  37.388  76.779  1.00  0.00          H   \nATOM   5018  H   LYS A 379      -6.739  37.239  75.253  1.00  0.00          H   \nATOM   5019  H   LYS A 379      -8.363  42.483  71.567  1.00  0.00          H   \nATOM   5020  H   LYS A 379      -9.835  41.854  72.029  1.00  0.00          H   \nATOM   5021  H   LYS A 379      -9.342  41.742  70.442  1.00  0.00          H   \nATOM   5022  H   ARG A 380      -7.833  34.935  71.400  1.00  0.00          H   \nATOM   5023  H   ARG A 380      -8.009  31.800  67.285  1.00  0.00          H   \nATOM   5024  H   ARG A 380      -4.913  29.402  68.048  1.00  0.00          H   \nATOM   5025  H   ARG A 380      -5.782  30.083  69.347  1.00  0.00          H   \nATOM   5026  H   ARG A 380      -6.871  30.869  65.575  1.00  0.00          H   \nATOM   5027  H   ARG A 380      -5.556  29.829  65.887  1.00  0.00          H   \nATOM   5028  H   VAL A 381      -3.869  35.834  69.347  1.00  0.00          H   \nATOM   5029  H   TYR A 382      -4.373  38.167  65.775  1.00  0.00          H   \nATOM   5030  H   TYR A 382      -6.845  31.853  63.565  1.00  0.00          H   \nATOM   5031  H   TYR A 383      -0.405  36.999  63.846  1.00  0.00          H   \nATOM   5032  H   TYR A 383       5.150  34.566  66.544  1.00  0.00          H   \nATOM   5033  H   LEU A 384       2.249  40.200  62.486  1.00  0.00          H   \nATOM   5034  H   THR A 385       4.666  36.798  60.720  1.00  0.00          H   \nATOM   5035  H   THR A 385       8.065  36.154  63.768  1.00  0.00          H   \nATOM   5036  H   ARG A 386       8.731  35.820  61.846  1.00  0.00          H   \nATOM   5037  H   ARG A 386      12.738  36.158  58.575  1.00  0.00          H   \nATOM   5038  H   ARG A 386      13.408  38.339  55.302  1.00  0.00          H   \nATOM   5039  H   ARG A 386      12.342  38.931  56.494  1.00  0.00          H   \nATOM   5040  H   ARG A 386      14.448  36.320  55.589  1.00  0.00          H   \nATOM   5041  H   ARG A 386      14.162  35.399  56.995  1.00  0.00          H   \nATOM   5042  H   ASP A 387      10.645  33.511  58.707  1.00  0.00          H   \nATOM   5043  H   THR A 389      15.580  33.053  60.654  1.00  0.00          H   \nATOM   5044  H   THR A 389      17.295  30.267  59.563  1.00  0.00          H   \nATOM   5045  H   THR A 390      16.946  34.802  58.535  1.00  0.00          H   \nATOM   5046  H   THR A 390      17.453  35.616  55.141  1.00  0.00          H   \nATOM   5047  H   LEU A 392      18.188  36.828  61.935  1.00  0.00          H   \nATOM   5048  H   ALA A 393      20.552  36.482  61.009  1.00  0.00          H   \nATOM   5049  H   ARG A 394      21.137  38.819  59.994  1.00  0.00          H   \nATOM   5050  H   ARG A 394      21.047  39.800  55.395  1.00  0.00          H   \nATOM   5051  H   ARG A 394      18.065  41.715  53.577  1.00  0.00          H   \nATOM   5052  H   ARG A 394      18.344  41.992  55.236  1.00  0.00          H   \nATOM   5053  H   ARG A 394      19.414  40.265  52.416  1.00  0.00          H   \nATOM   5054  H   ARG A 394      20.692  39.465  53.212  1.00  0.00          H   \nATOM   5055  H   ALA A 395      21.232  40.267  62.165  1.00  0.00          H   \nATOM   5056  H   ALA A 396      23.424  39.451  63.242  1.00  0.00          H   \nATOM   5057  H   TRP A 397      25.386  40.402  61.667  1.00  0.00          H   \nATOM   5058  H   TRP A 397      29.420  44.526  57.572  1.00  0.00          H   \nATOM   5059  H   GLU A 398      24.972  43.055  62.035  1.00  0.00          H   \nATOM   5060  H   THR A 399      26.310  43.149  64.316  1.00  0.00          H   \nATOM   5061  H   THR A 399      25.020  42.355  66.103  1.00  0.00          H   \nATOM   5062  H   ALA A 400      28.682  43.687  64.264  1.00  0.00          H   \nATOM   5063  H   ARG A 401      28.966  45.433  63.094  1.00  0.00          H   \nATOM   5064  H   ARG A 401      33.941  45.398  58.586  1.00  0.00          H   \nATOM   5065  H   ARG A 401      33.744  46.128  61.977  1.00  0.00          H   \nATOM   5066  H   ARG A 401      35.191  45.317  62.368  1.00  0.00          H   \nATOM   5067  H   ARG A 401      35.861  44.330  59.095  1.00  0.00          H   \nATOM   5068  H   ARG A 401      36.410  44.282  60.709  1.00  0.00          H   \nATOM   5069  H   HIS A 402      29.996  49.418  61.655  1.00  0.00          H   \nATOM   5070  H   THR A 403      25.783  49.872  60.165  1.00  0.00          H   \nATOM   5071  H   THR A 403      23.684  49.008  58.176  1.00  0.00          H   \nATOM   5072  H   VAL A 405      22.804  50.067  55.978  1.00  0.00          H   \nATOM   5073  H   ASN A 406      19.826  50.764  58.965  1.00  0.00          H   \nATOM   5074  H   ASN A 406      20.595  52.571  60.668  1.00  0.00          H   \nATOM   5075  H   ASN A 406      22.198  52.808  61.198  1.00  0.00          H   \nATOM   5076  H   SER A 407      20.294  46.748  59.391  1.00  0.00          H   \nATOM   5077  H   SER A 407      19.960  43.189  59.337  1.00  0.00          H   \nATOM   5078  H   TRP A 408      19.501  45.874  61.954  1.00  0.00          H   \nATOM   5079  H   TRP A 408      21.759  49.098  64.570  1.00  0.00          H   \nATOM   5080  H   LEU A 409      17.775  47.797  62.978  1.00  0.00          H   \nATOM   5081  H   GLY A 410      15.836  48.077  61.335  1.00  0.00          H   \nATOM   5082  H   ASN A 411      14.776  45.626  61.554  1.00  0.00          H   \nATOM   5083  H   ASN A 411      15.980  42.006  59.486  1.00  0.00          H   \nATOM   5084  H   ASN A 411      16.435  42.524  61.045  1.00  0.00          H   \nATOM   5085  H   ILE A 412      13.718  45.756  63.993  1.00  0.00          H   \nATOM   5086  H   ILE A 413      11.767  47.373  63.810  1.00  0.00          H   \nATOM   5087  H   MET A 414      10.076  46.242  61.977  1.00  0.00          H   \nATOM   5088  H   TYR A 415       9.498  44.033  63.159  1.00  0.00          H   \nATOM   5089  H   TYR A 415       9.535  40.479  57.103  1.00  0.00          H   \nATOM   5090  H   ALA A 416       7.599  44.372  65.001  1.00  0.00          H   \nATOM   5091  H   THR A 418       7.124  40.415  66.963  1.00  0.00          H   \nATOM   5092  H   THR A 418      10.031  38.808  65.631  1.00  0.00          H   \nATOM   5093  H   LEU A 419       7.765  36.083  68.484  1.00  0.00          H   \nATOM   5094  H   TRP A 420      10.500  35.950  68.072  1.00  0.00          H   \nATOM   5095  H   TRP A 420       9.492  35.955  64.119  1.00  0.00          H   \nATOM   5096  H   ALA A 421      11.186  38.613  67.554  1.00  0.00          H   \nATOM   5097  H   ARG A 422      10.844  39.851  69.832  1.00  0.00          H   \nATOM   5098  H   ARG A 422       7.680  41.948  73.450  1.00  0.00          H   \nATOM   5099  H   ARG A 422       5.831  39.147  72.511  1.00  0.00          H   \nATOM   5100  H   ARG A 422       4.867  39.155  73.917  1.00  0.00          H   \nATOM   5101  H   ARG A 422       6.414  41.935  75.333  1.00  0.00          H   \nATOM   5102  H   ARG A 422       5.202  40.753  75.536  1.00  0.00          H   \nATOM   5103  H   MET A 423      12.072  38.549  71.837  1.00  0.00          H   \nATOM   5104  H   ILE A 424      14.652  38.699  70.982  1.00  0.00          H   \nATOM   5105  H   LEU A 425      15.793  40.343  69.176  1.00  0.00          H   \nATOM   5106  H   MET A 426      14.946  42.725  70.456  1.00  0.00          H   \nATOM   5107  H   THR A 427      16.041  42.535  73.000  1.00  0.00          H   \nATOM   5108  H   THR A 427      15.563  41.506  74.364  1.00  0.00          H   \nATOM   5109  H   HIS A 428      18.755  42.225  72.463  1.00  0.00          H   \nATOM   5110  H   PHE A 429      19.479  44.600  70.874  1.00  0.00          H   \nATOM   5111  H   PHE A 430      18.765  46.593  72.560  1.00  0.00          H   \nATOM   5112  H   SER A 431      20.410  46.322  74.438  1.00  0.00          H   \nATOM   5113  H   SER A 431      23.577  44.281  76.756  1.00  0.00          H   \nATOM   5114  H   ILE A 432      22.819  46.896  73.399  1.00  0.00          H   \nATOM   5115  H   LEU A 433      22.406  49.522  72.630  1.00  0.00          H   \nATOM   5116  H   LEU A 434      22.166  50.683  75.205  1.00  0.00          H   \nATOM   5117  H   ALA A 435      24.623  50.276  76.480  1.00  0.00          H   \nATOM   5118  H   GLN A 436      26.033  51.997  74.708  1.00  0.00          H   \nATOM   5119  H   GLN A 436      30.961  50.698  73.742  1.00  0.00          H   \nATOM   5120  H   GLN A 436      29.393  50.028  73.758  1.00  0.00          H   \nATOM   5121  H   GLU A 437      25.494  54.145  75.740  1.00  0.00          H   \nATOM   5122  H   GLN A 438      23.746  55.122  73.608  1.00  0.00          H   \nATOM   5123  H   GLN A 438      28.073  56.193  68.585  1.00  0.00          H   \nATOM   5124  H   GLN A 438      27.776  57.178  69.945  1.00  0.00          H   \nATOM   5125  H   LEU A 439      21.741  56.367  73.761  1.00  0.00          H   \nATOM   5126  H   GLU A 440      20.244  58.510  72.598  1.00  0.00          H   \nATOM   5127  H   LYS A 441      20.662  58.027  69.951  1.00  0.00          H   \nATOM   5128  H   LYS A 441      26.717  56.847  64.457  1.00  0.00          H   \nATOM   5129  H   LYS A 441      25.973  55.840  65.555  1.00  0.00          H   \nATOM   5130  H   LYS A 441      26.913  57.106  66.090  1.00  0.00          H   \nATOM   5131  H   ALA A 442      19.864  60.125  66.087  1.00  0.00          H   \nATOM   5132  H   LEU A 443      18.032  56.469  64.029  1.00  0.00          H   \nATOM   5133  H   ASP A 444      20.378  55.215  60.418  1.00  0.00          H   \nATOM   5134  H   CYS A 445      16.559  53.959  58.237  1.00  0.00          H   \nATOM   5135  H   CYS A 445      13.985  51.474  61.052  1.00  0.00          H   \nATOM   5136  H   GLN A 446      15.702  49.945  56.783  1.00  0.00          H   \nATOM   5137  H   GLN A 446      17.665  50.938  50.530  1.00  0.00          H   \nATOM   5138  H   GLN A 446      17.193  52.224  51.545  1.00  0.00          H   \nATOM   5139  H   ILE A 447      12.126  51.461  54.587  1.00  0.00          H   \nATOM   5140  H   TYR A 448      10.204  48.009  53.805  1.00  0.00          H   \nATOM   5141  H   TYR A 448       3.136  46.454  54.583  1.00  0.00          H   \nATOM   5142  H   GLY A 449      11.953  48.909  52.048  1.00  0.00          H   \nATOM   5143  H   ALA A 450      11.167  51.730  51.517  1.00  0.00          H   \nATOM   5144  H   CYS A 451      14.086  55.243  51.944  1.00  0.00          H   \nATOM   5145  H   CYS A 451      18.669  56.058  54.181  1.00  0.00          H   \nATOM   5146  H   TYR A 452      14.784  54.799  56.372  1.00  0.00          H   \nATOM   5147  H   TYR A 452       8.012  54.562  53.851  1.00  0.00          H   \nATOM   5148  H   SER A 453      14.136  58.538  58.640  1.00  0.00          H   \nATOM   5149  H   SER A 453      17.371  60.905  60.867  1.00  0.00          H   \nATOM   5150  H   ILE A 454      16.170  56.644  61.943  1.00  0.00          H   \nATOM   5151  H   GLU A 455      13.903  57.855  65.499  1.00  0.00          H   \nATOM   5152  H   LEU A 457      15.283  56.240  70.499  1.00  0.00          H   \nATOM   5153  H   ASP A 458      12.732  56.316  69.594  1.00  0.00          H   \nATOM   5154  H   LEU A 459      11.644  54.097  70.024  1.00  0.00          H   \nATOM   5155  H   GLN A 461       7.254  54.572  71.157  1.00  0.00          H   \nATOM   5156  H   GLN A 461       8.617  59.203  71.771  1.00  0.00          H   \nATOM   5157  H   GLN A 461       8.360  57.656  72.441  1.00  0.00          H   \nATOM   5158  H   ILE A 462       7.031  54.315  68.483  1.00  0.00          H   \nATOM   5159  H   ILE A 463       6.526  51.595  68.348  1.00  0.00          H   \nATOM   5160  H   GLU A 464       4.066  51.459  69.633  1.00  0.00          H   \nATOM   5161  H   ARG A 465       2.418  52.919  68.049  1.00  0.00          H   \nATOM   5162  H   ARG A 465      -0.755  56.014  64.913  1.00  0.00          H   \nATOM   5163  H   ARG A 465       2.098  58.799  64.792  1.00  0.00          H   \nATOM   5164  H   ARG A 465       1.881  57.886  66.216  1.00  0.00          H   \nATOM   5165  H   ARG A 465       0.770  58.419  62.970  1.00  0.00          H   \nATOM   5166  H   ARG A 465      -0.443  57.222  63.026  1.00  0.00          H   \nATOM   5167  H   LEU A 466       2.629  51.406  65.562  1.00  0.00          H   \nATOM   5168  H   HIS A 467       1.918  48.978  66.309  1.00  0.00          H   \nATOM   5169  H   GLY A 468       0.205  49.025  67.674  1.00  0.00          H   \nATOM   5170  H   LEU A 469      -2.014  48.564  71.397  1.00  0.00          H   \nATOM   5171  H   SER A 470      -1.704  45.692  71.578  1.00  0.00          H   \nATOM   5172  H   SER A 470      -2.448  43.057  69.662  1.00  0.00          H   \nATOM   5173  H   ALA A 471       0.923  45.134  70.941  1.00  0.00          H   \nATOM   5174  H   PHE A 472       2.355  44.947  73.108  1.00  0.00          H   \nATOM   5175  H   SER A 473       2.027  42.696  74.111  1.00  0.00          H   \nATOM   5176  H   SER A 473      -1.145  42.404  76.223  1.00  0.00          H   \nATOM   5177  H   LEU A 474       2.668  40.687  73.572  1.00  0.00          H   \nATOM   5178  H   HIS A 475       1.130  36.327  73.818  1.00  0.00          H   \nATOM   5179  H   SER A 476       3.123  32.845  75.448  1.00  0.00          H   \nATOM   5180  H   SER A 476      -0.594  31.121  76.600  1.00  0.00          H   \nATOM   5181  H   TYR A 477       3.743  31.926  72.843  1.00  0.00          H   \nATOM   5182  H   TYR A 477       5.524  38.206  70.618  1.00  0.00          H   \nATOM   5183  H   SER A 478       2.819  30.819  68.447  1.00  0.00          H   \nATOM   5184  H   SER A 478       2.644  29.236  65.337  1.00  0.00          H   \nATOM   5185  H   GLY A 480       3.513  25.867  66.249  1.00  0.00          H   \nATOM   5186  H   GLU A 481       4.849  28.164  65.994  1.00  0.00          H   \nATOM   5187  H   ILE A 482       6.395  28.434  68.129  1.00  0.00          H   \nATOM   5188  H   ASN A 483       7.990  26.192  67.569  1.00  0.00          H   \nATOM   5189  H   ASN A 483       7.538  22.267  65.944  1.00  0.00          H   \nATOM   5190  H   ASN A 483       7.394  21.529  67.474  1.00  0.00          H   \nATOM   5191  H   ARG A 484       9.429  27.047  65.364  1.00  0.00          H   \nATOM   5192  H   ARG A 484       9.621  30.826  62.504  1.00  0.00          H   \nATOM   5193  H   ARG A 484       7.754  31.114  58.985  1.00  0.00          H   \nATOM   5194  H   ARG A 484       9.009  29.976  59.175  1.00  0.00          H   \nATOM   5195  H   ARG A 484       7.153  32.463  60.734  1.00  0.00          H   \nATOM   5196  H   ARG A 484       7.956  32.336  62.233  1.00  0.00          H   \nATOM   5197  H   VAL A 485      10.884  29.094  66.286  1.00  0.00          H   \nATOM   5198  H   ALA A 486      12.407  27.859  68.140  1.00  0.00          H   \nATOM   5199  H   SER A 487      13.884  26.117  66.473  1.00  0.00          H   \nATOM   5200  H   SER A 487      13.520  23.719  64.018  1.00  0.00          H   \nATOM   5201  H   CYS A 488      15.313  27.919  65.037  1.00  0.00          H   \nATOM   5202  H   CYS A 488      16.635  32.564  63.502  1.00  0.00          H   \nATOM   5203  H   LEU A 489      16.898  29.057  67.006  1.00  0.00          H   \nATOM   5204  H   ARG A 490      18.594  26.970  67.630  1.00  0.00          H   \nATOM   5205  H   ARG A 490      16.945  22.195  70.001  1.00  0.00          H   \nATOM   5206  H   ARG A 490      20.064  23.268  71.125  1.00  0.00          H   \nATOM   5207  H   ARG A 490      19.703  22.995  72.769  1.00  0.00          H   \nATOM   5208  H   ARG A 490      16.475  21.838  72.183  1.00  0.00          H   \nATOM   5209  H   ARG A 490      17.647  22.177  73.375  1.00  0.00          H   \nATOM   5210  H   LYS A 491      19.976  26.525  65.481  1.00  0.00          H   \nATOM   5211  H   LYS A 491      17.865  29.360  59.902  1.00  0.00          H   \nATOM   5212  H   LYS A 491      17.899  27.706  59.714  1.00  0.00          H   \nATOM   5213  H   LYS A 491      18.953  28.665  58.850  1.00  0.00          H   \nATOM   5214  H   LEU A 492      21.471  28.921  65.010  1.00  0.00          H   \nATOM   5215  H   GLY A 493      22.856  28.613  67.043  1.00  0.00          H   \nATOM   5216  H   VAL A 494      22.067  30.184  68.984  1.00  0.00          H   \nATOM   5217  H   LEU A 497      16.430  30.504  76.134  1.00  0.00          H   \nATOM   5218  H   ARG A 498      16.933  31.037  79.035  1.00  0.00          H   \nATOM   5219  H   ARG A 498      21.195  28.265  81.139  1.00  0.00          H   \nATOM   5220  H   ARG A 498      20.171  25.887  78.857  1.00  0.00          H   \nATOM   5221  H   ARG A 498      21.500  26.531  79.709  1.00  0.00          H   \nATOM   5222  H   ARG A 498      18.077  26.766  79.153  1.00  0.00          H   \nATOM   5223  H   ARG A 498      17.838  28.069  80.227  1.00  0.00          H   \nATOM   5224  H   VAL A 499      19.174  32.748  78.873  1.00  0.00          H   \nATOM   5225  H   TRP A 500      18.468  34.947  77.463  1.00  0.00          H   \nATOM   5226  H   TRP A 500      19.667  34.402  72.954  1.00  0.00          H   \nATOM   5227  H   ARG A 501      16.542  35.900  79.133  1.00  0.00          H   \nATOM   5228  H   ARG A 501      14.565  34.502  83.160  1.00  0.00          H   \nATOM   5229  H   ARG A 501      11.943  31.979  81.525  1.00  0.00          H   \nATOM   5230  H   ARG A 501      11.598  33.649  81.544  1.00  0.00          H   \nATOM   5231  H   ARG A 501      13.902  31.199  82.424  1.00  0.00          H   \nATOM   5232  H   ARG A 501      15.020  32.286  83.113  1.00  0.00          H   \nATOM   5233  H   HIS A 502      17.754  36.692  81.311  1.00  0.00          H   \nATOM   5234  H   ARG A 503      19.390  38.657  80.332  1.00  0.00          H   \nATOM   5235  H   ARG A 503      23.705  38.107  78.419  1.00  0.00          H   \nATOM   5236  H   ARG A 503      25.417  36.613  75.825  1.00  0.00          H   \nATOM   5237  H   ARG A 503      25.350  36.842  77.514  1.00  0.00          H   \nATOM   5238  H   ARG A 503      23.814  37.585  74.467  1.00  0.00          H   \nATOM   5239  H   ARG A 503      22.573  38.526  75.161  1.00  0.00          H   \nATOM   5240  H   ALA A 504      17.396  40.368  79.644  1.00  0.00          H   \nATOM   5241  H   ARG A 505      16.312  41.236  81.899  1.00  0.00          H   \nATOM   5242  H   ARG A 505      13.064  38.870  84.415  1.00  0.00          H   \nATOM   5243  H   ARG A 505      14.768  38.212  87.383  1.00  0.00          H   \nATOM   5244  H   ARG A 505      13.703  36.970  87.862  1.00  0.00          H   \nATOM   5245  H   ARG A 505      11.664  37.220  85.049  1.00  0.00          H   \nATOM   5246  H   ARG A 505      11.921  36.401  86.522  1.00  0.00          H   \nATOM   5247  H   SER A 506      18.320  42.682  82.951  1.00  0.00          H   \nATOM   5248  H   SER A 506      22.352  44.539  82.773  1.00  0.00          H   \nATOM   5249  H   VAL A 507      18.757  44.598  80.991  1.00  0.00          H   \nATOM   5250  H   ARG A 508      16.224  45.809  81.164  1.00  0.00          H   \nATOM   5251  H   ARG A 508       9.570  46.455  82.946  1.00  0.00          H   \nATOM   5252  H   ARG A 508      12.235  46.233  85.174  1.00  0.00          H   \nATOM   5253  H   ARG A 508      11.333  46.786  86.511  1.00  0.00          H   \nATOM   5254  H   ARG A 508       8.374  47.195  84.746  1.00  0.00          H   \nATOM   5255  H   ARG A 508       9.132  47.334  86.267  1.00  0.00          H   \nATOM   5256  H   ALA A 509      16.169  46.870  83.778  1.00  0.00          H   \nATOM   5257  H   ARG A 510      18.218  48.797  83.392  1.00  0.00          H   \nATOM   5258  H   ARG A 510      21.682  47.268  81.542  1.00  0.00          H   \nATOM   5259  H   ARG A 510      24.418  50.134  81.117  1.00  0.00          H   \nATOM   5260  H   ARG A 510      23.406  50.146  82.489  1.00  0.00          H   \nATOM   5261  H   ARG A 510      24.240  48.483  79.573  1.00  0.00          H   \nATOM   5262  H   ARG A 510      23.092  47.237  79.771  1.00  0.00          H   \nATOM   5263  H   LEU A 511      16.972  50.479  81.648  1.00  0.00          H   \nATOM   5264  H   LEU A 512      14.862  51.476  82.958  1.00  0.00          H   \nATOM   5265  H   SER A 513      16.095  53.069  84.745  1.00  0.00          H   \nATOM   5266  H   SER A 513      18.406  52.952  87.443  1.00  0.00          H   \nATOM   5267  H   GLN A 514      16.697  55.107  83.173  1.00  0.00          H   \nATOM   5268  H   GLN A 514      20.823  55.038  80.939  1.00  0.00          H   \nATOM   5269  H   GLN A 514      19.369  55.364  81.768  1.00  0.00          H   \nATOM   5270  H   GLY A 515      14.650  56.698  83.126  1.00  0.00          H   \nATOM   5271  H   GLY A 516      11.587  59.919  82.156  1.00  0.00          H   \nATOM   5272  H   ARG A 517      11.873  59.953  77.528  1.00  0.00          H   \nATOM   5273  H   ARG A 517      13.301  61.330  74.382  1.00  0.00          H   \nATOM   5274  H   ARG A 517      11.617  60.073  70.996  1.00  0.00          H   \nATOM   5275  H   ARG A 517      10.679  60.175  72.416  1.00  0.00          H   \nATOM   5276  H   ARG A 517      13.827  60.694  71.029  1.00  0.00          H   \nATOM   5277  H   ARG A 517      14.533  61.259  72.475  1.00  0.00          H   \nATOM   5278  H   ALA A 518      13.672  57.730  77.784  1.00  0.00          H   \nATOM   5279  H   ALA A 519      12.704  56.471  79.847  1.00  0.00          H   \nATOM   5280  H   THR A 520      10.404  55.635  79.006  1.00  0.00          H   \nATOM   5281  H   THR A 520       7.527  56.913  77.641  1.00  0.00          H   \nATOM   5282  H   CYS A 521      11.140  53.866  77.186  1.00  0.00          H   \nATOM   5283  H   CYS A 521      13.803  53.513  73.944  1.00  0.00          H   \nATOM   5284  H   GLY A 522      12.180  51.870  78.820  1.00  0.00          H   \nATOM   5285  H   LYS A 523       9.990  51.059  80.136  1.00  0.00          H   \nATOM   5286  H   LYS A 523       2.587  52.321  82.356  1.00  0.00          H   \nATOM   5287  H   LYS A 523       3.543  51.507  81.262  1.00  0.00          H   \nATOM   5288  H   LYS A 523       3.590  53.170  81.333  1.00  0.00          H   \nATOM   5289  H   TYR A 524       8.006  50.132  78.149  1.00  0.00          H   \nATOM   5290  H   TYR A 524       1.569  52.985  78.344  1.00  0.00          H   \nATOM   5291  H   LEU A 525       9.201  48.645  76.416  1.00  0.00          H   \nATOM   5292  H   PHE A 526      10.038  46.536  77.967  1.00  0.00          H   \nATOM   5293  H   ASN A 527       8.183  44.937  78.593  1.00  0.00          H   \nATOM   5294  H   ASN A 527       6.701  44.389  82.534  1.00  0.00          H   \nATOM   5295  H   ASN A 527       5.143  44.394  83.227  1.00  0.00          H   \nATOM   5296  H   TRP A 528       8.051  42.327  78.475  1.00  0.00          H   \nATOM   5297  H   TRP A 528      11.247  42.361  75.487  1.00  0.00          H   \nATOM   5298  H   ALA A 529       9.937  41.392  80.147  1.00  0.00          H   \nATOM   5299  H   VAL A 530       9.357  41.275  82.703  1.00  0.00          H   \nATOM   5300  H   LYS A 531       9.102  39.366  86.936  1.00  0.00          H   \nATOM   5301  H   LYS A 531      10.125  36.430  92.943  1.00  0.00          H   \nATOM   5302  H   LYS A 531      10.381  35.516  91.574  1.00  0.00          H   \nATOM   5303  H   LYS A 531       8.880  35.531  92.297  1.00  0.00          H   \nATOM   5304  H   THR A 532       7.058  40.447  87.880  1.00  0.00          H   \nATOM   5305  H   THR A 532       6.144  40.071  90.159  1.00  0.00          H   \nATOM   5306  H   LYS A 533       3.642  40.823  85.836  1.00  0.00          H   \nATOM   5307  H   LYS A 533       6.108  37.293  82.103  1.00  0.00          H   \nATOM   5308  H   LYS A 533       4.948  37.278  83.299  1.00  0.00          H   \nATOM   5309  H   LYS A 533       6.368  38.120  83.526  1.00  0.00          H   \nATOM   5310  H   LEU A 534       4.343  44.870  83.662  1.00  0.00          H   \nATOM   5311  H   LYS A 535       1.476  48.183  84.898  1.00  0.00          H   \nATOM   5312  H   LYS A 535      -2.387  53.997  84.917  1.00  0.00          H   \nATOM   5313  H   LYS A 535      -0.755  54.204  85.182  1.00  0.00          H   \nATOM   5314  H   LYS A 535      -1.664  53.300  86.246  1.00  0.00          H   \nATOM   5315  H   LEU A 536       1.324  49.481  82.196  1.00  0.00          H   \nATOM   5316  H   THR A 537       0.601  51.528  79.286  1.00  0.00          H   \nATOM   5317  H   THR A 537      -0.669  54.168  79.549  1.00  0.00          H   \nATOM   5318  H   ILE A 539      -0.214  55.750  74.082  1.00  0.00          H   \nATOM   5319  H   ALA A 541       3.408  60.850  74.237  1.00  0.00          H   \nATOM   5320  H   ALA A 542       2.705  59.115  72.038  1.00  0.00          H   \nATOM   5321  H   SER A 543       0.435  59.836  70.037  1.00  0.00          H   \nATOM   5322  H   SER A 543      -1.688  62.525  70.120  1.00  0.00          H   \nATOM   5323  H   GLN A 544       2.147  61.760  68.905  1.00  0.00          H   \nATOM   5324  H   GLN A 544       5.736  64.167  69.449  1.00  0.00          H   \nATOM   5325  H   GLN A 544       6.312  65.270  70.615  1.00  0.00          H   \nATOM   5326  H   LEU A 545       3.826  60.900  67.036  1.00  0.00          H   \nATOM   5327  H   ASP A 546       5.242  61.889  62.849  1.00  0.00          H   \nATOM   5328  H   LEU A 547       2.498  59.313  61.471  1.00  0.00          H   \nATOM   5329  H   SER A 548       2.619  59.953  58.683  1.00  0.00          H   \nATOM   5330  H   SER A 548       1.480  62.378  56.406  1.00  0.00          H   \nATOM   5331  H   GLY A 549       2.036  57.435  54.984  1.00  0.00          H   \nATOM   5332  H   TRP A 550       3.954  56.434  56.102  1.00  0.00          H   \nATOM   5333  H   TRP A 550       7.803  57.685  59.173  1.00  0.00          H   \nATOM   5334  H   PHE A 551       2.890  53.934  55.393  1.00  0.00          H   \nATOM   5335  H   VAL A 552       3.539  53.449  52.214  1.00  0.00          H   \nATOM   5336  H   ALA A 553       4.137  53.309  50.189  1.00  0.00          H   \nATOM   5337  H   GLY A 554       5.145  50.510  46.911  1.00  0.00          H   \nATOM   5338  H   TYR A 555       9.559  50.435  48.144  1.00  0.00          H   \nATOM   5339  H   TYR A 555       5.520  56.737  47.040  1.00  0.00          H   \nATOM   5340  H   SER A 556      10.830  49.215  46.867  1.00  0.00          H   \nATOM   5341  H   SER A 556      13.669  45.766  46.724  1.00  0.00          H   \nATOM   5342  H   GLY A 557      15.372  49.640  46.612  1.00  0.00          H   \nATOM   5343  H   GLY A 558      14.060  51.665  45.391  1.00  0.00          H   \nATOM   5344  H   ASP A 559      16.049  53.538  45.711  1.00  0.00          H   \nATOM   5345  H   ILE A 560      14.688  55.195  47.654  1.00  0.00          H   \nATOM   5346  H   TYR A 561      13.856  59.175  49.514  1.00  0.00          H   \nATOM   5347  H   TYR A 561      16.912  62.342  57.025  1.00  0.00          H   \nATOM   5348  H   HIS A 562      13.408  58.713  53.877  1.00  0.00          H   \nATOM   5349  H   SER A 563      11.850  62.511  55.272  1.00  0.00          H   \nATOM   5350  H   SER A 563      10.288  61.880  59.170  1.00  0.00          H   \nTER    5351      SER A 563\nATOM   5352  C1  LIG B   1      12.930  30.871  73.555  1.00  0.00          C   \nATOM   5353  C2  LIG B   1      12.904  29.594  72.915  1.00  0.00          C   \nATOM   5354  C3  LIG B   1      11.675  28.876  72.876  1.00  0.00          C   \nATOM   5355  C4  LIG B   1      10.504  29.416  73.455  1.00  0.00          C   \nATOM   5356  C5  LIG B   1      10.520  30.692  74.092  1.00  0.00          C   \nATOM   5357  C6  LIG B   1      11.753  31.411  74.134  1.00  0.00          C   \nATOM   5358  C7  LIG B   1       6.884  31.797  75.111  1.00  0.00          C   \nATOM   5359  C8  LIG B   1       7.733  32.392  76.033  1.00  0.00          C   \nATOM   5360  C9  LIG B   1       9.108  32.061  75.768  1.00  0.00          C   \nATOM   5361  C10 LIG B   1       9.277  31.234  74.669  1.00  0.00          C   \nATOM   5362  S11 LIG B   1       7.757  30.874  73.982  1.00  0.00          S   \nATOM   5363  C12 LIG B   1      14.083  29.040  72.318  1.00  0.00          C   \nATOM   5364  N13 LIG B   1      15.043  28.581  71.811  1.00  0.00          N   \nATOM   5365  C14 LIG B   1       5.402  31.944  75.107  1.00  0.00          C   \nATOM   5366  O15 LIG B   1       4.768  31.230  74.313  1.00  0.00          O   \nATOM   5367  O16 LIG B   1       4.868  32.787  75.871  1.00  0.00          O   \nATOM   5368  N17 LIG B   1       7.301  33.237  77.137  1.00  0.00          N   \nATOM   5369  S18 LIG B   1       8.282  34.502  77.689  1.00  0.00          S   \nATOM   5370  C19 LIG B   1       7.775  36.467  75.711  1.00  0.00          C   \nATOM   5371  C20 LIG B   1       8.733  35.682  76.427  1.00  0.00          C   \nATOM   5372  C21 LIG B   1      10.125  35.830  76.146  1.00  0.00          C   \nATOM   5373  C22 LIG B   1      10.578  36.740  75.166  1.00  0.00          C   \nATOM   5374  C23 LIG B   1       9.635  37.525  74.450  1.00  0.00          C   \nATOM   5375  C24 LIG B   1       8.246  37.387  74.720  1.00  0.00          C   \nATOM   5376  C25 LIG B   1       6.286  36.367  75.952  1.00  0.00          C   \nATOM   5377  O26 LIG B   1       9.481  33.897  78.252  1.00  0.00          O   \nATOM   5378  O27 LIG B   1       7.483  35.183  78.686  1.00  0.00          O   \nATOM   5379  H   LIG B   1       5.399  33.298  76.450  1.00  0.00          H   \nATOM   5380  H   LIG B   1       6.431  33.071  77.566  1.00  0.00          H   \nTER    5381      LIG B   1\nENDMDL\nCONECT 2317 2378\nCONECT 2378 2317\nEND\n",
+                    "type": "blob",
+                    "binary": false
+                  }
+                ],
+                "kwargs": {
+                  "ext": "pdb",
+                  "defaultRepresentation": true
+                },
+                "type": "call_method"
+              }
+            ]
+          }
+        }
+      }
     }
-   ],
-   "source": [
-    "def rf_model_builder(model_params, model_dir):\n",
-    "  sklearn_model = RandomForestRegressor(**model_params)\n",
-    "  sklearn_model.random_state = seed\n",
-    "  return dc.models.SklearnModel(sklearn_model, model_dir)\n",
-    "\n",
-    "params_dict = {\n",
-    "    \"n_estimators\": [10, 50, 100],\n",
-    "    \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n",
-    "}\n",
-    "\n",
-    "metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
-    "optimizer = dc.hyper.HyperparamOpt(rf_model_builder)\n",
-    "best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(\n",
-    "    params_dict, train_dataset, valid_dataset, transformers,\n",
-    "    metric=metric)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Ok, our best validation score is now `0.53` R^2. Let's make some predictions on the test set and see what they look like."
-   ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 432x288 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xzxzDJgjqTrx",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Tutorial Part 13: Modeling Protein-Ligand Interactions\n",
+        "\n",
+        "In this tutorial, we'll walk you through the use of machine learning methods to predict the binding energy of a protein-ligand complex. Recall that a ligand is some small molecule which interacts (usually non-covalently) with a protein. As you work through the tutorial, you'll trace an arc from loading a raw dataset to fitting a random forest model to predict binding affinities. We'll take the following steps to get there:\n",
+        "\n",
+        "1. Loading a chemical dataset, consisting of a series of protein-ligand complexes.\n",
+        "2. Featurizing each protein-ligand complexes with various featurization schemes. \n",
+        "3. Fitting a series of models with these featurized protein-ligand complexes.\n",
+        "4. Visualizing the results.\n",
+        "\n",
+        "To start the tutorial, we'll use a simple pre-processed dataset file that comes in the form of a gzipped file. Each row is a molecular system, and each column represents a different piece of information about that system. For instance, in this example, every row reflects a protein-ligand complex, and the following columns are present: a unique complex identifier; the SMILES string of the ligand; the binding affinity (Ki) of the ligand to the protein in the complex; a Python `list` of all lines in a PDB file for the protein alone; and a Python `list` of all lines in a ligand file for the ligand alone.\n",
+        "\n",
+        "## Colab\n",
+        "\n",
+        "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/13_Modeling_Protein_Ligand_Interactions.ipynb)\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "QsmBgrqsqTr0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "540e5b9a-acb9-4689-a66d-9879736aec60"
+      },
+      "source": [
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  59872      0 --:--:-- --:--:-- --:--:-- 59872\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.79 s, sys: 641 ms, total: 3.43 s\n",
+            "Wall time: 3min 57s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "F5yjhSAeqTr_",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "6bad6024-79a0-4ba8-ac65-3995402ef0f6"
+      },
+      "source": [
+        "import deepchem as dc\n",
+        "from deepchem.utils import download_url\n",
+        "\n",
+        "import os\n",
+        "\n",
+        "data_dir = dc.utils.get_data_dir()\n",
+        "dataset_file = os.path.join(data_dir, \"pdbbind_core_df.csv.gz\")\n",
+        "\n",
+        "if not os.path.exists(dataset_file):\n",
+        "    print('File does not exist. Downloading file...')\n",
+        "    download_url(\"https://s3-us-west-1.amazonaws.com/deepchem.io/datasets/pdbbind_core_df.csv.gz\")\n",
+        "    print('File downloaded...')\n",
+        "\n",
+        "raw_dataset = dc.utils.save.load_from_disk(dataset_file)"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "File does not exist. Downloading file...\n",
+            "File downloaded...\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-6LAaTG3qTsC",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's see what `dataset` looks like:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "hQW5CvXHqTsD",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "e4aeeea8-cac5-47ef-b75a-d92b1a19e493"
+      },
+      "source": [
+        "print(\"Type of dataset is: %s\" % str(type(raw_dataset)))\n",
+        "print(raw_dataset[:5])\n",
+        "print(\"Shape of dataset is: %s\" % str(raw_dataset.shape))"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Type of dataset is: <class 'pandas.core.frame.DataFrame'>\n",
+            "  pdb_id  ... label\n",
+            "0   2d3u  ...  6.92\n",
+            "1   3cyx  ...  8.00\n",
+            "2   3uo4  ...  6.52\n",
+            "3   1p1q  ...  4.89\n",
+            "4   3ag9  ...  8.05\n",
+            "\n",
+            "[5 rows x 7 columns]\n",
+            "Shape of dataset is: (193, 7)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "lOVvw1V4qTsI",
+        "colab_type": "text"
+      },
+      "source": [
+        "Visualizing what these proteins and ligands look like will help us build intuition and understanding about these systems. Let's write a bit of code to help us view our molecules. We'll use the `nglview` library to help us do this. You can install this library by calling `pip install nglview`."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "WCWAc-FSroM0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "b62cc915-8dae-4e6d-9163-71c427e83fc6"
+      },
+      "source": [
+        "!pip install -q nglview"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\u001b[K     |████████████████████████████████| 5.2MB 8.8MB/s \n",
+            "\u001b[?25h  Building wheel for nglview (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "aBRWy9I5qTsI",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "e9692ed27df045c7a86ccbab377a762d"
+          ]
+        },
+        "outputId": "ff0bdcc0-857a-4902-efed-821f0991c9e3"
+      },
+      "source": [
+        "import nglview\n",
+        "import tempfile\n",
+        "import os\n",
+        "import mdtraj as md\n",
+        "import numpy as np"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e9692ed27df045c7a86ccbab377a762d",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "_ColormakerRegistry()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "KOTeCo4WqTsM",
+        "colab_type": "text"
+      },
+      "source": [
+        "We'll use the `mdtraj` library to help us manipulate both ligand and protein objects. We'll use the following convenience function to parse in the ligand and protein representations above into mdtraj."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "OSODY3X7qTsM",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def convert_lines_to_mdtraj(molecule_lines):\n",
+        "  molecule_lines = molecule_lines.strip('[').strip(']').replace(\"'\",\"\").replace(\"\\\\n\", \"\").split(\", \")\n",
+        "  tempdir = tempfile.mkdtemp()\n",
+        "  molecule_file = os.path.join(tempdir, \"molecule.pdb\")\n",
+        "  with open(molecule_file, \"w\") as f:\n",
+        "    for line in molecule_lines:\n",
+        "        f.write(\"%s\\n\" % line)\n",
+        "  molecule_mdtraj = md.load(molecule_file)\n",
+        "  return molecule_mdtraj"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ECATxUI_qTsQ",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's take a look at the first protein ligand pair in our dataset:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "KIOhkfvSqTsR",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "first_protein, first_ligand = raw_dataset.iloc[0][\"protein_pdb\"], raw_dataset.iloc[0][\"ligand_pdb\"]\n",
+        "protein_mdtraj = convert_lines_to_mdtraj(first_protein)\n",
+        "ligand_mdtraj = convert_lines_to_mdtraj(first_ligand)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-t4jv9b_qTsZ",
+        "colab_type": "text"
+      },
+      "source": [
+        "We'll use the convenience function `nglview.show_mdtraj` in order to view our proteins and ligands."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5NyQYfzUqTsa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "d0f7e107871a4342a434a93292125e9e"
+          ]
+        },
+        "outputId": "014470d8-760d-4eff-f76e-b49692c5cc51"
+      },
+      "source": [
+        "v = nglview.show_mdtraj(ligand_mdtraj)\n",
+        "v"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d0f7e107871a4342a434a93292125e9e",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "NGLWidget()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vc9TYC-qqTsg",
+        "colab_type": "text"
+      },
+      "source": [
+        "Now that we have an idea of what the ligand looks like, let's take a look at our protein:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zKGqEq0wqTsi",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "655b10a5e1d8422ea4555272e0411fee"
+          ]
+        },
+        "outputId": "abb638fb-1d43-48c7-9305-581dd856b882"
+      },
+      "source": [
+        "view = nglview.show_mdtraj(protein_mdtraj)\n",
+        "view"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "655b10a5e1d8422ea4555272e0411fee",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "NGLWidget()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W-9e2Rl6qTso",
+        "colab_type": "text"
+      },
+      "source": [
+        "Can we view the complex with both protein and ligand? Yes, but we'll need the following helper function to join the two mdtraj files for the protein and ligand."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5YBB6ZTpqTsp",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "def combine_mdtraj(protein, ligand):\n",
+        "  chain = protein.topology.add_chain()\n",
+        "  residue = protein.topology.add_residue(\"LIG\", chain, resSeq=1)\n",
+        "  for atom in ligand.topology.atoms:\n",
+        "      protein.topology.add_atom(atom.name, atom.element, residue)\n",
+        "  protein.xyz = np.hstack([protein.xyz, ligand.xyz])\n",
+        "  protein.topology.create_standard_bonds()\n",
+        "  return protein\n",
+        "complex_mdtraj = combine_mdtraj(protein_mdtraj, ligand_mdtraj)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6zgCBe0mqTsw",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's now visualize our complex"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "YxM-ESaEqTsw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "a5a64350a6974d84b4346b77f6bd86d0"
+          ]
+        },
+        "outputId": "d2c234ee-f505-41ab-fe84-314e33bb5bf6"
+      },
+      "source": [
+        "v = nglview.show_mdtraj(complex_mdtraj)\n",
+        "v"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "a5a64350a6974d84b4346b77f6bd86d0",
+              "version_minor": 0,
+              "version_major": 2
+            },
+            "text/plain": [
+              "NGLWidget()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cysXf_3bqTs0",
+        "colab_type": "text"
+      },
+      "source": [
+        "We can see that the ligand slots into a groove on the outer edge of the protein. Ok, now that we've got our basic visualization tools up and running, let's see if we can use some machine learning to understand our dataset of protein-ligand systems better.\n",
+        "\n",
+        "In order to do this, we'll need a way to transform our protein-ligand complexes into representations which can be used by learning algorithms. Ideally, we'd have neural protein-ligand complex fingerprints, but DeepChem doesn't yet have a good learned fingerprint of this sort. We do however have well tuned manual featurizers that can help us with our challenge here.\n",
+        "\n",
+        "We'll make of two types of fingerprints in the rest of the tutorial, the circular fingerprints and the grid descriptors. The grid descriptors convert a 3D volume containing an arragment of atoms into a fingerprint. This is really useful for understanding protein-ligand complexes since it will allow us to transfer protein-ligand complexes into vectors that can be passed into a simple machine learning algorithms. Let's see how we can create such a fingerprint in DeepChem. We'll make use of the `dc.feat.RdkitGridFeaturizer` class."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "UpU1chIBqTs1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "grid_featurizer = dc.feat.RdkitGridFeaturizer(\n",
+        "    voxel_width=16.0, feature_types=[\"ecfp\", \"splif\", \"hbond\", \"pi_stack\", \"cation_pi\", \"salt_bridge\"], \n",
+        "    ecfp_power=5, splif_power=5, parallel=True, flatten=True, sanitize=True)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "wHaZzrBGqTtB",
+        "colab_type": "text"
+      },
+      "source": [
+        "Next we'll create circular fingerprints. These convert small molecules into a vector of fragments. You can create these fingerprints with the `dc.feat.CircularFingerprint` class."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "_RldLM7VqTtE",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "compound_featurizer = dc.feat.CircularFingerprint(size=128)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9471T63SqTtK",
+        "colab_type": "text"
+      },
+      "source": [
+        "The convenience loader `dc.molnet.load_pdbbind_grid` will take care of performing featurizing the pdbbind dataset under the hood for us. We'll use this helper method to perform our featurization for us. We'll featurize the \"refined\" subset of the PDBBIND dataset (which consists of only a couple thousand protein-ligand complexes) to keep this task manageable."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "1HNhZ9jHqTtL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 408
+        },
+        "outputId": "270325f7-6559-42a7-cec5-812c22d5e879"
+      },
+      "source": [
+        "pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid(\n",
+        "    featurizer=\"ECFP\", subset=\"refined\")"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading raw samples now.\n",
+            "shard_size: 8192\n",
+            "About to start loading CSV from /tmp/refined_smiles_labels.csv\n",
+            "Loading shard 1 of size 8192.\n",
+            "Featurizing sample 0\n",
+            "Featurizing sample 1000\n",
+            "Featurizing sample 2000\n",
+            "Featurizing sample 3000\n",
+            "TIMING: featurizing shard 0 took 10.019 s\n",
+            "TIMING: dataset construction took 10.161 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.160 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.082 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.080 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.138 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.021 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.020 s\n",
+            "Loading dataset from disk.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "uTIdwkdSqTtP",
+        "colab_type": "text"
+      },
+      "source": [
+        "Now, we're ready to do some learning! \n",
+        "\n",
+        "To fit a deepchem model, first we instantiate one of the provided (or user-written) model classes. In this case, we have a created a convenience class to wrap around any ML model available in Sci-Kit Learn that can in turn be used to interoperate with deepchem. To instantiate an ```SklearnModel```, you will need (a) task_types, (b) model_params, another ```dict``` as illustrated below, and (c) a ```model_instance``` defining the type of model you would like to fit, in this case a ```RandomForestRegressor```."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9kwi2GsvqTtQ",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from sklearn.ensemble import RandomForestRegressor\n",
+        "\n",
+        "seed=23 # Set a random seed to get stable results\n",
+        "sklearn_model = RandomForestRegressor(n_estimators=10, max_features='sqrt')\n",
+        "sklearn_model.random_state = seed\n",
+        "model = dc.models.SklearnModel(sklearn_model)\n",
+        "model.fit(train_dataset)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "d-imE_PBqTtT",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "e951d360-6783-4f3b-de1f-ef57fda22fb2"
+      },
+      "source": [
+        "from deepchem.utils.evaluate import Evaluator\n",
+        "import pandas as pd\n",
+        "\n",
+        "metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
+        "\n",
+        "evaluator = Evaluator(model, train_dataset, transformers)\n",
+        "train_r2score = evaluator.compute_model_performance([metric])\n",
+        "print(\"RF Train set R^2 %f\" % (train_r2score[\"r2_score\"]))\n",
+        "\n",
+        "evaluator = Evaluator(model, valid_dataset, transformers)\n",
+        "valid_r2score = evaluator.compute_model_performance([metric])\n",
+        "print(\"RF Valid set R^2 %f\" % (valid_r2score[\"r2_score\"]))"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "computed_metrics: [0.8402765501351457]\n",
+            "RF Train set R^2 0.840277\n",
+            "computed_metrics: [0.4348811019052432]\n",
+            "RF Valid set R^2 0.434881\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ioRs1T1tqTtZ",
+        "colab_type": "text"
+      },
+      "source": [
+        "This is decent performance on a validation set! It's interesting to note that a trivial prediction from just the ligand can do reasonably on the task of predicting the binding energy."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "CHAvWVCXqTtb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "6b8aa022-d8bd-4e3f-8ceb-2d093d10c34c"
+      },
+      "source": [
+        "predictions = model.predict(test_dataset)\n",
+        "print(predictions[:10])"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "[-1.40106366 -0.84870136 -1.18206478 -1.71648845 -0.81282409 -0.774844\n",
+            " -0.46336663 -0.79229381  0.47488378  0.18399203]\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1DCOhl9-qTtg",
+        "colab_type": "text"
+      },
+      "source": [
+        "# The protein-ligand complex view."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "nApZttv6qTth",
+        "colab_type": "text"
+      },
+      "source": [
+        "In the previous section, we featurized only the ligand. The signal we observed in R^2 reflects the ability of grid fingerprints and random forests to learn general features that make ligands \"drug-like.\" This time, let's see if we can do something sensible with our protein-ligand fingerprints that make use of our structural information. To start with, we need to re-featurize the dataset but using the \"grid\" fingerprints this time."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jhrZqqCDqTth",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 136
+        },
+        "outputId": "0c1b70e6-7249-4a07-a336-7f2ca94ebcb2"
+      },
+      "source": [
+        "pdbbind_tasks, (train_dataset, valid_dataset, test_dataset), transformers = dc.molnet.load_pdbbind_grid(\n",
+        "    featurizer=\"grid\", subset=\"refined\")"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.225 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.072 s\n",
+            "Loading dataset from disk.\n",
+            "TIMING: dataset construction took 0.072 s\n",
+            "Loading dataset from disk.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "vMp3LG6UqTtl",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's now train a simple random forest model on this dataset."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qeHrsc2PqTtm",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "seed=23 # Set a random seed to get stable results\n",
+        "sklearn_model = RandomForestRegressor(n_estimators=10, max_features='sqrt')\n",
+        "sklearn_model.random_state = seed\n",
+        "model = dc.models.SklearnModel(sklearn_model)\n",
+        "model.fit(train_dataset)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "P2VlBSPJqTto",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's see what our accuracies looks like!"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "zXyNarwnqTtp",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 85
+        },
+        "outputId": "cd568fc6-dc98-4fbb-8ceb-b8beece0e89c"
+      },
+      "source": [
+        "metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
+        "\n",
+        "evaluator = Evaluator(model, train_dataset, transformers)\n",
+        "train_r2score = evaluator.compute_model_performance([metric])\n",
+        "print(\"RF Train set R^2 %f\" % (train_r2score[\"r2_score\"]))\n",
+        "\n",
+        "evaluator = Evaluator(model, valid_dataset, transformers)\n",
+        "valid_r2score = evaluator.compute_model_performance([metric])\n",
+        "print(\"RF Valid set R^2 %f\" % (valid_r2score[\"r2_score\"]))"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "computed_metrics: [0.891357218591319]\n",
+            "RF Train set R^2 0.891357\n",
+            "computed_metrics: [0.43630320446768944]\n",
+            "RF Valid set R^2 0.436303\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3coX59A-qTt1",
+        "colab_type": "text"
+      },
+      "source": [
+        "Ok, there's some predictive performance here, but it looks like we have lower accuracy than the ligand-only dataset. What gives? There might be a few things going on. It's possible that for this particular dataset the pure ligand only features are quite predictive. But nonetheless, it's probably still useful to have a protein-ligand model since it's likely to learn different features than the the pure ligand-only model."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HC2XcoaIqTt2",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Doing Some Hyperparameter Optimization"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HDVZlzlrqTt2",
+        "colab_type": "text"
+      },
+      "source": [
+        "Ok, now that we've built a few models, let's do some hyperparameter optimization to see if we can get our numbers to be a little better. We'll use the `dc.hyper` module to do this for us."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "uxV2wE_5qTt3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "cdab9393-18ec-401b-e425-f6f1f9f74c78"
+      },
+      "source": [
+        "def rf_model_builder(model_params, model_dir):\n",
+        "  sklearn_model = RandomForestRegressor(**model_params)\n",
+        "  sklearn_model.random_state = seed\n",
+        "  return dc.models.SklearnModel(sklearn_model, model_dir)\n",
+        "\n",
+        "params_dict = {\n",
+        "    \"n_estimators\": [10, 50, 100],\n",
+        "    \"max_features\": [\"auto\", \"sqrt\", \"log2\", None],\n",
+        "}\n",
+        "\n",
+        "metric = dc.metrics.Metric(dc.metrics.r2_score)\n",
+        "optimizer = dc.hyper.HyperparamOpt(rf_model_builder)\n",
+        "best_rf, best_rf_hyperparams, all_rf_results = optimizer.hyperparam_search(\n",
+        "    params_dict, train_dataset, valid_dataset, transformers,\n",
+        "    metric=metric)"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Fitting model 1/12\n",
+            "hyperparameters: {'n_estimators': 10, 'max_features': 'auto'}\n",
+            "computed_metrics: [0.45050290646786983]\n",
+            "Model 1/12, Metric r2_score, Validation set 0: 0.450503\n",
+            "\tbest_validation_score so far: 0.450503\n",
+            "Fitting model 2/12\n",
+            "hyperparameters: {'n_estimators': 10, 'max_features': 'sqrt'}\n",
+            "computed_metrics: [0.43630320446768944]\n",
+            "Model 2/12, Metric r2_score, Validation set 1: 0.436303\n",
+            "\tbest_validation_score so far: 0.450503\n",
+            "Fitting model 3/12\n",
+            "hyperparameters: {'n_estimators': 10, 'max_features': 'log2'}\n",
+            "computed_metrics: [0.4171705642324419]\n",
+            "Model 3/12, Metric r2_score, Validation set 2: 0.417171\n",
+            "\tbest_validation_score so far: 0.450503\n",
+            "Fitting model 4/12\n",
+            "hyperparameters: {'n_estimators': 10, 'max_features': None}\n",
+            "computed_metrics: [0.45050290646786983]\n",
+            "Model 4/12, Metric r2_score, Validation set 3: 0.450503\n",
+            "\tbest_validation_score so far: 0.450503\n",
+            "Fitting model 5/12\n",
+            "hyperparameters: {'n_estimators': 50, 'max_features': 'auto'}\n",
+            "computed_metrics: [0.49574151173668746]\n",
+            "Model 5/12, Metric r2_score, Validation set 4: 0.495742\n",
+            "\tbest_validation_score so far: 0.495742\n",
+            "Fitting model 6/12\n",
+            "hyperparameters: {'n_estimators': 50, 'max_features': 'sqrt'}\n",
+            "computed_metrics: [0.5006038352000606]\n",
+            "Model 6/12, Metric r2_score, Validation set 5: 0.500604\n",
+            "\tbest_validation_score so far: 0.500604\n",
+            "Fitting model 7/12\n",
+            "hyperparameters: {'n_estimators': 50, 'max_features': 'log2'}\n",
+            "computed_metrics: [0.4644595610283615]\n",
+            "Model 7/12, Metric r2_score, Validation set 6: 0.464460\n",
+            "\tbest_validation_score so far: 0.500604\n",
+            "Fitting model 8/12\n",
+            "hyperparameters: {'n_estimators': 50, 'max_features': None}\n",
+            "computed_metrics: [0.49574151173668746]\n",
+            "Model 8/12, Metric r2_score, Validation set 7: 0.495742\n",
+            "\tbest_validation_score so far: 0.500604\n",
+            "Fitting model 9/12\n",
+            "hyperparameters: {'n_estimators': 100, 'max_features': 'auto'}\n",
+            "computed_metrics: [0.5137590613848505]\n",
+            "Model 9/12, Metric r2_score, Validation set 8: 0.513759\n",
+            "\tbest_validation_score so far: 0.513759\n",
+            "Fitting model 10/12\n",
+            "hyperparameters: {'n_estimators': 100, 'max_features': 'sqrt'}\n",
+            "computed_metrics: [0.5061380771850656]\n",
+            "Model 10/12, Metric r2_score, Validation set 9: 0.506138\n",
+            "\tbest_validation_score so far: 0.513759\n",
+            "Fitting model 11/12\n",
+            "hyperparameters: {'n_estimators': 100, 'max_features': 'log2'}\n",
+            "computed_metrics: [0.48516493298554586]\n",
+            "Model 11/12, Metric r2_score, Validation set 10: 0.485165\n",
+            "\tbest_validation_score so far: 0.513759\n",
+            "Fitting model 12/12\n",
+            "hyperparameters: {'n_estimators': 100, 'max_features': None}\n",
+            "computed_metrics: [0.5137590613848505]\n",
+            "Model 12/12, Metric r2_score, Validation set 11: 0.513759\n",
+            "\tbest_validation_score so far: 0.513759\n",
+            "computed_metrics: [0.9317458198277879]\n",
+            "Best hyperparameters: (100, None)\n",
+            "train_score: 0.931746\n",
+            "validation_score: 0.513759\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Lu25yEOdqTt8",
+        "colab_type": "text"
+      },
+      "source": [
+        "Ok, our best validation score is now `0.53` R^2. Let's make some predictions on the test set and see what they look like."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "5u96D9j1qTt9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 295
+        },
+        "outputId": "665b7830-c574-4ada-9a41-6ff760f3dc9b"
+      },
+      "source": [
+        "%matplotlib inline\n",
+        "\n",
+        "import matplotlib\n",
+        "import numpy as np\n",
+        "import matplotlib.pyplot as plt\n",
+        "\n",
+        "rf_predicted_test = best_rf.predict(test_dataset)\n",
+        "rf_true_test = test_dataset.y\n",
+        "plt.scatter(rf_predicted_test, rf_true_test)\n",
+        "plt.xlabel('Predicted pIC50s')\n",
+        "plt.ylabel('True IC50')\n",
+        "plt.title(r'RF predicted IC50 vs. True pIC50')\n",
+        "plt.xlim([2, 11])\n",
+        "plt.ylim([2, 11])\n",
+        "plt.plot([2, 11], [2, 11], color='k')\n",
+        "plt.show()"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "image/png": 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\n",
+            "text/plain": [
+              "<Figure size 432x288 with 1 Axes>"
+            ]
+          },
+          "metadata": {
+            "tags": [],
+            "needs_background": "light"
+          }
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "xtc2hABfqTuB",
+        "colab_type": "text"
+      },
+      "source": [
+        "This model seems reasonably predictive! It's likely that this model is still a ways from being good enough to use in a production setting, but this isn't bad for a quick and dirty tutorial."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "fZE3LA36qTuB",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Congratulations! Time to join the Community!\n",
+        "\n",
+        "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
+        "\n",
+        "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
+        "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
+        "\n",
+        "## Join the DeepChem Gitter\n",
+        "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
+      ]
     }
-   ],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "import matplotlib\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "\n",
-    "rf_predicted_test = best_rf.predict(test_dataset)\n",
-    "rf_true_test = test_dataset.y\n",
-    "plt.scatter(rf_predicted_test, rf_true_test)\n",
-    "plt.xlabel('Predicted pIC50s')\n",
-    "plt.ylabel('True IC50')\n",
-    "plt.title(r'RF predicted IC50 vs. True pIC50')\n",
-    "plt.xlim([2, 11])\n",
-    "plt.ylim([2, 11])\n",
-    "plt.plot([2, 11], [2, 11], color='k')\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This model seems reasonably predictive! It's likely that this model is still a ways from being good enough to use in a production setting, but this isn't bad for a quick and dirty tutorial."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Congratulations! Time to join the Community!\n",
-    "\n",
-    "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
-    "\n",
-    "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
-    "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
-    "\n",
-    "## Join the DeepChem Gitter\n",
-    "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  ]
+}
\ No newline at end of file
diff --git a/examples/tutorials/14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb b/examples/tutorials/14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb
index 27b61c5186..fc13e25e82 100644
--- a/examples/tutorials/14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb
+++ b/examples/tutorials/14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb
@@ -81,65 +81,46 @@
       "metadata": {
         "id": "Y2xCQyOInB_D",
         "colab_type": "code",
-        "colab": {}
-      },
-      "source": [
-        "%%capture\n",
-        "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
-      ],
-      "execution_count": 0,
-      "outputs": []
-    },
-    {
-      "cell_type": "code",
-      "metadata": {
-        "id": "W1cCOOYXnB_L",
-        "colab_type": "code",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 253
+          "height": 462
         },
-        "outputId": "37e914af-8a05-45a3-e60e-29a59226c6f0"
+        "outputId": "6ed5ba7e-fc17-47d7-df30-888de26b6da4"
       },
       "source": [
-        "import deepchem as dc\n",
-        "import os\n",
-        "from deepchem.utils import download_url"
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 2,
+      "execution_count": 1,
       "outputs": [
         {
           "output_type": "stream",
           "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  29010      0 --:--:-- --:--:-- --:--:-- 29010\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
             "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
             "  warnings.warn(msg, category=FutureWarning)\n"
           ],
           "name": "stderr"
         },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
         {
           "output_type": "stream",
           "text": [
@@ -153,9 +134,39 @@
             "\n"
           ],
           "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.78 s, sys: 632 ms, total: 3.41 s\n",
+            "Wall time: 3min 46s\n"
+          ],
+          "name": "stdout"
         }
       ]
     },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "W1cCOOYXnB_L",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import deepchem as dc\n",
+        "import os\n",
+        "from deepchem.utils import download_url"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
       "cell_type": "code",
       "metadata": {
@@ -177,11 +188,11 @@
       "metadata": {
         "id": "snei1ST1nB_a",
         "colab_type": "code",
+        "outputId": "a22b50ea-4c92-415d-f9c0-11dc2a52c42a",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 170
-        },
-        "outputId": "acd36ebf-eac6-422d-a79c-466701affc60"
+        }
       },
       "source": [
         "print(\"Type of dataset is: %s\" % str(type(raw_dataset)))\n",
diff --git a/examples/tutorials/15_Synthetic_Feasibility_Scoring.ipynb b/examples/tutorials/15_Synthetic_Feasibility_Scoring.ipynb
index 16ffc6355a..c74ac11196 100644
--- a/examples/tutorials/15_Synthetic_Feasibility_Scoring.ipynb
+++ b/examples/tutorials/15_Synthetic_Feasibility_Scoring.ipynb
@@ -59,31 +59,88 @@
       "metadata": {
         "id": "IlFeRa3qpbFz",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "a0a53b94-c133-44c7-c02d-f1acf3f97014"
       },
       "source": [
-        "%%capture\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')\n",
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')\n",
         "import deepchem as dc"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  65441      0 --:--:-- --:--:-- --:--:-- 65441\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.54 s, sys: 556 ms, total: 3.1 s\n",
+            "Wall time: 4min 20s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "code",
       "metadata": {
         "id": "d3QTjXKwpbF9",
         "colab_type": "code",
+        "outputId": "27683628-d72e-41b5-b0b5-9ff141c5dda7",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 306
-        },
-        "outputId": "5a4559b8-11bc-42f1-9e4d-a275fd36ed4b"
+        }
       },
       "source": [
         "# Lets get some molecules to play with\n",
@@ -109,10 +166,10 @@
             "Featurizing sample 5000\n",
             "Featurizing sample 6000\n",
             "Featurizing sample 7000\n",
-            "TIMING: featurizing shard 0 took 5.910 s\n",
-            "TIMING: dataset construction took 6.369 s\n",
+            "TIMING: featurizing shard 0 took 4.905 s\n",
+            "TIMING: dataset construction took 5.264 s\n",
             "Loading dataset from disk.\n",
-            "TIMING: dataset construction took 0.473 s\n",
+            "TIMING: dataset construction took 0.392 s\n",
             "Loading dataset from disk.\n"
           ],
           "name": "stdout"
@@ -253,11 +310,11 @@
       "metadata": {
         "id": "AZhS38JLpbGd",
         "colab_type": "code",
+        "outputId": "7cf7423c-9bcd-4130-e916-fe3e9c3668b0",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 34
-        },
-        "outputId": "c45a3112-07bd-47c4-cf51-801a4dee3010"
+          "height": 343
+        }
       },
       "source": [
         "from deepchem.models import ScScoreModel\n",
@@ -267,6 +324,29 @@
       ],
       "execution_count": 6,
       "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "If using Keras pass *_constraint arguments to layers.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:54: The name tf.losses.hinge_loss is deprecated. Please use tf.compat.v1.losses.hinge_loss instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/losses.py:55: The name tf.losses.Reduction is deprecated. Please use tf.compat.v1.losses.Reduction instead.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
         {
           "output_type": "execute_result",
           "data": {
@@ -336,11 +416,11 @@
       "metadata": {
         "id": "CNgjQWQRpbG4",
         "colab_type": "code",
+        "outputId": "95856282-2001-4aa5-cc19-4443c66c16ad",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 920
-        },
-        "outputId": "90bd3386-3b9b-443f-c89e-d7ae1809435e"
+        }
       },
       "source": [
         "plt.figure(figsize=(20,16))\n",
@@ -355,13 +435,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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7/di4thMRceI9hVEAAADUzUwlGBiGPL29fuR4LuTpbvcmcr/Nrd07gdJQf/8g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\n",
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\n",
             "text/plain": [
               "<Figure size 1440x1152 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
diff --git a/examples/tutorials/16_Conditional_Generative_Adversarial_Networks.ipynb b/examples/tutorials/16_Conditional_Generative_Adversarial_Networks.ipynb
index 29989b8a91..ed4dfc78d8 100644
--- a/examples/tutorials/16_Conditional_Generative_Adversarial_Networks.ipynb
+++ b/examples/tutorials/16_Conditional_Generative_Adversarial_Networks.ipynb
@@ -56,20 +56,76 @@
       "metadata": {
         "id": "gXeKc6O9qSSw",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "002abc70-3ff8-4f10-d74a-02e04737607e"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  90774      0 --:--:-- --:--:-- --:--:-- 90774\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.58 s, sys: 541 ms, total: 3.12 s\n",
+            "Wall time: 4min 12s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -86,11 +142,7 @@
       "metadata": {
         "id": "IdfLLsjGqSTC",
         "colab_type": "code",
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 253
-        },
-        "outputId": "96715d0f-8a61-4fdd-f838-0ae91ceb4e84"
+        "colab": {}
       },
       "source": [
         "import deepchem as dc\n",
@@ -109,49 +161,8 @@
         "    class_transforms.append(m)\n",
         "class_transforms = np.array(class_transforms)"
       ],
-      "execution_count": 2,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n"
-          ],
-          "name": "stdout"
-        }
-      ]
+      "execution_count": 0,
+      "outputs": []
     },
     {
       "cell_type": "markdown",
@@ -198,11 +209,11 @@
       "metadata": {
         "id": "CXy5-cJkqSTk",
         "colab_type": "code",
+        "outputId": "34e4d6bd-d68d-4d5d-9a57-82d235f9b133",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 282
-        },
-        "outputId": "58447aff-5365-464a-b5b7-81e5f1d3df30"
+        }
       },
       "source": [
         "%matplotlib inline\n",
@@ -216,7 +227,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<matplotlib.collections.PathCollection at 0x7fb4b32f9e80>"
+              "<matplotlib.collections.PathCollection at 0x7ff8cf6e5e10>"
             ]
           },
           "metadata": {
@@ -227,13 +238,14 @@
         {
           "output_type": "display_data",
           "data": {
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Cq16eejn5LJiyhBnvzvWu1hXgoVfu55lPBmu55Bp3DX/ph9qKvBCCIFCrRTVa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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
@@ -342,11 +354,11 @@
         "scrolled": true,
         "id": "3o85U5VJqSVG",
         "colab_type": "code",
+        "outputId": "7333127b-9d76-413d-ec3e-99154097dacd",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 700
-        },
-        "outputId": "09322dc4-5cb9-4e52-cd55-c66f7ba2f169"
+        }
       },
       "source": [
         "batch_size = model.batch_size\n",
@@ -377,45 +389,45 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/tensor_graph.py:714: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py:714: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "If using Keras pass *_constraint arguments to layers.\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/layers.py:1634: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/layers.py:1634: The name tf.log is deprecated. Please use tf.math.log instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/tensor_graph.py:727: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py:727: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/tensor_graph.py:1012: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py:1012: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/tensor_graph.py:1012: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py:1012: The name tf.GraphKeys is deprecated. Please use tf.compat.v1.GraphKeys instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/tensor_graph.py:738: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py:738: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/tensorgraph/tensor_graph.py:748: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/tensorgraph/tensor_graph.py:748: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead.\n",
             "\n",
-            "999 0.5353480346798897 0.42010479140281676\n",
-            "1999 0.4970605837404728 0.5927191683650017\n",
-            "2999 0.32337378211319445 0.7835518234968185\n",
-            "3999 0.6442742827236653 0.44793607395887375\n",
-            "4999 0.7197044906020165 0.3560899528264999\n",
-            "5999 0.6842574814558029 0.35353413671255113\n",
-            "6999 0.681817068874836 0.35413561046123504\n",
-            "7999 0.6773326816558838 0.36418179589509964\n",
-            "8999 0.680501739025116 0.3696627883315086\n",
-            "9999 0.6853346248865128 0.35303945660591124\n",
-            "10999 0.6879503725767135 0.35497990638017657\n",
-            "11999 0.6917924422621727 0.3506708189845085\n",
-            "12999 0.6924710651636123 0.3495020810961723\n",
-            "13999 0.6911373255252838 0.3482625074982643\n",
-            "14999 0.6910281682610512 0.35186264622211455\n",
-            "15999 0.6905803002119064 0.3522126387357712\n",
-            "16999 0.6895883530378342 0.3526522752642631\n",
-            "17999 0.6900975884199142 0.35136979585886\n",
-            "18999 0.6905963387489319 0.34954379898309706\n",
-            "19999 0.6901651693582534 0.3524894942045212\n"
+            "999 0.40659638777375223 0.46297103410959245\n",
+            "1999 0.3384278678447008 0.8597527861595153\n",
+            "2999 0.28886374438554047 0.9156105797290802\n",
+            "3999 0.42119870174676177 0.840492086827755\n",
+            "4999 0.6085479544401169 0.45372276908159254\n",
+            "5999 0.7160498830676079 0.35652754533290865\n",
+            "6999 0.6727188802361488 0.36548557299375534\n",
+            "7999 0.6745303119421006 0.36139536756277085\n",
+            "8999 0.6672571448087692 0.3725837562680244\n",
+            "9999 0.6735138981938362 0.36844821578264236\n",
+            "10999 0.6764357801675797 0.36131750684976577\n",
+            "11999 0.6835235329866409 0.3585679198503494\n",
+            "12999 0.6849101437330246 0.3555546105504036\n",
+            "13999 0.6862603163719178 0.35470631182193757\n",
+            "14999 0.6857899969816208 0.3557598451972008\n",
+            "15999 0.6868707528114318 0.35640183770656586\n",
+            "16999 0.6868409720659256 0.3557077826857567\n",
+            "17999 0.6868168808817864 0.3548824065327644\n",
+            "18999 0.6882137333750725 0.35393582719564437\n",
+            "19999 0.6891591399312019 0.3511381688117981\n"
           ],
           "name": "stdout"
         }
@@ -436,11 +448,11 @@
       "metadata": {
         "id": "JqJCBFIcqSV3",
         "colab_type": "code",
+        "outputId": "a51bb593-2547-43ee-cf79-9da56f84bf46",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 282
-        },
-        "outputId": "979bb631-6546-4e55-e801-e9fc52ef55c6"
+        }
       },
       "source": [
         "classes, points = generate_data(1000)\n",
@@ -455,7 +467,7 @@
           "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "<matplotlib.collections.PathCollection at 0x7fb4b323f3c8>"
+              "<matplotlib.collections.PathCollection at 0x7ff8c0208b38>"
             ]
           },
           "metadata": {
@@ -466,13 +478,14 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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+cGuejVHdP+f47pN+q0P+LVBhxvB5pARo3ycIAsVKFMGaa/WEnewWO1kp2Yx8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\n",
             "text/plain": [
               "<Figure size 432x288 with 1 Axes>"
             ]
           },
           "metadata": {
-            "tags": []
+            "tags": [],
+            "needs_background": "light"
           }
         }
       ]
diff --git a/examples/tutorials/17_Training_a_Generative_Adversarial_Network_on_MNIST.ipynb b/examples/tutorials/17_Training_a_Generative_Adversarial_Network_on_MNIST.ipynb
index c6bbca1baf..1ce1c921d9 100644
--- a/examples/tutorials/17_Training_a_Generative_Adversarial_Network_on_MNIST.ipynb
+++ b/examples/tutorials/17_Training_a_Generative_Adversarial_Network_on_MNIST.ipynb
@@ -54,20 +54,76 @@
       "metadata": {
         "id": "4qlydaTAr0bv",
         "colab_type": "code",
-        "colab": {}
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "ac58162b-1501-497e-ca5e-25ecf1a2391e"
       },
       "source": [
-        "%%capture\n",
         "%tensorflow_version 1.x\n",
-        "!wget -c https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
-        "!chmod +x Miniconda3-latest-Linux-x86_64.sh\n",
-        "!bash ./Miniconda3-latest-Linux-x86_64.sh -b -f -p /usr/local\n",
-        "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-        "import sys\n",
-        "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
       ],
-      "execution_count": 0,
-      "outputs": []
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  12908      0 --:--:-- --:--:-- --:--:-- 12908\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.41 s, sys: 557 ms, total: 2.97 s\n",
+            "Wall time: 4min 12s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
     },
     {
       "cell_type": "markdown",
@@ -84,11 +140,11 @@
       "metadata": {
         "id": "23zZTDoar0b7",
         "colab_type": "code",
+        "outputId": "7f706e98-a012-4dd4-d1d5-955f453629d1",
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 746
-        },
-        "outputId": "f7947e36-8045-4ffe-9818-63f9609f5380"
+          "height": 530
+        }
       },
       "source": [
         "import deepchem as dc\n",
@@ -109,40 +165,6 @@
         {
           "output_type": "stream",
           "text": [
-            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-            "  warnings.warn(msg, category=FutureWarning)\n"
-          ],
-          "name": "stderr"
-        },
-        {
-          "output_type": "display_data",
-          "data": {
-            "text/html": [
-              "<p style=\"color: red;\">\n",
-              "The default version of TensorFlow in Colab will switch to TensorFlow 2.x on the 27th of March, 2020.<br>\n",
-              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now\n",
-              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
-              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {
-            "tags": []
-          }
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "WARNING:tensorflow:\n",
-            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
-            "For more information, please see:\n",
-            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
-            "  * https://github.com/tensorflow/addons\n",
-            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
-            "If you depend on functionality not listed there, please file an issue.\n",
-            "\n",
             "WARNING:tensorflow:From <ipython-input-2-a137f3ef6722>:10: read_data_sets (from tensorflow.contrib.learn.python.learn.datasets.mnist) is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "Please use alternatives such as official/mnist/dataset.py from tensorflow/models.\n",
@@ -192,11 +214,11 @@
       "metadata": {
         "id": "mmhulNHor0cK",
         "colab_type": "code",
+        "outputId": "c1392973-8910-4a2e-e18d-34e235c59d5e",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 197
-        },
-        "outputId": "00cbc721-ac92-4c24-faf9-de92e02ec368"
+        }
       },
       "source": [
         "def plot_digits(im):\n",
@@ -215,7 +237,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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0Wi3FxcVkZGRIq3hdXR3nz58PqV2RkZFERERIhV1Op5OGhga+9a1vUVVVdU81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\n",
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\n",
             "text/plain": [
               "<Figure size 216x216 with 16 Axes>"
             ]
@@ -245,11 +267,11 @@
         "scrolled": true,
         "id": "8zLMNX5Xr0cW",
         "colab_type": "code",
+        "outputId": "5a0cb8db-18a9-4c8a-dfd7-f5fbf6158e85",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 122
-        },
-        "outputId": "664bd1a7-7884-45ce-95cc-f4ceb9497b75"
+        }
       },
       "source": [
         "class DigitGAN(dc.models.WGAN):\n",
@@ -285,8 +307,8 @@
             "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
             "Instructions for updating:\n",
             "If using Keras pass *_constraint arguments to layers.\n",
-            "WARNING:tensorflow:Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7ffa977f5eb8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
-            "WARNING: Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7ffa977f5eb8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n"
+            "WARNING:tensorflow:Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7fe629c7ca58>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
+            "WARNING: Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7fe629c7ca58>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n"
           ],
           "name": "stdout"
         }
@@ -307,11 +329,11 @@
       "metadata": {
         "id": "lP7x5ZT1r0cc",
         "colab_type": "code",
+        "outputId": "78c9ac0a-fe3f-402e-af5c-8eb95beee60b",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 513
-        },
-        "outputId": "0cf8ecdf-d144-42c7-f73e-9e1763ecb467"
+        }
       },
       "source": [
         "def iterbatches(epochs):\n",
@@ -326,34 +348,34 @@
         {
           "output_type": "stream",
           "text": [
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:191: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:191: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/gan.py:314: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/gan.py:314: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
             "\n",
-            "WARNING:tensorflow:From /usr/local/lib/python3.7/site-packages/deepchem/models/gan.py:315: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/gan.py:315: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
             "\n",
-            "WARNING:tensorflow:Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7ffa977f5eb8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
-            "WARNING: Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7ffa977f5eb8>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
-            "Ending global_step 4999: generator average loss 0.542297, discriminator average loss 0.536499\n",
-            "Ending global_step 9999: generator average loss 0.552842, discriminator average loss 0.547032\n",
-            "Ending global_step 14999: generator average loss 0.571728, discriminator average loss 0.565844\n",
-            "Ending global_step 19999: generator average loss 0.585051, discriminator average loss 0.579021\n",
-            "Ending global_step 24999: generator average loss 0.553326, discriminator average loss 0.54777\n",
-            "Ending global_step 29999: generator average loss 0.5744, discriminator average loss 0.5684\n",
-            "Ending global_step 34999: generator average loss 0.555237, discriminator average loss 0.54947\n",
-            "Ending global_step 39999: generator average loss 0.557185, discriminator average loss 0.551523\n",
-            "Ending global_step 44999: generator average loss 0.544566, discriminator average loss 0.538901\n",
-            "Ending global_step 49999: generator average loss 0.543208, discriminator average loss 0.537636\n",
-            "Ending global_step 54999: generator average loss 0.539997, discriminator average loss 0.534515\n",
-            "TIMING: model fitting took 555.919 s\n"
+            "WARNING:tensorflow:Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7fe629c7ca58>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
+            "WARNING: Entity <bound method GradientPenaltyLayer.call of <deepchem.models.gan.GradientPenaltyLayer object at 0x7fe629c7ca58>> could not be transformed and will be executed as-is. Please report this to the AutoGraph team. When filing the bug, set the verbosity to 10 (on Linux, `export AUTOGRAPH_VERBOSITY=10`) and attach the full output. Cause: Bad argument number for Name: 3, expecting 4\n",
+            "Ending global_step 4999: generator average loss 0.494329, discriminator average loss 0.4893\n",
+            "Ending global_step 9999: generator average loss 0.572398, discriminator average loss 0.566265\n",
+            "Ending global_step 14999: generator average loss 0.578495, discriminator average loss 0.57274\n",
+            "Ending global_step 19999: generator average loss 0.592253, discriminator average loss 0.586059\n",
+            "Ending global_step 24999: generator average loss 0.614171, discriminator average loss 0.607895\n",
+            "Ending global_step 29999: generator average loss 0.580813, discriminator average loss 0.574856\n",
+            "Ending global_step 34999: generator average loss 0.550079, discriminator average loss 0.544504\n",
+            "Ending global_step 39999: generator average loss 0.533705, discriminator average loss 0.528218\n",
+            "Ending global_step 44999: generator average loss 0.51776, discriminator average loss 0.512585\n",
+            "Ending global_step 49999: generator average loss 0.517917, discriminator average loss 0.512593\n",
+            "Ending global_step 54999: generator average loss 0.507587, discriminator average loss 0.502417\n",
+            "TIMING: model fitting took 390.000 s\n"
           ],
           "name": "stdout"
         }
@@ -374,11 +396,11 @@
       "metadata": {
         "id": "fSQtVhSer0ck",
         "colab_type": "code",
+        "outputId": "43a24f21-715f-40c7-971a-488c26ddca7d",
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 197
-        },
-        "outputId": "b03f0782-a009-4b79-c32d-83306f102678"
+        }
       },
       "source": [
         "plot_digits(gan.predict_gan_generator(batch_size=16))"
@@ -388,7 +410,7 @@
         {
           "output_type": "display_data",
           "data": {
-            "image/png": 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NkydPoqmpadocjLQAj51KmimTUmR1qaqqQnV1NSorKyGRSBAIBDA4OIhdu3bhwIEDYUtP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\n",
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JGF2yQS22gEL6NTZs2ICysjIIhUJ6Y4VCIYyMjKCpqQnj4+N0LRkZGTAajbBYLNDpdFCpVODz+UgkErQzLycnh2aS3G43uru7MTExQSuc8wWXy51VHEkkEhgbG6Mdi3a7nWZ7LoZEIgG/34+jR4+iv78fBoMBZWVluOeee5CRkQG9Xo/Vq1ejvb19XvnkK4GEF9u3b6ftFfv374fD4UAgEJiXUZIuU7L7sCwLv9+Pd999Fy6Xa37rmtejzwPZGoknIGlDk8kElUoFsVhMww6/308PlOd/8eR5mZmZtOCS/JhU5MFJG2lpaSmys7NpRicSicDj8aC3txenTp3C4OAggsEg5HI5MjMzkZWVhZqaGpSXl4PP50OhUNCbjRziuFwueDwe+vr64PP54PV651xpOx/EgyfvLjMzM3C73XC73fD5fPT3FwPLsgiHw+jr64PVaoVarcbk5CS2bt0KjUYDhUKB2tpa2O32lBk48dr5+fm45ZZbIBAIYLPZ8MwzzyAajc77Rid1EBJCkvPFkSNH5l1Im7eBX26xHA4HMpkMjz32GNasWYPs7Gy43W6cPXsWzc3NeOaZZ2C32+n2SErGXC4XarUaq1evRl1dHSQSCc0GMAyDQCBwRe9/JfD5fKjVahpGkR6XyclJ9Pf3Y9euXejo6IDL5UIgEKA7kEAgQEtLC/7mb/4GGzZsQElJCQ2XSJGK7DIWi4Xmwx0Ox4LWKZVKaaqNpNgMBgP0ej20Wi3sdvslU6bE25EfkukZHBzEyMgIrTPccMMN6OzsRHt7e0oOxXw+H7fffjseffRRZGZmYnBwEOFweEHGDQDZ2dnQ6/W0EjozM4PBwUG89tpr8w6rUlqqJx6upqYGBoNhVl7baDRixYoVmJiYgMvlooemeDwOHo+H7OxsmuclKUWyO5DYazEIh8Ow2Wzo7OyEXq9HWVkZAFDvKxAI6NZPjIMYr9frhc1mw8jICPR6Pe1XCQaDNO612WyYmZmBy+Wa9zaaDHLATc4eyGQy1NbWIhgMYnJyElNTUwiFQvQwJhQKIRaLaf8J2SVJp2N+fj70ej1kMhl9D7FYDC6Xu2jHAQDFxcXIz89HRkYG+Hw+rQjPF6Q78/vf/z7WrFlDd/I9e/bgxRdfXNCZIeW9KCzLwuFwQKlU0jSiSqWCRCKBTqfD1NQU3G43nE4njSdJTt1isUCpVAI4F0uStFhPT8+iDZy8Xm9vL8rKysCyLI2dFQoFvSFJ6ALMrtSSaippmyWhAOlvttlsGBsbg9PpXHC5nhhzYWEhzR4A5xyHXq+HxWKhDkMkEtE2X61WC7VajenpaXoD+Hw+OkxSUVEBlUpF++3JDZKqFggOh0MbuhiGoQ1z5P3m6sUVCgXKyspQX18Pk8kEn8+HpqYmHDx4ECdOnFjQ2lJq4NFoFG63G7/73e9w8803o76+HllZWdRDkjAB+LBkT6qapGeBdPIlEglMTEygo6MDBw8eTEmPB8uyOHbsGEpKShCLxSAUCqHVasHj8bBmzRp0dnbC4/HQbjc+nw+ZTIb8/Hy6bSZXZBmGgd/vh91uR3d3N44dO4axsTH4fL4Fbc3EwCsqKuj2TMI4mUwGlUqFWCwGkUgEkUgEs9mMRx55BKWlpcjKyoLdbsfRo0dx6tQpdHZ2YvPmzSgoKKCTU1wuF16vF4cOHaKH6VRgZGQELpeLrtVoNNIGKZ/PN6ddgmEYFBUV4Yc//CH0ej2i0ShGR0dxzz33wOPxLDwrtaBnXQLxeBw+nw9vvfUW3n//fajVaqxduxYPP/wwysvLIRQKaboNAO1NJsZAvGMkEoHb7UZ7ezv27t1L88ypwNTUFI4cOYKnn34aDz/8MJ0uuummm8CyLHp6ejA6OopIJAKlUomsrCzU19fTtk0SF5MbUiAQIBAIwOFw0GLQYtZqs9nw4osv4stf/jK0Wi0YhoFAIEBmZiZqamrw+OOPIysrC0qlEnK5HBkZGXRNYrEYer0eO3bsQDgcpiEicRzBYBA+nw85OTnQ6XSQy+WYmppa9DXNy8uDTqej/xYKhSguLkZjYyPeffddHDt2DH/9618vaegMw+DWW2/Fli1bsHbtWvD5fLz66qt46qmn4PV6F3U90xKi+Hw++Hw+WnnMzs7G+Pg46urqYDQaZ8XYycWfeDxOexBGRkYwMDCAgYGBlFbd4vE4RkZGcPDgQWzZsgX5+flQq9XQaDSora1FVlYWXC4XwuEwJBIJNBoNcnNzIZVK6cAAmT4in3V6ehp9fX2YnJxEIBBY8HpZlsXMzAyamppw7733QiqVQiKR0N4ZLpeLuro6aLVaiMXiWRVi4MNQIXm8jmSJrFYrAoEAgsEgbSArKipCc3Pzog+ahMSHgBSYyE5pNBphNBrh8/kwNDSE/v5+RCIRZGRkQKvVIiMjAzfccAMqKiogEonQ0tKClpYW9Pb2Lvq7T+tETyQSQVdXF/7whz+gvb0dPB4PQqGQxmckFCEXmAwZOBwO9Pb2oq+vDyMjIynvnRgbG8PU1BSOHj1KzwhCoRBVVVW00zEYDFImKOKZAdCBAXJjer1e9Pf34+jRoxgeHp5XzHk+WJbF1NQUmpqaMDw8DLlcTiecJBIJ5Yy53PPJn5FIBLFYDF6vF1arFQcOHKDhTSQSgcVigVgsRktLy6Krr8Qx+f1+2rlIbso1a9Zg1apVuPvuu2G323Hw4EG88sor8Hg8qKqqQllZGcrKylBYWAipVAqPx4M333wTx44dS0lvPTOfD7ZQhtHkw1ppaSnq6+uxadMmnDp1CnK5HFKpFCzLQqvVgsvl0ni7paUFe/fuxfDw8FwrYPNibRUKhXjkkUdw3333obKykrwGgA/DjORdhhh/IpGggwOPP/44mpqa5jWGdaV1MgyD6upqrF+/Htu3b8fWrVtnHTqTkbwmj8cD4Fy/zwsvvIDe3l5YrVa0tbWBz+dj5cqV2L59O/72b/8WLpcL7e3teOCBBy7nJU+wLLvqcmsFALVaDaPRiJKSEjzxxBPIzMyc1f+f9LlnHdJJlTIej2N8fBzNzc146qmn0NvbSzNCcwV7CXbZJRk6Jh/M7/ejv78fPp8P3d3dcDqdEAgEdKsl2RaRSASlUonx8fG08maEw2G89dZbcDqd+PKXv4ycnBzIZLJZIRRwzohIVZFUWH0+H+0LGR0dTUm6jYBlWVitVoRCIfT39+O1116btYuQPHxySwTx2iS709vbC7fbDY/Hg5mZGfB4PHR0dMDv9+Ps2bN0p0zF2cbv99PD9aOPPgqFQgGTyYTPfvazyMzMpEMKpCcmeeY2GAzi5MmTeP3119Ha2krtI1Vh6ZJ48Cu8Jv3gMpkMMpkMGRkZ0Gg0mJycxMDAAPVMV8J8PTiBTqfDD37wA1RWVsJsNsNsNs+aPiLjasTQI5EIHA4HWlpa8LOf/WzeN+JC10luOpJhIQY9HxDaDpIvv4KBz8mDnw+RSASLxYJ/+7d/g8VioWN9xLj5fD4t38/MzKCxsRG7du1aVMx9KQ9+1Q08lVio4RDI5XI0NDTgV7/6FVQqFWWwIt1x5Is5ceIEjhw5gt/85jfw+/3z9oKLXecSYkEGTkCyZQKBACqVig5jWCwWNDY2Ynh4GC6XKyXeetnA5wDSMlBaWjqLWiGZFSqRSGBmZgbT09MYHR1d0Jfzf8XACUiFkoSfYrEYLpcLoVAoZW27ywa+SCSzay02q/N/zcCXAlf1kPlxQCoMexlLj2uD7n8Zy0gTlg18GR9rLBv4Mj7WmG8M7sS1Ky1nSfr78jpTg4/KWi2X+o/lLMpVwFKsM0V86ksiBEtakAFcdhzvcljOoqQZ6Sbony9SRGGRdo9NxhwJU6/D4YDP50sdi1lKXmUZ15RxA0g7r3qqQPhasrKykJGRkXL66WUPvoyrBsIy9tRTT4HH4+Hs2bP41a9+taD2h0thyQ2cEPokEom0K2ylCmR4Vy6XQ61Ww+12w+v1Ympq6iPhJa81cLlc6PV63HXXXbjhhhtQXV1NhzVqa2spU1oqsKQGzjAMNm7cCIlEglAohMbGxquiSDYXMAwDs9mMwsJC2r5LDHx6ehpOpxODg4Po7+9HMBhMabvsfNcpFoshlUopF+TVWsuVwOFwkJmZidzcXEqft379ekilUgDnho6T6fRSguSG/iv9AGAX88Pn89menh42Go2yDoeD1Wq1LJfLXdRrJv+kap0AWKFQyD788MOsy+ViQ6EQG4lE2Gg0ysZiMTYQCLBWq5V9+eWX2ZqaGlalUl21dQoEAra4uJi97bbb2MLCQlahUKTsegJoSfU1/cQnPsHu3buXnZycZIPBIJtIJNh4PM5Go1H26NGj7NatW1mxWLzg7/4Cm10qA6+oqGCfeeYZdmZmhmVZlg2Hw+yf//xnduvWrXN6PofDYXk8Hvu/6aq0GY5IJGItFgv77rvvsjabjY3H42wikZj1E4/H2VAoxE5PT7PHjx9nv/Wtb7F8Pn9JDVyhULCrVq1i9+/fz3Z1dbGdnZ3stm3bWKPReM0aeGZmJvsP//AP7MjICBsIBNhoNEqv5/T0NPviiy+yUqn0st/xfA18SUIUHo+HjIwMrFu3btZ0R/KE/ZVQXFyMvLw8yOVyNDc3w263IxgMpnytZrMZn/rUp1BZWUmn2gHQkTXC1kTI+3NycmCxWGA0GtMyP3op6HQ6fOITn8CKFSugUqloau1qSp5fCStXrkRZWRmUSuUsvsl4PI6jR4/igw8+SLkwbNoNnPRYm81myiZFRtj6+vrmTOhTX1+PHTt2ID8/H//yL/+CY8eOpdzAeTweSkpK8P/+3/+bNU9IhmoJHTChciCy2EqlErm5uRgbG1sSKW+ifvHggw9Sokty8wEf5sCvJTAMg4aGBtTU1NCYm9QOwuEwXn/9dbz77rspf9+0GrhQKMSKFSvwxBNPoL6+nv7e6/VieHgYTz755JwMXCAQoKSkBNdddx0UCgWqqqooM1YqsXHjRmzcuHGWccdiMfT29uKVV17Be++9h87OTmi1WhQVFeGWW26B3+9HZ2dnWsWdzofJZEJubi50Oh0dwhCLxbj//vuxb98+nDhxAlardUnWMlewLIvR0dELeFhCoRCOHz+O48ePo7e3N+Xvm1YDV6vV+PrXv46SkhKIxWKwLIvnn38eXV1d6OrqgsfjuazHI57ytttuQ3l5OeXUu/nmmykp5kIJHi+GhoYGbNq0if6bCFLt3r0bH3zwAbq6uuByueDz+eDxeOhwrNvtTqs0yPm45ZZbcOONN9Itnlyn9evXIycnBzt27EBvby+ef/75a8bQiXpG8vB0IpGAy+XCr3/9awwPD6dl10mrgctkMtx+++10S4rH49i7dy+amprQ1dV1+YX9L1OTVqvF1q1bqc4mn89HXV0dpqenIZFIKPPRYo2Lz+ejsrISFRUVAEA5T6xWK/bu3YvBwUGa9yb8LUSNgDx+KQycw+Fg3bp1s3ZE8vvCwkIUFxcjEolgcHAQ3d3dCAaDKd/pFoqMjAzI5XIAoBP1drsdb775ZlrOU8ASxOCEQzsWi8Hn86GlpQWDg4OXfDw5fJrNZlRVVWHjxo24/vrrqZ4ieYxEIkFubi56enoQDocXFfvyeDxaKiZfQCKRQFtbG3bv3o2Ojo4LBKZI7EhYrpYin8/hcKBQKGCxWGA2m+nvCYl/KBSCRCKBWCxGaWkp/uu//gvvvPMOHnroobSv7UpgGAY333wzSkpKAJxbc3d3N5qamhYsFjAXpNXASZdYMg/4ihUrwOFw4HK5kJmZic2bN6O6uhoajYZSGRNebqlUCrlcDoVCMUtrk8PhwGQy4e6778avf/1rTE5OLsrADQYDfvnLX9JDMABMTEzg1KlTeOedd2YZL/kcQqGQ8o0TaY5Ua2SeD51Oh5/97GcoLS0FcO4me+2113Ds2DE0NTWBZVlkZWWhqKgIDz74IORyOdauXYuf/OQnePLJJxdF67wYkKFjwn+TSCTgdrvR1taGI0eOpPW90+7BCXsRKcWuW7cOJpMJ09PT0Ol02LhxI2pqai7QwiEkO0SLkrwO+RGLxVT9bDF3P+EK37Bhw6xQ6ujRozhx4sQsFlby3kKhEBKJBDKZDF6vN7WVt0uAMMw2NDRAo10pr4MAACAASURBVNFQSb6DBw/i0KFDaGlpAcMw0Ov1sFqtyMvLw+bNm6HX67F161YcPHiQkhQtNVQqFRWkJRkeQqgqEAig1WoxMzOTll0wrQaebHiEcOarX/3qLLpkYrgXAzFwIkWYPNUej8fptryYC3PdddfhlltumSUbEgwG8aMf/Qg9PT30MyTvQkqlklIgOJ3ORYdIc4FAIKCiuxwOBw6HA0eOHMGePXswMTEB4Nz1ttlscDgc6OzsxK5du7Bp0yZUVlbim9/8Jl566SU888wzS94/U1JSgn//93+nWR+y25WWlkIkEsFut+PEiRPpOains5KZnZ3NHjp0iJ2ammITiQTLsuysamDy32OxGBuLxdhoNMpGIhHW6/WyDoeDtVqt7NTUFK18xeNx1uFwsC+88AJrNBpZHo+3oAohwzCsXq9nd+7cyU5PT7MELS0t7I4dO1ipVHrJ50mlUlaj0bAmk4kViUQsl8udV/VtIdfzO9/5DtvW1kav4f79+1mTyXTJVgcOh8N+4hOfYHfu3Mm63W52amqKbWpqYv/jP/6DValUs67bpT4nUlDJNJvN7Oc+9zlalk8kEmwsFmMdDgfrcDhYm83GWq1WdteuXeznPve5BVdJr0ol0+v1Ys+ePbjjjjtQWloKk8lEvfX59MmkUpicjSCxLiHhAc7Rle3btw+NjY2w2+0LTi0JhULce++9qKuro6oSRHO+ra3tsp2O5FBJFB/Iukn4Qs4L4XCY6vgsFgaDAfn5+QDOcQFOTU1hYmLikh4vkUigt7cXBoMBU1NTyMzMRE5ODhiGwUMPPURFWDs6OhAMBuH1ejExMUHZc4mixUJBwsgdO3Zg69attIKdTJFNmsJkMhlKSkoQDAbR2dmJ06dPz1ly8EpIq4G73W788pe/hEgkQjQapbImwOx2WXIIAT7cUQi5PFGGYP5XK3F6ehrPPfcc3n///QUbN4fDgUQiwSOPPEK3TeCc2JHD4bio4SQT/wDnsgCEkJOAaGaqVCokEglMTk6mTL+eyOoB567rXApLQ0ND0Ov1GB4eppqfWq0WtbW1VFx2165dsNvtGBgYmEWjtlgDJ+JZn/nMZ7B+/XoA575bcnP29/fD6/UiFotRdb3y8nLceuutsNvtKWvFWJJelJ07d+Lll1/G+vXrsW3bNoTDYVitVjz33HOUiP0zn/kMiouLKSegUCikMTg5dff39+Oxxx5Da2vronoWyIH3fIHZ0dFRjI6OXjB+xufzIRKJwOPx6OGI/BDPzeVyYbFY8PDDD2Pz5s1UVe5K+f6F4Hwd0UshEAjg9OnTeOyxx3DnnXeipqYGFRUVUCgU4HK50Ol0+PSnP422tjZ88MEHNKORip6WrKwsvPHGG8jOzqYOxG6347nnnsPjjz+OYDBI34PD4aCoqAj19fX44he/iFWrVuHVV1/F008/vag1AEtk4JFIhOrHjI2NUWJ2Mn8XDAZx+vRpmEwmqNXqC5qwOBwO3G43hoeHcebMmUXr9SSHQ8m/Y9lzkioajQbRaBQKhYJ6lrGxMYyNjdGK2/lhiVKpxB133IHq6mro9XqYzWYIBIKUVec8Hg+cTifVCZJIJDAYDJfUHiUIBAKwWq04fvw4BAIBjEYjxGIxOBwOQqEQBgYGcOjQIZw8efKCXP9CQdK8XC4XIyMjsNvtaGpqQl9fHzo6OjA9PX3B7jAxMYHp6WlkZGRApVIhKysLAoFg0anXJRt4CAaDVJIkGeSiWq1WhMPhWYoKJCxIJBIYHx/H2bNnYbfbF/0lkCzM+dVHoVAIjUZDq4Ekbq2oqEBzczMmJydnGTfLnpMzUalUyM/PR0NDA/Ly8qgCGrA4vZ5k2Gw29Pf3Q6fTgc/nQ6VSobS0FAMDA/D5fAiFQrPOAyzLUinsaDQKm80Gp9M5ayhiamoKJ06cQFNTE3p7exdEx3wxkLDS4XBgZGQEnZ2deOWVV+h7XAxer5cK8BI22o+UgV8OJB7ncDjUeyfrlDscDuzZswe7d+9OyRdA1N2SX4thGBQXFyMzMxMrVqyg0oGRSAS7du1Ca2sruru7Zz2PYRgoFAps3LgR999/P9avXw8ul4upqSkcPnwYTqczZenD119/HTabDfX19ZDL5airq8OPf/xjDAwM4OzZs2htbaUk9CR9KpPJoNfrUVVVhZycHCoT4na70dfXh9bWVjz66KMp161XqVQQiUTYtWsX9u3bN6cuS3Lukkql9PxFiPIXg2vCwPl8PlVXSFZiIxLO3/3ud9Hc3HxJffaFIBaL4fDhw1i5ciWys7MBfDh7mZeXBy6XC5vNBqvViqNHj2JiYoIaN7kBeTwebr/9dqxfvx7FxcWIx+MYHh5Gb28vzU4QjvHFwuFwYHBwEIlEAlwuFwqFAqWlpbBYLFi1ahVuueUWjI+PY2ZmBoFAgJ4J1Go1lEolxGIxFRjweDw0C0PEtaLR6KIPdSRzwuFw4PP5cPjw4Tnzf4vFYohEIsRiMTgcDthsNirnuBhcdQMnMSzxLsRzA+e+1Pb2dhw+fJiGB6lCLBbD+++/T+NlcmAjBSnymKmpKTidTqo9zzAM5bjWaDSor69HSUkJlEolPB4PRkZGqCEqlUpEIhFMTk4uer1EApCsUSgUQigUQqVSQafTIScnB2azGTMzMwiFQpBKpTCbzdQjcjgc2lZw8uRJdHR0wGq1QigUIjs7G6FQaFEHYhJaZmRkQCaT0WJULBabE2eMyWSCyWQCAHR3d6dMFuaqGzjxRlu2bIFWq6XGnUgkcOzYMezcuRPj4+MprxRGIhH89re/RXFxMVatWgUej4dwOIxIJEIzN16vlyp9ESPhcDjQarXIyspCTU0NNm/eDK1Wi0QigcHBQXR0dGBwcBCFhYVQKBQYGhpKiYEDoLtHck8OAcMw0Gg01AtqtVoqXEWMKxgMYmxsDD/60Y9oBVYoFGLHjh0IBAKLMnAejwepVIoVK1aAy+UiEokgFArRGPpK8f3GjRuxadMm8Pl8vPnmmzh58uSC1zJrXSl5lUXgrrvuwm233QatVktjrkQigW9/+9s4evQo2tra0lYGDwQCcLvdmJqaQkZGBj39EwnqgoIC6PV6lJaWwmq1wuv1oqamBgaDgarDabVa2lloNBqxbt061NbWIjs7m0pRnz59etFsTSQsSj4gJxs78OE2T7JByc7i5Zdfxv79+/H222/DbrfPeo3f/OY3ixNb/V/1hszMTGzcuBEOhwNTU1PIycmBRCJBOBzG+Pg4WltbMT09PSvFy+VyIZfLUVNTg7KyMkQiEQwMDMDhcCx4PbPWlpJXWQA4HA5ycnJQU1OD2tpaKhHi8XjQ3d2N48ePU0qGdIE03I+Pj1NJ72R9eKFQCJlMBpFIhKysLIRCIeTm5tKCFTkEk6ELIu/NsixcLhf6+vrQ09OTkqGMrKwslJSU0IMvEcWKxWJU4IkcjJOLUiMjI3jrrbdw8OBBtLe3Y3h4+ILXJoK9CwX57OFwGO3t7XC73QgEArSRjTSHVVVV0UKa0+mE1+tFPB5Hbm4uMjMzwefzkUgk4PP5UnbeumoGzuVysXLlSlRXV6OoqIhupXa7HW+88QY6OztTIjN9JYyNjaGnpwcVFRX04Jg8xAAASqUSOTk5AD4UVkpuLyCxrc/ng8vlgsvlwvT0NN5991309PSk5CYtKSnB2rVraSwdDocRjUYRCoUgEAggkUjoTUeGCWKxGFpbW/G9730PMzMzaZvTTCQSVFH57bffRiwWA4/HQ35+Pm699VaqIC0UCjE2Nobu7m60t7dTBb3Vq1dTrhxSxErVrn1VDJykg77whS+gvLyc5ronJibwwQcf4Kc//Wnae6sJXnvtNfT29mLr1q1QKBSzKpvJ23/y32OxGJ3qCYVCGB4eRmdnJ44ePUoHOojgaaqMauXKlbj55ptpnwiZGyVqyKSwEg6HMTExgSeffBKNjY0YGRmhB+R0gWXPSS0me10ul4vh4WEajkgkEiQSCUilUhgMBnR1dcHtdqOnpwcnT56kO9Qtt9wCg8FAw5zF4qoYuF6vR0VFBQoLC6FSqcCyLDweD3bv3o133313yYwb+LAA9d3vfhcVFRUoKytDQ0MDNXTSUEV6T/x+P1577TUMDAzA7XbTqqzT6YTNZqN69alu+wwEAvB4PEgkErTVIPnMQNKqe/fuxYEDB7B//36Mjo6m3biTkfyZ4/E4fD4fmpubaQ/K4cOHYbfb4XQ60dPTA5vNhunpaUSjUXi9XkxPTyMSiWD9+vXIzc1FVlYW3nvvvcX1xKTig80Xer0ea9asgU6ng1gsRjgcRn9/P9588000Nzcv6VoSiQSmpqbw+9//HmvWrMHatWuRkZFBU4WkH4ZkBNxuN1566SW0tbXB4/Es2Szm1NQUxsbGaB4cABVVTSQSNEPS2NiIV1999aoMNiSDeHXSGRgMBvHiiy/S/Pb09PSs6+b1euH1ehEIBHDHHXfAaDRCIBDg8OHDiwtX0tkPfqmfT37yk2xXVxcbiUTYSCTC9vX1sRs2bJg3Bdr5P6laJ8Mwl/1Z7OsvZJ0rVqxgH3roITYYDLLhcJiNRCJsPB5n4/E4a7fb2UOHDrFGo5EVCAQp+Y7+9yelzFZzvfb/9E//xD777LPsSy+9NGemq6vSD34xyGQyqFQqaDQahMNhjIyM4NSpUylpokoVlsIjzxfhcJj2bPf29qK/vx/Hjx+HzWajvd0ul+uaJTOdK1iWxZ49eyCRSGj35mK+jyU3cMIeGggEkEgkMDQ0RA8c16JhXSvw+/0YHR3Fvn37Zhm43W7/yBv1+Ugll8uSG7hIJILX68WZM2eg1+vR3t6O1tbWZeO+AiYmJjAxMYHDhw9f7aV8pLDkIlQ8Ho+GKVVVVRgYGMDY2Niiiw0AwC6LUKUay1Le80UsFsPMzAz8fj8dZEhF19gylnExXJU0IcuyiEajGBoaSgnt2jKWcSlcVSHYFDdRWZL+/lERLb2W1wl8dNZqudR/zCsGX8YyPmqYlwf/qBw0lteZMiyJ0nEqcKlD5rIQ7DIuh2s1JJkzlg18GR9rXPWJno8Kksk3xWIxpWZL9UT6MlKLZQO/BAwGAzQaDTQaDW1HJdP+MpkMPB6PNvmPjY2hv79/SQSo5gMyCCyRSCCVShEOh2kP+/8VLImBJw8LfBSyNhwOB5s3b8b111+PG264ARKJhBp3LBaDXC6n42F2ux1/+MMf8K//+q/XTLMYADrGplQqUVBQgLKyMthsNnR0dGBkZOT/zK6TUgNPniwh/IJmsxl5eXnQ6/UwmUx44403KJ3XUg42zAVarRZr167Fo48+CpPJBLlcDolEcoFWJhlrYxiGktxcKw1PEokEZrMZX/va1ygRqF6vh16vp/OOjY2NeOGFF9KurpAKzIVy4nJImYFzOBwYDAYUFhbSqROhUIi8vDyUl5fDaDRCp9NBKBTCZrPBbrfj5MmTGB8fv6D5falBtG9uvPFGNDQ0oLy8nKoREEQiEUqFIJPJKCE/4Va8FvTh+Xw+Vq9ejfr6eqxZs2aWCptSqaSEphs2bIDb7YZAIMCBAweu8qpng2EY5OTkIDs7G3l5eeBwOBgbG8P4+Dg6Ozvn/XopMXBCR1xcXIzt27fTHl4+n4+8vDxUVVUhMzMTUqkUlZWVCAaDcLlcePbZZ3HkyBGqBpZKScD5rF0sFiM3Nxef+9znsHbtWshkslmPiUaj8Hg8lBGKMF8xDAO73X5RMsmlBtlNtm/fjrvuugsajQajo6OYnJzExMQEtFot1Go1xGIxampqIBKJkJ2djQ8++CBlnIQLXbdIJJolc7Nq1So0NDRg+/bt4PP5OHLkCA4fPoyhoSE6mDxnLGaih2EYVigUsjqdjv3iF7/I/v73v2e7u7vZ3t5etq2tjW1ubmZbW1vZkZER1u12s+FwmE6jRKNR1u/3s5OTk2x7ezv77W9/m7VYLFdlUuaBBx5ge3t7WY/Hw0ajUZYgHo+zwWCQff3119kvfOEL7MqVK9m6ujr2nXfeYWdmZth4PM7+6U9/Yu+55560r/NyPwzDsDKZjN25cyd77Ngx1mq1si+99BJ7/fXXs3q9npXJZOytt97K7ty5k52YmGBjsRgbDofZoaEh9uGHH2bNZvNVmehhGIbNyspi//jHP7Jvv/0229LSwrpcLtbv97PhcJgqf4RCIdbhcLB/+ctf2Orq6vRN9CSzvQKgKTOdTofa2lqUlJRQIhyRSET5O4hQVPLzyHMFAgGEQiHuuusuFBUVoampCbt27VoSjyIQCGAymVBcXAydTkcPjsC5kOTMmTM4dOgQDhw4QIdkuVwupqenEQwGIZVKcfToUfT09KR9rZeDQqFAdnY2MjMzEQwG0dPTg927d6Onp4cO9XZ2diIvLw82mw0qlYpmWAhdXrrB4XBQV1eHyspK1NXV0TOMVCrFddddB7FYDLFYDIVCMSs0JGceQjiq1+shkUjmPEw9bwMnKlnk7yKRCJmZmcjPz4fJZIJUKqVkNEKhkGpJJh/UyGsBoIuvr69HWVkZTCYTDh06hImJiflvR/P8LAKBAFlZWSgoKKCZEgK324329na88soraGlpoZQICoWCku6wLIuOjo6rOuDLMAwyMjJQUVFBec3Hxsbw3nvvUQUFAJQ+2efzUd4S4NwIYboMXCAQQKfT0fGzzZs3Y8uWLbjpppuu+J6k45RcWz6fD7PZjIKCAgwMDKCvr29Oa5h3DE7uPEICKRKJYDabaTaByJEQGjZyI5z/GheDQqHA9u3bsWHDBtx3331oa2ujCmLpgFqtxsqVK7Fly5ZZFzwej1PPffz4cQSDQQgEAiiVSmzcuJHuVJFIBMPDw3A6nWlb45VApBm/9a1vITs7GxMTE7DZbBfIpAsEAigUChgMBkoeRMiXXn311ZSvSyAQID8/Hzt37sTatWtpcex8urmLgdBMjI+P484774RIJEJpaSmeeeYZfP3rX0d5eTm+8Y1vzMn5LciDA6CCnsFgECMjIzh79uws1tPkxRIQBiQAFzA08Xg8KJVK8Pl8SCQSPProo/jzn/+MX/ziF2kJV7hcLqqrqyltM7nosVgMfr8ff/3rX3H8+HFEIhGo1Wrccsst2LBhA+rq6pCbm4uBgQH8+c9/Tskk0mKgUqmQmZkJg8EALpeLkydP4u23374gzy2TyaDRaKDT6SAQCKgY1P/8z/+k/AblcrkwmUz4+c9/jqqqKur4bDYbAFAR3eRrTjJRsVgML7zwArq6utDd3Q2r1QqFQgGtVkuJWuVy+ZxtYkExOPBhTjgYDFJVYODDrYSEJWSLJB9ifHwcwWCQsrj6fD5EIhFIpVJUV1dDp9NBKpWipqYGLS0tkEqlaSmg8Hg8rFq1ikplEESjUbjdbqpNLxQKsX79ejQ0NGD9+vXIy8ujHNb79+9fUmKdi4Ewgh09ehSJRAJHjhxBe3v7BQagUqmgUqkofzchDD116lTKJ6qKioqwbt06rFu3jp5ryPsRp6ZQKMAwDEKhEHp7ezE6OkrDp3feeQe9vb00a1JYWEjp/QgXTNoMnCyWGLjX64Xf78crr7yCgYEBOJ1O7Nixg/JSBwIBRCIReL1ejI2N4Z133sHQ0BDGx8dp+i0Wi8FoNOKRRx5BfX09CgoKIBAIoFarkZOTg66urpRW3khq6r777qPCqsC5m5Zo2oRCIfD5fBgMBvz0pz9FVlYW5b0OBAKw2WzXRKHE5XLhhRdewIsvvnjZLZsU25IdTzAYRGtra0oLblwuFw888AAefPBBqhxN7IXP5yMUCsHj8cBgMCAej2N8fBw///nPsX///ovSZHO5XNxxxx34zGc+Q+1vPsWfeRl4NBq9oKBB6HzHxsYQDAYxOjoKp9NJY73Dhw9Tii6XyzVLI4bQjQHnqMl27dqFgYEBfO1rX4NSqcSqVavwzW9+E1/72tdS2j/B4/EgFouhVqupfiNwbpfp7+/HU089BavVCpZloVQqqcIaubDBYDCtrLfzwVyZtUjG6vy2iVTVHkhG5Pvf/z62bdtGQxA2ieaZ2A7hLff5fFScbGpqapZxk/rEt771LezYsQNmsxkA8Ic//AHvvPPOnNc170PmpS5GNBql2o0tLS1QKpVgGAanT5/G9PQ0AoEA5ey72GuwLIv+/n5KvEi04C0WyyUPpQtFZmYmKisrKRsreX8iWWK1WiEQCJCbm0s1cUjWATh3gxAlio9CTwfDMMjPz4der6e/I2T+qTBuLpdLw5INGzYgKytr1qGd5qR5PCos5fV64fF4KGVbsuMkZzm1Wk1V60iq+ezZs/Mi6k9pLwrh7ztx4gT1zoQw8kqIx+OYmJiA1WrFxMQEjEYjVT270ql7PuBwOMjOzsbmzZtppofsQr29vejs7MTk5CQUCgXq6+vx4IMPUu1O8liRSAS5XA65XA6v13vNGznDMKiurobFYpn1u1RdV3Ke+frXv47S0lIq3AuAHhxJyjcSidDEBBGFJSlbctPx+XwoFAqYTCYUFRVBKpXSc1tvb++8iIFS3k1IRFuBuW+fBOSxJAUpFovp6XmxTTcEMpkMZWVluOmmm8AwDD3sTk5O4le/+hU++OADTE1NUa7wjIyMWTsIy7IQCARYs2YNnn/+eXzpS19KmZ5MOkDaKGpqapCbm0t/H41GUxZ7RyIRuFwuDA0NwWKxgGEYSgp6+vRptLS0YM+ePejo6KBtHBwOB2VlZSguLsZ3vvMdeL1eyOVyWgshcukZGRk4evQojh49iqeffhoej2dea0tLu+x8DZsgmQqYsKheLI++EJDCU21tLUpLS2kemzCd9vT0wG63IxwOQyAQoKKiAvn5+RCLxRekOhmGgVwux4oVK/DDH/4Qp06dwvHjx3H8+PFrpquQID8/H5///Oeh0+lmXce//OUveO2111LiNFj2nJLEe++9h02bNs1SmSD8352dnRf07AwMDIDD4eCTn/wkJBIJPRc5nU4qGkskTaxWK9xu97x3y7QZ+HxBjIYIgAKYpSa8WA/O5XIhlUpRVlYGi8UCiURC6ZDtdjv6+voQDAap9yktLaVqxUQ2O5FIwO/3QyAQUNXg++67D2azGX6/HydPnrys6vDVgNlsxqc+9SmaliNiWW+99RZeeumllL3P5OQkWltbZzkkDoeDoaEh9Pb2XlRzx+VyQSaTISMjgypARCIRtLS0oLm5GQcOHIDL5aLSiAtByg38/JL8XJ8jEomwcuVKrFu3Djk5ORAKhTR+W6xSAvHeWq2WboEsy9IW2GAwCKfTCT6fD6lUCr/fTx8Xj8cpUWggEMCpU6dQXFxMJaeTCemvNRIjPp8PuVyOrKws+r14vV5s2rQppSK1wDn+8p6engti++HhYYyMjFz0OSzLIhaLwe12w2AwIBaLYXx8HC+88ALa29sxODi46DWmxMDJISH5w11MC57IYCc/j8TZ27Ztw0033YTi4mJqOENDQzh27FhKtv3MzEzcdNNNVC9GIBAgEAhAJBJBp9OhuroaY2NjYFkWMzMzlHBeJBLB7XZjeHgY4+PjGBoaQjgchslkoroyJDy52i2z5+POO++kZw0A9LDn9/tTHkqRvPr5jqi8vBwjIyOX7R0JhUJUEOzAgQM4ffo0JiYmUnI9F23gZCzKYDBQISQSVgDnJEL8fj/dupPTRTKZDEqlEnq9Hg0NDbRvPJFIoK+vD6dOnVo8wz8+zHxkZ2dDp9NBoVCAy+UiFotRZWWdTgetVguPxwOxWEy1HmdmZuBwODA8PEz7Tnw+HwKBAPU+NpsNNpvtmsumVFVVobKykv7b6XSio6MjLX33ZEdsbm5GZWUljEYjAKC0tBSjo6N49913EQwGZ70v6WgkM64+nw9nzpyZJby7WCzawBUKBWpqarBt2zaYzWbodDqEQiE6ETM4OEg1JoFz4YJarYbBYEBeXh4d7iVtm+FwGA6HA7/97W9x7NgxtLW1LfrLIM8XCoWzxtCi0SjUajUEAgEmJyeRmZlJwyFywwaDQUxNTcHj8SAQCEAoFEKhUEChUAD4UIHY7/fPOidcC3OopaWlKCkpof9ubW3F008/nbZRwWAwiH/8x3/E9773Pdx1113gcDi4/vrrwePx8Oqrr2JwcHDWzUUawAoKCiCTyeDz+WhzW6qwKAOXSCQwGAyoq6vDddddB6PRCIVCQTMNiUQCOTk5WLlyJa1cEu8tFoupQhgpnMTjcYyMjOCPf/wj3njjDTidzpSd8icnJ7F3715s3LiR9h4bDAaIRCLw+XyaRguHw4jH41AqlTSjU1VVhVAohGg0Cg6HQ9cNAIWFhaitrcU999yDvXv3oqOjA16vF3fffTcKCwsxNjaG3/zmN+jq6loyigkul4uMjAyo1WpIJBIAgM/nQ09PDxobG9OW0kwkEujs7MQ3vvENPPPMM3jrrbfA4XCwZs0avPnmm/jTn/6EY8eOobm5GdFoFNXV1Vi7di3UajWAcyT/Vqs1peHTvA082UvF43H4/X7YbDaa30yWj04kEtSYibIumfYWCAS0y4x4u+npaQwODtLSbarEQIFzF6+rqwvPP/887V0XiUQoKiqi8TQ5KMbjcVqsSB7ySM7okOsgEokgEonouaG0tBShUAjr1q2DXq+f1Ri0VJ6cx+NBr9fP+i727NmDAwcOzFIZTgcikQgmJiaQSCTwi1/8AnfccQfMZjMsFgs2bNhAQ9iTJ0/SrBmXy4XL5cLU1NSiJUvOx6IMPBqNYnp6Gm1tbdi4cSPkcjlVDCZ5bCIpHYvFEI/HZ3WEJRt3OBzG8PAwzpw5Q9tUU4lAIIC+vj78/Oc/R15eHlasWAG9Xo+bb74ZUqkUYrGY3niXqvAlGyox9mTukS1bttDPSTx1IpHAzMwMvcGXAjweD7m5uVQpLpFI4IknnkBvb++SvD8A2O12fOMb30B2djZtg66srIRQKASfz8eZM2eocyC9TMPDwynfXeZtw8CBXQAAGEdJREFU4MlbbCKRgMfjwZkzZ/Dkk09i27ZtuPvuu1FeXk4/CPHk0WiUDg6QUAX4UFzpxIkTePbZZ9Ha2ppSz30+/H4/Ojs70dXVBYZh8Lvf/Q5arRZ33XUXPv3pT6OoqOiCoWPgQ6MmLcLRaJQKm5ID1vT0NBobG3HkyBHs27cPgUAA4XA45V7pStBqtfjBD36A/Px8xONx2mu91IdglmXxyU9+EllZWdi8eTPEYjGMRiOMRiOUSiWysrKQnZ0Nj8eDZ599FgcPHkz5GhZ9yCS5zPHxcRw6dAg+nw8PPfQQsrOzodFoaP8B6RxTqVRQKBTg8/kIh8MYGBhAd3c39uzZg9bWVkxOTqbic10WyenKaDSKeDyOffv2YWRkBBaLBaWlpWhoaKCGPj4+ju7ubthsNsjlcgwNDdHeZYlEQnU0PR4PhoaG6HjY1WAJAM5ltkjhpL+/H7t27VoSWfSLgcwAHDx4ECqVCpWVlVCr1TCZTFCr1eDz+XC5XBgbG0vLd5+yQo/H40FXVxcmJydRV1dHW1JJeEKMgEzwEK9utVrR3NyMffv2pXUG83IIh8Po6OhAR0cHMjIysGrVKohEImg0GgBAb28vmpqaMDAwAK1Wi56eHszMzNA0I9mdroUWWhICqlQqMAyDkZER7N69+6rKxJDwUKFQQK1Wo7S0FJmZmTR7Rlom0nE+SHk34fj4OL7//e/jS1/6Eu644w4UFRVBLBbTgxiJv3k8HrxeL06fPo1Dhw7B7/dfE1VAp9OJt99+G3v37qW/uxbWNVeQ0TQulwuv10tz+NcCPB4PRkZG0NXVBblcjoKCAlRXV0MoFEIqlaa0a5Qg5aV6MjD6wgsv4P3334daraasSjwej4YnhLGIlGSvNSO61tYzVxQXF6O+vh7hcBj/+Z//icbGxqu9pFkYHR1FY2MjHVFcu3btrIGSVCMtzVbxeBxDQ0MYHR2lxRVy6CRVKw6HA4fDAZfLtayylkJkZWWhqKgI4XAYbW1tOHPmzNVe0iz4/X4EAgGIxWK43W6Ew2HI5fIL2jhShbSxy5LDJ5lSX0b6QYY5SkpK6KTMtXAuSAbJRpFhB6fTCaVSSTNOqcYyP/jHCIlEArt378Ybb7wBhUKBwcHBa3YQAwAaGxtht9tRUVGBsbGxtLzHsoF/zOB2u+HxeCAUCq95ovuxsTFEo1HYbDa4XK60vMeSS3mnE+xHRL3so7JOpFnKmwxuk5nYxRzs2RRJeX9UxECX15kapHWt8Xg8VXUPy6X+Y1kIdhkfaywLwV4FfFTWiSUSgr1Y/nu+jjdVIcoy/m8h7eET6ShNJmBKZXvxsoEv46qBGLZEIpl1yEylpMqygS9jyUC0RslgiV6vh06ng8lkgsfjgdvtxtjY2IL4Ty6FZQNfJNI9e5kqRq9rAQzDQK1WQ6/XIxaLoaysDLm5uTAajZicnKQSMeFwmA51LxbXpIEn0zRfa5PqySAeiRh5qqkYyLRQuvrKF8Jhsxjw+XyUlJTgxhtvRDQaxfr161FeXg4+n08pOQwGA1wuF+Lx+MfLwMm0vdlsxoYNG1BdXY3u7m40Njbi9OnT15Shc7lcZGdnY8eOHdDr9QgEApiYmEBjYyPcbnfK+j94PB5EIhEd2AbOGX0qxt/+7u/+Dg0NDdBoNHj77bfR3t6OlpaWVCz7kjCbzVi/fj0+/elPIxwOQ6VSQSqVIh6PUw5IHo8Hs9mMEydO4L333sPIyMiiPutVNXDi/bhcLpRKJbKzs1FWVoZNmzah9P+3d20xTZ5/+On5SM8HWmG0HCwMQTwNJbKJBzBx55iwmCwxy7yaS5bMZVmy7HJLll3tZssutmjc1GRB55Jp4mRIEBQdcloLQqmUQ4GW0jNtaen/wv/7rmWwA7TFMZ6kN1jb9/v6+973d3ye0lJKOm82m9ddFZkYm1arxbZt22hbqkQiQSgUgtvthlKphNVqhd1ux9DQECW0WS2Iv0o0j/h8Pp2Wl0qlkMvldPQvHo/D7/fD6XTC4/EgGAzC6/VSlgAmkwkWiwU+n4/6+no0NDRg165dEIlEGB8fx9zcXMYN/MiRI6iuroZGo0E0GqVsCqFQCOFwGAwGAzqdDkqlEhKJBAKBAOfPn1+WUOjvYt0MnMlkQiwW0+FkjUaDoqIiVFRUYN++fdBoNCguLkZZWRk+++yzjBt4snoAgBTCe0IVIZfLsXv3brz++usoLS2limsLCwtYWFjAwYMH0dnZic7OTszOzsLpdK66Q448+ESpTi6XQ6lUYtu2baisrITBYIDJZKK0ELFYDKOjo+jt7cXw8DAcDgdGRkbg9XqpMXG5XCiVSrz//vsoKCiAVCpFOBzGli1boFar03YvV7qexsZGVFZWpjAYkMF1wuibm5sLuVwOg8GA4uJi/PDDD2tiwl0XA+fz+VAqlXjzzTfR3t6Onp4eqqggl8vpkUR+4EyDyWRCKpWiqqoKMpkM0WgUDQ0N4HA4WFhYgMFggNFopJzlhBNlKQ0dm81GfX09TCYTBgcH0dHRsWoDV6lU0Gq10Ol0mJ6exjPPPIPdu3ejvr4eCoWC8rkko6SkBHV1dbS3eumLrJNIi0xNTeHTTz/FTz/9lJWpn9zcXMhkMgCP4xW32w2Hw4GrV69Cq9WisLAQZWVlVJGNnOB+v//fY+BKpRJarRZ6vR6tra2w2Wzw+/2Uo9vtdtPdM3n6PpNgs9nQ6XSoqKiA0WiESqVCeXk5NSCpVAqpVEp1hwh3Cjn6yYvBYMDtdsNms6Gvr2/V4lnk2hcWFuD3+8FkMlFYWIidO3dCpVLR6ajlaKWXU1YgREzJn+90OjE8PIzm5mZMTU1lnPY5WRUjkUggEAhgeHgYFosF/f39UCqV9KFlMBgIhUKYmJiAz+db0+mdVQNnMBjIy8uDWq1GTk4OmpubKW8h8HhmLxAIpPx4mRhjWgoiT6fX62EymfD0009DqVRSQyIgQxyhUIjqFSX7yMluwujo6JqCI/I95CGXSqXQ6XSULIkY71LpD0JQtFRodalC3sTEBHp7e2E2mzOeRSF0EYQWO5FIwOVywWazYXBwEC6XC3w+HxKJhN7v2dlZWCwWuvmtFlkzcMK7/eKLL8JisaCpqWnZwCHZLVlcXMx4wz7ZVTweDzweD0Kh0B+0e8haotEopqam0NXVBa/XC4FAgPz8fEgkEsqtd/78eVy5cmWtrZ9wu90p8h6Dg4OwWq1QqVRgsVi0xXRmZobeI4FAQANbpVJJsxJLjT0Wi6GpqQnnzp3LSoqwvLwcn3zyCTQaDf3+W7duwWKxwOl0YseOHdi7dy9MJhOl8Gtra8NHH3205p72rBh4eXk5iouLsWvXLlgsFgwPDy9r3GTiPjnYy2R6UCgUQqPRQKVSQa/Xo7S0FAaDgQZ2hIvQ4XBQ8aMbN27A4/GAwWBAqVTCZDJBoVBAKBSivb2dchOmC0QJraWlBeFwGBUVFeDz+VTC8bvvvoPD4YDf76e7tFgsRk1NDbZv3w61Wp2ijRmNRtHW1ka1iLKBnJwcVFVVgcvlYnFxEeFwGHfu3MHMzAzYbDYOHjxIOVISiQQGBgYwMjICt9u95gcwowbOYDDA4/GwZ88eqgc/OjqK6enpZd8vlUopVx3hA8yEb0iI7vft2we9Xg+FQgG5XI6ioiKo1WpwuVzK3RKLxeB2uzE6Ogqz2Yy7d+8iFotRHc9IJAKxWAwOh4M7d+7A6/Wmfc2Li4sYHx/HwMAApYUjhEpjY2OwWq3UWMViMdRqNfx+P1WmIIjH4wiHw2hvb4fdbs/KxI9MJoNGo6FiYtFoFMFgEDMzM1QQNj8/nzL+xmIxdHR0wGKxpCVzllEDZ7PZyM3NxcmTJ8FisXDmzBl0d3evmFkg2QoSaCwsLFDpwXRCKpVi69at+Oqrr5CTk0PdES6XS49zFotF01N+v5+SEpG1JRIJ+Hw+PHz4kKpErCVf+1cIBAIp7FQkrUk4FQnxqVarhcFgQEFBAWQyGVUaBh67Bj6fDxcuXMD4+HhG1rkU5eXlKC8vpwWqUChEWYO1Wi2MRiMKCgogEAgoLd7nn3+eNh7FjBm4WCyGyWTCxYsXkZubi7a2NvT09PzpU1lYWAij0UiNLBKJYG5uLq0GzuFw0NDQgPfeew8ajYZmGVgsFj05yIsUV6qrq1FRUQGPx4Pnn38eFosFo6OjGBwchMPhwPz8PCKRSEbdKaLOMD8/T1OVcrkcr7zyClwuFzweDzQaDWQyGaRSKRQKBSQSCTWsQCCA1tZWXLp0iapUZAOnTp3CkSNHADxODc7OzuLRo0coKSnBU089hcLCQlqwCoVCNKuWrrpHxgx83759OHLkCIxGI7q7u/HgwYO/PBKVSiVUKlVKYLc0xbUWMBgMHD9+HEePHkVhYSFlwU0u8iztTyZyggKBAGKxmHJvGwwGyOVydHV1wW63w+v1pmWNK4G4S7OzsxCJRDTDYzAYoNFoMD8/D4lEAj6fT0WyWCwWFc5qaWlBa2srurq60tqOuhJIcExiHAKilbR7927k5uZCq9VSSmW/309PxCe+Xfbo0aM4deoUFhcX8fPPP+PmzZt/+n4Gg0FvRnL+Np0qa2KxGO+++y5MJhMtdgCPA7nkh4+UtUmGhbzYbDYKCwuh0+mwdetW2hUXjUYxOpr50cpoNIpHjx5BqVTSaiBRL05uTCP3irh5Xq8XFy5cQHd3N6xWa8bXCfxePCMuFFmXRCKB0WiEXq+HSCQCn8+nGSGfz4fBwUHae/NEDzzY7XZ0dnYiEong+++/R3d394rvJbtiUVER8vLyaA7Y6XTC4XCkRQh27969+PLLL1FSUkIpnGOxGMbGxjA0NIRz585hcnISAKhqRXFxMUwmE/Lz82l2h5DeE+kVmUwGJpOZFRGqmZkZvP3223jrrbdQW1uL6upqMBgMGnBOTk7SlCWJFcLhMGZmZmC1WpeV8ssUJBIJTpw4QbV6gFRWXyKPTkiASHp4z549kMvlNO5ZKzJm4DKZDDqdDi6XCwBWzGczmUzIZDK88MILyMvLozlw8ndSIid+5GqgVquRn5+PvLw8GpAFg0F88cUXsFqtGB8fh8VioQxcNpsNIyMj1JfNycmBWCyGXq/Ha6+9BoVCQcnyyb8TXzeTiMfjmJ2dxa1bt8BkMlFVVUVz+BMTE7h+/TpqampQWVmZonq3tM8mG2Cz2cjPzwefz6d/I0acXPklGSHSEqxSqfDyyy+jvb0dHR0dT4aM4HKQSCQpXWNLQW66XC5HQUEBamtrqeQH8HszllarpTvSakGI1sViMRgMBoLBIBwOBy5evAibzYa5ubk//J9kPXQGgwGZTEabwcrLy6lgLYfDSekJzySIK9Xb24ucnBzY7XYIhUI4HA4MDAzg5s2b0Ol0KCsrSzFu4I+yjtnA0lYCkjwgFVdyTQBo24NcLsehQ4cQjUYxMDCA2dnZJ7NdlsvlQiQSQSgU0tI7uShS2pbJZHjppZfw3HPPoby8nPYjJB+7gUAAdrt91bs3ANTW1qK2thbA45Okr68PP/74I5WK/iskEgnMzc2hq6sLJ06cwMcff4za2lps2bIFk5OTcLvdWeU1dzgcaG5uhtPpxJ49ezA3N4ehoSH09/fD6/XS2AF4bNiRSIRWabOFQCCAy5cvo6amBvn5+SntBaTYk/wAiEQiquek1+vBYDDg9/tx7ty5J7dUT5qYiFqC2+3G0aNHaXEFANWnVKlUKb0K09PT6OnpQWtrKwKBwJoMiDRvRaNRxONxDA8P48aNG/+4DYA0CY2MjECj0WBxcRHt7e0YGhrK+u7o9/vx22+/wev1Qi6XQ61Wo7CwkDZjEUNyOBy4fPky1QnKFuLxOBUSI5kqIqcSCATg8XggFoupC8Nms1MolD0eD6xW65pTrxlllwUet8YWFxfTiztw4ADy8vKgUCgQjUZp37NAIEhp3rfZbOjp6UFHRwctmZO0HilekKpeIpH407wuUZYgN5rL5SInJ+cfuxUsFivFZSIVxrUeo6sBEaFls9m0f1qlUkGn09HgLRKJwOl0oqurK+s8hYlEAvPz83T6iLQckOGQZDkbsrGRIJ4UpBwOx5NbqieBA4fDgdFopC2y+fn5NHWU/HQmBx2k96KtrY2OqxGO8Wg0SvVngsEg1a9cqfwPPO57Jn0PHA4HO3fuxBtvvIE7d+787fEy0uNx+PBh1NXVoaioiBrZWtyn1YKk1lgsFvLy8vDss89Cq9WioKAAfD6fpgfHxsZw7969rBs46ZUhpwbpg3G73bDb7RgZGYFarYZKpYJGo0nZcILBIJxOJ+x2+5O7g/f396OlpQUHDhyA0WikwwzJpWPg93ZYYtzE5zabzXA4HGCz2aisrMSrr76K6upqxONxDAwMYGxsDDabDc3NzVQvZyX09PSAx+Nh+/btqKqqotJ2H3zwAa5evYqurq4VbySDwYBUKsXJkydx7NgxmEwmqn8TCARoU9Z6gMFgYP/+/aitrcWOHTsgFAohFArp7rlU0jDbIG4S+f7kWVJSOJNIJDT2IkFoOBymG9dakTED7+vrozLf8Xic+ljA7xF9NBpNSRkBoCNiZWVlkMvlmJ+fh8lkQlVVFVQqFVpaWtDT0wOHw4GZmRkEg8G/vBEulwsWiwVXrlyhfqpMJkNdXR0kEglqampSovrkAhOLxYJIJEJdXR0qKiqgUCgo5W9bWxtsNhu8Xu+6UTts27YNJSUlVIuS+LFEjIrP51OhrGyukfy+RL6QVIRFIhHUajVtNZDJZLRoRRIR09PTaeNGyZiB379/Hy6XC42NjeDxeBCLxTQVlDw4kBxckN5lqVSKQ4cOIRaL0WZ/DoeDmZkZfPPNN3j48CF8Pt/fXovP54PZbMbExAQaGxshFoshEomwf/9+7N+/n75vaZRP5hiTT5x4PA6v14v+/n58/fXXMJvNWc1OLMX27dtppybZHIDH8QLZ0fl8ftZVNkgMQE4Romwtl8vB5XKRm5sLLpcLHo8HgUCQUsyz2WyYnp5+sg2cRPBnzpzBsWPHUFVVBYVCQXdrFosFLpdL/WJi5DweD3w+H0KhkOpTnj59mu7Wq51UJ40+x48fR2NjI9555x1otdo/jHKR3W85MViyc3/44Ydoa2vLiDLvP0EikaCZCJKlICDDEOPj43C5XOsiAksSC5FIhD5oRLOJIDlXT8R0L126hAcPHqRlHRlNE0ajUQwNDWF0dBR5eXk0H57cK5HsnpDdcXh4GE1NTZicnITT6cTY2FhamI5I+vHatWuYmpqCUCikLaelpaVoaGiAXq9PydkvLi7CbDajt7cXVqsVNpsN7e3tmJ6eTllPcr9MNl0BgUAAHo+Xci/JvX348CGGhobWzQcnqcK5uTnI5XL6dxJvJW8uJPZqbm6GxWJJm+JDRg2c7CJut5u6I8mzlktZoYhe+b179/Dtt9+mjGOlC/Pz8+jt7UVvby8AUEqImpoaKJVKbN26lY5NkV3x9u3b+OWXX/DgwQPYbLYV88nrQbNGWmeXVi6ZTCYePXqUlSawlZBIJGC32zE2NgaDwZDCVkaC4FgsBr/fD5fLBbPZjGvXrmF8fDxtbl/GR9YSiQT1v4g/m8w9QvKjk5OTOH36NG7fvp1VWcHFxUV4vV5cu3YN169fX/Ea/grJwWm2sNSok08PBoOBkZGRrHUPLodEIoHz58/D4XBg165dlKVrfn4eFosFNpsNQ0NDaGpqgt1uRzAYTPtpk5WZzK6uLuTk5ODw4cPg8Xj05fV60dLSgs7OTjqmtJ4B21qNcz16PZKHislpF4/H4XK54PP51l2Iyu12U3qKSCQCi8WCu3fvwuVyIRwOIxAIwOl0rpkFbCVkxcAnJyfR19eHGzdu0KhZIBDA5XLh1q1b6OzsRE9Pz4ZhUc0GOBwOpVkg/SZOp5NWC/v7+zExMbHuGqVE3r25uRmhUAgDAwP49ddfs/bgZU1ljYyAkUwJj8fD7OxsWp/af4s0SDrWKZVKUVZWhrNnz0KtViMYDKKzsxMejweTk5M4e/YspqamEAqF1nKP06KyRgLgTPbCrCRhknUZwWS/Md0X/F8ycMIzWF9fj0AgAJvNBp/PR0fbXC4XJd5cAzIqI5hOrGTgWaduy3YabaOCsMnev38fwWAQLpcLCwsLNOAkFcT/OjaFYNcB6VonGSr+/2fi/5+Z/D1r+XjgP7iDP8nCpf8WgdW0rXM59yPNp+O/8Z6mYFMIdhMbGn/k393EJjYQNg18Exsamwa+iQ2NTQPfxIbGpoFvYkNj08A3saGxaeCb2NDYNPBNbGhsGvgmNjT+B9IB/uG0A3EsAAAAAElFTkSuQmCC\n",
             "text/plain": [
               "<Figure size 216x216 with 16 Axes>"
             ]
diff --git a/examples/tutorials/18_Using_Reinforcement_Learning_to_Play_Pong.ipynb b/examples/tutorials/18_Using_Reinforcement_Learning_to_Play_Pong.ipynb
index 0fef300233..7d7287c975 100644
--- a/examples/tutorials/18_Using_Reinforcement_Learning_to_Play_Pong.ipynb
+++ b/examples/tutorials/18_Using_Reinforcement_Learning_to_Play_Pong.ipynb
@@ -1,272 +1,392 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial Part 18: Using Reinforcement Learning to Play Pong\n",
-    "\n",
-    "This notebook demonstrates using reinforcement learning to train an agent to play Pong.\n",
-    "\n",
-    "The first step is to create an `Environment` that implements this task.  Fortunately,\n",
-    "OpenAI Gym already provides an implementation of Pong (and many other tasks appropriate\n",
-    "for reinforcement learning).  DeepChem's `GymEnvironment` class provides an easy way to\n",
-    "use environments from OpenAI Gym.  We could just use it directly, but in this case we\n",
-    "subclass it and preprocess the screen image a little bit to make learning easier.\n",
-    "\n",
-    "## Colab\n",
-    "\n",
-    "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/18_Using_Reinforcement_Learning_to_Play_Pong.ipynb)\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. To install `gym` you should also use `pip install 'gym[atari]'` (We need the extra modifier since we'll be using an atari game). We'll add this command onto our usual Colab installation commands for you"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!chmod +x Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local\n",
-    "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-    "import sys\n",
-    "sys.path.append('/usr/local/lib/python3.7/site-packages/')\n",
-    "import deepchem as dc\n",
-    "!conda install pip\n",
-    "!pip install 'gym[atari]'"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
-      "  warnings.warn(msg, category=FutureWarning)\n",
-      "RDKit WARNING: [17:47:46] Enabling RDKit 2019.09.3 jupyter extensions\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:516: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:517: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:518: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:519: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:520: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:541: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:542: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:543: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:544: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:545: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
-      "/Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorboard/compat/tensorflow_stub/dtypes.py:550: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
-      "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
-     ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    },
+    "colab": {
+      "name": "18_Using_Reinforcement_Learning_to_Play_Pong.ipynb",
+      "provenance": []
     }
-   ],
-   "source": [
-    "import deepchem as dc\n",
-    "import numpy as np\n",
-    "\n",
-    "class PongEnv(dc.rl.GymEnvironment):\n",
-    "  def __init__(self):\n",
-    "    super(PongEnv, self).__init__('Pong-v0')\n",
-    "    self._state_shape = (80, 80)\n",
-    "  \n",
-    "  @property\n",
-    "  def state(self):\n",
-    "    # Crop everything outside the play area, reduce the image size,\n",
-    "    # and convert it to black and white.\n",
-    "    cropped = np.array(self._state)[34:194, :, :]\n",
-    "    reduced = cropped[0:-1:2, 0:-1:2]\n",
-    "    grayscale = np.sum(reduced, axis=2)\n",
-    "    bw = np.zeros(grayscale.shape)\n",
-    "    bw[grayscale != 233] = 1\n",
-    "    return bw\n",
-    "\n",
-    "  def __deepcopy__(self, memo):\n",
-    "    return PongEnv()\n",
-    "\n",
-    "env = PongEnv()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Next we create a network to implement the policy.  We begin with two convolutional layers to process\n",
-    "the image.  That is followed by a dense (fully connected) layer to provide plenty of capacity for game\n",
-    "logic.  We also add a small Gated Recurrent Unit.  That gives the network a little bit of memory, so\n",
-    "it can keep track of which way the ball is moving.\n",
-    "\n",
-    "We concatenate the dense and GRU outputs together, and use them as inputs to two final layers that serve as the\n",
-    "network's outputs.  One computes the action probabilities, and the other computes an estimate of the\n",
-    "state value function.\n",
-    "\n",
-    "We also provide an input for the initial state of the GRU, and returned its final state at the end.  This is required by the learning algorithm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import tensorflow as tf\n",
-    "from tensorflow.keras.layers import Input, Concatenate, Conv2D, Dense, Flatten, GRU, Reshape\n",
-    "\n",
-    "class PongPolicy(dc.rl.Policy):\n",
-    "    def __init__(self):\n",
-    "        super(PongPolicy, self).__init__(['action_prob', 'value', 'rnn_state'], [np.zeros(16)])\n",
-    "\n",
-    "    def create_model(self, **kwargs):\n",
-    "        state = Input(shape=(80, 80))\n",
-    "        rnn_state = Input(shape=(16,))\n",
-    "        conv1 = Conv2D(16, kernel_size=8, strides=4, activation=tf.nn.relu)(Reshape((80, 80, 1))(state))\n",
-    "        conv2 = Conv2D(32, kernel_size=4, strides=2, activation=tf.nn.relu)(conv1)\n",
-    "        dense = Dense(256, activation=tf.nn.relu)(Flatten()(conv2))\n",
-    "        gru, rnn_final_state = GRU(16, return_state=True, return_sequences=True)(\n",
-    "            Reshape((-1, 256))(dense), initial_state=rnn_state)\n",
-    "        concat = Concatenate()([dense, Reshape((16,))(gru)])\n",
-    "        action_prob = Dense(env.n_actions, activation=tf.nn.softmax)(concat)\n",
-    "        value = Dense(1)(concat)\n",
-    "        return tf.keras.Model(inputs=[state, rnn_state], outputs=[action_prob, value, rnn_final_state])\n",
-    "\n",
-    "policy = PongPolicy()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We will optimize the policy using the Asynchronous Advantage Actor Critic (A3C) algorithm.  There are lots of hyperparameters we could specify at this point, but the default values for most of them work well on this problem.  The only one we need to customize is the learning rate."
-   ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "m0jRtbRGsoZy",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Tutorial Part 18: Using Reinforcement Learning to Play Pong\n",
+        "\n",
+        "This notebook demonstrates using reinforcement learning to train an agent to play Pong.\n",
+        "\n",
+        "The first step is to create an `Environment` that implements this task.  Fortunately,\n",
+        "OpenAI Gym already provides an implementation of Pong (and many other tasks appropriate\n",
+        "for reinforcement learning).  DeepChem's `GymEnvironment` class provides an easy way to\n",
+        "use environments from OpenAI Gym.  We could just use it directly, but in this case we\n",
+        "subclass it and preprocess the screen image a little bit to make learning easier.\n",
+        "\n",
+        "## Colab\n",
+        "\n",
+        "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/18_Using_Reinforcement_Learning_to_Play_Pong.ipynb)\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment. To install `gym` you should also use `pip install 'gym[atari]'` (We need the extra modifier since we'll be using an atari game). We'll add this command onto our usual Colab installation commands for you"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qXdmcnhtst-z",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "30790158-71f8-40de-f11d-7ea9c936b71c"
+      },
+      "source": [
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  35620      0 --:--:-- --:--:-- --:--:-- 35175\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.69 s, sys: 598 ms, total: 3.28 s\n",
+            "Wall time: 3min 48s\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "9sv6kX_VsoZ1",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 187
+        },
+        "outputId": "4563471c-497e-42a7-b5ed-22f205381510"
+      },
+      "source": [
+        "!pip install 'gym[atari]'"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: gym[atari] in /usr/local/lib/python3.6/dist-packages (0.17.2)\n",
+            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (1.18.4)\n",
+            "Requirement already satisfied: cloudpickle<1.4.0,>=1.2.0 in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (1.3.0)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (1.4.1)\n",
+            "Requirement already satisfied: pyglet<=1.5.0,>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (1.5.0)\n",
+            "Requirement already satisfied: Pillow; extra == \"atari\" in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (7.0.0)\n",
+            "Requirement already satisfied: atari-py~=0.2.0; extra == \"atari\" in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (0.2.6)\n",
+            "Requirement already satisfied: opencv-python; extra == \"atari\" in /usr/local/lib/python3.6/dist-packages (from gym[atari]) (4.1.2.30)\n",
+            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyglet<=1.5.0,>=1.4.0->gym[atari]) (0.16.0)\n",
+            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from atari-py~=0.2.0; extra == \"atari\"->gym[atari]) (1.12.0)\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "EuRrb3vpsoZ_",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import deepchem as dc\n",
+        "import numpy as np\n",
+        "\n",
+        "class PongEnv(dc.rl.GymEnvironment):\n",
+        "  def __init__(self):\n",
+        "    super(PongEnv, self).__init__('Pong-v0')\n",
+        "    self._state_shape = (80, 80)\n",
+        "  \n",
+        "  @property\n",
+        "  def state(self):\n",
+        "    # Crop everything outside the play area, reduce the image size,\n",
+        "    # and convert it to black and white.\n",
+        "    cropped = np.array(self._state)[34:194, :, :]\n",
+        "    reduced = cropped[0:-1:2, 0:-1:2]\n",
+        "    grayscale = np.sum(reduced, axis=2)\n",
+        "    bw = np.zeros(grayscale.shape)\n",
+        "    bw[grayscale != 233] = 1\n",
+        "    return bw\n",
+        "\n",
+        "  def __deepcopy__(self, memo):\n",
+        "    return PongEnv()\n",
+        "\n",
+        "env = PongEnv()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "GNnO3MZ_soaG",
+        "colab_type": "text"
+      },
+      "source": [
+        "Next we create a network to implement the policy.  We begin with two convolutional layers to process\n",
+        "the image.  That is followed by a dense (fully connected) layer to provide plenty of capacity for game\n",
+        "logic.  We also add a small Gated Recurrent Unit.  That gives the network a little bit of memory, so\n",
+        "it can keep track of which way the ball is moving.\n",
+        "\n",
+        "We concatenate the dense and GRU outputs together, and use them as inputs to two final layers that serve as the\n",
+        "network's outputs.  One computes the action probabilities, and the other computes an estimate of the\n",
+        "state value function.\n",
+        "\n",
+        "We also provide an input for the initial state of the GRU, and returned its final state at the end.  This is required by the learning algorithm"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BLdt8WAQsoaH",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "import tensorflow as tf\n",
+        "from tensorflow.keras.layers import Input, Concatenate, Conv2D, Dense, Flatten, GRU, Reshape\n",
+        "\n",
+        "class PongPolicy(dc.rl.Policy):\n",
+        "    def __init__(self):\n",
+        "        super(PongPolicy, self).__init__(['action_prob', 'value', 'rnn_state'], [np.zeros(16)])\n",
+        "\n",
+        "    def create_model(self, **kwargs):\n",
+        "        state = Input(shape=(80, 80))\n",
+        "        rnn_state = Input(shape=(16,))\n",
+        "        conv1 = Conv2D(16, kernel_size=8, strides=4, activation=tf.nn.relu)(Reshape((80, 80, 1))(state))\n",
+        "        conv2 = Conv2D(32, kernel_size=4, strides=2, activation=tf.nn.relu)(conv1)\n",
+        "        dense = Dense(256, activation=tf.nn.relu)(Flatten()(conv2))\n",
+        "        gru, rnn_final_state = GRU(16, return_state=True, return_sequences=True)(\n",
+        "            Reshape((-1, 256))(dense), initial_state=rnn_state)\n",
+        "        concat = Concatenate()([dense, Reshape((16,))(gru)])\n",
+        "        action_prob = Dense(env.n_actions, activation=tf.nn.softmax)(concat)\n",
+        "        value = Dense(1)(concat)\n",
+        "        return tf.keras.Model(inputs=[state, rnn_state], outputs=[action_prob, value, rnn_final_state])\n",
+        "\n",
+        "policy = PongPolicy()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "WARNING:tensorflow:From /Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
-      "WARNING:tensorflow:From /Users/bharath/Code/deepchem/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bharath/Code/deepchem/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bharath/Code/deepchem/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bharath/Code/deepchem/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bharath/Code/deepchem/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bharath/Code/deepchem/deepchem/rl/a3c.py:32: The name tf.log is deprecated. Please use tf.math.log instead.\n",
-      "\n",
-      "WARNING:tensorflow:From /Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/tensorflow/python/ops/math_grad.py:1250: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
-      "Instructions for updating:\n",
-      "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
-     ]
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "YU19h0aUsoaN",
+        "colab_type": "text"
+      },
+      "source": [
+        "We will optimize the policy using the Asynchronous Advantage Actor Critic (A3C) algorithm.  There are lots of hyperparameters we could specify at this point, but the default values for most of them work well on this problem.  The only one we need to customize is the learning rate."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "scrolled": true,
+        "id": "Fw_wu511soaO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 343
+        },
+        "outputId": "64a01d40-960f-4f4a-a21e-cd42457fcc37"
+      },
+      "source": [
+        "from deepchem.models.optimizers import Adam\n",
+        "a3c = dc.rl.A3C(env, policy, model_dir='model', optimizer=Adam(learning_rate=0.0002))"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1630: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "If using Keras pass *_constraint arguments to layers.\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:169: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/optimizers.py:76: The name tf.train.AdamOptimizer is deprecated. Please use tf.compat.v1.train.AdamOptimizer instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:258: The name tf.global_variables is deprecated. Please use tf.compat.v1.global_variables instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:260: The name tf.variables_initializer is deprecated. Please use tf.compat.v1.variables_initializer instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/models/keras_model.py:237: The name tf.placeholder is deprecated. Please use tf.compat.v1.placeholder instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/rl/a3c.py:32: The name tf.log is deprecated. Please use tf.math.log instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /tensorflow-1.15.2/python3.6/tensorflow_core/python/ops/math_grad.py:1424: where (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
+            "Instructions for updating:\n",
+            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "-PUD4JG2soaU",
+        "colab_type": "text"
+      },
+      "source": [
+        "Optimize for as long as you have patience to.  By 1 million steps you should see clear signs of learning.  Around 3 million steps it should start to occasionally beat the game's built in AI.  By 7 million steps it should be winning almost every time.  Running on my laptop, training takes about 20 minutes for every million steps."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Wa18EQlmsoaV",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 105
+        },
+        "outputId": "39aa4c1a-6da2-4b18-a83b-0bac0a62155a"
+      },
+      "source": [
+        "# Change this to train as many steps as you have patience for.\n",
+        "a3c.fit(1000)"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/rl/a3c.py:412: The name tf.assign is deprecated. Please use tf.compat.v1.assign instead.\n",
+            "\n",
+            "WARNING:tensorflow:From /root/miniconda/lib/python3.6/site-packages/deepchem/rl/a3c.py:253: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_xHNjusSsoaa",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's watch it play and see how it does! "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ud6DB_ndsoab",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "# This code doesn't work well on Colab\n",
+        "env.reset()\n",
+        "while not env.terminated:\n",
+        "    env.env.render()\n",
+        "    env.step(a3c.select_action(env.state))"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "3MGK4nrhsoah",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Congratulations! Time to join the Community!\n",
+        "\n",
+        "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
+        "\n",
+        "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
+        "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
+        "\n",
+        "## Join the DeepChem Gitter\n",
+        "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
+      ]
     }
-   ],
-   "source": [
-    "from deepchem.models.optimizers import Adam\n",
-    "a3c = dc.rl.A3C(env, policy, model_dir='model', optimizer=Adam(learning_rate=0.0002))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Optimize for as long as you have patience to.  By 1 million steps you should see clear signs of learning.  Around 3 million steps it should start to occasionally beat the game's built in AI.  By 7 million steps it should be winning almost every time.  Running on my laptop, training takes about 20 minutes for every million steps."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Change this to train as many steps as you have patience for.\n",
-    "a3c.fit(1000)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's watch it play and see how it does! "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "env.reset()\n",
-    "while not env.terminated:\n",
-    "    env.env.render()\n",
-    "    env.step(a3c.select_action(env.state))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Congratulations! Time to join the Community!\n",
-    "\n",
-    "Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with DeepChem, we encourage you to finish the rest of the tutorials in this series. You can also help the DeepChem community in the following ways:\n",
-    "\n",
-    "## Star DeepChem on [GitHub](https://github.com/deepchem/deepchem)\n",
-    "This helps build awareness of the DeepChem project and the tools for open source drug discovery that we're trying to build.\n",
-    "\n",
-    "## Join the DeepChem Gitter\n",
-    "The DeepChem [Gitter](https://gitter.im/deepchem/Lobby) hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation!"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
+  ]
+}
\ No newline at end of file
diff --git a/examples/tutorials/21_Introduction_to_Bioinformatics.ipynb b/examples/tutorials/21_Introduction_to_Bioinformatics.ipynb
index 8313bd7036..3b0144641a 100644
--- a/examples/tutorials/21_Introduction_to_Bioinformatics.ipynb
+++ b/examples/tutorials/21_Introduction_to_Bioinformatics.ipynb
@@ -1,2106 +1,2596 @@
 {
- "cells": [
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Tutorial Part 21: Introduction to Bioinformatics\n",
-    "\n",
-    "So far in this tutorial, we've primarily worked on the problems of cheminformatics. We've been interested in seeing how we can use the techniques of machine learning to make predictions about the properties of molecules. In this tutorial, we're going to shift a bit and see how we can use classical computer science techniques and machine learning to tackle problems in bioinformatics.\n",
-    "\n",
-    "For this, we're going to use the venerable [biopython](https://biopython.org/) library to do some basic bioinformatics. A lot of the material in this notebook is adapted from the extensive official [Biopython tutorial]http://biopython.org/DIST/docs/tutorial/Tutorial.html). We strongly recommend checking out the official tutorial after you work through this notebook!\n",
-    "\n",
-    "## Colab\n",
-    "\n",
-    "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
-    "\n",
-    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/21_Introduction_to_Bioinformatics.ipynb)\n",
-    "\n",
-    "## Setup\n",
-    "\n",
-    "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "!wget -c https://repo.anaconda.com/archive/Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!chmod +x Anaconda3-2019.10-Linux-x86_64.sh\n",
-    "!bash ./Anaconda3-2019.10-Linux-x86_64.sh -b -f -p /usr/local\n",
-    "!conda install -y -c deepchem -c rdkit -c conda-forge -c omnia deepchem-gpu=2.3.0\n",
-    "import sys\n",
-    "sys.path.append('/usr/local/lib/python3.7/site-packages/')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We'll use `pip` to install `biopython`"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Requirement already satisfied: biopython in /Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages (1.76)\r\n",
-      "Requirement already satisfied: numpy in /Users/bharath/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages (from biopython) (1.18.1)\r\n"
-     ]
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "kernelspec": {
+      "display_name": "Python 3",
+      "language": "python",
+      "name": "python3"
+    },
+    "language_info": {
+      "codemirror_mode": {
+        "name": "ipython",
+        "version": 3
+      },
+      "file_extension": ".py",
+      "mimetype": "text/x-python",
+      "name": "python",
+      "nbconvert_exporter": "python",
+      "pygments_lexer": "ipython3",
+      "version": "3.6.10"
+    },
+    "colab": {
+      "name": "21_Introduction_to_Bioinformatics.ipynb",
+      "provenance": []
     }
-   ],
-   "source": [
-    "!pip install biopython"
-   ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
+  "cells": [
     {
-     "data": {
-      "text/plain": [
-       "'1.76'"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Euiq_EOntgQm",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Tutorial Part 21: Introduction to Bioinformatics\n",
+        "\n",
+        "So far in this tutorial, we've primarily worked on the problems of cheminformatics. We've been interested in seeing how we can use the techniques of machine learning to make predictions about the properties of molecules. In this tutorial, we're going to shift a bit and see how we can use classical computer science techniques and machine learning to tackle problems in bioinformatics.\n",
+        "\n",
+        "For this, we're going to use the venerable [biopython](https://biopython.org/) library to do some basic bioinformatics. A lot of the material in this notebook is adapted from the extensive official [Biopython tutorial]http://biopython.org/DIST/docs/tutorial/Tutorial.html). We strongly recommend checking out the official tutorial after you work through this notebook!\n",
+        "\n",
+        "## Colab\n",
+        "\n",
+        "This tutorial and the rest in this sequence are designed to be done in Google colab. If you'd like to open this notebook in colab, you can use the following link.\n",
+        "\n",
+        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/deepchem/deepchem/blob/master/examples/tutorials/21_Introduction_to_Bioinformatics.ipynb)\n",
+        "\n",
+        "## Setup\n",
+        "\n",
+        "To run DeepChem within Colab, you'll need to run the following cell of installation commands. This will take about 5 minutes to run to completion and install your environment."
       ]
-     },
-     "execution_count": 2,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import Bio\n",
-    "Bio.__version__"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('AGTACACATTG')"
+      "cell_type": "code",
+      "metadata": {
+        "id": "9k2qhejltgQo",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 462
+        },
+        "outputId": "d10d7d7d-5fb6-4805-c225-212cfb94a466"
+      },
+      "source": [
+        "%tensorflow_version 1.x\n",
+        "!curl -Lo deepchem_installer.py https://raw.githubusercontent.com/deepchem/deepchem/master/scripts/colab_install.py\n",
+        "import deepchem_installer\n",
+        "%time deepchem_installer.install(version='2.3.0')"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "TensorFlow 1.x selected.\n",
+            "  % Total    % Received % Xferd  Average Speed   Time    Time     Time  Current\n",
+            "                                 Dload  Upload   Total   Spent    Left  Speed\n",
+            "100  2814  100  2814    0     0  35175      0 --:--:-- --:--:-- --:--:-- 35175\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "add /root/miniconda/lib/python3.6/site-packages to PYTHONPATH\n",
+            "python version: 3.6.9\n",
+            "fetching installer from https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh\n",
+            "done\n",
+            "installing miniconda to /root/miniconda\n",
+            "done\n",
+            "installing deepchem\n",
+            "done\n",
+            "/usr/local/lib/python3.6/dist-packages/sklearn/externals/joblib/__init__.py:15: FutureWarning: sklearn.externals.joblib is deprecated in 0.21 and will be removed in 0.23. Please import this functionality directly from joblib, which can be installed with: pip install joblib. If this warning is raised when loading pickled models, you may need to re-serialize those models with scikit-learn 0.21+.\n",
+            "  warnings.warn(msg, category=FutureWarning)\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "WARNING:tensorflow:\n",
+            "The TensorFlow contrib module will not be included in TensorFlow 2.0.\n",
+            "For more information, please see:\n",
+            "  * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n",
+            "  * https://github.com/tensorflow/addons\n",
+            "  * https://github.com/tensorflow/io (for I/O related ops)\n",
+            "If you depend on functionality not listed there, please file an issue.\n",
+            "\n"
+          ],
+          "name": "stdout"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "deepchem-2.3.0 installation finished!\n"
+          ],
+          "name": "stderr"
+        },
+        {
+          "output_type": "stream",
+          "text": [
+            "CPU times: user 2.95 s, sys: 647 ms, total: 3.6 s\n",
+            "Wall time: 4min 2s\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from Bio.Seq import Seq\n",
-    "my_seq = Seq(\"AGTACACATTG\")\n",
-    "my_seq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('TCATGTGTAAC')"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "APnQxtIKtgQs",
+        "colab_type": "text"
+      },
+      "source": [
+        "We'll use `pip` to install `biopython`"
       ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_seq.complement()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('CAATGTGTACT')"
+      "cell_type": "code",
+      "metadata": {
+        "id": "HeYSJWSAtgQt",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "67253897-cae0-4b69-82b2-be881731982c"
+      },
+      "source": [
+        "!pip install biopython"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Collecting biopython\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/66/134dbd5f885fc71493c61b6cf04c9ea08082da28da5ed07709b02857cbd0/biopython-1.77-cp36-cp36m-manylinux1_x86_64.whl (2.3MB)\n",
+            "\u001b[K     |████████████████████████████████| 2.3MB 2.7MB/s \n",
+            "\u001b[?25hRequirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from biopython) (1.18.4)\n",
+            "Installing collected packages: biopython\n",
+            "Successfully installed biopython-1.77\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_seq.reverse_complement()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Parsing Sequence Records\n",
-    "\n",
-    "We're going to download a sample `fasta` file from the Biopython tutorial to use in some exercises. This file is a set of hits for a sequence (of lady slipper orcid genes)."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2020-03-07 20:24:08--  https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta\n",
-      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.40.133\n",
-      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.40.133|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 76480 (75K) [text/plain]\n",
-      "Saving to: ‘ls_orchid.fasta’\n",
-      "\n",
-      "ls_orchid.fasta     100%[===================>]  74.69K  --.-KB/s    in 0.03s   \n",
-      "\n",
-      "2020-03-07 20:24:08 (2.81 MB/s) - ‘ls_orchid.fasta’ saved [76480/76480]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's take a look at what the contents of this file look like:"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "4CxSQrxptgQx",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "0e6411b1-8709-4010-b1b6-d3cfcf1788f1"
+      },
+      "source": [
+        "import Bio\n",
+        "Bio.__version__"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'1.77'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 3
+        }
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "gi|2765658|emb|Z78533.1|CIZ78533\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765657|emb|Z78532.1|CCZ78532\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAG...GGC', SingleLetterAlphabet())\n",
-      "753\n",
-      "gi|2765656|emb|Z78531.1|CFZ78531\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAG...TAA', SingleLetterAlphabet())\n",
-      "748\n",
-      "gi|2765655|emb|Z78530.1|CMZ78530\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAAACAACAT...CAT', SingleLetterAlphabet())\n",
-      "744\n",
-      "gi|2765654|emb|Z78529.1|CLZ78529\n",
-      "Seq('ACGGCGAGCTGCCGAAGGACATTGTTGAGACAGCAGAATATACGATTGAGTGAA...AAA', SingleLetterAlphabet())\n",
-      "733\n",
-      "gi|2765652|emb|Z78527.1|CYZ78527\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAG...CCC', SingleLetterAlphabet())\n",
-      "718\n",
-      "gi|2765651|emb|Z78526.1|CGZ78526\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAG...TGT', SingleLetterAlphabet())\n",
-      "730\n",
-      "gi|2765650|emb|Z78525.1|CAZ78525\n",
-      "Seq('TGTTGAGATAGCAGAATATACATCGAGTGAATCCGGAGGACCTGTGGTTATTCG...GCA', SingleLetterAlphabet())\n",
-      "704\n",
-      "gi|2765649|emb|Z78524.1|CFZ78524\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATAGTAG...AGC', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765648|emb|Z78523.1|CHZ78523\n",
-      "Seq('CGTAACCAGGTTTCCGTAGGTGAACCTGCGGCAGGATCATTGTTGAGACAGCAG...AAG', SingleLetterAlphabet())\n",
-      "709\n",
-      "gi|2765647|emb|Z78522.1|CMZ78522\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAG...GAG', SingleLetterAlphabet())\n",
-      "700\n",
-      "gi|2765646|emb|Z78521.1|CCZ78521\n",
-      "Seq('GTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAGAATATATGATCGAGT...ACC', SingleLetterAlphabet())\n",
-      "726\n",
-      "gi|2765645|emb|Z78520.1|CSZ78520\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAG...TTT', SingleLetterAlphabet())\n",
-      "753\n",
-      "gi|2765644|emb|Z78519.1|CPZ78519\n",
-      "Seq('ATATGATCGAGTGAATCTGGTGGACTTGTGGTTACTCAGCTCGCCATAGGCTTT...TTA', SingleLetterAlphabet())\n",
-      "699\n",
-      "gi|2765643|emb|Z78518.1|CRZ78518\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGGAGGATCATTGTTGAGATAGTAG...TCC', SingleLetterAlphabet())\n",
-      "658\n",
-      "gi|2765642|emb|Z78517.1|CFZ78517\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAG...AGC', SingleLetterAlphabet())\n",
-      "752\n",
-      "gi|2765641|emb|Z78516.1|CPZ78516\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAT...TAA', SingleLetterAlphabet())\n",
-      "726\n",
-      "gi|2765640|emb|Z78515.1|MXZ78515\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGCTGAGACCGTAG...AGC', SingleLetterAlphabet())\n",
-      "765\n",
-      "gi|2765639|emb|Z78514.1|PSZ78514\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAAGCCCCCA...CTA', SingleLetterAlphabet())\n",
-      "755\n",
-      "gi|2765638|emb|Z78513.1|PBZ78513\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...GAG', SingleLetterAlphabet())\n",
-      "742\n",
-      "gi|2765637|emb|Z78512.1|PWZ78512\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAAGCCCCCA...AGC', SingleLetterAlphabet())\n",
-      "762\n",
-      "gi|2765636|emb|Z78511.1|PEZ78511\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTTCGGAAGGATCATTGTTGAGACCCCCA...GGA', SingleLetterAlphabet())\n",
-      "745\n",
-      "gi|2765635|emb|Z78510.1|PCZ78510\n",
-      "Seq('CTAACCAGGGTTCCGAGGTGACCTTCGGGAGGATTCCTTTTTAAGCCCCCGAAA...TTA', SingleLetterAlphabet())\n",
-      "750\n",
-      "gi|2765634|emb|Z78509.1|PPZ78509\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...GGA', SingleLetterAlphabet())\n",
-      "731\n",
-      "gi|2765633|emb|Z78508.1|PLZ78508\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...TGA', SingleLetterAlphabet())\n",
-      "741\n",
-      "gi|2765632|emb|Z78507.1|PLZ78507\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCCCCA...TGA', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765631|emb|Z78506.1|PLZ78506\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCAA...TGA', SingleLetterAlphabet())\n",
-      "727\n",
-      "gi|2765630|emb|Z78505.1|PSZ78505\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...TTT', SingleLetterAlphabet())\n",
-      "711\n",
-      "gi|2765629|emb|Z78504.1|PKZ78504\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTTCGGAAGGATCATTGTTGAGACCGCAA...TAA', SingleLetterAlphabet())\n",
-      "743\n",
-      "gi|2765628|emb|Z78503.1|PCZ78503\n",
-      "Seq('CGTAACCAGGTTTCCGTAGGTGAACCTCCGGAAGGATCCTTGTTGAGACCGCCA...TAA', SingleLetterAlphabet())\n",
-      "727\n",
-      "gi|2765627|emb|Z78502.1|PBZ78502\n",
-      "Seq('CGTAACCAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGACCGCCA...CGC', SingleLetterAlphabet())\n",
-      "757\n",
-      "gi|2765626|emb|Z78501.1|PCZ78501\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCAA...AGA', SingleLetterAlphabet())\n",
-      "770\n",
-      "gi|2765625|emb|Z78500.1|PWZ78500\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGCTCATTGTTGAGACCGCAA...AAG', SingleLetterAlphabet())\n",
-      "767\n",
-      "gi|2765624|emb|Z78499.1|PMZ78499\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAGGGATCATTGTTGAGATCGCAT...ACC', SingleLetterAlphabet())\n",
-      "759\n",
-      "gi|2765623|emb|Z78498.1|PMZ78498\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAAGGTCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "750\n",
-      "gi|2765622|emb|Z78497.1|PDZ78497\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "788\n",
-      "gi|2765621|emb|Z78496.1|PAZ78496\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...AGC', SingleLetterAlphabet())\n",
-      "774\n",
-      "gi|2765620|emb|Z78495.1|PEZ78495\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...GTG', SingleLetterAlphabet())\n",
-      "789\n",
-      "gi|2765619|emb|Z78494.1|PNZ78494\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGGTCGCAT...AAG', SingleLetterAlphabet())\n",
-      "688\n",
-      "gi|2765618|emb|Z78493.1|PGZ78493\n",
-      "Seq('CGTAACAAGGATTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...CCC', SingleLetterAlphabet())\n",
-      "719\n",
-      "gi|2765617|emb|Z78492.1|PBZ78492\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...ATA', SingleLetterAlphabet())\n",
-      "743\n",
-      "gi|2765616|emb|Z78491.1|PCZ78491\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...AGC', SingleLetterAlphabet())\n",
-      "737\n",
-      "gi|2765615|emb|Z78490.1|PFZ78490\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
-      "728\n",
-      "gi|2765614|emb|Z78489.1|PDZ78489\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGC', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765613|emb|Z78488.1|PTZ78488\n",
-      "Seq('CTGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACGCAATAATTGATCGA...GCT', SingleLetterAlphabet())\n",
-      "696\n",
-      "gi|2765612|emb|Z78487.1|PHZ78487\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TAA', SingleLetterAlphabet())\n",
-      "732\n",
-      "gi|2765611|emb|Z78486.1|PBZ78486\n",
-      "Seq('CGTCACGAGGTTTCCGTAGGTGAATCTGCGGGAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
-      "731\n",
-      "gi|2765610|emb|Z78485.1|PHZ78485\n",
-      "Seq('CTGAACCTGGTGTCCGAAGGTGAATCTGCGGATGGATCATTGTTGAGATATCAT...GTA', SingleLetterAlphabet())\n",
-      "735\n",
-      "gi|2765609|emb|Z78484.1|PCZ78484\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGGGGAAGGATCATTGTTGAGATCACAT...TTT', SingleLetterAlphabet())\n",
-      "720\n",
-      "gi|2765608|emb|Z78483.1|PVZ78483\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765607|emb|Z78482.1|PEZ78482\n",
-      "Seq('TCTACTGCAGTGACCGAGATTTGCCATCGAGCCTCCTGGGAGCTTTCTTGCTGG...GCA', SingleLetterAlphabet())\n",
-      "629\n",
-      "gi|2765606|emb|Z78481.1|PIZ78481\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
-      "572\n",
-      "gi|2765605|emb|Z78480.1|PGZ78480\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
-      "587\n",
-      "gi|2765604|emb|Z78479.1|PPZ78479\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGT', SingleLetterAlphabet())\n",
-      "700\n",
-      "gi|2765603|emb|Z78478.1|PVZ78478\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCAGTGTTGAGATCACAT...GGC', SingleLetterAlphabet())\n",
-      "636\n",
-      "gi|2765602|emb|Z78477.1|PVZ78477\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGC', SingleLetterAlphabet())\n",
-      "716\n",
-      "gi|2765601|emb|Z78476.1|PGZ78476\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...CCC', SingleLetterAlphabet())\n",
-      "592\n",
-      "gi|2765600|emb|Z78475.1|PSZ78475\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', SingleLetterAlphabet())\n",
-      "716\n",
-      "gi|2765599|emb|Z78474.1|PKZ78474\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACGT...CTT', SingleLetterAlphabet())\n",
-      "733\n",
-      "gi|2765598|emb|Z78473.1|PSZ78473\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGG', SingleLetterAlphabet())\n",
-      "626\n",
-      "gi|2765597|emb|Z78472.1|PLZ78472\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "737\n",
-      "gi|2765596|emb|Z78471.1|PDZ78471\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765595|emb|Z78470.1|PPZ78470\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GTT', SingleLetterAlphabet())\n",
-      "574\n",
-      "gi|2765594|emb|Z78469.1|PHZ78469\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GTT', SingleLetterAlphabet())\n",
-      "594\n",
-      "gi|2765593|emb|Z78468.1|PAZ78468\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...GTT', SingleLetterAlphabet())\n",
-      "610\n",
-      "gi|2765592|emb|Z78467.1|PSZ78467\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
-      "730\n",
-      "gi|2765591|emb|Z78466.1|PPZ78466\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...CCC', SingleLetterAlphabet())\n",
-      "641\n",
-      "gi|2765590|emb|Z78465.1|PRZ78465\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGC', SingleLetterAlphabet())\n",
-      "702\n",
-      "gi|2765589|emb|Z78464.1|PGZ78464\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAGCGGAAGGGTCATTGTTGAGATCACATAATA...AGC', SingleLetterAlphabet())\n",
-      "733\n",
-      "gi|2765588|emb|Z78463.1|PGZ78463\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGTTCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "738\n",
-      "gi|2765587|emb|Z78462.1|PSZ78462\n",
-      "Seq('CGTCACGAGGTCTCCGGATGTGACCCTGCGGAAGGATCATTGTTGAGATCACAT...CAT', SingleLetterAlphabet())\n",
-      "736\n",
-      "gi|2765586|emb|Z78461.1|PWZ78461\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...TAA', SingleLetterAlphabet())\n",
-      "732\n",
-      "gi|2765585|emb|Z78460.1|PCZ78460\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...TTA', SingleLetterAlphabet())\n",
-      "745\n",
-      "gi|2765584|emb|Z78459.1|PDZ78459\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TTT', SingleLetterAlphabet())\n",
-      "744\n",
-      "gi|2765583|emb|Z78458.1|PHZ78458\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TTG', SingleLetterAlphabet())\n",
-      "738\n",
-      "gi|2765582|emb|Z78457.1|PCZ78457\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...GAG', SingleLetterAlphabet())\n",
-      "739\n",
-      "gi|2765581|emb|Z78456.1|PTZ78456\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "740\n",
-      "gi|2765580|emb|Z78455.1|PJZ78455\n",
-      "Seq('CGTAACCAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
-      "745\n",
-      "gi|2765579|emb|Z78454.1|PFZ78454\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AAC', SingleLetterAlphabet())\n",
-      "695\n",
-      "gi|2765578|emb|Z78453.1|PSZ78453\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
-      "745\n",
-      "gi|2765577|emb|Z78452.1|PBZ78452\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
-      "743\n",
-      "gi|2765576|emb|Z78451.1|PHZ78451\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGTACCTCCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "730\n",
-      "gi|2765575|emb|Z78450.1|PPZ78450\n",
-      "Seq('GGAAGGATCATTGCTGATATCACATAATAATTGATCGAGTTAAGCTGGAGGATC...GAG', SingleLetterAlphabet())\n",
-      "706\n",
-      "gi|2765574|emb|Z78449.1|PMZ78449\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGC', SingleLetterAlphabet())\n",
-      "744\n",
-      "gi|2765573|emb|Z78448.1|PAZ78448\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGG', SingleLetterAlphabet())\n",
-      "742\n",
-      "gi|2765572|emb|Z78447.1|PVZ78447\n",
-      "Seq('CGTAACAAGGATTCCGTAGGTGAACCTGCGGGAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
-      "694\n",
-      "gi|2765571|emb|Z78446.1|PAZ78446\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...CCC', SingleLetterAlphabet())\n",
-      "712\n",
-      "gi|2765570|emb|Z78445.1|PUZ78445\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGT', SingleLetterAlphabet())\n",
-      "715\n",
-      "gi|2765569|emb|Z78444.1|PAZ78444\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGGTGAACTGCGGAAGGATCATTGTTGAGATCACAT...ATT', SingleLetterAlphabet())\n",
-      "688\n",
-      "gi|2765568|emb|Z78443.1|PLZ78443\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGG', SingleLetterAlphabet())\n",
-      "784\n",
-      "gi|2765567|emb|Z78442.1|PBZ78442\n",
-      "Seq('GTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGT...AGT', SingleLetterAlphabet())\n",
-      "721\n",
-      "gi|2765566|emb|Z78441.1|PSZ78441\n",
-      "Seq('GGAAGGTCATTGCCGATATCACATAATAATTGATCGAGTTAATCTGGAGGATCT...GAG', SingleLetterAlphabet())\n",
-      "703\n",
-      "gi|2765565|emb|Z78440.1|PPZ78440\n",
-      "Seq('CGTAACAAGGTTTCCGTAGGTGGACCTCCGGGAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
-      "744\n",
-      "gi|2765564|emb|Z78439.1|PBZ78439\n",
-      "Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet())\n",
-      "592\n"
-     ]
-    }
-   ],
-   "source": [
-    "from Bio import SeqIO\n",
-    "\n",
-    "for seq_record in SeqIO.parse('ls_orchid.fasta', 'fasta'):\n",
-    "    print(seq_record.id)\n",
-    "    print(repr(seq_record.seq))\n",
-    "    print(len(seq_record))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Sequence Objects\n",
-    "\n",
-    "A large part of the biopython infrastructure deals with tools for handlings sequences. These could be DNA sequences, RNA sequences, amino acid sequences or even more exotic constructs. To tell biopython what type of sequence it's dealing with, you can specify the alphabet explicitly."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "7eXZ-43CtgQ6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "67edb7fd-f16a-457f-81f7-e7f1995670f7"
+      },
+      "source": [
+        "from Bio.Seq import Seq\n",
+        "my_seq = Seq(\"AGTACACATTG\")\n",
+        "my_seq"
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('AGTACACATTG')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 4
+        }
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('ACAGTAGAC', IUPACUnambiguousDNA())"
+      "cell_type": "code",
+      "metadata": {
+        "id": "Fd-wViuTtgRB",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ef3c2283-eb10-4c55-808c-41fd0283da2c"
+      },
+      "source": [
+        "my_seq.complement()"
+      ],
+      "execution_count": 5,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('TCATGTGTAAC')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 5
+        }
       ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from Bio.Seq import Seq\n",
-    "from Bio.Alphabet import IUPAC\n",
-    "my_seq = Seq(\"ACAGTAGAC\", IUPAC.unambiguous_dna)\n",
-    "my_seq"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "IUPACUnambiguousDNA()"
+      "cell_type": "code",
+      "metadata": {
+        "id": "GlO-43FNtgRF",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "0466b5b6-ff69-4a00-b071-0bcb857a5291"
+      },
+      "source": [
+        "my_seq.reverse_complement()"
+      ],
+      "execution_count": 6,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('CAATGTGTACT')"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 6
+        }
       ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_seq.alphabet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "If we want to code a protein sequence, we can do that just as easily."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('AAAAA', IUPACProtein())"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "W-LumeWptgRJ",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Parsing Sequence Records\n",
+        "\n",
+        "We're going to download a sample `fasta` file from the Biopython tutorial to use in some exercises. This file is a set of hits for a sequence (of lady slipper orcid genes)."
       ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_prot = Seq(\"AAAAA\", IUPAC.protein) # Alanine pentapeptide\n",
-    "my_prot"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "IUPACProtein()"
+      "cell_type": "code",
+      "metadata": {
+        "id": "U0A0B3-FtgRK",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "24b1cd34-25ae-410c-dd19-6370337da8cf"
+      },
+      "source": [
+        "!wget https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta"
+      ],
+      "execution_count": 7,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2020-05-31 03:16:22--  https://raw.githubusercontent.com/biopython/biopython/master/Doc/examples/ls_orchid.fasta\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 76480 (75K) [text/plain]\n",
+            "Saving to: ‘ls_orchid.fasta’\n",
+            "\n",
+            "\rls_orchid.fasta       0%[                    ]       0  --.-KB/s               \rls_orchid.fasta     100%[===================>]  74.69K  --.-KB/s    in 0.03s   \n",
+            "\n",
+            "2020-05-31 03:16:23 (2.70 MB/s) - ‘ls_orchid.fasta’ saved [76480/76480]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_prot.alphabet"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can take the length of sequences and index into them like strings."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "5\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(len(my_prot))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "iNwHJES1tgRP",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's take a look at what the contents of this file look like:"
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "'A'"
+      "cell_type": "code",
+      "metadata": {
+        "id": "5ZudMHxttgRQ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "1b9b1d3b-11ca-4d5b-df82-bb81b3191fec"
+      },
+      "source": [
+        "from Bio import SeqIO\n",
+        "\n",
+        "for seq_record in SeqIO.parse('ls_orchid.fasta', 'fasta'):\n",
+        "    print(seq_record.id)\n",
+        "    print(repr(seq_record.seq))\n",
+        "    print(len(seq_record))"
+      ],
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "gi|2765658|emb|Z78533.1|CIZ78533\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGATGAGACCGTGG...CGC', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765657|emb|Z78532.1|CCZ78532\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAACAG...GGC', SingleLetterAlphabet())\n",
+            "753\n",
+            "gi|2765656|emb|Z78531.1|CFZ78531\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAG...TAA', SingleLetterAlphabet())\n",
+            "748\n",
+            "gi|2765655|emb|Z78530.1|CMZ78530\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAAACAACAT...CAT', SingleLetterAlphabet())\n",
+            "744\n",
+            "gi|2765654|emb|Z78529.1|CLZ78529\n",
+            "Seq('ACGGCGAGCTGCCGAAGGACATTGTTGAGACAGCAGAATATACGATTGAGTGAA...AAA', SingleLetterAlphabet())\n",
+            "733\n",
+            "gi|2765652|emb|Z78527.1|CYZ78527\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAG...CCC', SingleLetterAlphabet())\n",
+            "718\n",
+            "gi|2765651|emb|Z78526.1|CGZ78526\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAG...TGT', SingleLetterAlphabet())\n",
+            "730\n",
+            "gi|2765650|emb|Z78525.1|CAZ78525\n",
+            "Seq('TGTTGAGATAGCAGAATATACATCGAGTGAATCCGGAGGACCTGTGGTTATTCG...GCA', SingleLetterAlphabet())\n",
+            "704\n",
+            "gi|2765649|emb|Z78524.1|CFZ78524\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATAGTAG...AGC', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765648|emb|Z78523.1|CHZ78523\n",
+            "Seq('CGTAACCAGGTTTCCGTAGGTGAACCTGCGGCAGGATCATTGTTGAGACAGCAG...AAG', SingleLetterAlphabet())\n",
+            "709\n",
+            "gi|2765647|emb|Z78522.1|CMZ78522\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAG...GAG', SingleLetterAlphabet())\n",
+            "700\n",
+            "gi|2765646|emb|Z78521.1|CCZ78521\n",
+            "Seq('GTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAGAATATATGATCGAGT...ACC', SingleLetterAlphabet())\n",
+            "726\n",
+            "gi|2765645|emb|Z78520.1|CSZ78520\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGCAG...TTT', SingleLetterAlphabet())\n",
+            "753\n",
+            "gi|2765644|emb|Z78519.1|CPZ78519\n",
+            "Seq('ATATGATCGAGTGAATCTGGTGGACTTGTGGTTACTCAGCTCGCCATAGGCTTT...TTA', SingleLetterAlphabet())\n",
+            "699\n",
+            "gi|2765643|emb|Z78518.1|CRZ78518\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGGAGGATCATTGTTGAGATAGTAG...TCC', SingleLetterAlphabet())\n",
+            "658\n",
+            "gi|2765642|emb|Z78517.1|CFZ78517\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAG...AGC', SingleLetterAlphabet())\n",
+            "752\n",
+            "gi|2765641|emb|Z78516.1|CPZ78516\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACAGTAT...TAA', SingleLetterAlphabet())\n",
+            "726\n",
+            "gi|2765640|emb|Z78515.1|MXZ78515\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGCTGAGACCGTAG...AGC', SingleLetterAlphabet())\n",
+            "765\n",
+            "gi|2765639|emb|Z78514.1|PSZ78514\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAAGCCCCCA...CTA', SingleLetterAlphabet())\n",
+            "755\n",
+            "gi|2765638|emb|Z78513.1|PBZ78513\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...GAG', SingleLetterAlphabet())\n",
+            "742\n",
+            "gi|2765637|emb|Z78512.1|PWZ78512\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAAGCCCCCA...AGC', SingleLetterAlphabet())\n",
+            "762\n",
+            "gi|2765636|emb|Z78511.1|PEZ78511\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTTCGGAAGGATCATTGTTGAGACCCCCA...GGA', SingleLetterAlphabet())\n",
+            "745\n",
+            "gi|2765635|emb|Z78510.1|PCZ78510\n",
+            "Seq('CTAACCAGGGTTCCGAGGTGACCTTCGGGAGGATTCCTTTTTAAGCCCCCGAAA...TTA', SingleLetterAlphabet())\n",
+            "750\n",
+            "gi|2765634|emb|Z78509.1|PPZ78509\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...GGA', SingleLetterAlphabet())\n",
+            "731\n",
+            "gi|2765633|emb|Z78508.1|PLZ78508\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...TGA', SingleLetterAlphabet())\n",
+            "741\n",
+            "gi|2765632|emb|Z78507.1|PLZ78507\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCCCCA...TGA', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765631|emb|Z78506.1|PLZ78506\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCAA...TGA', SingleLetterAlphabet())\n",
+            "727\n",
+            "gi|2765630|emb|Z78505.1|PSZ78505\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCCA...TTT', SingleLetterAlphabet())\n",
+            "711\n",
+            "gi|2765629|emb|Z78504.1|PKZ78504\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTTCGGAAGGATCATTGTTGAGACCGCAA...TAA', SingleLetterAlphabet())\n",
+            "743\n",
+            "gi|2765628|emb|Z78503.1|PCZ78503\n",
+            "Seq('CGTAACCAGGTTTCCGTAGGTGAACCTCCGGAAGGATCCTTGTTGAGACCGCCA...TAA', SingleLetterAlphabet())\n",
+            "727\n",
+            "gi|2765627|emb|Z78502.1|PBZ78502\n",
+            "Seq('CGTAACCAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGACCGCCA...CGC', SingleLetterAlphabet())\n",
+            "757\n",
+            "gi|2765626|emb|Z78501.1|PCZ78501\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGACCGCAA...AGA', SingleLetterAlphabet())\n",
+            "770\n",
+            "gi|2765625|emb|Z78500.1|PWZ78500\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGCTCATTGTTGAGACCGCAA...AAG', SingleLetterAlphabet())\n",
+            "767\n",
+            "gi|2765624|emb|Z78499.1|PMZ78499\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAGGGATCATTGTTGAGATCGCAT...ACC', SingleLetterAlphabet())\n",
+            "759\n",
+            "gi|2765623|emb|Z78498.1|PMZ78498\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAAGGTCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "750\n",
+            "gi|2765622|emb|Z78497.1|PDZ78497\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "788\n",
+            "gi|2765621|emb|Z78496.1|PAZ78496\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...AGC', SingleLetterAlphabet())\n",
+            "774\n",
+            "gi|2765620|emb|Z78495.1|PEZ78495\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...GTG', SingleLetterAlphabet())\n",
+            "789\n",
+            "gi|2765619|emb|Z78494.1|PNZ78494\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGGTCGCAT...AAG', SingleLetterAlphabet())\n",
+            "688\n",
+            "gi|2765618|emb|Z78493.1|PGZ78493\n",
+            "Seq('CGTAACAAGGATTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...CCC', SingleLetterAlphabet())\n",
+            "719\n",
+            "gi|2765617|emb|Z78492.1|PBZ78492\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...ATA', SingleLetterAlphabet())\n",
+            "743\n",
+            "gi|2765616|emb|Z78491.1|PCZ78491\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...AGC', SingleLetterAlphabet())\n",
+            "737\n",
+            "gi|2765615|emb|Z78490.1|PFZ78490\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
+            "728\n",
+            "gi|2765614|emb|Z78489.1|PDZ78489\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGC', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765613|emb|Z78488.1|PTZ78488\n",
+            "Seq('CTGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACGCAATAATTGATCGA...GCT', SingleLetterAlphabet())\n",
+            "696\n",
+            "gi|2765612|emb|Z78487.1|PHZ78487\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TAA', SingleLetterAlphabet())\n",
+            "732\n",
+            "gi|2765611|emb|Z78486.1|PBZ78486\n",
+            "Seq('CGTCACGAGGTTTCCGTAGGTGAATCTGCGGGAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
+            "731\n",
+            "gi|2765610|emb|Z78485.1|PHZ78485\n",
+            "Seq('CTGAACCTGGTGTCCGAAGGTGAATCTGCGGATGGATCATTGTTGAGATATCAT...GTA', SingleLetterAlphabet())\n",
+            "735\n",
+            "gi|2765609|emb|Z78484.1|PCZ78484\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGGGGAAGGATCATTGTTGAGATCACAT...TTT', SingleLetterAlphabet())\n",
+            "720\n",
+            "gi|2765608|emb|Z78483.1|PVZ78483\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765607|emb|Z78482.1|PEZ78482\n",
+            "Seq('TCTACTGCAGTGACCGAGATTTGCCATCGAGCCTCCTGGGAGCTTTCTTGCTGG...GCA', SingleLetterAlphabet())\n",
+            "629\n",
+            "gi|2765606|emb|Z78481.1|PIZ78481\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
+            "572\n",
+            "gi|2765605|emb|Z78480.1|PGZ78480\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
+            "587\n",
+            "gi|2765604|emb|Z78479.1|PPZ78479\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGT', SingleLetterAlphabet())\n",
+            "700\n",
+            "gi|2765603|emb|Z78478.1|PVZ78478\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCAGTGTTGAGATCACAT...GGC', SingleLetterAlphabet())\n",
+            "636\n",
+            "gi|2765602|emb|Z78477.1|PVZ78477\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGC', SingleLetterAlphabet())\n",
+            "716\n",
+            "gi|2765601|emb|Z78476.1|PGZ78476\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...CCC', SingleLetterAlphabet())\n",
+            "592\n",
+            "gi|2765600|emb|Z78475.1|PSZ78475\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GGT', SingleLetterAlphabet())\n",
+            "716\n",
+            "gi|2765599|emb|Z78474.1|PKZ78474\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACGT...CTT', SingleLetterAlphabet())\n",
+            "733\n",
+            "gi|2765598|emb|Z78473.1|PSZ78473\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGG', SingleLetterAlphabet())\n",
+            "626\n",
+            "gi|2765597|emb|Z78472.1|PLZ78472\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "737\n",
+            "gi|2765596|emb|Z78471.1|PDZ78471\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765595|emb|Z78470.1|PPZ78470\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GTT', SingleLetterAlphabet())\n",
+            "574\n",
+            "gi|2765594|emb|Z78469.1|PHZ78469\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GTT', SingleLetterAlphabet())\n",
+            "594\n",
+            "gi|2765593|emb|Z78468.1|PAZ78468\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCGCAT...GTT', SingleLetterAlphabet())\n",
+            "610\n",
+            "gi|2765592|emb|Z78467.1|PSZ78467\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGA', SingleLetterAlphabet())\n",
+            "730\n",
+            "gi|2765591|emb|Z78466.1|PPZ78466\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...CCC', SingleLetterAlphabet())\n",
+            "641\n",
+            "gi|2765590|emb|Z78465.1|PRZ78465\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGC', SingleLetterAlphabet())\n",
+            "702\n",
+            "gi|2765589|emb|Z78464.1|PGZ78464\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAGCGGAAGGGTCATTGTTGAGATCACATAATA...AGC', SingleLetterAlphabet())\n",
+            "733\n",
+            "gi|2765588|emb|Z78463.1|PGZ78463\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGTTCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "738\n",
+            "gi|2765587|emb|Z78462.1|PSZ78462\n",
+            "Seq('CGTCACGAGGTCTCCGGATGTGACCCTGCGGAAGGATCATTGTTGAGATCACAT...CAT', SingleLetterAlphabet())\n",
+            "736\n",
+            "gi|2765586|emb|Z78461.1|PWZ78461\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...TAA', SingleLetterAlphabet())\n",
+            "732\n",
+            "gi|2765585|emb|Z78460.1|PCZ78460\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...TTA', SingleLetterAlphabet())\n",
+            "745\n",
+            "gi|2765584|emb|Z78459.1|PDZ78459\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TTT', SingleLetterAlphabet())\n",
+            "744\n",
+            "gi|2765583|emb|Z78458.1|PHZ78458\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TTG', SingleLetterAlphabet())\n",
+            "738\n",
+            "gi|2765582|emb|Z78457.1|PCZ78457\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...GAG', SingleLetterAlphabet())\n",
+            "739\n",
+            "gi|2765581|emb|Z78456.1|PTZ78456\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "740\n",
+            "gi|2765580|emb|Z78455.1|PJZ78455\n",
+            "Seq('CGTAACCAGGTTTCCGTAGGTGGACCTTCGGGAGGATCATTTTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
+            "745\n",
+            "gi|2765579|emb|Z78454.1|PFZ78454\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AAC', SingleLetterAlphabet())\n",
+            "695\n",
+            "gi|2765578|emb|Z78453.1|PSZ78453\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
+            "745\n",
+            "gi|2765577|emb|Z78452.1|PBZ78452\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
+            "743\n",
+            "gi|2765576|emb|Z78451.1|PHZ78451\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGTACCTCCGGAAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "730\n",
+            "gi|2765575|emb|Z78450.1|PPZ78450\n",
+            "Seq('GGAAGGATCATTGCTGATATCACATAATAATTGATCGAGTTAAGCTGGAGGATC...GAG', SingleLetterAlphabet())\n",
+            "706\n",
+            "gi|2765574|emb|Z78449.1|PMZ78449\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGC', SingleLetterAlphabet())\n",
+            "744\n",
+            "gi|2765573|emb|Z78448.1|PAZ78448\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGG', SingleLetterAlphabet())\n",
+            "742\n",
+            "gi|2765572|emb|Z78447.1|PVZ78447\n",
+            "Seq('CGTAACAAGGATTCCGTAGGTGAACCTGCGGGAGGATCATTGTTGAGATCACAT...AGC', SingleLetterAlphabet())\n",
+            "694\n",
+            "gi|2765571|emb|Z78446.1|PAZ78446\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTCCGGAAGGATCATTGTTGAGATCACAT...CCC', SingleLetterAlphabet())\n",
+            "712\n",
+            "gi|2765570|emb|Z78445.1|PUZ78445\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...TGT', SingleLetterAlphabet())\n",
+            "715\n",
+            "gi|2765569|emb|Z78444.1|PAZ78444\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGGTGAACTGCGGAAGGATCATTGTTGAGATCACAT...ATT', SingleLetterAlphabet())\n",
+            "688\n",
+            "gi|2765568|emb|Z78443.1|PLZ78443\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACAT...AGG', SingleLetterAlphabet())\n",
+            "784\n",
+            "gi|2765567|emb|Z78442.1|PBZ78442\n",
+            "Seq('GTAGGTGAACCTGCGGAAGGATCATTGTTGAGATCACATAATAATTGATCGAGT...AGT', SingleLetterAlphabet())\n",
+            "721\n",
+            "gi|2765566|emb|Z78441.1|PSZ78441\n",
+            "Seq('GGAAGGTCATTGCCGATATCACATAATAATTGATCGAGTTAATCTGGAGGATCT...GAG', SingleLetterAlphabet())\n",
+            "703\n",
+            "gi|2765565|emb|Z78440.1|PPZ78440\n",
+            "Seq('CGTAACAAGGTTTCCGTAGGTGGACCTCCGGGAGGATCATTGTTGAGATCACAT...GCA', SingleLetterAlphabet())\n",
+            "744\n",
+            "gi|2765564|emb|Z78439.1|PBZ78439\n",
+            "Seq('CATTGTTGAGATCACATAATAATTGATCGAGTTAATCTGGAGGATCTGTTTACT...GCC', SingleLetterAlphabet())\n",
+            "592\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_prot[0]"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "You can also use slice notation on sequences to get subsequences."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('AAA', IUPACProtein())"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "UV-0Mvv-tgRV",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Sequence Objects\n",
+        "\n",
+        "A large part of the biopython infrastructure deals with tools for handlings sequences. These could be DNA sequences, RNA sequences, amino acid sequences or even more exotic constructs. To tell biopython what type of sequence it's dealing with, you can specify the alphabet explicitly."
       ]
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_prot[0:3]"
-   ]
-  },
-  {
-   "cell_type": "raw",
-   "metadata": {},
-   "source": [
-    "You can concatenate sequences if they have the same type so this works."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('AAAAAAAAAA', IUPACProtein())"
+      "cell_type": "code",
+      "metadata": {
+        "id": "kdkqKHmgtgRW",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "2b14f4ac-5a4e-4e9e-fb4e-c919acb317c2"
+      },
+      "source": [
+        "from Bio.Seq import Seq\n",
+        "from Bio.Alphabet import IUPAC\n",
+        "my_seq = Seq(\"ACAGTAGAC\", IUPAC.unambiguous_dna)\n",
+        "my_seq"
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('ACAGTAGAC', IUPACUnambiguousDNA())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 9
+        }
       ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "my_prot + my_prot"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "But this fails"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "ename": "TypeError",
-     "evalue": "Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA()",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
-      "\u001b[0;32m<ipython-input-21-0b91ff3c1125>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_prot\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmy_seq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[0;32m~/opt/anaconda3/envs/deepchem/lib/python3.6/site-packages/Bio/Seq.py\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    381\u001b[0m                 raise TypeError(\n\u001b[1;32m    382\u001b[0m                     \"Incompatible alphabets {0!r} and {1!r}\".format(\n\u001b[0;32m--> 383\u001b[0;31m                         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malphabet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malphabet\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m                     )\n\u001b[1;32m    385\u001b[0m                 )\n",
-      "\u001b[0;31mTypeError\u001b[0m: Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA()"
-     ]
-    }
-   ],
-   "source": [
-    "my_prot + my_seq"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Transcription\n",
-    "\n",
-    "Transcription is the process by which a DNA sequence is converted into messenger RNA. Remember that this is part of the \"central dogma\" of biology in which DNA engenders messenger RNA which engenders proteins. Here's a nice representation of this cycle borrowed from a Khan academy [lesson](https://cdn.kastatic.org/ka-perseus-images/20ce29384b2e7ff0cdea72acaa5b1dbd7287ab00.png).\n",
-    "\n",
-    "<img src=\"https://cdn.kastatic.org/ka-perseus-images/20ce29384b2e7ff0cdea72acaa5b1dbd7287ab00.png\">"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note from the image above that DNA has two strands. The top strand is typically called the coding strand, and the bottom the template strand. The template strand is used for the actual transcription process of conversion into messenger RNA, but in bioinformatics, it's more common to work with the coding strand. Let's now see how we can execute a transcription computationally using Biopython."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "j5xDuf7DtgRb",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b71c66eb-9d9d-47fc-c1b2-6c695bed8072"
+      },
+      "source": [
+        "my_seq.alphabet"
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "IUPACUnambiguousDNA()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 10
+        }
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('ATGATCTCGTAA', IUPACUnambiguousDNA())"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "dUw07rNrtgRr",
+        "colab_type": "text"
+      },
+      "source": [
+        "If we want to code a protein sequence, we can do that just as easily."
       ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from Bio.Seq import Seq\n",
-    "from Bio.Alphabet import IUPAC\n",
-    "\n",
-    "coding_dna = Seq(\"ATGATCTCGTAA\", IUPAC.unambiguous_dna)\n",
-    "coding_dna"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('TTACGAGATCAT', IUPACUnambiguousDNA())"
+      "cell_type": "code",
+      "metadata": {
+        "id": "O6WUnJEftgRs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "79c056e1-c85a-4a72-84e7-f6d414c4679d"
+      },
+      "source": [
+        "my_prot = Seq(\"AAAAA\", IUPAC.protein) # Alanine pentapeptide\n",
+        "my_prot"
+      ],
+      "execution_count": 11,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('AAAAA', IUPACProtein())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 11
+        }
       ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "template_dna = coding_dna.reverse_complement()\n",
-    "template_dna"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note that these sequences match those in the image below. You might be confused about why the `template_dna` sequence is shown reversed. The reason is that by convention, the template strand is read in the reverse direction.\n",
-    "\n",
-    "Let's now see how we can transcribe our `coding_dna` strand into messenger RNA. This will only swap 'T' for 'U' and change the alphabet for our object."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('AUGAUCUCGUAA', IUPACUnambiguousRNA())"
+      "cell_type": "code",
+      "metadata": {
+        "id": "jdgRxL6qtgR0",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "87748f50-1267-48cb-a8bb-fb27d82a2fe6"
+      },
+      "source": [
+        "my_prot.alphabet"
+      ],
+      "execution_count": 12,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "IUPACProtein()"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 12
+        }
       ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messenger_rna = coding_dna.transcribe()\n",
-    "messenger_rna"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We can also perform a \"back-transcription\" to recover the original coding strand from the messenger RNA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('ATGATCTCGTAA', IUPACUnambiguousDNA())"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pTPKw7cHtgR3",
+        "colab_type": "text"
+      },
+      "source": [
+        "We can take the length of sequences and index into them like strings."
       ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "messenger_rna.back_transcribe()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Translation\n",
-    "\n",
-    "Translation is the next step in the process, whereby a messenger RNA is transformed into a protein sequence. Here's a beautiful diagram [from Wikipedia](https://en.wikipedia.org/wiki/Translation_(biology)#/media/File:Ribosome_mRNA_translation_en.svg) that lays out the basics of this process.\n",
-    "\n",
-    "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b1/Ribosome_mRNA_translation_en.svg/1000px-Ribosome_mRNA_translation_en.svg.png\">\n",
-    "\n",
-    "Note how 3 nucleotides at a time correspond to one new amino acid added to the growing protein chain. A set of 3 nucleotides which codes for a given amino acid is called a \"codon.\" We can use the `translate()` method on the messenger rna to perform this transformation in code."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "messenger_rna.translate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The translation can also be performed directly from the coding sequence DNA"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('MIS*', HasStopCodon(IUPACProtein(), '*'))"
+      "cell_type": "code",
+      "metadata": {
+        "id": "OkY6Tx60tgR4",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "90d9fa0c-0f34-4ead-dfbe-f5bda3d8c34a"
+      },
+      "source": [
+        "print(len(my_prot))"
+      ],
+      "execution_count": 13,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "5\n"
+          ],
+          "name": "stdout"
+        }
       ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coding_dna.translate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now consider a longer genetic sequence that has some more interesting structure for us to look at."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))"
+      "cell_type": "code",
+      "metadata": {
+        "id": "YSOUpm8FtgR8",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "2e1efe60-ad60-40cf-ba79-07e85b8f8af4"
+      },
+      "source": [
+        "my_prot[0]"
+      ],
+      "execution_count": 14,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'A'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 14
+        }
       ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coding_dna = Seq(\"ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG\", IUPAC.unambiguous_dna)\n",
-    "coding_dna.translate()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "In both of the sequences above, '*' represents the [stop codon](https://en.wikipedia.org/wiki/Stop_codon). A stop codon is a sequence of 3 nucleotides that turns off the protein machinery. In DNA, the stop codons are 'TGA', 'TAA', 'TAG'. Note that this latest sequence has multiple stop codons. It's possible to run the machinery up to the first stop codon and pause too."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('MAIVMGR', IUPACProtein())"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PdQ8weemtgR_",
+        "colab_type": "text"
+      },
+      "source": [
+        "You can also use slice notation on sequences to get subsequences."
       ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "coding_dna.translate(to_stop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "We're going to introduce a bit of terminology here. A complete coding sequence CDS is a nucleotide sequence of messenger RNA which is made of a whole number of codons (that is, the length of the sequence is a multiple of 3), starts with a \"start codon\" and ends with a \"stop codon\". A start codon is basically the opposite of a stop codon and is mostly commonly the sequence \"AUG\", but can be different (especially if you're dealing with something like bacterial DNA).\n",
-    "\n",
-    "Let's see how we can translate a complete CDS of bacterial messenger RNA."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*', HasStopCodon(ExtendedIUPACProtein(), '*'))"
+      "cell_type": "code",
+      "metadata": {
+        "id": "U5v3swWFtgSA",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "4cc11741-2f62-4854-afcd-ebf34973b533"
+      },
+      "source": [
+        "my_prot[0:3]"
+      ],
+      "execution_count": 15,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('AAA', IUPACProtein())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 15
+        }
       ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from Bio.Alphabet import generic_dna\n",
-    "\n",
-    "gene = Seq(\"GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA\" + \\\n",
-    "           \"GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT\" + \\\n",
-    "           \"AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT\" + \\\n",
-    "           \"TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT\" + \\\n",
-    "           \"AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA\",\n",
-    "           generic_dna)\n",
-    "# We specify a \"table\" to use a different translation table for bacterial proteins\n",
-    "gene.translate(table=\"Bacterial\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR', ExtendedIUPACProtein())"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ng_LjVQ6tgSI",
+        "colab_type": "raw"
+      },
+      "source": [
+        "You can concatenate sequences if they have the same type so this works."
       ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "gene.translate(table=\"Bacterial\", to_stop=True)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Handling Annotated Sequences\n",
-    "\n",
-    "Sometimes it will be useful for us to be able to handle annotated sequences where there's richer annotations, as in GenBank or EMBL files. For these purposes, we'll want to use the `SeqRecord` class."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Help on class SeqRecord in module Bio.SeqRecord:\n",
-      "\n",
-      "class SeqRecord(builtins.object)\n",
-      " |  A SeqRecord object holds a sequence and information about it.\n",
-      " |  \n",
-      " |  Main attributes:\n",
-      " |   - id          - Identifier such as a locus tag (string)\n",
-      " |   - seq         - The sequence itself (Seq object or similar)\n",
-      " |  \n",
-      " |  Additional attributes:\n",
-      " |   - name        - Sequence name, e.g. gene name (string)\n",
-      " |   - description - Additional text (string)\n",
-      " |   - dbxrefs     - List of database cross references (list of strings)\n",
-      " |   - features    - Any (sub)features defined (list of SeqFeature objects)\n",
-      " |   - annotations - Further information about the whole sequence (dictionary).\n",
-      " |     Most entries are strings, or lists of strings.\n",
-      " |   - letter_annotations - Per letter/symbol annotation (restricted\n",
-      " |     dictionary). This holds Python sequences (lists, strings\n",
-      " |     or tuples) whose length matches that of the sequence.\n",
-      " |     A typical use would be to hold a list of integers\n",
-      " |     representing sequencing quality scores, or a string\n",
-      " |     representing the secondary structure.\n",
-      " |  \n",
-      " |  You will typically use Bio.SeqIO to read in sequences from files as\n",
-      " |  SeqRecord objects.  However, you may want to create your own SeqRecord\n",
-      " |  objects directly (see the __init__ method for further details):\n",
-      " |  \n",
-      " |  >>> from Bio.Seq import Seq\n",
-      " |  >>> from Bio.SeqRecord import SeqRecord\n",
-      " |  >>> from Bio.Alphabet import IUPAC\n",
-      " |  >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\",\n",
-      " |  ...                         IUPAC.protein),\n",
-      " |  ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
-      " |  ...                    description=\"toxic membrane protein\")\n",
-      " |  >>> print(record)\n",
-      " |  ID: YP_025292.1\n",
-      " |  Name: HokC\n",
-      " |  Description: toxic membrane protein\n",
-      " |  Number of features: 0\n",
-      " |  Seq('MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF', IUPACProtein())\n",
-      " |  \n",
-      " |  If you want to save SeqRecord objects to a sequence file, use Bio.SeqIO\n",
-      " |  for this.  For the special case where you want the SeqRecord turned into\n",
-      " |  a string in a particular file format there is a format method which uses\n",
-      " |  Bio.SeqIO internally:\n",
-      " |  \n",
-      " |  >>> print(record.format(\"fasta\"))\n",
-      " |  >YP_025292.1 toxic membrane protein\n",
-      " |  MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
-      " |  <BLANKLINE>\n",
-      " |  \n",
-      " |  You can also do things like slicing a SeqRecord, checking its length, etc\n",
-      " |  \n",
-      " |  >>> len(record)\n",
-      " |  44\n",
-      " |  >>> edited = record[:10] + record[11:]\n",
-      " |  >>> print(edited.seq)\n",
-      " |  MKQHKAMIVAIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
-      " |  >>> print(record.seq)\n",
-      " |  MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
-      " |  \n",
-      " |  Methods defined here:\n",
-      " |  \n",
-      " |  __add__(self, other)\n",
-      " |      Add another sequence or string to this sequence.\n",
-      " |      \n",
-      " |      The other sequence can be a SeqRecord object, a Seq object (or\n",
-      " |      similar, e.g. a MutableSeq) or a plain Python string. If you add\n",
-      " |      a plain string or a Seq (like) object, the new SeqRecord will simply\n",
-      " |      have this appended to the existing data. However, any per letter\n",
-      " |      annotation will be lost:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
-      " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
-      " |      >>> print(list(record.letter_annotations))\n",
-      " |      ['solexa_quality']\n",
-      " |      \n",
-      " |      >>> new = record + \"ACT\"\n",
-      " |      >>> print(\"%s %s\" % (new.id, new.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNNACT\n",
-      " |      >>> print(list(new.letter_annotations))\n",
-      " |      []\n",
-      " |      \n",
-      " |      The new record will attempt to combine the annotation, but for any\n",
-      " |      ambiguities (e.g. different names) it defaults to omitting that\n",
-      " |      annotation.\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> with open(\"GenBank/pBAD30.gb\") as handle:\n",
-      " |      ...     plasmid = SeqIO.read(handle, \"gb\")\n",
-      " |      >>> print(\"%s %i\" % (plasmid.id, len(plasmid)))\n",
-      " |      pBAD30 4923\n",
-      " |      \n",
-      " |      Now let's cut the plasmid into two pieces, and join them back up the\n",
-      " |      other way round (i.e. shift the starting point on this plasmid, have\n",
-      " |      a look at the annotated features in the original file to see why this\n",
-      " |      particular split point might make sense):\n",
-      " |      \n",
-      " |      >>> left = plasmid[:3765]\n",
-      " |      >>> right = plasmid[3765:]\n",
-      " |      >>> new = right + left\n",
-      " |      >>> print(\"%s %i\" % (new.id, len(new)))\n",
-      " |      pBAD30 4923\n",
-      " |      >>> str(new.seq) == str(right.seq + left.seq)\n",
-      " |      True\n",
-      " |      >>> len(new.features) == len(left.features) + len(right.features)\n",
-      " |      True\n",
-      " |      \n",
-      " |      When we add the left and right SeqRecord objects, their annotation\n",
-      " |      is all consistent, so it is all conserved in the new SeqRecord:\n",
-      " |      \n",
-      " |      >>> new.id == left.id == right.id == plasmid.id\n",
-      " |      True\n",
-      " |      >>> new.name == left.name == right.name == plasmid.name\n",
-      " |      True\n",
-      " |      >>> new.description == plasmid.description\n",
-      " |      True\n",
-      " |      >>> new.annotations == left.annotations == right.annotations\n",
-      " |      True\n",
-      " |      >>> new.letter_annotations == plasmid.letter_annotations\n",
-      " |      True\n",
-      " |      >>> new.dbxrefs == left.dbxrefs == right.dbxrefs\n",
-      " |      True\n",
-      " |      \n",
-      " |      However, we should point out that when we sliced the SeqRecord,\n",
-      " |      any annotations dictionary or dbxrefs list entries were lost.\n",
-      " |      You can explicitly copy them like this:\n",
-      " |      \n",
-      " |      >>> new.annotations = plasmid.annotations.copy()\n",
-      " |      >>> new.dbxrefs = plasmid.dbxrefs[:]\n",
-      " |  \n",
-      " |  __bool__(self)\n",
-      " |      Boolean value of an instance of this class (True).\n",
-      " |      \n",
-      " |      This behaviour is for backwards compatibility, since until the\n",
-      " |      __len__ method was added, a SeqRecord always evaluated as True.\n",
-      " |      \n",
-      " |      Note that in comparison, a Seq object will evaluate to False if it\n",
-      " |      has a zero length sequence.\n",
-      " |      \n",
-      " |      WARNING: The SeqRecord may in future evaluate to False when its\n",
-      " |      sequence is of zero length (in order to better match the Seq\n",
-      " |      object behaviour)!\n",
-      " |  \n",
-      " |  __contains__(self, char)\n",
-      " |      Implement the 'in' keyword, searches the sequence.\n",
-      " |      \n",
-      " |      e.g.\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Fasta/sweetpea.nu\", \"fasta\")\n",
-      " |      >>> \"GAATTC\" in record\n",
-      " |      False\n",
-      " |      >>> \"AAA\" in record\n",
-      " |      True\n",
-      " |      \n",
-      " |      This essentially acts as a proxy for using \"in\" on the sequence:\n",
-      " |      \n",
-      " |      >>> \"GAATTC\" in record.seq\n",
-      " |      False\n",
-      " |      >>> \"AAA\" in record.seq\n",
-      " |      True\n",
-      " |      \n",
-      " |      Note that you can also use Seq objects as the query,\n",
-      " |      \n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.Alphabet import generic_dna\n",
-      " |      >>> Seq(\"AAA\") in record\n",
-      " |      True\n",
-      " |      >>> Seq(\"AAA\", generic_dna) in record\n",
-      " |      True\n",
-      " |      \n",
-      " |      See also the Seq object's __contains__ method.\n",
-      " |  \n",
-      " |  __eq__(self, other)\n",
-      " |      Define the equal-to operand (not implemented).\n",
-      " |  \n",
-      " |  __format__(self, format_spec)\n",
-      " |      Return the record as a string in the specified file format.\n",
-      " |      \n",
-      " |      This method supports the python format() function added in\n",
-      " |      Python 2.6/3.0.  The format_spec should be a lower case string\n",
-      " |      supported by Bio.SeqIO as an output file format. See also the\n",
-      " |      SeqRecord's format() method.\n",
-      " |      \n",
-      " |      Under Python 3 binary formats raise a ValueError, while on\n",
-      " |      Python 2 this will work with a deprecation warning.\n",
-      " |  \n",
-      " |  __ge__(self, other)\n",
-      " |      Define the greater-than-or-equal-to operand (not implemented).\n",
-      " |  \n",
-      " |  __getitem__(self, index)\n",
-      " |      Return a sub-sequence or an individual letter.\n",
-      " |      \n",
-      " |      Slicing, e.g. my_record[5:10], returns a new SeqRecord for\n",
-      " |      that sub-sequence with some annotation preserved as follows:\n",
-      " |      \n",
-      " |      * The name, id and description are kept as-is.\n",
-      " |      * Any per-letter-annotations are sliced to match the requested\n",
-      " |        sub-sequence.\n",
-      " |      * Unless a stride is used, all those features which fall fully\n",
-      " |        within the subsequence are included (with their locations\n",
-      " |        adjusted accordingly). If you want to preserve any truncated\n",
-      " |        features (e.g. GenBank/EMBL source features), you must\n",
-      " |        explicitly add them to the new SeqRecord yourself.\n",
-      " |      * The annotations dictionary and the dbxrefs list are not used\n",
-      " |        for the new SeqRecord, as in general they may not apply to the\n",
-      " |        subsequence. If you want to preserve them, you must explicitly\n",
-      " |        copy them to the new SeqRecord yourself.\n",
-      " |      \n",
-      " |      Using an integer index, e.g. my_record[5] is shorthand for\n",
-      " |      extracting that letter from the sequence, my_record.seq[5].\n",
-      " |      \n",
-      " |      For example, consider this short protein and its secondary\n",
-      " |      structure as encoded by the PDB (e.g. H for alpha helices),\n",
-      " |      plus a simple feature for its histidine self phosphorylation\n",
-      " |      site:\n",
-      " |      \n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> from Bio.SeqFeature import SeqFeature, FeatureLocation\n",
-      " |      >>> from Bio.Alphabet import IUPAC\n",
-      " |      >>> rec = SeqRecord(Seq(\"MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLAT\"\n",
-      " |      ...                     \"EMMSEQDGYLAESINKDIEECNAIIEQFIDYLR\",\n",
-      " |      ...                     IUPAC.protein),\n",
-      " |      ...                 id=\"1JOY\", name=\"EnvZ\",\n",
-      " |      ...                 description=\"Homodimeric domain of EnvZ from E. coli\")\n",
-      " |      >>> rec.letter_annotations[\"secondary_structure\"] = \"  S  SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT  \"\n",
-      " |      >>> rec.features.append(SeqFeature(FeatureLocation(20, 21),\n",
-      " |      ...                     type = \"Site\"))\n",
-      " |      \n",
-      " |      Now let's have a quick look at the full record,\n",
-      " |      \n",
-      " |      >>> print(rec)\n",
-      " |      ID: 1JOY\n",
-      " |      Name: EnvZ\n",
-      " |      Description: Homodimeric domain of EnvZ from E. coli\n",
-      " |      Number of features: 1\n",
-      " |      Per letter annotation for: secondary_structure\n",
-      " |      Seq('MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEE...YLR', IUPACProtein())\n",
-      " |      >>> rec.letter_annotations[\"secondary_structure\"]\n",
-      " |      '  S  SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT  '\n",
-      " |      >>> print(rec.features[0].location)\n",
-      " |      [20:21]\n",
-      " |      \n",
-      " |      Now let's take a sub sequence, here chosen as the first (fractured)\n",
-      " |      alpha helix which includes the histidine phosphorylation site:\n",
-      " |      \n",
-      " |      >>> sub = rec[11:41]\n",
-      " |      >>> print(sub)\n",
-      " |      ID: 1JOY\n",
-      " |      Name: EnvZ\n",
-      " |      Description: Homodimeric domain of EnvZ from E. coli\n",
-      " |      Number of features: 1\n",
-      " |      Per letter annotation for: secondary_structure\n",
-      " |      Seq('RTLLMAGVSHDLRTPLTRIRLATEMMSEQD', IUPACProtein())\n",
-      " |      >>> sub.letter_annotations[\"secondary_structure\"]\n",
-      " |      'HHHHHTTTHHHHHHHHHHHHHHHHHHHHHH'\n",
-      " |      >>> print(sub.features[0].location)\n",
-      " |      [9:10]\n",
-      " |      \n",
-      " |      You can also of course omit the start or end values, for\n",
-      " |      example to get the first ten letters only:\n",
-      " |      \n",
-      " |      >>> print(rec[:10])\n",
-      " |      ID: 1JOY\n",
-      " |      Name: EnvZ\n",
-      " |      Description: Homodimeric domain of EnvZ from E. coli\n",
-      " |      Number of features: 0\n",
-      " |      Per letter annotation for: secondary_structure\n",
-      " |      Seq('MAAGVKQLAD', IUPACProtein())\n",
-      " |      \n",
-      " |      Or for the last ten letters:\n",
-      " |      \n",
-      " |      >>> print(rec[-10:])\n",
-      " |      ID: 1JOY\n",
-      " |      Name: EnvZ\n",
-      " |      Description: Homodimeric domain of EnvZ from E. coli\n",
-      " |      Number of features: 0\n",
-      " |      Per letter annotation for: secondary_structure\n",
-      " |      Seq('IIEQFIDYLR', IUPACProtein())\n",
-      " |      \n",
-      " |      If you omit both, then you get a copy of the original record (although\n",
-      " |      lacking the annotations and dbxrefs):\n",
-      " |      \n",
-      " |      >>> print(rec[:])\n",
-      " |      ID: 1JOY\n",
-      " |      Name: EnvZ\n",
-      " |      Description: Homodimeric domain of EnvZ from E. coli\n",
-      " |      Number of features: 1\n",
-      " |      Per letter annotation for: secondary_structure\n",
-      " |      Seq('MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEE...YLR', IUPACProtein())\n",
-      " |      \n",
-      " |      Finally, indexing with a simple integer is shorthand for pulling out\n",
-      " |      that letter from the sequence directly:\n",
-      " |      \n",
-      " |      >>> rec[5]\n",
-      " |      'K'\n",
-      " |      >>> rec.seq[5]\n",
-      " |      'K'\n",
-      " |  \n",
-      " |  __gt__(self, other)\n",
-      " |      Define the greater-than operand (not implemented).\n",
-      " |  \n",
-      " |  __init__(self, seq, id='<unknown id>', name='<unknown name>', description='<unknown description>', dbxrefs=None, features=None, annotations=None, letter_annotations=None)\n",
-      " |      Create a SeqRecord.\n",
-      " |      \n",
-      " |      Arguments:\n",
-      " |       - seq         - Sequence, required (Seq, MutableSeq or UnknownSeq)\n",
-      " |       - id          - Sequence identifier, recommended (string)\n",
-      " |       - name        - Sequence name, optional (string)\n",
-      " |       - description - Sequence description, optional (string)\n",
-      " |       - dbxrefs     - Database cross references, optional (list of strings)\n",
-      " |       - features    - Any (sub)features, optional (list of SeqFeature objects)\n",
-      " |       - annotations - Dictionary of annotations for the whole sequence\n",
-      " |       - letter_annotations - Dictionary of per-letter-annotations, values\n",
-      " |         should be strings, list or tuples of the same length as the full\n",
-      " |         sequence.\n",
-      " |      \n",
-      " |      You will typically use Bio.SeqIO to read in sequences from files as\n",
-      " |      SeqRecord objects.  However, you may want to create your own SeqRecord\n",
-      " |      objects directly.\n",
-      " |      \n",
-      " |      Note that while an id is optional, we strongly recommend you supply a\n",
-      " |      unique id string for each record.  This is especially important\n",
-      " |      if you wish to write your sequences to a file.\n",
-      " |      \n",
-      " |      If you don't have the actual sequence, but you do know its length,\n",
-      " |      then using the UnknownSeq object from Bio.Seq is appropriate.\n",
-      " |      \n",
-      " |      You can create a 'blank' SeqRecord object, and then populate the\n",
-      " |      attributes later.\n",
-      " |  \n",
-      " |  __iter__(self)\n",
-      " |      Iterate over the letters in the sequence.\n",
-      " |      \n",
-      " |      For example, using Bio.SeqIO to read in a protein FASTA file:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Fasta/loveliesbleeding.pro\", \"fasta\")\n",
-      " |      >>> for amino in record:\n",
-      " |      ...     print(amino)\n",
-      " |      ...     if amino == \"L\": break\n",
-      " |      X\n",
-      " |      A\n",
-      " |      G\n",
-      " |      L\n",
-      " |      >>> print(record.seq[3])\n",
-      " |      L\n",
-      " |      \n",
-      " |      This is just a shortcut for iterating over the sequence directly:\n",
-      " |      \n",
-      " |      >>> for amino in record.seq:\n",
-      " |      ...     print(amino)\n",
-      " |      ...     if amino == \"L\": break\n",
-      " |      X\n",
-      " |      A\n",
-      " |      G\n",
-      " |      L\n",
-      " |      >>> print(record.seq[3])\n",
-      " |      L\n",
-      " |      \n",
-      " |      Note that this does not facilitate iteration together with any\n",
-      " |      per-letter-annotation.  However, you can achieve that using the\n",
-      " |      python zip function on the record (or its sequence) and the relevant\n",
-      " |      per-letter-annotation:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> rec = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
-      " |      >>> print(\"%s %s\" % (rec.id, rec.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
-      " |      >>> print(list(rec.letter_annotations))\n",
-      " |      ['solexa_quality']\n",
-      " |      >>> for nuc, qual in zip(rec, rec.letter_annotations[\"solexa_quality\"]):\n",
-      " |      ...     if qual > 35:\n",
-      " |      ...         print(\"%s %i\" % (nuc, qual))\n",
-      " |      A 40\n",
-      " |      C 39\n",
-      " |      G 38\n",
-      " |      T 37\n",
-      " |      A 36\n",
-      " |      \n",
-      " |      You may agree that using zip(rec.seq, ...) is more explicit than using\n",
-      " |      zip(rec, ...) as shown above.\n",
-      " |  \n",
-      " |  __le___(self, other)\n",
-      " |      Define the less-than-or-equal-to operand (not implemented).\n",
-      " |  \n",
-      " |  __len__(self)\n",
-      " |      Return the length of the sequence.\n",
-      " |      \n",
-      " |      For example, using Bio.SeqIO to read in a FASTA nucleotide file:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Fasta/sweetpea.nu\", \"fasta\")\n",
-      " |      >>> len(record)\n",
-      " |      309\n",
-      " |      >>> len(record.seq)\n",
-      " |      309\n",
-      " |  \n",
-      " |  __lt__(self, other)\n",
-      " |      Define the less-than operand (not implemented).\n",
-      " |  \n",
-      " |  __ne__(self, other)\n",
-      " |      Define the not-equal-to operand (not implemented).\n",
-      " |  \n",
-      " |  __nonzero__ = __bool__(self)\n",
-      " |  \n",
-      " |  __radd__(self, other)\n",
-      " |      Add another sequence or string to this sequence (from the left).\n",
-      " |      \n",
-      " |      This method handles adding a Seq object (or similar, e.g. MutableSeq)\n",
-      " |      or a plain Python string (on the left) to a SeqRecord (on the right).\n",
-      " |      See the __add__ method for more details, but for example:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
-      " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
-      " |      >>> print(list(record.letter_annotations))\n",
-      " |      ['solexa_quality']\n",
-      " |      \n",
-      " |      >>> new = \"ACT\" + record\n",
-      " |      >>> print(\"%s %s\" % (new.id, new.seq))\n",
-      " |      slxa_0001_1_0001_01 ACTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
-      " |      >>> print(list(new.letter_annotations))\n",
-      " |      []\n",
-      " |  \n",
-      " |  __repr__(self)\n",
-      " |      Return a concise summary of the record for debugging (string).\n",
-      " |      \n",
-      " |      The python built in function repr works by calling the object's ___repr__\n",
-      " |      method.  e.g.\n",
-      " |      \n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> from Bio.Alphabet import generic_protein\n",
-      " |      >>> rec = SeqRecord(Seq(\"MASRGVNKVILVGNLGQDPEVRYMPNGGAVANITLATSESWRDKAT\"\n",
-      " |      ...                    +\"GEMKEQTEWHRVVLFGKLAEVASEYLRKGSQVYIEGQLRTRKWTDQ\"\n",
-      " |      ...                    +\"SGQDRYTTEVVVNVGGTMQMLGGRQGGGAPAGGNIGGGQPQGGWGQ\"\n",
-      " |      ...                    +\"PQQPQGGNQFSGGAQSRPQQSAPAAPSNEPPMDFDDDIPF\",\n",
-      " |      ...                    generic_protein),\n",
-      " |      ...                 id=\"NP_418483.1\", name=\"b4059\",\n",
-      " |      ...                 description=\"ssDNA-binding protein\",\n",
-      " |      ...                 dbxrefs=[\"ASAP:13298\", \"GI:16131885\", \"GeneID:948570\"])\n",
-      " |      >>> print(repr(rec))\n",
-      " |      SeqRecord(seq=Seq('MASRGVNKVILVGNLGQDPEVRYMPNGGAVANITLATSESWRDKATGEMKEQTE...IPF', ProteinAlphabet()), id='NP_418483.1', name='b4059', description='ssDNA-binding protein', dbxrefs=['ASAP:13298', 'GI:16131885', 'GeneID:948570'])\n",
-      " |      \n",
-      " |      At the python prompt you can also use this shorthand:\n",
-      " |      \n",
-      " |      >>> rec\n",
-      " |      SeqRecord(seq=Seq('MASRGVNKVILVGNLGQDPEVRYMPNGGAVANITLATSESWRDKATGEMKEQTE...IPF', ProteinAlphabet()), id='NP_418483.1', name='b4059', description='ssDNA-binding protein', dbxrefs=['ASAP:13298', 'GI:16131885', 'GeneID:948570'])\n",
-      " |      \n",
-      " |      Note that long sequences are shown truncated. Also note that any\n",
-      " |      annotations, letter_annotations and features are not shown (as they\n",
-      " |      would lead to a very long string).\n",
-      " |  \n",
-      " |  __str__(self)\n",
-      " |      Return a human readable summary of the record and its annotation (string).\n",
-      " |      \n",
-      " |      The python built in function str works by calling the object's ___str__\n",
-      " |      method.  e.g.\n",
-      " |      \n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> from Bio.Alphabet import IUPAC\n",
-      " |      >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\",\n",
-      " |      ...                         IUPAC.protein),\n",
-      " |      ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
-      " |      ...                    description=\"toxic membrane protein, small\")\n",
-      " |      >>> print(str(record))\n",
-      " |      ID: YP_025292.1\n",
-      " |      Name: HokC\n",
-      " |      Description: toxic membrane protein, small\n",
-      " |      Number of features: 0\n",
-      " |      Seq('MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF', IUPACProtein())\n",
-      " |      \n",
-      " |      In this example you don't actually need to call str explicity, as the\n",
-      " |      print command does this automatically:\n",
-      " |      \n",
-      " |      >>> print(record)\n",
-      " |      ID: YP_025292.1\n",
-      " |      Name: HokC\n",
-      " |      Description: toxic membrane protein, small\n",
-      " |      Number of features: 0\n",
-      " |      Seq('MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF', IUPACProtein())\n",
-      " |      \n",
-      " |      Note that long sequences are shown truncated.\n",
-      " |  \n",
-      " |  format(self, format)\n",
-      " |      Return the record as a string in the specified file format.\n",
-      " |      \n",
-      " |      The format should be a lower case string supported as an output\n",
-      " |      format by Bio.SeqIO, which is used to turn the SeqRecord into a\n",
-      " |      string.  e.g.\n",
-      " |      \n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> from Bio.Alphabet import IUPAC\n",
-      " |      >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\",\n",
-      " |      ...                         IUPAC.protein),\n",
-      " |      ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
-      " |      ...                    description=\"toxic membrane protein\")\n",
-      " |      >>> record.format(\"fasta\")\n",
-      " |      '>YP_025292.1 toxic membrane protein\\nMKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\\n'\n",
-      " |      >>> print(record.format(\"fasta\"))\n",
-      " |      >YP_025292.1 toxic membrane protein\n",
-      " |      MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
-      " |      <BLANKLINE>\n",
-      " |      \n",
-      " |      The python print command automatically appends a new line, meaning\n",
-      " |      in this example a blank line is shown.  If you look at the string\n",
-      " |      representation you can see there is a trailing new line (shown as\n",
-      " |      slash n) which is important when writing to a file or if\n",
-      " |      concatenating multiple sequence strings together.\n",
-      " |      \n",
-      " |      Note that this method will NOT work on every possible file format\n",
-      " |      supported by Bio.SeqIO (e.g. some are for multiple sequences only,\n",
-      " |      and binary formats are not supported).\n",
-      " |  \n",
-      " |  lower(self)\n",
-      " |      Return a copy of the record with a lower case sequence.\n",
-      " |      \n",
-      " |      All the annotation is preserved unchanged. e.g.\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Fasta/aster.pro\", \"fasta\")\n",
-      " |      >>> print(record.format(\"fasta\"))\n",
-      " |      >gi|3298468|dbj|BAA31520.1| SAMIPF\n",
-      " |      GGHVNPAVTFGAFVGGNITLLRGIVYIIAQLLGSTVACLLLKFVTNDMAVGVFSLSAGVG\n",
-      " |      VTNALVFEIVMTFGLVYTVYATAIDPKKGSLGTIAPIAIGFIVGANI\n",
-      " |      <BLANKLINE>\n",
-      " |      >>> print(record.lower().format(\"fasta\"))\n",
-      " |      >gi|3298468|dbj|BAA31520.1| SAMIPF\n",
-      " |      gghvnpavtfgafvggnitllrgivyiiaqllgstvaclllkfvtndmavgvfslsagvg\n",
-      " |      vtnalvfeivmtfglvytvyataidpkkgslgtiapiaigfivgani\n",
-      " |      <BLANKLINE>\n",
-      " |      \n",
-      " |      To take a more annotation rich example,\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> old = SeqIO.read(\"EMBL/TRBG361.embl\", \"embl\")\n",
-      " |      >>> len(old.features)\n",
-      " |      3\n",
-      " |      >>> new = old.lower()\n",
-      " |      >>> len(old.features) == len(new.features)\n",
-      " |      True\n",
-      " |      >>> old.annotations[\"organism\"] == new.annotations[\"organism\"]\n",
-      " |      True\n",
-      " |      >>> old.dbxrefs == new.dbxrefs\n",
-      " |      True\n",
-      " |  \n",
-      " |  reverse_complement(self, id=False, name=False, description=False, features=True, annotations=False, letter_annotations=True, dbxrefs=False)\n",
-      " |      Return new SeqRecord with reverse complement sequence.\n",
-      " |      \n",
-      " |      By default the new record does NOT preserve the sequence identifier,\n",
-      " |      name, description, general annotation or database cross-references -\n",
-      " |      these are unlikely to apply to the reversed sequence.\n",
-      " |      \n",
-      " |      You can specify the returned record's id, name and description as\n",
-      " |      strings, or True to keep that of the parent, or False for a default.\n",
-      " |      \n",
-      " |      You can specify the returned record's features with a list of\n",
-      " |      SeqFeature objects, or True to keep that of the parent, or False to\n",
-      " |      omit them. The default is to keep the original features (with the\n",
-      " |      strand and locations adjusted).\n",
-      " |      \n",
-      " |      You can also specify both the returned record's annotations and\n",
-      " |      letter_annotations as dictionaries, True to keep that of the parent,\n",
-      " |      or False to omit them. The default is to keep the original\n",
-      " |      annotations (with the letter annotations reversed).\n",
-      " |      \n",
-      " |      To show what happens to the pre-letter annotations, consider an\n",
-      " |      example Solexa variant FASTQ file with a single entry, which we'll\n",
-      " |      read in as a SeqRecord:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
-      " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
-      " |      >>> print(list(record.letter_annotations))\n",
-      " |      ['solexa_quality']\n",
-      " |      >>> print(record.letter_annotations[\"solexa_quality\"])\n",
-      " |      [40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5]\n",
-      " |      \n",
-      " |      Now take the reverse complement, here we explicitly give a new\n",
-      " |      identifier (the old identifier with a suffix):\n",
-      " |      \n",
-      " |      >>> rc_record = record.reverse_complement(id=record.id + \"_rc\")\n",
-      " |      >>> print(\"%s %s\" % (rc_record.id, rc_record.seq))\n",
-      " |      slxa_0001_1_0001_01_rc NNNNNNACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT\n",
-      " |      \n",
-      " |      Notice that the per-letter-annotations have also been reversed,\n",
-      " |      although this may not be appropriate for all cases.\n",
-      " |      \n",
-      " |      >>> print(rc_record.letter_annotations[\"solexa_quality\"])\n",
-      " |      [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]\n",
-      " |      \n",
-      " |      Now for the features, we need a different example. Parsing a GenBank\n",
-      " |      file is probably the easiest way to get an nice example with features\n",
-      " |      in it...\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> with open(\"GenBank/pBAD30.gb\") as handle:\n",
-      " |      ...     plasmid = SeqIO.read(handle, \"gb\")\n",
-      " |      >>> print(\"%s %i\" % (plasmid.id, len(plasmid)))\n",
-      " |      pBAD30 4923\n",
-      " |      >>> plasmid.seq\n",
-      " |      Seq('GCTAGCGGAGTGTATACTGGCTTACTATGTTGGCACTGATGAGGGTGTCAGTGA...ATG', IUPACAmbiguousDNA())\n",
-      " |      >>> len(plasmid.features)\n",
-      " |      13\n",
-      " |      \n",
-      " |      Now, let's take the reverse complement of this whole plasmid:\n",
-      " |      \n",
-      " |      >>> rc_plasmid = plasmid.reverse_complement(id=plasmid.id+\"_rc\")\n",
-      " |      >>> print(\"%s %i\" % (rc_plasmid.id, len(rc_plasmid)))\n",
-      " |      pBAD30_rc 4923\n",
-      " |      >>> rc_plasmid.seq\n",
-      " |      Seq('CATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCA...AGC', IUPACAmbiguousDNA())\n",
-      " |      >>> len(rc_plasmid.features)\n",
-      " |      13\n",
-      " |      \n",
-      " |      Let's compare the first CDS feature - it has gone from being the\n",
-      " |      second feature (index 1) to the second last feature (index -2), its\n",
-      " |      strand has changed, and the location switched round.\n",
-      " |      \n",
-      " |      >>> print(plasmid.features[1])\n",
-      " |      type: CDS\n",
-      " |      location: [1081:1960](-)\n",
-      " |      qualifiers:\n",
-      " |          Key: label, Value: ['araC']\n",
-      " |          Key: note, Value: ['araC regulator of the arabinose BAD promoter']\n",
-      " |          Key: vntifkey, Value: ['4']\n",
-      " |      <BLANKLINE>\n",
-      " |      >>> print(rc_plasmid.features[-2])\n",
-      " |      type: CDS\n",
-      " |      location: [2963:3842](+)\n",
-      " |      qualifiers:\n",
-      " |          Key: label, Value: ['araC']\n",
-      " |          Key: note, Value: ['araC regulator of the arabinose BAD promoter']\n",
-      " |          Key: vntifkey, Value: ['4']\n",
-      " |      <BLANKLINE>\n",
-      " |      \n",
-      " |      You can check this new location, based on the length of the plasmid:\n",
-      " |      \n",
-      " |      >>> len(plasmid) - 1081\n",
-      " |      3842\n",
-      " |      >>> len(plasmid) - 1960\n",
-      " |      2963\n",
-      " |      \n",
-      " |      Note that if the SeqFeature annotation includes any strand specific\n",
-      " |      information (e.g. base changes for a SNP), this information is not\n",
-      " |      amended, and would need correction after the reverse complement.\n",
-      " |      \n",
-      " |      Note trying to reverse complement a protein SeqRecord raises an\n",
-      " |      exception:\n",
-      " |      \n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.Alphabet import IUPAC\n",
-      " |      >>> protein_rec = SeqRecord(Seq(\"MAIVMGR\", IUPAC.protein), id=\"Test\")\n",
-      " |      >>> protein_rec.reverse_complement()\n",
-      " |      Traceback (most recent call last):\n",
-      " |         ...\n",
-      " |      ValueError: Proteins do not have complements!\n",
-      " |      \n",
-      " |      Also note you can reverse complement a SeqRecord using a MutableSeq:\n",
-      " |      \n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> from Bio.Seq import MutableSeq\n",
-      " |      >>> from Bio.Alphabet import generic_dna\n",
-      " |      >>> rec = SeqRecord(MutableSeq(\"ACGT\", generic_dna), id=\"Test\")\n",
-      " |      >>> rec.seq[0] = \"T\"\n",
-      " |      >>> print(\"%s %s\" % (rec.id, rec.seq))\n",
-      " |      Test TCGT\n",
-      " |      >>> rc = rec.reverse_complement(id=True)\n",
-      " |      >>> print(\"%s %s\" % (rc.id, rc.seq))\n",
-      " |      Test ACGA\n",
-      " |  \n",
-      " |  translate(self, table='Standard', stop_symbol='*', to_stop=False, cds=False, gap=None, id=False, name=False, description=False, features=False, annotations=False, letter_annotations=False, dbxrefs=False)\n",
-      " |      Return new SeqRecord with translated sequence.\n",
-      " |      \n",
-      " |      This calls the record's .seq.translate() method (which describes\n",
-      " |      the translation related arguments, like table for the genetic code),\n",
-      " |      \n",
-      " |      By default the new record does NOT preserve the sequence identifier,\n",
-      " |      name, description, general annotation or database cross-references -\n",
-      " |      these are unlikely to apply to the translated sequence.\n",
-      " |      \n",
-      " |      You can specify the returned record's id, name and description as\n",
-      " |      strings, or True to keep that of the parent, or False for a default.\n",
-      " |      \n",
-      " |      You can specify the returned record's features with a list of\n",
-      " |      SeqFeature objects, or False (default) to omit them.\n",
-      " |      \n",
-      " |      You can also specify both the returned record's annotations and\n",
-      " |      letter_annotations as dictionaries, True to keep that of the parent\n",
-      " |      (annotations only), or False (default) to omit them.\n",
-      " |      \n",
-      " |      e.g. Loading a FASTA gene and translating it,\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> gene_record = SeqIO.read(\"Fasta/sweetpea.nu\", \"fasta\")\n",
-      " |      >>> print(gene_record.format(\"fasta\"))\n",
-      " |      >gi|3176602|gb|U78617.1|LOU78617 Lathyrus odoratus phytochrome A (PHYA) gene, partial cds\n",
-      " |      CAGGCTGCGCGGTTTCTATTTATGAAGAACAAGGTCCGTATGATAGTTGATTGTCATGCA\n",
-      " |      AAACATGTGAAGGTTCTTCAAGACGAAAAACTCCCATTTGATTTGACTCTGTGCGGTTCG\n",
-      " |      ACCTTAAGAGCTCCACATAGTTGCCATTTGCAGTACATGGCTAACATGGATTCAATTGCT\n",
-      " |      TCATTGGTTATGGCAGTGGTCGTCAATGACAGCGATGAAGATGGAGATAGCCGTGACGCA\n",
-      " |      GTTCTACCACAAAAGAAAAAGAGACTTTGGGGTTTGGTAGTTTGTCATAACACTACTCCG\n",
-      " |      AGGTTTGTT\n",
-      " |      <BLANKLINE>\n",
-      " |      \n",
-      " |      And now translating the record, specifying the new ID and description:\n",
-      " |      \n",
-      " |      >>> protein_record = gene_record.translate(table=11,\n",
-      " |      ...                                        id=\"phya\",\n",
-      " |      ...                                        description=\"translation\")\n",
-      " |      >>> print(protein_record.format(\"fasta\"))\n",
-      " |      >phya translation\n",
-      " |      QAARFLFMKNKVRMIVDCHAKHVKVLQDEKLPFDLTLCGSTLRAPHSCHLQYMANMDSIA\n",
-      " |      SLVMAVVVNDSDEDGDSRDAVLPQKKKRLWGLVVCHNTTPRFV\n",
-      " |      <BLANKLINE>\n",
-      " |  \n",
-      " |  upper(self)\n",
-      " |      Return a copy of the record with an upper case sequence.\n",
-      " |      \n",
-      " |      All the annotation is preserved unchanged. e.g.\n",
-      " |      \n",
-      " |      >>> from Bio.Alphabet import generic_dna\n",
-      " |      >>> from Bio.Seq import Seq\n",
-      " |      >>> from Bio.SeqRecord import SeqRecord\n",
-      " |      >>> record = SeqRecord(Seq(\"acgtACGT\", generic_dna), id=\"Test\",\n",
-      " |      ...                    description = \"Made up for this example\")\n",
-      " |      >>> record.letter_annotations[\"phred_quality\"] = [1, 2, 3, 4, 5, 6, 7, 8]\n",
-      " |      >>> print(record.upper().format(\"fastq\"))\n",
-      " |      @Test Made up for this example\n",
-      " |      ACGTACGT\n",
-      " |      +\n",
-      " |      \"#$%&'()\n",
-      " |      <BLANKLINE>\n",
-      " |      \n",
-      " |      Naturally, there is a matching lower method:\n",
-      " |      \n",
-      " |      >>> print(record.lower().format(\"fastq\"))\n",
-      " |      @Test Made up for this example\n",
-      " |      acgtacgt\n",
-      " |      +\n",
-      " |      \"#$%&'()\n",
-      " |      <BLANKLINE>\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data descriptors defined here:\n",
-      " |  \n",
-      " |  __dict__\n",
-      " |      dictionary for instance variables (if defined)\n",
-      " |  \n",
-      " |  __weakref__\n",
-      " |      list of weak references to the object (if defined)\n",
-      " |  \n",
-      " |  letter_annotations\n",
-      " |      Dictionary of per-letter-annotation for the sequence.\n",
-      " |      \n",
-      " |      For example, this can hold quality scores used in FASTQ or QUAL files.\n",
-      " |      Consider this example using Bio.SeqIO to read in an example Solexa\n",
-      " |      variant FASTQ file as a SeqRecord:\n",
-      " |      \n",
-      " |      >>> from Bio import SeqIO\n",
-      " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
-      " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
-      " |      >>> print(list(record.letter_annotations))\n",
-      " |      ['solexa_quality']\n",
-      " |      >>> print(record.letter_annotations[\"solexa_quality\"])\n",
-      " |      [40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5]\n",
-      " |      \n",
-      " |      The letter_annotations get sliced automatically if you slice the\n",
-      " |      parent SeqRecord, for example taking the last ten bases:\n",
-      " |      \n",
-      " |      >>> sub_record = record[-10:]\n",
-      " |      >>> print(\"%s %s\" % (sub_record.id, sub_record.seq))\n",
-      " |      slxa_0001_1_0001_01 ACGTNNNNNN\n",
-      " |      >>> print(sub_record.letter_annotations[\"solexa_quality\"])\n",
-      " |      [4, 3, 2, 1, 0, -1, -2, -3, -4, -5]\n",
-      " |      \n",
-      " |      Any python sequence (i.e. list, tuple or string) can be recorded in\n",
-      " |      the SeqRecord's letter_annotations dictionary as long as the length\n",
-      " |      matches that of the SeqRecord's sequence.  e.g.\n",
-      " |      \n",
-      " |      >>> len(sub_record.letter_annotations)\n",
-      " |      1\n",
-      " |      >>> sub_record.letter_annotations[\"dummy\"] = \"abcdefghij\"\n",
-      " |      >>> len(sub_record.letter_annotations)\n",
-      " |      2\n",
-      " |      \n",
-      " |      You can delete entries from the letter_annotations dictionary as usual:\n",
-      " |      \n",
-      " |      >>> del sub_record.letter_annotations[\"solexa_quality\"]\n",
-      " |      >>> sub_record.letter_annotations\n",
-      " |      {'dummy': 'abcdefghij'}\n",
-      " |      \n",
-      " |      You can completely clear the dictionary easily as follows:\n",
-      " |      \n",
-      " |      >>> sub_record.letter_annotations = {}\n",
-      " |      >>> sub_record.letter_annotations\n",
-      " |      {}\n",
-      " |      \n",
-      " |      Note that if replacing the record's sequence with a sequence of a\n",
-      " |      different length you must first clear the letter_annotations dict.\n",
-      " |  \n",
-      " |  seq\n",
-      " |      The sequence itself, as a Seq or MutableSeq object.\n",
-      " |  \n",
-      " |  ----------------------------------------------------------------------\n",
-      " |  Data and other attributes defined here:\n",
-      " |  \n",
-      " |  __hash__ = None\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "from Bio.SeqRecord import SeqRecord\n",
-    "help(SeqRecord)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's write a bit of code involving `SeqRecord` and see how it comes out looking."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from Bio.SeqRecord import SeqRecord\n",
-    "\n",
-    "simple_seq = Seq(\"GATC\")\n",
-    "simple_seq_r = SeqRecord(simple_seq)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "ZG77QUj2tgSJ",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "cdbfa06f-c65e-4ada-e5af-47577e9b4a91"
+      },
+      "source": [
+        "my_prot + my_prot"
+      ],
+      "execution_count": 16,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('AAAAAAAAAA', IUPACProtein())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 16
+        }
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "AC12345\n",
-      "Made up sequence\n"
-     ]
-    }
-   ],
-   "source": [
-    "simple_seq_r.id = \"AC12345\"\n",
-    "simple_seq_r.description = \"Made up sequence\"\n",
-    "print(simple_seq_r.id)\n",
-    "print(simple_seq_r.description)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now see how we can use `SeqRecord` to parse a large fasta file. We'll pull down a file hosted on the biopython site."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "y2XacTYttgSM",
+        "colab_type": "text"
+      },
+      "source": [
+        "But this fails"
+      ]
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2020-03-09 11:07:05--  https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.fna\n",
-      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.40.133\n",
-      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.40.133|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 9853 (9.6K) [text/plain]\n",
-      "Saving to: ‘NC_005816.fna’\n",
-      "\n",
-      "NC_005816.fna       100%[===================>]   9.62K  --.-KB/s    in 0s      \n",
-      "\n",
-      "2020-03-09 11:07:06 (42.9 MB/s) - ‘NC_005816.fna’ saved [9853/9853]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.fna"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "code",
+      "metadata": {
+        "id": "MZ53Yjr1tgSO",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 287
+        },
+        "outputId": "418c486c-8e8e-4ef0-cac0-176b29d1107e"
+      },
+      "source": [
+        "my_prot + my_seq"
+      ],
+      "execution_count": 17,
+      "outputs": [
+        {
+          "output_type": "error",
+          "ename": "TypeError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-17-0b91ff3c1125>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmy_prot\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mmy_seq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/Bio/Seq.py\u001b[0m in \u001b[0;36m__add__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m    335\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mAlphabet\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_type_compatible\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malphabet\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malphabet\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m                 raise TypeError(\n\u001b[0;32m--> 337\u001b[0;31m                     \u001b[0;34mf\"Incompatible alphabets {self.alphabet!r} and {other.alphabet!r}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    338\u001b[0m                 )\n\u001b[1;32m    339\u001b[0m             \u001b[0;31m# They should be the same sequence type (or one of them is generic)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mTypeError\u001b[0m: Incompatible alphabets IUPACProtein() and IUPACUnambiguousDNA()"
+          ]
+        }
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet()), id='gi|45478711|ref|NC_005816.1|', name='gi|45478711|ref|NC_005816.1|', description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence', dbxrefs=[])"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "_Z-KdC2WtgSR",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Transcription\n",
+        "\n",
+        "Transcription is the process by which a DNA sequence is converted into messenger RNA. Remember that this is part of the \"central dogma\" of biology in which DNA engenders messenger RNA which engenders proteins. Here's a nice representation of this cycle borrowed from a Khan academy [lesson](https://cdn.kastatic.org/ka-perseus-images/20ce29384b2e7ff0cdea72acaa5b1dbd7287ab00.png).\n",
+        "\n",
+        "<img src=\"https://cdn.kastatic.org/ka-perseus-images/20ce29384b2e7ff0cdea72acaa5b1dbd7287ab00.png\">"
       ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "from Bio import SeqIO\n",
-    "\n",
-    "record = SeqIO.read(\"NC_005816.fna\", \"fasta\")\n",
-    "record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Note how there's a number of annotations attached to the `SeqRecord` object!\n",
-    "\n",
-    "Let's take a closer look."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'gi|45478711|ref|NC_005816.1|'"
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "1ZjlCDDmtgSU",
+        "colab_type": "text"
+      },
+      "source": [
+        "Note from the image above that DNA has two strands. The top strand is typically called the coding strand, and the bottom the template strand. The template strand is used for the actual transcription process of conversion into messenger RNA, but in bioinformatics, it's more common to work with the coding strand. Let's now see how we can execute a transcription computationally using Biopython."
       ]
-     },
-     "execution_count": 48,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "record.id"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'gi|45478711|ref|NC_005816.1|'"
+      "cell_type": "code",
+      "metadata": {
+        "id": "TvPiRx_0tgSU",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "b14310f2-9e00-43d3-e3d4-2e87ad1dd12b"
+      },
+      "source": [
+        "from Bio.Seq import Seq\n",
+        "from Bio.Alphabet import IUPAC\n",
+        "\n",
+        "coding_dna = Seq(\"ATGATCTCGTAA\", IUPAC.unambiguous_dna)\n",
+        "coding_dna"
+      ],
+      "execution_count": 18,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('ATGATCTCGTAA', IUPACUnambiguousDNA())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 18
+        }
       ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "record.name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "data": {
-      "text/plain": [
-       "'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence'"
+      "cell_type": "code",
+      "metadata": {
+        "id": "arGizrBztgSX",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "f0b34687-7cfc-410c-a5d8-ed7e0db81611"
+      },
+      "source": [
+        "template_dna = coding_dna.reverse_complement()\n",
+        "template_dna"
+      ],
+      "execution_count": 19,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('TTACGAGATCAT', IUPACUnambiguousDNA())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 19
+        }
       ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "record.description"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Let's now look at the same sequence, but downloaded from GenBank. We'll download the hosted file from the biopython tutorial website as before."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--2020-03-09 11:19:25--  https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.gb\n",
-      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.40.133\n",
-      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.40.133|:443... connected.\n",
-      "HTTP request sent, awaiting response... 200 OK\n",
-      "Length: 31838 (31K) [text/plain]\n",
-      "Saving to: ‘NC_005816.gb’\n",
-      "\n",
-      "NC_005816.gb        100%[===================>]  31.09K  --.-KB/s    in 0.02s   \n",
-      "\n",
-      "2020-03-09 11:19:25 (2.01 MB/s) - ‘NC_005816.gb’ saved [31838/31838]\n",
-      "\n"
-     ]
-    }
-   ],
-   "source": [
-    "!wget https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.gb"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "x8FjupA9tgSa",
+        "colab_type": "text"
+      },
+      "source": [
+        "Note that these sequences match those in the image below. You might be confused about why the `template_dna` sequence is shown reversed. The reason is that by convention, the template strand is read in the reverse direction.\n",
+        "\n",
+        "Let's now see how we can transcribe our `coding_dna` strand into messenger RNA. This will only swap 'T' for 'U' and change the alphabet for our object."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oo8bBugUtgSa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "ec994b9b-19fb-47e8-eb20-9e5931c72adb"
+      },
+      "source": [
+        "messenger_rna = coding_dna.transcribe()\n",
+        "messenger_rna"
+      ],
+      "execution_count": 20,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('AUGAUCUCGUAA', IUPACUnambiguousRNA())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 20
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6UTMKVfAtgSe",
+        "colab_type": "text"
+      },
+      "source": [
+        "We can also perform a \"back-transcription\" to recover the original coding strand from the messenger RNA."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "edClUMputgSf",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "0a7e0b83-18ee-4146-ef9b-9c13f9d0e652"
+      },
+      "source": [
+        "messenger_rna.back_transcribe()"
+      ],
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('ATGATCTCGTAA', IUPACUnambiguousDNA())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "0GqZSdFptgSk",
+        "colab_type": "text"
+      },
+      "source": [
+        "## Translation\n",
+        "\n",
+        "Translation is the next step in the process, whereby a messenger RNA is transformed into a protein sequence. Here's a beautiful diagram [from Wikipedia](https://en.wikipedia.org/wiki/Translation_(biology)#/media/File:Ribosome_mRNA_translation_en.svg) that lays out the basics of this process.\n",
+        "\n",
+        "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/b/b1/Ribosome_mRNA_translation_en.svg/1000px-Ribosome_mRNA_translation_en.svg.png\">\n",
+        "\n",
+        "Note how 3 nucleotides at a time correspond to one new amino acid added to the growing protein chain. A set of 3 nucleotides which codes for a given amino acid is called a \"codon.\" We can use the `translate()` method on the messenger rna to perform this transformation in code."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "7K_pm48HtgSm",
+        "colab_type": "text"
+      },
+      "source": [
+        "messenger_rna.translate()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "IiUYHWmRtgSm",
+        "colab_type": "text"
+      },
+      "source": [
+        "The translation can also be performed directly from the coding sequence DNA"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "cy8y6y9CtgSn",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "24fd9456-7d23-4bb5-c0a9-d9f9472f07f4"
+      },
+      "source": [
+        "coding_dna.translate()"
+      ],
+      "execution_count": 22,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('MIS*', HasStopCodon(IUPACProtein(), '*'))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 22
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "6hHsJnfQtgSq",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's now consider a longer genetic sequence that has some more interesting structure for us to look at."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iwpB4lYatgSs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "aba507e8-0321-4745-c14e-fc1110666d1a"
+      },
+      "source": [
+        "coding_dna = Seq(\"ATGGCCATTGTAATGGGCCGCTGAAAGGGTGCCCGATAG\", IUPAC.unambiguous_dna)\n",
+        "coding_dna.translate()"
+      ],
+      "execution_count": 23,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('MAIVMGR*KGAR*', HasStopCodon(IUPACProtein(), '*'))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 23
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ulq6Gc06tgSv",
+        "colab_type": "text"
+      },
+      "source": [
+        "In both of the sequences above, '*' represents the [stop codon](https://en.wikipedia.org/wiki/Stop_codon). A stop codon is a sequence of 3 nucleotides that turns off the protein machinery. In DNA, the stop codons are 'TGA', 'TAA', 'TAG'. Note that this latest sequence has multiple stop codons. It's possible to run the machinery up to the first stop codon and pause too."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "6uScm61FtgSw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "eaaa0949-37d7-45c0-bf07-324a2062ee53"
+      },
+      "source": [
+        "coding_dna.translate(to_stop=True)"
+      ],
+      "execution_count": 24,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('MAIVMGR', IUPACProtein())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 24
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "aVLT-471tgS2",
+        "colab_type": "text"
+      },
+      "source": [
+        "We're going to introduce a bit of terminology here. A complete coding sequence CDS is a nucleotide sequence of messenger RNA which is made of a whole number of codons (that is, the length of the sequence is a multiple of 3), starts with a \"start codon\" and ends with a \"stop codon\". A start codon is basically the opposite of a stop codon and is mostly commonly the sequence \"AUG\", but can be different (especially if you're dealing with something like bacterial DNA).\n",
+        "\n",
+        "Let's see how we can translate a complete CDS of bacterial messenger RNA."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "iy9-Co_WtgS3",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "d6bc133f-1753-40a9-c621-4b0ba52be8d7"
+      },
+      "source": [
+        "from Bio.Alphabet import generic_dna\n",
+        "\n",
+        "gene = Seq(\"GTGAAAAAGATGCAATCTATCGTACTCGCACTTTCCCTGGTTCTGGTCGCTCCCATGGCA\" + \\\n",
+        "           \"GCACAGGCTGCGGAAATTACGTTAGTCCCGTCAGTAAAATTACAGATAGGCGATCGTGAT\" + \\\n",
+        "           \"AATCGTGGCTATTACTGGGATGGAGGTCACTGGCGCGACCACGGCTGGTGGAAACAACAT\" + \\\n",
+        "           \"TATGAATGGCGAGGCAATCGCTGGCACCTACACGGACCGCCGCCACCGCCGCGCCACCAT\" + \\\n",
+        "           \"AAGAAAGCTCCTCATGATCATCACGGCGGTCATGGTCCAGGCAAACATCACCGCTAA\",\n",
+        "           generic_dna)\n",
+        "# We specify a \"table\" to use a different translation table for bacterial proteins\n",
+        "gene.translate(table=\"Bacterial\")"
+      ],
+      "execution_count": 25,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HR*', HasStopCodon(ExtendedIUPACProtein(), '*'))"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 25
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yWmqHt3GtgS6",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "6205609f-6925-4271-b13d-1dc466d6b22c"
+      },
+      "source": [
+        "gene.translate(table=\"Bacterial\", to_stop=True)"
+      ],
+      "execution_count": 26,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "Seq('VKKMQSIVLALSLVLVAPMAAQAAEITLVPSVKLQIGDRDNRGYYWDGGHWRDH...HHR', ExtendedIUPACProtein())"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 26
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "hqQlZA2dtgS8",
+        "colab_type": "text"
+      },
+      "source": [
+        "# Handling Annotated Sequences\n",
+        "\n",
+        "Sometimes it will be useful for us to be able to handle annotated sequences where there's richer annotations, as in GenBank or EMBL files. For these purposes, we'll want to use the `SeqRecord` class."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nnHQ_fObtgS9",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "outputId": "b751ab6b-cabc-454b-e9f7-691e95e4d282"
+      },
+      "source": [
+        "from Bio.SeqRecord import SeqRecord\n",
+        "help(SeqRecord)"
+      ],
+      "execution_count": 27,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Help on class SeqRecord in module Bio.SeqRecord:\n",
+            "\n",
+            "class SeqRecord(builtins.object)\n",
+            " |  A SeqRecord object holds a sequence and information about it.\n",
+            " |  \n",
+            " |  Main attributes:\n",
+            " |   - id          - Identifier such as a locus tag (string)\n",
+            " |   - seq         - The sequence itself (Seq object or similar)\n",
+            " |  \n",
+            " |  Additional attributes:\n",
+            " |   - name        - Sequence name, e.g. gene name (string)\n",
+            " |   - description - Additional text (string)\n",
+            " |   - dbxrefs     - List of database cross references (list of strings)\n",
+            " |   - features    - Any (sub)features defined (list of SeqFeature objects)\n",
+            " |   - annotations - Further information about the whole sequence (dictionary).\n",
+            " |     Most entries are strings, or lists of strings.\n",
+            " |   - letter_annotations - Per letter/symbol annotation (restricted\n",
+            " |     dictionary). This holds Python sequences (lists, strings\n",
+            " |     or tuples) whose length matches that of the sequence.\n",
+            " |     A typical use would be to hold a list of integers\n",
+            " |     representing sequencing quality scores, or a string\n",
+            " |     representing the secondary structure.\n",
+            " |  \n",
+            " |  You will typically use Bio.SeqIO to read in sequences from files as\n",
+            " |  SeqRecord objects.  However, you may want to create your own SeqRecord\n",
+            " |  objects directly (see the __init__ method for further details):\n",
+            " |  \n",
+            " |  >>> from Bio.Seq import Seq\n",
+            " |  >>> from Bio.SeqRecord import SeqRecord\n",
+            " |  >>> from Bio.Alphabet import IUPAC\n",
+            " |  >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\",\n",
+            " |  ...                         IUPAC.protein),\n",
+            " |  ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
+            " |  ...                    description=\"toxic membrane protein\")\n",
+            " |  >>> print(record)\n",
+            " |  ID: YP_025292.1\n",
+            " |  Name: HokC\n",
+            " |  Description: toxic membrane protein\n",
+            " |  Number of features: 0\n",
+            " |  Seq('MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF', IUPACProtein())\n",
+            " |  \n",
+            " |  If you want to save SeqRecord objects to a sequence file, use Bio.SeqIO\n",
+            " |  for this.  For the special case where you want the SeqRecord turned into\n",
+            " |  a string in a particular file format there is a format method which uses\n",
+            " |  Bio.SeqIO internally:\n",
+            " |  \n",
+            " |  >>> print(record.format(\"fasta\"))\n",
+            " |  >YP_025292.1 toxic membrane protein\n",
+            " |  MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
+            " |  <BLANKLINE>\n",
+            " |  \n",
+            " |  You can also do things like slicing a SeqRecord, checking its length, etc\n",
+            " |  \n",
+            " |  >>> len(record)\n",
+            " |  44\n",
+            " |  >>> edited = record[:10] + record[11:]\n",
+            " |  >>> print(edited.seq)\n",
+            " |  MKQHKAMIVAIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
+            " |  >>> print(record.seq)\n",
+            " |  MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
+            " |  \n",
+            " |  Methods defined here:\n",
+            " |  \n",
+            " |  __add__(self, other)\n",
+            " |      Add another sequence or string to this sequence.\n",
+            " |      \n",
+            " |      The other sequence can be a SeqRecord object, a Seq object (or\n",
+            " |      similar, e.g. a MutableSeq) or a plain Python string. If you add\n",
+            " |      a plain string or a Seq (like) object, the new SeqRecord will simply\n",
+            " |      have this appended to the existing data. However, any per letter\n",
+            " |      annotation will be lost:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
+            " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
+            " |      >>> print(list(record.letter_annotations))\n",
+            " |      ['solexa_quality']\n",
+            " |      \n",
+            " |      >>> new = record + \"ACT\"\n",
+            " |      >>> print(\"%s %s\" % (new.id, new.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNNACT\n",
+            " |      >>> print(list(new.letter_annotations))\n",
+            " |      []\n",
+            " |      \n",
+            " |      The new record will attempt to combine the annotation, but for any\n",
+            " |      ambiguities (e.g. different names) it defaults to omitting that\n",
+            " |      annotation.\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> with open(\"GenBank/pBAD30.gb\") as handle:\n",
+            " |      ...     plasmid = SeqIO.read(handle, \"gb\")\n",
+            " |      >>> print(\"%s %i\" % (plasmid.id, len(plasmid)))\n",
+            " |      pBAD30 4923\n",
+            " |      \n",
+            " |      Now let's cut the plasmid into two pieces, and join them back up the\n",
+            " |      other way round (i.e. shift the starting point on this plasmid, have\n",
+            " |      a look at the annotated features in the original file to see why this\n",
+            " |      particular split point might make sense):\n",
+            " |      \n",
+            " |      >>> left = plasmid[:3765]\n",
+            " |      >>> right = plasmid[3765:]\n",
+            " |      >>> new = right + left\n",
+            " |      >>> print(\"%s %i\" % (new.id, len(new)))\n",
+            " |      pBAD30 4923\n",
+            " |      >>> str(new.seq) == str(right.seq + left.seq)\n",
+            " |      True\n",
+            " |      >>> len(new.features) == len(left.features) + len(right.features)\n",
+            " |      True\n",
+            " |      \n",
+            " |      When we add the left and right SeqRecord objects, their annotation\n",
+            " |      is all consistent, so it is all conserved in the new SeqRecord:\n",
+            " |      \n",
+            " |      >>> new.id == left.id == right.id == plasmid.id\n",
+            " |      True\n",
+            " |      >>> new.name == left.name == right.name == plasmid.name\n",
+            " |      True\n",
+            " |      >>> new.description == plasmid.description\n",
+            " |      True\n",
+            " |      >>> new.annotations == left.annotations == right.annotations\n",
+            " |      True\n",
+            " |      >>> new.letter_annotations == plasmid.letter_annotations\n",
+            " |      True\n",
+            " |      >>> new.dbxrefs == left.dbxrefs == right.dbxrefs\n",
+            " |      True\n",
+            " |      \n",
+            " |      However, we should point out that when we sliced the SeqRecord,\n",
+            " |      any annotations dictionary or dbxrefs list entries were lost.\n",
+            " |      You can explicitly copy them like this:\n",
+            " |      \n",
+            " |      >>> new.annotations = plasmid.annotations.copy()\n",
+            " |      >>> new.dbxrefs = plasmid.dbxrefs[:]\n",
+            " |  \n",
+            " |  __bool__(self)\n",
+            " |      Boolean value of an instance of this class (True).\n",
+            " |      \n",
+            " |      This behaviour is for backwards compatibility, since until the\n",
+            " |      __len__ method was added, a SeqRecord always evaluated as True.\n",
+            " |      \n",
+            " |      Note that in comparison, a Seq object will evaluate to False if it\n",
+            " |      has a zero length sequence.\n",
+            " |      \n",
+            " |      WARNING: The SeqRecord may in future evaluate to False when its\n",
+            " |      sequence is of zero length (in order to better match the Seq\n",
+            " |      object behaviour)!\n",
+            " |  \n",
+            " |  __contains__(self, char)\n",
+            " |      Implement the 'in' keyword, searches the sequence.\n",
+            " |      \n",
+            " |      e.g.\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Fasta/sweetpea.nu\", \"fasta\")\n",
+            " |      >>> \"GAATTC\" in record\n",
+            " |      False\n",
+            " |      >>> \"AAA\" in record\n",
+            " |      True\n",
+            " |      \n",
+            " |      This essentially acts as a proxy for using \"in\" on the sequence:\n",
+            " |      \n",
+            " |      >>> \"GAATTC\" in record.seq\n",
+            " |      False\n",
+            " |      >>> \"AAA\" in record.seq\n",
+            " |      True\n",
+            " |      \n",
+            " |      Note that you can also use Seq objects as the query,\n",
+            " |      \n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.Alphabet import generic_dna\n",
+            " |      >>> Seq(\"AAA\") in record\n",
+            " |      True\n",
+            " |      >>> Seq(\"AAA\", generic_dna) in record\n",
+            " |      True\n",
+            " |      \n",
+            " |      See also the Seq object's __contains__ method.\n",
+            " |  \n",
+            " |  __eq__(self, other)\n",
+            " |      Define the equal-to operand (not implemented).\n",
+            " |  \n",
+            " |  __format__(self, format_spec)\n",
+            " |      Return the record as a string in the specified file format.\n",
+            " |      \n",
+            " |      This method supports the Python format() function and f-strings.\n",
+            " |      The format_spec should be a lower case string supported by\n",
+            " |      Bio.SeqIO as a text output file format. Requesting a binary file\n",
+            " |      format raises a ValueError. e.g.\n",
+            " |      \n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\"),\n",
+            " |      ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
+            " |      ...                    description=\"toxic membrane protein\")\n",
+            " |      ...\n",
+            " |      >>> format(record, \"fasta\")\n",
+            " |      '>YP_025292.1 toxic membrane protein\\nMKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\\n'\n",
+            " |      >>> print(f\"Here is {record.id} in FASTA format:\\n{record:fasta}\")\n",
+            " |      Here is YP_025292.1 in FASTA format:\n",
+            " |      >YP_025292.1 toxic membrane protein\n",
+            " |      MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
+            " |      <BLANKLINE>\n",
+            " |      \n",
+            " |      See also the SeqRecord's format() method.\n",
+            " |  \n",
+            " |  __ge__(self, other)\n",
+            " |      Define the greater-than-or-equal-to operand (not implemented).\n",
+            " |  \n",
+            " |  __getitem__(self, index)\n",
+            " |      Return a sub-sequence or an individual letter.\n",
+            " |      \n",
+            " |      Slicing, e.g. my_record[5:10], returns a new SeqRecord for\n",
+            " |      that sub-sequence with some annotation preserved as follows:\n",
+            " |      \n",
+            " |      * The name, id and description are kept as-is.\n",
+            " |      * Any per-letter-annotations are sliced to match the requested\n",
+            " |        sub-sequence.\n",
+            " |      * Unless a stride is used, all those features which fall fully\n",
+            " |        within the subsequence are included (with their locations\n",
+            " |        adjusted accordingly). If you want to preserve any truncated\n",
+            " |        features (e.g. GenBank/EMBL source features), you must\n",
+            " |        explicitly add them to the new SeqRecord yourself.\n",
+            " |      * The annotations dictionary and the dbxrefs list are not used\n",
+            " |        for the new SeqRecord, as in general they may not apply to the\n",
+            " |        subsequence. If you want to preserve them, you must explicitly\n",
+            " |        copy them to the new SeqRecord yourself.\n",
+            " |      \n",
+            " |      Using an integer index, e.g. my_record[5] is shorthand for\n",
+            " |      extracting that letter from the sequence, my_record.seq[5].\n",
+            " |      \n",
+            " |      For example, consider this short protein and its secondary\n",
+            " |      structure as encoded by the PDB (e.g. H for alpha helices),\n",
+            " |      plus a simple feature for its histidine self phosphorylation\n",
+            " |      site:\n",
+            " |      \n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> from Bio.SeqFeature import SeqFeature, FeatureLocation\n",
+            " |      >>> from Bio.Alphabet import IUPAC\n",
+            " |      >>> rec = SeqRecord(Seq(\"MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLAT\"\n",
+            " |      ...                     \"EMMSEQDGYLAESINKDIEECNAIIEQFIDYLR\",\n",
+            " |      ...                     IUPAC.protein),\n",
+            " |      ...                 id=\"1JOY\", name=\"EnvZ\",\n",
+            " |      ...                 description=\"Homodimeric domain of EnvZ from E. coli\")\n",
+            " |      >>> rec.letter_annotations[\"secondary_structure\"] = \"  S  SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT  \"\n",
+            " |      >>> rec.features.append(SeqFeature(FeatureLocation(20, 21),\n",
+            " |      ...                     type = \"Site\"))\n",
+            " |      \n",
+            " |      Now let's have a quick look at the full record,\n",
+            " |      \n",
+            " |      >>> print(rec)\n",
+            " |      ID: 1JOY\n",
+            " |      Name: EnvZ\n",
+            " |      Description: Homodimeric domain of EnvZ from E. coli\n",
+            " |      Number of features: 1\n",
+            " |      Per letter annotation for: secondary_structure\n",
+            " |      Seq('MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEE...YLR', IUPACProtein())\n",
+            " |      >>> rec.letter_annotations[\"secondary_structure\"]\n",
+            " |      '  S  SSSSSSHHHHHTTTHHHHHHHHHHHHHHHHHHHHHHTHHHHHHHHHHHHHHHHHHHHHTT  '\n",
+            " |      >>> print(rec.features[0].location)\n",
+            " |      [20:21]\n",
+            " |      \n",
+            " |      Now let's take a sub sequence, here chosen as the first (fractured)\n",
+            " |      alpha helix which includes the histidine phosphorylation site:\n",
+            " |      \n",
+            " |      >>> sub = rec[11:41]\n",
+            " |      >>> print(sub)\n",
+            " |      ID: 1JOY\n",
+            " |      Name: EnvZ\n",
+            " |      Description: Homodimeric domain of EnvZ from E. coli\n",
+            " |      Number of features: 1\n",
+            " |      Per letter annotation for: secondary_structure\n",
+            " |      Seq('RTLLMAGVSHDLRTPLTRIRLATEMMSEQD', IUPACProtein())\n",
+            " |      >>> sub.letter_annotations[\"secondary_structure\"]\n",
+            " |      'HHHHHTTTHHHHHHHHHHHHHHHHHHHHHH'\n",
+            " |      >>> print(sub.features[0].location)\n",
+            " |      [9:10]\n",
+            " |      \n",
+            " |      You can also of course omit the start or end values, for\n",
+            " |      example to get the first ten letters only:\n",
+            " |      \n",
+            " |      >>> print(rec[:10])\n",
+            " |      ID: 1JOY\n",
+            " |      Name: EnvZ\n",
+            " |      Description: Homodimeric domain of EnvZ from E. coli\n",
+            " |      Number of features: 0\n",
+            " |      Per letter annotation for: secondary_structure\n",
+            " |      Seq('MAAGVKQLAD', IUPACProtein())\n",
+            " |      \n",
+            " |      Or for the last ten letters:\n",
+            " |      \n",
+            " |      >>> print(rec[-10:])\n",
+            " |      ID: 1JOY\n",
+            " |      Name: EnvZ\n",
+            " |      Description: Homodimeric domain of EnvZ from E. coli\n",
+            " |      Number of features: 0\n",
+            " |      Per letter annotation for: secondary_structure\n",
+            " |      Seq('IIEQFIDYLR', IUPACProtein())\n",
+            " |      \n",
+            " |      If you omit both, then you get a copy of the original record (although\n",
+            " |      lacking the annotations and dbxrefs):\n",
+            " |      \n",
+            " |      >>> print(rec[:])\n",
+            " |      ID: 1JOY\n",
+            " |      Name: EnvZ\n",
+            " |      Description: Homodimeric domain of EnvZ from E. coli\n",
+            " |      Number of features: 1\n",
+            " |      Per letter annotation for: secondary_structure\n",
+            " |      Seq('MAAGVKQLADDRTLLMAGVSHDLRTPLTRIRLATEMMSEQDGYLAESINKDIEE...YLR', IUPACProtein())\n",
+            " |      \n",
+            " |      Finally, indexing with a simple integer is shorthand for pulling out\n",
+            " |      that letter from the sequence directly:\n",
+            " |      \n",
+            " |      >>> rec[5]\n",
+            " |      'K'\n",
+            " |      >>> rec.seq[5]\n",
+            " |      'K'\n",
+            " |  \n",
+            " |  __gt__(self, other)\n",
+            " |      Define the greater-than operand (not implemented).\n",
+            " |  \n",
+            " |  __init__(self, seq, id='<unknown id>', name='<unknown name>', description='<unknown description>', dbxrefs=None, features=None, annotations=None, letter_annotations=None)\n",
+            " |      Create a SeqRecord.\n",
+            " |      \n",
+            " |      Arguments:\n",
+            " |       - seq         - Sequence, required (Seq, MutableSeq or UnknownSeq)\n",
+            " |       - id          - Sequence identifier, recommended (string)\n",
+            " |       - name        - Sequence name, optional (string)\n",
+            " |       - description - Sequence description, optional (string)\n",
+            " |       - dbxrefs     - Database cross references, optional (list of strings)\n",
+            " |       - features    - Any (sub)features, optional (list of SeqFeature objects)\n",
+            " |       - annotations - Dictionary of annotations for the whole sequence\n",
+            " |       - letter_annotations - Dictionary of per-letter-annotations, values\n",
+            " |         should be strings, list or tuples of the same length as the full\n",
+            " |         sequence.\n",
+            " |      \n",
+            " |      You will typically use Bio.SeqIO to read in sequences from files as\n",
+            " |      SeqRecord objects.  However, you may want to create your own SeqRecord\n",
+            " |      objects directly.\n",
+            " |      \n",
+            " |      Note that while an id is optional, we strongly recommend you supply a\n",
+            " |      unique id string for each record.  This is especially important\n",
+            " |      if you wish to write your sequences to a file.\n",
+            " |      \n",
+            " |      If you don't have the actual sequence, but you do know its length,\n",
+            " |      then using the UnknownSeq object from Bio.Seq is appropriate.\n",
+            " |      \n",
+            " |      You can create a 'blank' SeqRecord object, and then populate the\n",
+            " |      attributes later.\n",
+            " |  \n",
+            " |  __iter__(self)\n",
+            " |      Iterate over the letters in the sequence.\n",
+            " |      \n",
+            " |      For example, using Bio.SeqIO to read in a protein FASTA file:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Fasta/loveliesbleeding.pro\", \"fasta\")\n",
+            " |      >>> for amino in record:\n",
+            " |      ...     print(amino)\n",
+            " |      ...     if amino == \"L\": break\n",
+            " |      X\n",
+            " |      A\n",
+            " |      G\n",
+            " |      L\n",
+            " |      >>> print(record.seq[3])\n",
+            " |      L\n",
+            " |      \n",
+            " |      This is just a shortcut for iterating over the sequence directly:\n",
+            " |      \n",
+            " |      >>> for amino in record.seq:\n",
+            " |      ...     print(amino)\n",
+            " |      ...     if amino == \"L\": break\n",
+            " |      X\n",
+            " |      A\n",
+            " |      G\n",
+            " |      L\n",
+            " |      >>> print(record.seq[3])\n",
+            " |      L\n",
+            " |      \n",
+            " |      Note that this does not facilitate iteration together with any\n",
+            " |      per-letter-annotation.  However, you can achieve that using the\n",
+            " |      python zip function on the record (or its sequence) and the relevant\n",
+            " |      per-letter-annotation:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> rec = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
+            " |      >>> print(\"%s %s\" % (rec.id, rec.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
+            " |      >>> print(list(rec.letter_annotations))\n",
+            " |      ['solexa_quality']\n",
+            " |      >>> for nuc, qual in zip(rec, rec.letter_annotations[\"solexa_quality\"]):\n",
+            " |      ...     if qual > 35:\n",
+            " |      ...         print(\"%s %i\" % (nuc, qual))\n",
+            " |      A 40\n",
+            " |      C 39\n",
+            " |      G 38\n",
+            " |      T 37\n",
+            " |      A 36\n",
+            " |      \n",
+            " |      You may agree that using zip(rec.seq, ...) is more explicit than using\n",
+            " |      zip(rec, ...) as shown above.\n",
+            " |  \n",
+            " |  __le___(self, other)\n",
+            " |      Define the less-than-or-equal-to operand (not implemented).\n",
+            " |  \n",
+            " |  __len__(self)\n",
+            " |      Return the length of the sequence.\n",
+            " |      \n",
+            " |      For example, using Bio.SeqIO to read in a FASTA nucleotide file:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Fasta/sweetpea.nu\", \"fasta\")\n",
+            " |      >>> len(record)\n",
+            " |      309\n",
+            " |      >>> len(record.seq)\n",
+            " |      309\n",
+            " |  \n",
+            " |  __lt__(self, other)\n",
+            " |      Define the less-than operand (not implemented).\n",
+            " |  \n",
+            " |  __ne__(self, other)\n",
+            " |      Define the not-equal-to operand (not implemented).\n",
+            " |  \n",
+            " |  __radd__(self, other)\n",
+            " |      Add another sequence or string to this sequence (from the left).\n",
+            " |      \n",
+            " |      This method handles adding a Seq object (or similar, e.g. MutableSeq)\n",
+            " |      or a plain Python string (on the left) to a SeqRecord (on the right).\n",
+            " |      See the __add__ method for more details, but for example:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
+            " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
+            " |      >>> print(list(record.letter_annotations))\n",
+            " |      ['solexa_quality']\n",
+            " |      \n",
+            " |      >>> new = \"ACT\" + record\n",
+            " |      >>> print(\"%s %s\" % (new.id, new.seq))\n",
+            " |      slxa_0001_1_0001_01 ACTACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
+            " |      >>> print(list(new.letter_annotations))\n",
+            " |      []\n",
+            " |  \n",
+            " |  __repr__(self)\n",
+            " |      Return a concise summary of the record for debugging (string).\n",
+            " |      \n",
+            " |      The python built in function repr works by calling the object's ___repr__\n",
+            " |      method.  e.g.\n",
+            " |      \n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> from Bio.Alphabet import generic_protein\n",
+            " |      >>> rec = SeqRecord(Seq(\"MASRGVNKVILVGNLGQDPEVRYMPNGGAVANITLATSESWRDKAT\"\n",
+            " |      ...                    +\"GEMKEQTEWHRVVLFGKLAEVASEYLRKGSQVYIEGQLRTRKWTDQ\"\n",
+            " |      ...                    +\"SGQDRYTTEVVVNVGGTMQMLGGRQGGGAPAGGNIGGGQPQGGWGQ\"\n",
+            " |      ...                    +\"PQQPQGGNQFSGGAQSRPQQSAPAAPSNEPPMDFDDDIPF\",\n",
+            " |      ...                    generic_protein),\n",
+            " |      ...                 id=\"NP_418483.1\", name=\"b4059\",\n",
+            " |      ...                 description=\"ssDNA-binding protein\",\n",
+            " |      ...                 dbxrefs=[\"ASAP:13298\", \"GI:16131885\", \"GeneID:948570\"])\n",
+            " |      >>> print(repr(rec))\n",
+            " |      SeqRecord(seq=Seq('MASRGVNKVILVGNLGQDPEVRYMPNGGAVANITLATSESWRDKATGEMKEQTE...IPF', ProteinAlphabet()), id='NP_418483.1', name='b4059', description='ssDNA-binding protein', dbxrefs=['ASAP:13298', 'GI:16131885', 'GeneID:948570'])\n",
+            " |      \n",
+            " |      At the python prompt you can also use this shorthand:\n",
+            " |      \n",
+            " |      >>> rec\n",
+            " |      SeqRecord(seq=Seq('MASRGVNKVILVGNLGQDPEVRYMPNGGAVANITLATSESWRDKATGEMKEQTE...IPF', ProteinAlphabet()), id='NP_418483.1', name='b4059', description='ssDNA-binding protein', dbxrefs=['ASAP:13298', 'GI:16131885', 'GeneID:948570'])\n",
+            " |      \n",
+            " |      Note that long sequences are shown truncated. Also note that any\n",
+            " |      annotations, letter_annotations and features are not shown (as they\n",
+            " |      would lead to a very long string).\n",
+            " |  \n",
+            " |  __str__(self)\n",
+            " |      Return a human readable summary of the record and its annotation (string).\n",
+            " |      \n",
+            " |      The python built in function str works by calling the object's ___str__\n",
+            " |      method.  e.g.\n",
+            " |      \n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> from Bio.Alphabet import IUPAC\n",
+            " |      >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\",\n",
+            " |      ...                         IUPAC.protein),\n",
+            " |      ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
+            " |      ...                    description=\"toxic membrane protein, small\")\n",
+            " |      >>> print(str(record))\n",
+            " |      ID: YP_025292.1\n",
+            " |      Name: HokC\n",
+            " |      Description: toxic membrane protein, small\n",
+            " |      Number of features: 0\n",
+            " |      Seq('MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF', IUPACProtein())\n",
+            " |      \n",
+            " |      In this example you don't actually need to call str explicity, as the\n",
+            " |      print command does this automatically:\n",
+            " |      \n",
+            " |      >>> print(record)\n",
+            " |      ID: YP_025292.1\n",
+            " |      Name: HokC\n",
+            " |      Description: toxic membrane protein, small\n",
+            " |      Number of features: 0\n",
+            " |      Seq('MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF', IUPACProtein())\n",
+            " |      \n",
+            " |      Note that long sequences are shown truncated.\n",
+            " |  \n",
+            " |  format(self, format)\n",
+            " |      Return the record as a string in the specified file format.\n",
+            " |      \n",
+            " |      The format should be a lower case string supported as an output\n",
+            " |      format by Bio.SeqIO, which is used to turn the SeqRecord into a\n",
+            " |      string.  e.g.\n",
+            " |      \n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> from Bio.Alphabet import IUPAC\n",
+            " |      >>> record = SeqRecord(Seq(\"MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\",\n",
+            " |      ...                         IUPAC.protein),\n",
+            " |      ...                    id=\"YP_025292.1\", name=\"HokC\",\n",
+            " |      ...                    description=\"toxic membrane protein\")\n",
+            " |      >>> record.format(\"fasta\")\n",
+            " |      '>YP_025292.1 toxic membrane protein\\nMKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\\n'\n",
+            " |      >>> print(record.format(\"fasta\"))\n",
+            " |      >YP_025292.1 toxic membrane protein\n",
+            " |      MKQHKAMIVALIVICITAVVAALVTRKDLCEVHIRTGQTEVAVF\n",
+            " |      <BLANKLINE>\n",
+            " |      \n",
+            " |      The Python print function automatically appends a new line, meaning\n",
+            " |      in this example a blank line is shown.  If you look at the string\n",
+            " |      representation you can see there is a trailing new line (shown as\n",
+            " |      slash n) which is important when writing to a file or if\n",
+            " |      concatenating multiple sequence strings together.\n",
+            " |      \n",
+            " |      Note that this method will NOT work on every possible file format\n",
+            " |      supported by Bio.SeqIO (e.g. some are for multiple sequences only,\n",
+            " |      and binary formats are not supported).\n",
+            " |  \n",
+            " |  lower(self)\n",
+            " |      Return a copy of the record with a lower case sequence.\n",
+            " |      \n",
+            " |      All the annotation is preserved unchanged. e.g.\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Fasta/aster.pro\", \"fasta\")\n",
+            " |      >>> print(record.format(\"fasta\"))\n",
+            " |      >gi|3298468|dbj|BAA31520.1| SAMIPF\n",
+            " |      GGHVNPAVTFGAFVGGNITLLRGIVYIIAQLLGSTVACLLLKFVTNDMAVGVFSLSAGVG\n",
+            " |      VTNALVFEIVMTFGLVYTVYATAIDPKKGSLGTIAPIAIGFIVGANI\n",
+            " |      <BLANKLINE>\n",
+            " |      >>> print(record.lower().format(\"fasta\"))\n",
+            " |      >gi|3298468|dbj|BAA31520.1| SAMIPF\n",
+            " |      gghvnpavtfgafvggnitllrgivyiiaqllgstvaclllkfvtndmavgvfslsagvg\n",
+            " |      vtnalvfeivmtfglvytvyataidpkkgslgtiapiaigfivgani\n",
+            " |      <BLANKLINE>\n",
+            " |      \n",
+            " |      To take a more annotation rich example,\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> old = SeqIO.read(\"EMBL/TRBG361.embl\", \"embl\")\n",
+            " |      >>> len(old.features)\n",
+            " |      3\n",
+            " |      >>> new = old.lower()\n",
+            " |      >>> len(old.features) == len(new.features)\n",
+            " |      True\n",
+            " |      >>> old.annotations[\"organism\"] == new.annotations[\"organism\"]\n",
+            " |      True\n",
+            " |      >>> old.dbxrefs == new.dbxrefs\n",
+            " |      True\n",
+            " |  \n",
+            " |  reverse_complement(self, id=False, name=False, description=False, features=True, annotations=False, letter_annotations=True, dbxrefs=False)\n",
+            " |      Return new SeqRecord with reverse complement sequence.\n",
+            " |      \n",
+            " |      By default the new record does NOT preserve the sequence identifier,\n",
+            " |      name, description, general annotation or database cross-references -\n",
+            " |      these are unlikely to apply to the reversed sequence.\n",
+            " |      \n",
+            " |      You can specify the returned record's id, name and description as\n",
+            " |      strings, or True to keep that of the parent, or False for a default.\n",
+            " |      \n",
+            " |      You can specify the returned record's features with a list of\n",
+            " |      SeqFeature objects, or True to keep that of the parent, or False to\n",
+            " |      omit them. The default is to keep the original features (with the\n",
+            " |      strand and locations adjusted).\n",
+            " |      \n",
+            " |      You can also specify both the returned record's annotations and\n",
+            " |      letter_annotations as dictionaries, True to keep that of the parent,\n",
+            " |      or False to omit them. The default is to keep the original\n",
+            " |      annotations (with the letter annotations reversed).\n",
+            " |      \n",
+            " |      To show what happens to the pre-letter annotations, consider an\n",
+            " |      example Solexa variant FASTQ file with a single entry, which we'll\n",
+            " |      read in as a SeqRecord:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
+            " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
+            " |      >>> print(list(record.letter_annotations))\n",
+            " |      ['solexa_quality']\n",
+            " |      >>> print(record.letter_annotations[\"solexa_quality\"])\n",
+            " |      [40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5]\n",
+            " |      \n",
+            " |      Now take the reverse complement, here we explicitly give a new\n",
+            " |      identifier (the old identifier with a suffix):\n",
+            " |      \n",
+            " |      >>> rc_record = record.reverse_complement(id=record.id + \"_rc\")\n",
+            " |      >>> print(\"%s %s\" % (rc_record.id, rc_record.seq))\n",
+            " |      slxa_0001_1_0001_01_rc NNNNNNACGTACGTACGTACGTACGTACGTACGTACGTACGTACGT\n",
+            " |      \n",
+            " |      Notice that the per-letter-annotations have also been reversed,\n",
+            " |      although this may not be appropriate for all cases.\n",
+            " |      \n",
+            " |      >>> print(rc_record.letter_annotations[\"solexa_quality\"])\n",
+            " |      [-5, -4, -3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40]\n",
+            " |      \n",
+            " |      Now for the features, we need a different example. Parsing a GenBank\n",
+            " |      file is probably the easiest way to get an nice example with features\n",
+            " |      in it...\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> with open(\"GenBank/pBAD30.gb\") as handle:\n",
+            " |      ...     plasmid = SeqIO.read(handle, \"gb\")\n",
+            " |      >>> print(\"%s %i\" % (plasmid.id, len(plasmid)))\n",
+            " |      pBAD30 4923\n",
+            " |      >>> plasmid.seq\n",
+            " |      Seq('GCTAGCGGAGTGTATACTGGCTTACTATGTTGGCACTGATGAGGGTGTCAGTGA...ATG', IUPACAmbiguousDNA())\n",
+            " |      >>> len(plasmid.features)\n",
+            " |      13\n",
+            " |      \n",
+            " |      Now, let's take the reverse complement of this whole plasmid:\n",
+            " |      \n",
+            " |      >>> rc_plasmid = plasmid.reverse_complement(id=plasmid.id+\"_rc\")\n",
+            " |      >>> print(\"%s %i\" % (rc_plasmid.id, len(rc_plasmid)))\n",
+            " |      pBAD30_rc 4923\n",
+            " |      >>> rc_plasmid.seq\n",
+            " |      Seq('CATGGGCAAATATTATACGCAAGGCGACAAGGTGCTGATGCCGCTGGCGATTCA...AGC', IUPACAmbiguousDNA())\n",
+            " |      >>> len(rc_plasmid.features)\n",
+            " |      13\n",
+            " |      \n",
+            " |      Let's compare the first CDS feature - it has gone from being the\n",
+            " |      second feature (index 1) to the second last feature (index -2), its\n",
+            " |      strand has changed, and the location switched round.\n",
+            " |      \n",
+            " |      >>> print(plasmid.features[1])\n",
+            " |      type: CDS\n",
+            " |      location: [1081:1960](-)\n",
+            " |      qualifiers:\n",
+            " |          Key: label, Value: ['araC']\n",
+            " |          Key: note, Value: ['araC regulator of the arabinose BAD promoter']\n",
+            " |          Key: vntifkey, Value: ['4']\n",
+            " |      <BLANKLINE>\n",
+            " |      >>> print(rc_plasmid.features[-2])\n",
+            " |      type: CDS\n",
+            " |      location: [2963:3842](+)\n",
+            " |      qualifiers:\n",
+            " |          Key: label, Value: ['araC']\n",
+            " |          Key: note, Value: ['araC regulator of the arabinose BAD promoter']\n",
+            " |          Key: vntifkey, Value: ['4']\n",
+            " |      <BLANKLINE>\n",
+            " |      \n",
+            " |      You can check this new location, based on the length of the plasmid:\n",
+            " |      \n",
+            " |      >>> len(plasmid) - 1081\n",
+            " |      3842\n",
+            " |      >>> len(plasmid) - 1960\n",
+            " |      2963\n",
+            " |      \n",
+            " |      Note that if the SeqFeature annotation includes any strand specific\n",
+            " |      information (e.g. base changes for a SNP), this information is not\n",
+            " |      amended, and would need correction after the reverse complement.\n",
+            " |      \n",
+            " |      Note trying to reverse complement a protein SeqRecord raises an\n",
+            " |      exception:\n",
+            " |      \n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.Alphabet import IUPAC\n",
+            " |      >>> protein_rec = SeqRecord(Seq(\"MAIVMGR\", IUPAC.protein), id=\"Test\")\n",
+            " |      >>> protein_rec.reverse_complement()\n",
+            " |      Traceback (most recent call last):\n",
+            " |         ...\n",
+            " |      ValueError: Proteins do not have complements!\n",
+            " |      \n",
+            " |      Also note you can reverse complement a SeqRecord using a MutableSeq:\n",
+            " |      \n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> from Bio.Seq import MutableSeq\n",
+            " |      >>> from Bio.Alphabet import generic_dna\n",
+            " |      >>> rec = SeqRecord(MutableSeq(\"ACGT\", generic_dna), id=\"Test\")\n",
+            " |      >>> rec.seq[0] = \"T\"\n",
+            " |      >>> print(\"%s %s\" % (rec.id, rec.seq))\n",
+            " |      Test TCGT\n",
+            " |      >>> rc = rec.reverse_complement(id=True)\n",
+            " |      >>> print(\"%s %s\" % (rc.id, rc.seq))\n",
+            " |      Test ACGA\n",
+            " |  \n",
+            " |  translate(self, table='Standard', stop_symbol='*', to_stop=False, cds=False, gap=None, id=False, name=False, description=False, features=False, annotations=False, letter_annotations=False, dbxrefs=False)\n",
+            " |      Return new SeqRecord with translated sequence.\n",
+            " |      \n",
+            " |      This calls the record's .seq.translate() method (which describes\n",
+            " |      the translation related arguments, like table for the genetic code),\n",
+            " |      \n",
+            " |      By default the new record does NOT preserve the sequence identifier,\n",
+            " |      name, description, general annotation or database cross-references -\n",
+            " |      these are unlikely to apply to the translated sequence.\n",
+            " |      \n",
+            " |      You can specify the returned record's id, name and description as\n",
+            " |      strings, or True to keep that of the parent, or False for a default.\n",
+            " |      \n",
+            " |      You can specify the returned record's features with a list of\n",
+            " |      SeqFeature objects, or False (default) to omit them.\n",
+            " |      \n",
+            " |      You can also specify both the returned record's annotations and\n",
+            " |      letter_annotations as dictionaries, True to keep that of the parent\n",
+            " |      (annotations only), or False (default) to omit them.\n",
+            " |      \n",
+            " |      e.g. Loading a FASTA gene and translating it,\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> gene_record = SeqIO.read(\"Fasta/sweetpea.nu\", \"fasta\")\n",
+            " |      >>> print(gene_record.format(\"fasta\"))\n",
+            " |      >gi|3176602|gb|U78617.1|LOU78617 Lathyrus odoratus phytochrome A (PHYA) gene, partial cds\n",
+            " |      CAGGCTGCGCGGTTTCTATTTATGAAGAACAAGGTCCGTATGATAGTTGATTGTCATGCA\n",
+            " |      AAACATGTGAAGGTTCTTCAAGACGAAAAACTCCCATTTGATTTGACTCTGTGCGGTTCG\n",
+            " |      ACCTTAAGAGCTCCACATAGTTGCCATTTGCAGTACATGGCTAACATGGATTCAATTGCT\n",
+            " |      TCATTGGTTATGGCAGTGGTCGTCAATGACAGCGATGAAGATGGAGATAGCCGTGACGCA\n",
+            " |      GTTCTACCACAAAAGAAAAAGAGACTTTGGGGTTTGGTAGTTTGTCATAACACTACTCCG\n",
+            " |      AGGTTTGTT\n",
+            " |      <BLANKLINE>\n",
+            " |      \n",
+            " |      And now translating the record, specifying the new ID and description:\n",
+            " |      \n",
+            " |      >>> protein_record = gene_record.translate(table=11,\n",
+            " |      ...                                        id=\"phya\",\n",
+            " |      ...                                        description=\"translation\")\n",
+            " |      >>> print(protein_record.format(\"fasta\"))\n",
+            " |      >phya translation\n",
+            " |      QAARFLFMKNKVRMIVDCHAKHVKVLQDEKLPFDLTLCGSTLRAPHSCHLQYMANMDSIA\n",
+            " |      SLVMAVVVNDSDEDGDSRDAVLPQKKKRLWGLVVCHNTTPRFV\n",
+            " |      <BLANKLINE>\n",
+            " |  \n",
+            " |  upper(self)\n",
+            " |      Return a copy of the record with an upper case sequence.\n",
+            " |      \n",
+            " |      All the annotation is preserved unchanged. e.g.\n",
+            " |      \n",
+            " |      >>> from Bio.Alphabet import generic_dna\n",
+            " |      >>> from Bio.Seq import Seq\n",
+            " |      >>> from Bio.SeqRecord import SeqRecord\n",
+            " |      >>> record = SeqRecord(Seq(\"acgtACGT\", generic_dna), id=\"Test\",\n",
+            " |      ...                    description = \"Made up for this example\")\n",
+            " |      >>> record.letter_annotations[\"phred_quality\"] = [1, 2, 3, 4, 5, 6, 7, 8]\n",
+            " |      >>> print(record.upper().format(\"fastq\"))\n",
+            " |      @Test Made up for this example\n",
+            " |      ACGTACGT\n",
+            " |      +\n",
+            " |      \"#$%&'()\n",
+            " |      <BLANKLINE>\n",
+            " |      \n",
+            " |      Naturally, there is a matching lower method:\n",
+            " |      \n",
+            " |      >>> print(record.lower().format(\"fastq\"))\n",
+            " |      @Test Made up for this example\n",
+            " |      acgtacgt\n",
+            " |      +\n",
+            " |      \"#$%&'()\n",
+            " |      <BLANKLINE>\n",
+            " |  \n",
+            " |  ----------------------------------------------------------------------\n",
+            " |  Data descriptors defined here:\n",
+            " |  \n",
+            " |  __dict__\n",
+            " |      dictionary for instance variables (if defined)\n",
+            " |  \n",
+            " |  __weakref__\n",
+            " |      list of weak references to the object (if defined)\n",
+            " |  \n",
+            " |  letter_annotations\n",
+            " |      Dictionary of per-letter-annotation for the sequence.\n",
+            " |      \n",
+            " |      For example, this can hold quality scores used in FASTQ or QUAL files.\n",
+            " |      Consider this example using Bio.SeqIO to read in an example Solexa\n",
+            " |      variant FASTQ file as a SeqRecord:\n",
+            " |      \n",
+            " |      >>> from Bio import SeqIO\n",
+            " |      >>> record = SeqIO.read(\"Quality/solexa_faked.fastq\", \"fastq-solexa\")\n",
+            " |      >>> print(\"%s %s\" % (record.id, record.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTACGTACGTACGTACGTACGTACGTACGTACGTACGTNNNNNN\n",
+            " |      >>> print(list(record.letter_annotations))\n",
+            " |      ['solexa_quality']\n",
+            " |      >>> print(record.letter_annotations[\"solexa_quality\"])\n",
+            " |      [40, 39, 38, 37, 36, 35, 34, 33, 32, 31, 30, 29, 28, 27, 26, 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0, -1, -2, -3, -4, -5]\n",
+            " |      \n",
+            " |      The letter_annotations get sliced automatically if you slice the\n",
+            " |      parent SeqRecord, for example taking the last ten bases:\n",
+            " |      \n",
+            " |      >>> sub_record = record[-10:]\n",
+            " |      >>> print(\"%s %s\" % (sub_record.id, sub_record.seq))\n",
+            " |      slxa_0001_1_0001_01 ACGTNNNNNN\n",
+            " |      >>> print(sub_record.letter_annotations[\"solexa_quality\"])\n",
+            " |      [4, 3, 2, 1, 0, -1, -2, -3, -4, -5]\n",
+            " |      \n",
+            " |      Any python sequence (i.e. list, tuple or string) can be recorded in\n",
+            " |      the SeqRecord's letter_annotations dictionary as long as the length\n",
+            " |      matches that of the SeqRecord's sequence.  e.g.\n",
+            " |      \n",
+            " |      >>> len(sub_record.letter_annotations)\n",
+            " |      1\n",
+            " |      >>> sub_record.letter_annotations[\"dummy\"] = \"abcdefghij\"\n",
+            " |      >>> len(sub_record.letter_annotations)\n",
+            " |      2\n",
+            " |      \n",
+            " |      You can delete entries from the letter_annotations dictionary as usual:\n",
+            " |      \n",
+            " |      >>> del sub_record.letter_annotations[\"solexa_quality\"]\n",
+            " |      >>> sub_record.letter_annotations\n",
+            " |      {'dummy': 'abcdefghij'}\n",
+            " |      \n",
+            " |      You can completely clear the dictionary easily as follows:\n",
+            " |      \n",
+            " |      >>> sub_record.letter_annotations = {}\n",
+            " |      >>> sub_record.letter_annotations\n",
+            " |      {}\n",
+            " |      \n",
+            " |      Note that if replacing the record's sequence with a sequence of a\n",
+            " |      different length you must first clear the letter_annotations dict.\n",
+            " |  \n",
+            " |  seq\n",
+            " |      The sequence itself, as a Seq or MutableSeq object.\n",
+            " |  \n",
+            " |  ----------------------------------------------------------------------\n",
+            " |  Data and other attributes defined here:\n",
+            " |  \n",
+            " |  __hash__ = None\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "20ZEptbZtgTC",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's write a bit of code involving `SeqRecord` and see how it comes out looking."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "yD3E6wrYtgTC",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "from Bio.SeqRecord import SeqRecord\n",
+        "\n",
+        "simple_seq = Seq(\"GATC\")\n",
+        "simple_seq_r = SeqRecord(simple_seq)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3FItR96PtgTG",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 51
+        },
+        "outputId": "1cc21f88-bce0-421c-c407-79368717351d"
+      },
+      "source": [
+        "simple_seq_r.id = \"AC12345\"\n",
+        "simple_seq_r.description = \"Made up sequence\"\n",
+        "print(simple_seq_r.id)\n",
+        "print(simple_seq_r.description)"
+      ],
+      "execution_count": 29,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "AC12345\n",
+            "Made up sequence\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "cxAH3YE0tgTK",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's now see how we can use `SeqRecord` to parse a large fasta file. We'll pull down a file hosted on the biopython site."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "vNxAQJkqtgTL",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "73621273-91f0-4cdb-8a71-52c03a1c6d18"
+      },
+      "source": [
+        "!wget https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.fna"
+      ],
+      "execution_count": 30,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2020-05-31 03:18:24--  https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.fna\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 9853 (9.6K) [text/plain]\n",
+            "Saving to: ‘NC_005816.fna’\n",
+            "\n",
+            "\rNC_005816.fna         0%[                    ]       0  --.-KB/s               \rNC_005816.fna       100%[===================>]   9.62K  --.-KB/s    in 0s      \n",
+            "\n",
+            "2020-05-31 03:18:24 (95.0 MB/s) - ‘NC_005816.fna’ saved [9853/9853]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
     {
-     "data": {
-      "text/plain": [
-       "SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence', dbxrefs=['Project:58037'])"
+      "cell_type": "code",
+      "metadata": {
+        "id": "mvFt3fVqtgTP",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        },
+        "outputId": "2cd7e864-6a4c-4ef7-d6bd-bca9a9ebf5a2"
+      },
+      "source": [
+        "from Bio import SeqIO\n",
+        "\n",
+        "record = SeqIO.read(\"NC_005816.fna\", \"fasta\")\n",
+        "record"
+      ],
+      "execution_count": 31,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', SingleLetterAlphabet()), id='gi|45478711|ref|NC_005816.1|', name='gi|45478711|ref|NC_005816.1|', description='gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence', dbxrefs=[])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 31
+        }
       ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "5_fCYXkttgTW",
+        "colab_type": "text"
+      },
+      "source": [
+        "Note how there's a number of annotations attached to the `SeqRecord` object!\n",
+        "\n",
+        "Let's take a closer look."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "N7OdmewwtgTa",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "6e06606b-471f-4aa1-f4a5-31a746882b7d"
+      },
+      "source": [
+        "record.id"
+      ],
+      "execution_count": 32,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'gi|45478711|ref|NC_005816.1|'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 32
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "156aQviwtgTd",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "3b15a4ae-af16-4828-ba71-89539eba8edf"
+      },
+      "source": [
+        "record.name"
+      ],
+      "execution_count": 33,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'gi|45478711|ref|NC_005816.1|'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 33
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Ov2neH1XtgTk",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 34
+        },
+        "outputId": "1834c5b8-93aa-4b15-fee6-5456a2fc79ed"
+      },
+      "source": [
+        "record.description"
+      ],
+      "execution_count": 34,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "'gi|45478711|ref|NC_005816.1| Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence'"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 34
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "HTOwKix8tgTr",
+        "colab_type": "text"
+      },
+      "source": [
+        "Let's now look at the same sequence, but downloaded from GenBank. We'll download the hosted file from the biopython tutorial website as before."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LpqMN5Z_tgTs",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 204
+        },
+        "outputId": "65c34e55-317d-49d4-f804-fb28c9662ce5"
+      },
+      "source": [
+        "!wget https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.gb"
+      ],
+      "execution_count": 35,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "--2020-05-31 03:18:38--  https://raw.githubusercontent.com/biopython/biopython/master/Tests/GenBank/NC_005816.gb\n",
+            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
+            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 31838 (31K) [text/plain]\n",
+            "Saving to: ‘NC_005816.gb’\n",
+            "\n",
+            "\rNC_005816.gb          0%[                    ]       0  --.-KB/s               \rNC_005816.gb        100%[===================>]  31.09K  --.-KB/s    in 0.01s   \n",
+            "\n",
+            "2020-05-31 03:18:39 (2.50 MB/s) - ‘NC_005816.gb’ saved [31838/31838]\n",
+            "\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "PhalU4PRtgTw",
+        "colab_type": "code",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 54
+        },
+        "outputId": "6c84a014-cdf7-4e26-a282-b366267f9424"
+      },
+      "source": [
+        "from Bio import SeqIO\n",
+        "\n",
+        "record = SeqIO.read(\"NC_005816.gb\", \"genbank\")\n",
+        "record"
+      ],
+      "execution_count": 36,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "SeqRecord(seq=Seq('TGTAACGAACGGTGCAATAGTGATCCACACCCAACGCCTGAAATCAGATCCAGG...CTG', IUPACAmbiguousDNA()), id='NC_005816.1', name='NC_005816', description='Yersinia pestis biovar Microtus str. 91001 plasmid pPCP1, complete sequence', dbxrefs=['Project:58037'])"
+            ]
+          },
+          "metadata": {
+            "tags": []
+          },
+          "execution_count": 36
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "inhAKrVItgT2",
+        "colab_type": "text"
+      },
+      "source": [
+        "## SeqIO Objects\n",
+        "\n",
+        "TODO(rbharath): Continue filling this up in future PRs."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "okON0bUHtgT6",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
     }
-   ],
-   "source": [
-    "from Bio import SeqIO\n",
-    "\n",
-    "record = SeqIO.read(\"NC_005816.gb\", \"genbank\")\n",
-    "record"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## SeqIO Objects\n",
-    "\n",
-    "TODO(rbharath): Continue filling this up in future PRs."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
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-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.10"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
+  ]
+}
\ No newline at end of file