AMP_Activity_Prediction.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyOcIggxGR3brbbbKHARnc4m",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Trailblazer29/AMP-Activity-Prediction/blob/main/AMP_Activity_Prediction.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<center><H1>Antimicrobial Peptide Activity Prediction</</H1></center>"
      ],
      "metadata": {
        "id": "PMsH8O1QPw7A"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this project, we will build a machine learning model that predicts the activity of antimicrobial peptides. This model will be trained on a filtered [subset](https://cbbio.online/AxPEP/?action=dataset) of the AmPEP dataset."
      ],
      "metadata": {
        "id": "6f1uzdhYP42T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Installations"
      ],
      "metadata": {
        "id": "03yHTDzoJYgm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Conda"
      ],
      "metadata": {
        "id": "eQGWlwfKMl5p"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bH9-ydupHHJP",
        "outputId": "57ee2c32-182c-4e7f-e096-b5112aecea25"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "⏬ Downloading https://github.com/conda-forge/miniforge/releases/download/23.1.0-1/Mambaforge-23.1.0-1-Linux-x86_64.sh...\n",
            "📦 Installing...\n",
            "📌 Adjusting configuration...\n",
            "🩹 Patching environment...\n",
            "⏲ Done in 0:00:28\n",
            "🔁 Restarting kernel...\n"
          ]
        }
      ],
      "source": [
        "# Install Conda\n",
        "!pip install -q condacolab\n",
        "import condacolab\n",
        "condacolab.install()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Pfeature"
      ],
      "metadata": {
        "id": "KWCdQQecML46"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The [Pfeature](https://webs.iiitd.edu.in/raghava/pfeature/Pfeature_Manual.pdf) library allows for the computing of protein/peptide properties/features.\n",
        "\n",
        "This work is focused on short peptides, consisting of 5-30 amino acids each, that have antimicrobial activity. We will use Pfeature to calculate the molecular properties of peptides, which will serve as input features for our AMP activity prediction model."
      ],
      "metadata": {
        "id": "PHv8AXKTQNYw"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t5809Cl51Ogn",
        "outputId": "c0ad9bd3-e339-4a9a-b7cb-ac943713481b"
      },
      "source": [
        "! wget https://github.com/raghavagps/Pfeature/raw/master/PyLib/Pfeature.zip\n",
        "! unzip Pfeature.zip"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-11-27 18:20:09--  https://github.com/raghavagps/Pfeature/raw/master/PyLib/Pfeature.zip\n",
            "Resolving github.com (github.com)... 140.82.121.3\n",
            "Connecting to github.com (github.com)|140.82.121.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/raghavagps/Pfeature/master/PyLib/Pfeature.zip [following]\n",
            "--2023-11-27 18:20:10--  https://raw.githubusercontent.com/raghavagps/Pfeature/master/PyLib/Pfeature.zip\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.111.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.111.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 519486 (507K) [application/zip]\n",
            "Saving to: ‘Pfeature.zip’\n",
            "\n",
            "Pfeature.zip        100%[===================>] 507.31K  --.-KB/s    in 0.02s   \n",
            "\n",
            "2023-11-27 18:20:10 (30.7 MB/s) - ‘Pfeature.zip’ saved [519486/519486]\n",
            "\n",
            "Archive:  Pfeature.zip\n",
            "   creating: Pfeature/\n",
            "  inflating: Pfeature/PKG-INFO       \n",
            "   creating: __MACOSX/\n",
            "   creating: __MACOSX/Pfeature/\n",
            "  inflating: __MACOSX/Pfeature/._PKG-INFO  \n",
            "  inflating: Pfeature/README         \n",
            "  inflating: __MACOSX/Pfeature/._README  \n",
            "  inflating: Pfeature/setup.py       \n",
            "  inflating: __MACOSX/Pfeature/._setup.py  \n",
            "  inflating: Pfeature/Functions_Tables.pdf  \n",
            "  inflating: __MACOSX/Pfeature/._Functions_Tables.pdf  \n",
            "   creating: Pfeature/build/\n",
            "   creating: Pfeature/build/lib/\n",
            "   creating: Pfeature/build/lib/Pfeature/\n",
            "  inflating: Pfeature/build/lib/Pfeature/bonds.csv  \n",
            "   creating: __MACOSX/Pfeature/build/\n",
            "   creating: __MACOSX/Pfeature/build/lib/\n",
            "   creating: __MACOSX/Pfeature/build/lib/Pfeature/\n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._bonds.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/pfeature.py  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._pfeature.py  \n",
            "  inflating: Pfeature/build/lib/Pfeature/AAIndexNames.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._AAIndexNames.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aaindices.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._aaindices.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/atom.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._atom.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/PhysicoChemical.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._PhysicoChemical.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/__init__.py  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/.___init__.py  \n",
            "  inflating: Pfeature/build/lib/Pfeature/z_aaindex.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._z_aaindex.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aaindex.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._aaindex.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/bin_di.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._bin_di.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/Grantham.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._Grantham.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/can_pat.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._can_pat.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aa_attr_group.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._aa_attr_group.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/Schneider-Wrede.csv  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._Schneider-Wrede.csv  \n",
            "  inflating: Pfeature/build/lib/Pfeature/data  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._data  \n",
            "  inflating: Pfeature/build/lib/Pfeature/aaind.txt  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/Pfeature/._aaind.txt  \n",
            "  inflating: __MACOSX/Pfeature/build/lib/._Pfeature  \n",
            "  inflating: __MACOSX/Pfeature/build/._lib  \n",
            "  inflating: __MACOSX/Pfeature/._build  \n",
            "  inflating: Pfeature/Pfeature_Descriptors.pdf  \n",
            "  inflating: __MACOSX/Pfeature/._Pfeature_Descriptors.pdf  \n",
            "   creating: Pfeature/Pfeature/\n",
            "  inflating: Pfeature/Pfeature/bonds.csv  \n",
            "   creating: __MACOSX/Pfeature/Pfeature/\n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._bonds.csv  \n",
            "  inflating: Pfeature/Pfeature/pfeature.py  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._pfeature.py  \n",
            "  inflating: Pfeature/Pfeature/AAIndexNames.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._AAIndexNames.csv  \n",
            "  inflating: Pfeature/Pfeature/aaindices.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._aaindices.csv  \n",
            "  inflating: Pfeature/Pfeature/atom.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._atom.csv  \n",
            "  inflating: Pfeature/Pfeature/PhysicoChemical.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._PhysicoChemical.csv  \n",
            "  inflating: Pfeature/Pfeature/__init__.py  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/.___init__.py  \n",
            "  inflating: Pfeature/Pfeature/z_aaindex.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._z_aaindex.csv  \n",
            "  inflating: Pfeature/Pfeature/aaindex.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._aaindex.csv  \n",
            "  inflating: Pfeature/Pfeature/bin_di.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._bin_di.csv  \n",
            "  inflating: Pfeature/Pfeature/Grantham.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._Grantham.csv  \n",
            "  inflating: Pfeature/Pfeature/can_pat.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._can_pat.csv  \n",
            "  inflating: Pfeature/Pfeature/aa_attr_group.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._aa_attr_group.csv  \n",
            "  inflating: Pfeature/Pfeature/Schneider-Wrede.csv  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._Schneider-Wrede.csv  \n",
            "  inflating: Pfeature/Pfeature/data  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._data  \n",
            "  inflating: Pfeature/Pfeature/aaind.txt  \n",
            "  inflating: __MACOSX/Pfeature/Pfeature/._aaind.txt  \n",
            "  inflating: __MACOSX/Pfeature/._Pfeature  \n",
            "  inflating: __MACOSX/._Pfeature     \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TzCcA2aJ2MTA",
        "outputId": "877144d1-afd4-4d5a-93c4-04ccbb48c4ce"
      },
      "source": [
        "# Install Pfeature\n",
        "%cd Pfeature\n",
        "! python setup.py install"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Pfeature\n",
            "/usr/local/lib/python3.10/site-packages/setuptools/command/install.py:34: SetuptoolsDeprecationWarning: setup.py install is deprecated. Use build and pip and other standards-based tools.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/site-packages/setuptools/command/easy_install.py:144: EasyInstallDeprecationWarning: easy_install command is deprecated. Use build and pip and other standards-based tools.\n",
            "  warnings.warn(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CD-HIT"
      ],
      "metadata": {
        "id": "Zl60ntVMMcex"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Install CD-HIT\n",
        "! conda install -c bioconda cd-hit"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AwNYVn4xIhnu",
        "outputId": "d189a765-ebac-4610-8a60-a36c717a0f2d"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting package metadata (current_repodata.json): - \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\bdone\n",
            "Solving environment: / \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\bdone\n",
            "\n",
            "\n",
            "==> WARNING: A newer version of conda exists. <==\n",
            "  current version: 23.1.0\n",
            "  latest version: 23.10.0\n",
            "\n",
            "Please update conda by running\n",
            "\n",
            "    $ conda update -n base -c conda-forge conda\n",
            "\n",
            "Or to minimize the number of packages updated during conda update use\n",
            "\n",
            "     conda install conda=23.10.0\n",
            "\n",
            "\n",
            "\n",
            "## Package Plan ##\n",
            "\n",
            "  environment location: /usr/local\n",
            "\n",
            "  added / updated specs:\n",
            "    - cd-hit\n",
            "\n",
            "\n",
            "The following packages will be downloaded:\n",
            "\n",
            "    package                    |            build\n",
            "    ---------------------------|-----------------\n",
            "    ca-certificates-2023.11.17 |       hbcca054_0         151 KB  conda-forge\n",
            "    cd-hit-4.8.1               |       h43eeafb_9         232 KB  bioconda\n",
            "    certifi-2023.11.17         |     pyhd8ed1ab_0         155 KB  conda-forge\n",
            "    openssl-3.2.0              |       hd590300_0         2.7 MB  conda-forge\n",
            "    zlib-1.2.13                |       h166bdaf_4          92 KB  conda-forge\n",
            "    ------------------------------------------------------------\n",
            "                                           Total:         3.3 MB\n",
            "\n",
            "The following NEW packages will be INSTALLED:\n",
            "\n",
            "  cd-hit             bioconda/linux-64::cd-hit-4.8.1-h43eeafb_9 \n",
            "  zlib               conda-forge/linux-64::zlib-1.2.13-h166bdaf_4 \n",
            "\n",
            "The following packages will be UPDATED:\n",
            "\n",
            "  ca-certificates                      2022.12.7-ha878542_0 --> 2023.11.17-hbcca054_0 \n",
            "  certifi                            2022.12.7-pyhd8ed1ab_0 --> 2023.11.17-pyhd8ed1ab_0 \n",
            "  openssl                                  3.1.0-h0b41bf4_0 --> 3.2.0-hd590300_0 \n",
            "\n",
            "\n",
            "\n",
            "Downloading and Extracting Packages\n",
            "ca-certificates-2023 | 151 KB    | :   0% 0/1 [00:00<?, ?it/s]\n",
            "openssl-3.2.0        | 2.7 MB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\n",
            "\n",
            "certifi-2023.11.17   | 155 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "cd-hit-4.8.1         | 232 KB    | :   0% 0/1 [00:00<?, ?it/s]\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "ca-certificates-2023 | 151 KB    | : 100% 1.0/1 [00:00<00:00,  1.42s/it]               \n",
            "\n",
            "\n",
            "\n",
            "zlib-1.2.13          | 92 KB     | :  17% 0.1741144964346061/1 [00:00<00:00,  1.04s/it]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "openssl-3.2.0        | 2.7 MB    | :   1% 0.0057479228506946865/1 [00:00<00:33, 34.16s/it]\u001b[A\n",
            "\n",
            "certifi-2023.11.17   | 155 KB    | :  10% 0.1030835729430788/1 [00:00<00:01,  1.93s/it]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "cd-hit-4.8.1         | 232 KB    | :   7% 0.06904253212138069/1 [00:00<00:02,  2.91s/it]\u001b[A\u001b[A\u001b[A\n",
            "openssl-3.2.0        | 2.7 MB    | :  82% 0.8219529676493402/1 [00:00<00:00,  3.42it/s]   \u001b[A\n",
            "\n",
            "certifi-2023.11.17   | 155 KB    | : 100% 1.0/1 [00:00<00:00,  4.02it/s]               \u001b[A\u001b[A\n",
            "\n",
            "certifi-2023.11.17   | 155 KB    | : 100% 1.0/1 [00:00<00:00,  4.02it/s]\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "zlib-1.2.13          | 92 KB     | : 100% 1.0/1 [00:00<00:00,  3.60it/s]               \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "zlib-1.2.13          | 92 KB     | : 100% 1.0/1 [00:00<00:00,  3.60it/s]\u001b[A\u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "cd-hit-4.8.1         | 232 KB    | : 100% 1.0/1 [00:00<00:00,  1.80it/s]                \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "cd-hit-4.8.1         | 232 KB    | : 100% 1.0/1 [00:00<00:00,  1.80it/s]\u001b[A\u001b[A\u001b[A\n",
            "                                                                        \n",
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            "\n",
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            "\n",
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            "                                                                        \u001b[A\u001b[A\u001b[A\n",
            "\n",
            "\n",
            "\n",
            "                                                                        \u001b[A\u001b[A\u001b[A\u001b[A\n",
            "Preparing transaction: - \b\bdone\n",
            "Verifying transaction: | \b\b/ \b\bdone\n",
            "Executing transaction: \\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\b\\ \b\b| \b\b/ \b\b- \b\bdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Lazypredict"
      ],
      "metadata": {
        "id": "CbRYqnf_cPuh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install lazypredict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 896
        },
        "id": "uy58ye68cTst",
        "outputId": "72ba7cb0-a82e-4b43-fc49-8fc785bfb4fb"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lazypredict\n",
            "  Downloading lazypredict-0.2.12-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/site-packages (from lazypredict) (4.65.0)\n",
            "Collecting pandas\n",
            "  Downloading pandas-2.1.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m12.3/12.3 MB\u001b[0m \u001b[31m58.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25hCollecting scikit-learn\n",
            "  Downloading scikit_learn-1.3.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m10.8/10.8 MB\u001b[0m \u001b[31m68.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting xgboost\n",
            "  Downloading xgboost-2.0.2-py3-none-manylinux2014_x86_64.whl (297.1 MB)\n",
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            "\u001b[?25hCollecting scipy\n",
            "  Downloading scipy-1.11.4-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (36.4 MB)\n",
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            "\u001b[?25hCollecting numpy\n",
            "  Downloading numpy-1.26.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (18.2 MB)\n",
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            "\u001b[?25hCollecting python-dateutil>=2.8.2\n",
            "  Downloading python_dateutil-2.8.2-py2.py3-none-any.whl (247 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m247.7/247.7 kB\u001b[0m \u001b[31m26.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting tzdata>=2022.1\n",
            "  Downloading tzdata-2023.3-py2.py3-none-any.whl (341 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m341.8/341.8 kB\u001b[0m \u001b[31m35.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting pytz>=2020.1\n",
            "  Downloading pytz-2023.3.post1-py2.py3-none-any.whl (502 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m502.5/502.5 kB\u001b[0m \u001b[31m44.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting threadpoolctl>=2.0.0\n",
            "  Downloading threadpoolctl-3.2.0-py3-none-any.whl (15 kB)\n",
            "Collecting six>=1.5\n",
            "  Downloading six-1.16.0-py2.py3-none-any.whl (11 kB)\n",
            "Installing collected packages: pytz, tzdata, threadpoolctl, six, numpy, joblib, click, scipy, python-dateutil, xgboost, scikit-learn, pandas, lightgbm, lazypredict\n",
            "Successfully installed click-8.1.7 joblib-1.3.2 lazypredict-0.2.12 lightgbm-4.1.0 numpy-1.26.2 pandas-2.1.3 python-dateutil-2.8.2 pytz-2023.3.post1 scikit-learn-1.3.2 scipy-1.11.4 six-1.16.0 threadpoolctl-3.2.0 tzdata-2023.3 xgboost-2.0.2\n",
            "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\n",
            "\u001b[0m"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "dateutil",
                  "six"
                ]
              }
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Import Required Libraries & Packages"
      ],
      "metadata": {
        "id": "fhe7EevjJov7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import sys\n",
        "import pandas as pd\n",
        "from Pfeature.pfeature import aac_wp, dpc_wp, tpc_wp\n",
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "from sklearn.metrics import matthews_corrcoef, confusion_matrix, classification_report, RocCurveDisplay"
      ],
      "metadata": {
        "id": "Ajy919S5I0E9"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Download the AmPEP Training & Testing Datasets"
      ],
      "metadata": {
        "id": "5n6VkvHIJ8Cf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! wget https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip\n",
        "! wget https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Qy1O2YdrI733",
        "outputId": "3a9cc9e6-875f-4570-d87e-cd7ba9b5a04e"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-11-27 18:23:30--  https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip\n",
            "Resolving sourceforge.net (sourceforge.net)... 172.64.150.145, 104.18.37.111, 2606:4700:4400::ac40:9691, ...\n",
            "Connecting to sourceforge.net (sourceforge.net)|172.64.150.145|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip/ [following]\n",
            "--2023-11-27 18:23:30--  https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip/\n",
            "Reusing existing connection to sourceforge.net:443.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip/download [following]\n",
            "--2023-11-27 18:23:30--  https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip/download\n",
            "Reusing existing connection to sourceforge.net:443.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://downloads.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip?ts=gAAAAABlZN6jrPkEoW7pcwP-r5hxH0oBF9mzjNFfc-lB6c5P4LYCpC61tTFDBzn5sQIWkcrnvI3CCj5BE-6jVnPRM-EhPYXb9Q%3D%3D&use_mirror=altushost-swe&r= [following]\n",
            "--2023-11-27 18:23:31--  https://downloads.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip?ts=gAAAAABlZN6jrPkEoW7pcwP-r5hxH0oBF9mzjNFfc-lB6c5P4LYCpC61tTFDBzn5sQIWkcrnvI3CCj5BE-6jVnPRM-EhPYXb9Q%3D%3D&use_mirror=altushost-swe&r=\n",
            "Resolving downloads.sourceforge.net (downloads.sourceforge.net)... 204.68.111.105\n",
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            "Location: https://altushost-swe.dl.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip [following]\n",
            "--2023-11-27 18:23:31--  https://altushost-swe.dl.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-train_set.zip\n",
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            "Length: 77275 (75K) [application/octet-stream]\n",
            "Saving to: ‘deep-ampep30-train_set.zip’\n",
            "\n",
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            "\n",
            "2023-11-27 18:23:35 (1.56 MB/s) - ‘deep-ampep30-train_set.zip’ saved [77275/77275]\n",
            "\n",
            "--2023-11-27 18:23:35--  https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip\n",
            "Resolving sourceforge.net (sourceforge.net)... 172.64.150.145, 104.18.37.111, 2606:4700:4400::ac40:9691, ...\n",
            "Connecting to sourceforge.net (sourceforge.net)|172.64.150.145|:443... connected.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip/ [following]\n",
            "--2023-11-27 18:23:36--  https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip/\n",
            "Reusing existing connection to sourceforge.net:443.\n",
            "HTTP request sent, awaiting response... 301 Moved Permanently\n",
            "Location: https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip/download [following]\n",
            "--2023-11-27 18:23:36--  https://sourceforge.net/projects/axpep/files/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip/download\n",
            "Reusing existing connection to sourceforge.net:443.\n",
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            "Location: https://downloads.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip?ts=gAAAAABlZN6oXqsJFGsP0FXMgNc6TwY1B5ZaK7LDlSW-f9B3XcpYAnwPMSzVip8B1osdJkNSnBC8swSS9SC7DqeTuGP0R_fZZA%3D%3D&use_mirror=altushost-swe&r= [following]\n",
            "--2023-11-27 18:23:36--  https://downloads.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip?ts=gAAAAABlZN6oXqsJFGsP0FXMgNc6TwY1B5ZaK7LDlSW-f9B3XcpYAnwPMSzVip8B1osdJkNSnBC8swSS9SC7DqeTuGP0R_fZZA%3D%3D&use_mirror=altushost-swe&r=\n",
            "Resolving downloads.sourceforge.net (downloads.sourceforge.net)... 204.68.111.105\n",
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            "--2023-11-27 18:23:37--  https://altushost-swe.dl.sourceforge.net/project/axpep/Deep-AmPEP30_datasets/deep-ampep30-test_set.zip\n",
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            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! unzip deep-ampep30-train_set.zip\n",
        "! unzip deep-ampep30-test_set.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eH37bY-dLUUO",
        "outputId": "f306bdf0-b1b1-4020-88ab-a6d968e58d5c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  deep-ampep30-train_set.zip\n",
            "   creating: train_set/\n",
            "  inflating: train_set/train_ne.fasta  \n",
            "  inflating: train_set/train_po.fasta  \n",
            "  inflating: train_set/train_po_ne.fasta  \n",
            "Archive:  deep-ampep30-test_set.zip\n",
            "   creating: test_set/\n",
            "  inflating: test_set/test_ne.fasta  \n",
            "  inflating: test_set/test_po.fasta  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5RJtsv1tLmbc",
        "outputId": "a4d752de-8cdf-4103-def2-7a72744f2476"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 448\n",
            "drwxr-xr-x 4 root root   4096 Nov 27 18:20 \u001b[0m\u001b[01;34mbuild\u001b[0m/\n",
            "-rw-r--r-- 1 root root   4332 Nov 10 06:36 cuda-keyring_1.0-1_all.deb\n",
            "-rw-r--r-- 1 root root   3326 Jan 18  2020 deep-ampep30-test_set.zip\n",
            "-rw-r--r-- 1 root root  77275 Jan 18  2020 deep-ampep30-train_set.zip\n",
            "drwxr-xr-x 2 root root   4096 Nov 27 18:20 \u001b[01;34mdist\u001b[0m/\n",
            "-rw-r--r-- 1 root root  86079 Sep 29  2020 Functions_Tables.pdf\n",
            "-rw-r--r-- 1 root root  17294 Nov 10 06:36 NGC-DL-CONTAINER-LICENSE\n",
            "drwxr-xr-x 3 root root   4096 Nov 27 18:22 \u001b[01;34mPfeature\u001b[0m/\n",
            "-rw-r--r-- 1 root root 221146 Sep 29  2020 Pfeature_Descriptors.pdf\n",
            "drwxr-xr-x 2 root root   4096 Nov 27 18:20 \u001b[01;34mPfeature.egg-info\u001b[0m/\n",
            "-rw-r--r-- 1 root root    971 Sep 28  2020 PKG-INFO\n",
            "-rw-r--r-- 1 root root   1265 Sep 29  2020 README\n",
            "-rw-r--r-- 1 root root   1267 Sep 29  2020 setup.py\n",
            "drwxr-xr-x 2 root root   4096 Jan  8  2020 \u001b[01;34mtest_set\u001b[0m/\n",
            "drwxr-xr-x 2 root root   4096 Jan  6  2020 \u001b[01;34mtrain_set\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd train_set"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wME1B5O6Mcw6",
        "outputId": "89545abe-24f1-43a0-fc95-ce2744abb3d6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Pfeature/train_set\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FS2YayQUJyEf",
        "outputId": "10f69701-f06e-4ab7-b336-eb75aae659ff"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 216\n",
            "-rw-r--r-- 1 root root  57206 Jan  5  2020 train_ne.fasta\n",
            "-rw-r--r-- 1 root root  52619 Jan  5  2020 train_po.fasta\n",
            "-rw-r--r-- 1 root root 109825 Jan  5  2020 train_po_ne.fasta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cat train_po.fasta"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XpB5DjC1Mgin",
        "outputId": "893ca483-7a24-446b-aea2-67072fb0b4c2"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            ">amp5_30_1\n",
            "ACSAG\n",
            ">amp5_30_2\n",
            "AMVGT\n",
            ">amp5_30_3\n",
            "AMVSS\n",
            ">amp5_30_4\n",
            "CPFVC\n",
            ">amp5_30_5\n",
            "SPGGA\n",
            ">amp5_30_6\n",
            "DDDDDD\n",
            ">amp5_30_7\n",
            "FRWWHR\n",
            ">amp5_30_8\n",
            "RKKWFW\n",
            ">amp5_30_9\n",
            "RRWWRF\n",
            ">amp5_30_10\n",
            "TVVTNA\n",
            ">amp5_30_11\n",
            "TVVTQA\n",
            ">amp5_30_12\n",
            "TVYTNA\n",
            ">amp5_30_13\n",
            "TVYTQA\n",
            ">amp5_30_14\n",
            "CSYTMEA\n",
            ">amp5_30_15\n",
            "GSEIQPR\n",
            ">amp5_30_16\n",
            "KVFLGLK\n",
            ">amp5_30_17\n",
            "LKLLKKL\n",
            ">amp5_30_18\n",
            "MRTGNAD\n",
            ">amp5_30_19\n",
            "MRTGNAN\n",
            ">amp5_30_20\n",
            "VISIIPV\n",
            ">amp5_30_21\n",
            "ASILTNAS\n",
            ">amp5_30_22\n",
            "FFFLSRIF\n",
            ">amp5_30_23\n",
            "FFHLHFHY\n",
            ">amp5_30_24\n",
            "KIIFLIAI\n",
            ">amp5_30_25\n",
            "PFKLSLHL\n",
            ">amp5_30_26\n",
            "QPFSLERW\n",
            ">amp5_30_27\n",
            "TCSYTMEA\n",
            ">amp5_30_28\n",
            "VAGRAQGM\n",
            ">amp5_30_29\n",
            "VRPYLVAF\n",
            ">amp5_30_30\n",
            "AHCLAIGRK\n",
            ">amp5_30_31\n",
            "ALLLAIRKR\n",
            ">amp5_30_32\n",
            "ALRLAIRKR\n",
            ">amp5_30_33\n",
            "ALWLAIRKR\n",
            ">amp5_30_34\n",
            "ALYLAIRKR\n",
            ">amp5_30_35\n",
            "AWKLFDDGV\n",
            ">amp5_30_36\n",
            "AWLLAIRKR\n",
            ">amp5_30_37\n",
            "DEKGPKWKR\n",
            ">amp5_30_38\n",
            "DYHHGVRVL\n",
            ">amp5_30_39\n",
            "EPFKISIHL\n",
            ">amp5_30_40\n",
            "EPFKLSLHL\n",
            ">amp5_30_41\n",
            "FLGFLHHLF\n",
            ">amp5_30_42\n",
            "FLGFLKNLF\n",
            ">amp5_30_43\n",
            "FLHFLHHLF\n",
            ">amp5_30_44\n",
            "GVCDMADLA\n",
            ">amp5_30_45\n",
            "GVVPHDFRI\n",
            ">amp5_30_46\n",
            "IAKVALKAL\n",
            ">amp5_30_47\n",
            "KIKFLKVLT\n",
            ">amp5_30_48\n",
            "NIPQLTPTP\n",
            ">amp5_30_49\n",
            "NRWCFAGDD\n",
            ">amp5_30_50\n",
            "RPPGFSPFR\n",
            ">amp5_30_51\n",
            "RSELIVHQR\n",
            ">amp5_30_52\n",
            "SVAGRAQGM\n",
            ">amp5_30_53\n",
            "TPFKLSLHL\n",
            ">amp5_30_54\n",
            "AAGMGFFGAR\n",
            ">amp5_30_55\n",
            "APPPGLSAGV\n",
            ">amp5_30_56\n",
            "ATCDLLSMWN\n",
            ">amp5_30_57\n",
            "EKYTEVPEYV\n",
            ">amp5_30_58\n",
            "EQCREEEDDR\n",
            ">amp5_30_59\n",
            "ESYFVFSVGM\n",
            ">amp5_30_60\n",
            "FFPIVGKLLS\n",
            ">amp5_30_61\n",
            "FIFDLLKKLV\n",
            ">amp5_30_62\n",
            "FLGALLSKIF\n",
            ">amp5_30_63\n",
            "FLPGLLAGLL\n",
            ">amp5_30_64\n",
            "GDGTGPGPGP\n",
            ">amp5_30_65\n",
            "GILDAITGLL\n",
            ">amp5_30_66\n",
            "GILDALTGIL\n",
            ">amp5_30_67\n",
            "GLLKRIKTLL\n",
            ">amp5_30_68\n",
            "HKTDSFVGLM\n",
            ">amp5_30_69\n",
            "IGGYCSWLRL\n",
            ">amp5_30_70\n",
            "IIIQYEGHKH\n",
            ">amp5_30_71\n",
            "KIAKVALKAL\n",
            ">amp5_30_72\n",
            "KRWWKWWRRC\n",
            ">amp5_30_73\n",
            "KTCENLADTY\n",
            ">amp5_30_74\n",
            "KTKKKFLKKT\n",
            ">amp5_30_75\n",
            "LKKLLKKLKK\n",
            ">amp5_30_76\n",
            "LLGGLLQSLL\n",
            ">amp5_30_77\n",
            "LLPNLLKSLL\n",
            ">amp5_30_78\n",
            "LRQSQFVGSR\n",
            ">amp5_30_79\n",
            "LSAIWSGIKS\n",
            ">amp5_30_80\n",
            "LSPNLLKSLL\n",
            ">amp5_30_81\n",
            "MCTHPLDCSN\n",
            ">amp5_30_82\n",
            "SNDIYFNFQR\n",
            ">amp5_30_83\n",
            "SRSELIVHQR\n",
            ">amp5_30_84\n",
            "VKLFPVKLFP\n",
            ">amp5_30_85\n",
            "AVPDVAFNAYG\n",
            ">amp5_30_86\n",
            "CTTCECCCSCS\n",
            ">amp5_30_87\n",
            "EPHPDEFVGLM\n",
            ">amp5_30_88\n",
            "EPHPNEFVGLM\n",
            ">amp5_30_89\n",
            "EPNPDEFFGLM\n",
            ">amp5_30_90\n",
            "EPNPDEFVGLM\n",
            ">amp5_30_91\n",
            "EPNPNEFFGLM\n",
            ">amp5_30_92\n",
            "FCTMIPIPRCY\n",
            ">amp5_30_93\n",
            "FQWQRNMRKVR\n",
            ">amp5_30_94\n",
            "FQWQRNPRKVR\n",
            ">amp5_30_95\n",
            "GIADILKGLLG\n",
            ">amp5_30_96\n",
            "IFGAIWNGIKS\n",
            ">amp5_30_97\n",
            "ILGKIWEGVKS\n",
            ">amp5_30_98\n",
            "KKLLKWLKKLL\n",
            ">amp5_30_99\n",
            "KVHGSLARAGK\n",
            ">amp5_30_100\n",
            "LMCTHPLDCSN\n",
            ">amp5_30_101\n",
            "NRILPTLIGPL\n",
            ">amp5_30_102\n",
            "RPKPQQFFGLM\n",
            ">amp5_30_103\n",
            "RVCYAIPLPIC\n",
            ">amp5_30_104\n",
            "SLLSLIRKLIT\n",
            ">amp5_30_105\n",
            "TESYFVFSVGM\n",
            ">amp5_30_106\n",
            "AERVGAGAPVYL\n",
            ">amp5_30_107\n",
            "AIHDILKYGKPS\n",
            ">amp5_30_108\n",
            "FCKSLPLPLSVK\n",
            ">amp5_30_109\n",
            "FLPIASLLGKYL\n",
            ">amp5_30_110\n",
            "FLPLKKLRFGLL\n",
            ">amp5_30_111\n",
            "FLPVLAGVLSRA\n",
            ">amp5_30_112\n",
            "GAHDILKYGKPS\n",
            ">amp5_30_113\n",
            "GIADILKYGKPS\n",
            ">amp5_30_114\n",
            "GIHAILKYGKPS\n",
            ">amp5_30_115\n",
            "GIHDALKYGKPS\n",
            ">amp5_30_116\n",
            "GIHDIAKYGKPS\n",
            ">amp5_30_117\n",
            "GIHDILAYGKPS\n",
            ">amp5_30_118\n",
            "GIHDILKAGKPS\n",
            ">amp5_30_119\n",
            "GIHDILKYAKPS\n",
            ">amp5_30_120\n",
            "GIHDILKYGAPS\n",
            ">amp5_30_121\n",
            "GIHDILKYGKAS\n",
            ">amp5_30_122\n",
            "GIHDILKYGKPA\n",
            ">amp5_30_123\n",
            "GIHDILKYGKPS\n",
            ">amp5_30_124\n",
            "GIHRILKYGKPS\n",
            ">amp5_30_125\n",
            "GIRDILKYGKPS\n",
            ">amp5_30_126\n",
            "GKWMSLLKHILK\n",
            ">amp5_30_127\n",
            "GLLDALSGILGL\n",
            ">amp5_30_128\n",
            "GLLSGTSVRGSI\n",
            ">amp5_30_129\n",
            "GMWSKILGHLIR\n",
            ">amp5_30_130\n",
            "GVSGHGQHGVHG\n",
            ">amp5_30_131\n",
            "HPLKQYWWRPSI\n",
            ">amp5_30_132\n",
            "IGGIISFFKRLF\n",
            ">amp5_30_133\n",
            "ILGKIWKIKKLF\n",
            ">amp5_30_134\n",
            "ILGTILGLLKGL\n",
            ">amp5_30_135\n",
            "ILGTILGLLKSL\n",
            ">amp5_30_136\n",
            "ILPIRSLIKKLL\n",
            ">amp5_30_137\n",
            "IPLPLGYFAKKT\n",
            ">amp5_30_138\n",
            "ISLEICAIFHDN\n",
            ">amp5_30_139\n",
            "KFHEKHHSHRGY\n",
            ">amp5_30_140\n",
            "KRIVQRIKDFLR\n",
            ">amp5_30_141\n",
            "LKKLLKKLKKLL\n",
            ">amp5_30_142\n",
            "LNWGAILKHIIK\n",
            ">amp5_30_143\n",
            "LRPAVIVRTKAL\n",
            ">amp5_30_144\n",
            "RICRIIFLRVCR\n",
            ">amp5_30_145\n",
            "RLCRIVVIRVCR\n",
            ">amp5_30_146\n",
            "RVCFAIPLPICH\n",
            ">amp5_30_147\n",
            "RVCYAIPLPICY\n",
            ">amp5_30_148\n",
            "SRSELIVHQRLF\n",
            ">amp5_30_149\n",
            "SRSELIVHQRMK\n",
            ">amp5_30_150\n",
            "SRSELIVHQRRC\n",
            ">amp5_30_151\n",
            "TPVVNPPFLQQT\n",
            ">amp5_30_152\n",
            "TWLKKRRWKKAK\n",
            ">amp5_30_153\n",
            "YSKSLPLSVLNP\n",
            ">amp5_30_154\n",
            "AGFVLKGYTKTSQ\n",
            ">amp5_30_155\n",
            "AVPAVRKTNETLD\n",
            ">amp5_30_156\n",
            "CTCTTIISSSSTF\n",
            ">amp5_30_157\n",
            "EGGGPQWAVGHFM\n",
            ">amp5_30_158\n",
            "ELYENKPRRPYIL\n",
            ">amp5_30_159\n",
            "FFGSVLKLIPKIL\n",
            ">amp5_30_160\n",
            "FFPIVGKLLFGLL\n",
            ">amp5_30_161\n",
            "FFPIVGKLLSGLF\n",
            ">amp5_30_162\n",
            "FFPIVGKLLSGLL\n",
            ">amp5_30_163\n",
            "FFPIVGKRLYGLL\n",
            ">amp5_30_164\n",
            "FFPVIGRILNGIL\n",
            ">amp5_30_165\n",
            "FIGAIARLLSKIF\n",
            ">amp5_30_166\n",
            "FIGAVAGLLSKIF\n",
            ">amp5_30_167\n",
            "FIGGIISFFKRLF\n",
            ">amp5_30_168\n",
            "FIGGIISFIKKLF\n",
            ">amp5_30_169\n",
            "FIGGIISLIKKLF\n",
            ">amp5_30_170\n",
            "FIPLVSGLFSRLL\n",
            ">amp5_30_171\n",
            "FISAIASFLGKFL\n",
            ">amp5_30_172\n",
            "FISAIASMLGKFL\n",
            ">amp5_30_173\n",
            "FISGLIGGLMKAL\n",
            ">amp5_30_174\n",
            "FITGLIGGLMKAL\n",
            ">amp5_30_175\n",
            "FITGLISGLMKAL\n",
            ">amp5_30_176\n",
            "FLAGLIGGLAKML\n",
            ">amp5_30_177\n",
            "FLGALAKIISGIF\n",
            ">amp5_30_178\n",
            "FLGALWNVAKSVF\n",
            ">amp5_30_179\n",
            "FLGGILNTITGLL\n",
            ">amp5_30_180\n",
            "FLGGLIKWWPWRR\n",
            ">amp5_30_181\n",
            "FLGGLLASLLGKI\n",
            ">amp5_30_182\n",
            "FLGGLWKAMSNLL\n",
            ">amp5_30_183\n",
            "FLPAIAGILSQLF\n",
            ">amp5_30_184\n",
            "FLPFLAKILTGVL\n",
            ">amp5_30_185\n",
            "FLPFLASLLSKVL\n",
            ">amp5_30_186\n",
            "FLPFLASLLTKVL\n",
            ">amp5_30_187\n",
            "FLPFLATLLSKVL\n",
            ">amp5_30_188\n",
            "FLPFLKSILGKIL\n",
            ">amp5_30_189\n",
            "FLPFVGNLLKGLL\n",
            ">amp5_30_190\n",
            "FLPGLIAGIAKML\n",
            ">amp5_30_191\n",
            "FLPIIAKVLSGLL\n",
            ">amp5_30_192\n",
            "FLPIIGKLLSGLL\n",
            ">amp5_30_193\n",
            "FLPIIGQLLSGLL\n",
            ">amp5_30_194\n",
            "FLPIITNLLGKLL\n",
            ">amp5_30_195\n",
            "FLPILGKLLSGIL\n",
            ">amp5_30_196\n",
            "FLPILGNLLSGLL\n",
            ">amp5_30_197\n",
            "FLPIVGKLLSGLF\n",
            ">amp5_30_198\n",
            "FLPIVGKLLSGLL\n",
            ">amp5_30_199\n",
            "FLPIVGRLISGIL\n",
            ">amp5_30_200\n",
            "FLPIVGRLISGLL\n",
            ">amp5_30_201\n",
            "FLPIVTNLLSGLL\n",
            ">amp5_30_202\n",
            "FLPLFASLIGKLL\n",
            ">amp5_30_203\n",
            "FLPLIAGLIGKLF\n",
            ">amp5_30_204\n",
            "FLPLIGKILGTIL\n",
            ">amp5_30_205\n",
            "FLPLIGKVLSGIL\n",
            ">amp5_30_206\n",
            "FLPLIGRVLSGIL\n",
            ">amp5_30_207\n",
            "FLPLILRKIVTAL\n",
            ">amp5_30_208\n",
            "FLPLLASLFSGLF\n",
            ">amp5_30_209\n",
            "FLPLLASLFSRLF\n",
            ">amp5_30_210\n",
            "FLPLLASLFSRLL\n",
            ">amp5_30_211\n",
            "FLPLLFGAISHLL\n",
            ">amp5_30_212\n",
            "FLPLLGNLLRGLL\n",
            ">amp5_30_213\n",
            "FLPLVGKILSGLI\n",
            ">amp5_30_214\n",
            "FLPLVTGLLSGLL\n",
            ">amp5_30_215\n",
            "FLPLVTMLLGKLF\n",
            ">amp5_30_216\n",
            "FLPVIAGLLSKLF\n",
            ">amp5_30_217\n",
            "FLSAIASMLGKFL\n",
            ">amp5_30_218\n",
            "FLSAITSILGKFF\n",
            ">amp5_30_219\n",
            "FLSAITSLLGKLL\n",
            ">amp5_30_220\n",
            "FLSGIVGMLGKLF\n",
            ">amp5_30_221\n",
            "FLSHIAGFLSNLF\n",
            ">amp5_30_222\n",
            "FLSIIAKVLGSLF\n",
            ">amp5_30_223\n",
            "FLSSIGKILGNLL\n",
            ">amp5_30_224\n",
            "FLSTIWNGIKSLL\n",
            ">amp5_30_225\n",
            "FLSTLWNAAKSIF\n",
            ">amp5_30_226\n",
            "FMPIIGRLMSGSL\n",
            ">amp5_30_227\n",
            "FMPILSCSRFKRC\n",
            ">amp5_30_228\n",
            "FPLALLCKVFKKC\n",
            ">amp5_30_229\n",
            "FVQWFSKFLGRIL\n",
            ">amp5_30_230\n",
            "FWGKLWEGVKNAI\n",
            ">amp5_30_231\n",
            "GFGMALKLLKKVL\n",
            ">amp5_30_232\n",
            "GLFDIIKKIAESF\n",
            ">amp5_30_233\n",
            "GLFDIIKKIAESI\n",
            ">amp5_30_234\n",
            "GLFDIIKNIFSGL\n",
            ">amp5_30_235\n",
            "GLFDIIKNIVSTL\n",
            ">amp5_30_236\n",
            "GLFDIVKKLVSDF\n",
            ">amp5_30_237\n",
            "GLFDKLKSLVSDF\n",
            ">amp5_30_238\n",
            "GLLSLLSLLGKLL\n",
            ">amp5_30_239\n",
            "GWFDVVKHIASAV\n",
            ">amp5_30_240\n",
            "HGVSGHGQHGVHG\n",
            ">amp5_30_241\n",
            "IFGAIAGLLKNIF\n",
            ">amp5_30_242\n",
            "IFGAIWKGISSLL\n",
            ">amp5_30_243\n",
            "IFGAIWNGIKSLF\n",
            ">amp5_30_244\n",
            "IFKAIWSGIKRLC\n",
            ">amp5_30_245\n",
            "IFKAIWSGIKSLF\n",
            ">amp5_30_246\n",
            "IFSAIAGLLSNLL\n",
            ">amp5_30_247\n",
            "IIPLPLGYFAKKT\n",
            ">amp5_30_248\n",
            "ILGEIWKGIKDIL\n",
            ">amp5_30_249\n",
            "ILGKFCDEIKRIV\n",
            ">amp5_30_250\n",
            "ILGKIAEGIKSLF\n",
            ">amp5_30_251\n",
            "ILGKILEGIKSLF\n",
            ">amp5_30_252\n",
            "ILGKILKGIKKLF\n",
            ">amp5_30_253\n",
            "ILGKIWEGIKSIF\n",
            ">amp5_30_254\n",
            "ILGKIWEGIKSLF\n",
            ">amp5_30_255\n",
            "ILGKIWKGIKSLF\n",
            ">amp5_30_256\n",
            "ILPIIGKILSTIF\n",
            ">amp5_30_257\n",
            "ILPILAPLIGGLL\n",
            ">amp5_30_258\n",
            "ILPILGNLLNGLL\n",
            ">amp5_30_259\n",
            "ILPILGNLLNSLL\n",
            ">amp5_30_260\n",
            "ILPLVGNLLNDLL\n",
            ">amp5_30_261\n",
            "ILPWKWPWWPWRR\n",
            ">amp5_30_262\n",
            "ILSAIWSGIKSLF\n",
            ">amp5_30_263\n",
            "ILSYLWNGIKSIF\n",
            ">amp5_30_264\n",
            "IPPFIKKVLTTVF\n",
            ">amp5_30_265\n",
            "IWSAIWSGIKGLL\n",
            ">amp5_30_266\n",
            "LLPILGNLLNGLL\n",
            ">amp5_30_267\n",
            "LLPIVGNLLKSLL\n",
            ">amp5_30_268\n",
            "LLPIVGNLLNSLL\n",
            ">amp5_30_269\n",
            "LLPWKWPWWKWRR\n",
            ">amp5_30_270\n",
            "LLRHVVKILEKYL\n",
            ">amp5_30_271\n",
            "NLKALAALAKKIL\n",
            ">amp5_30_272\n",
            "PKRKSATKGDEPA\n",
            ">amp5_30_273\n",
            "QKIAEKFSGTRRG\n",
            ">amp5_30_274\n",
            "RKFHEKHHSHRGY\n",
            ">amp5_30_275\n",
            "RLPWRWPRRPWRR\n",
            ">amp5_30_276\n",
            "RRPWRWPRWPWRR\n",
            ">amp5_30_277\n",
            "RRPWRWPWWPWRR\n",
            ">amp5_30_278\n",
            "SGFVLKGYTKTSQ\n",
            ">amp5_30_279\n",
            "SPPSEQLGKSFNF\n",
            ">amp5_30_280\n",
            "SYSMEHFRWGKPV\n",
            ">amp5_30_281\n",
            "TSRCIFYRRKKCS\n",
            ">amp5_30_282\n",
            "VIPIVSGLLSSLL\n",
            ">amp5_30_283\n",
            "VLPIIGNLLNSLL\n",
            ">amp5_30_284\n",
            "VLPLISMALGKLL\n",
            ">amp5_30_285\n",
            "VRRFPWWWPFLRR\n",
            ">amp5_30_286\n",
            "WGIHRILKYGKPS\n",
            ">amp5_30_287\n",
            "WGIRDILKYGKPS\n",
            ">amp5_30_288\n",
            "WGIRRILKYGKRS\n",
            ">amp5_30_289\n",
            "WNDTGKDADGSEY\n",
            ">amp5_30_290\n",
            "YPELQQDLIARLL\n",
            ">amp5_30_291\n",
            "AILTTLANWARKFL\n",
            ">amp5_30_292\n",
            "APPPGYAMESDSFS\n",
            ">amp5_30_293\n",
            "CMSTRGDQARKICE\n",
            ">amp5_30_294\n",
            "DSLSFSYNNFEEDD\n",
            ">amp5_30_295\n",
            "FDIVKKVVGALGSL\n",
            ">amp5_30_296\n",
            "FDLGGLIKGVVDLF\n",
            ">amp5_30_297\n",
            "FFPIGVFCKIFKTC\n",
            ">amp5_30_298\n",
            "FFPLALLCKVFKKC\n",
            ">amp5_30_299\n",
            "FIGPIISALASLFG\n",
            ">amp5_30_300\n",
            "FIITGLVRGLTKLF\n",
            ">amp5_30_301\n",
            "FILPLIASFLSKFL\n",
            ">amp5_30_302\n",
            "FLFPLITSFLSKVL\n",
            ">amp5_30_303\n",
            "FLGGLIKPWWPWRR\n",
            ">amp5_30_304\n",
            "FLKPLFNAALKLLP\n",
            ">amp5_30_305\n",
            "FLLFPLMCKIQGKC\n",
            ">amp5_30_306\n",
            "FLPILINLIHKGLL\n",
            ">amp5_30_307\n",
            "FLPPSPWKETFRTS\n",
            ">amp5_30_308\n",
            "FVLPLVMCKILRKC\n",
            ">amp5_30_309\n",
            "GFSPNLPGKGLRIS\n",
            ">amp5_30_310\n",
            "GFWGKLWEGVKNAI\n",
            ">amp5_30_311\n",
            "GFWGKLWEGVKSAI\n",
            ">amp5_30_312\n",
            "GFWGSLWEGVKSVV\n",
            ">amp5_30_313\n",
            "GILDAIKAIAKAAG\n",
            ">amp5_30_314\n",
            "GILGKLWEGFKSIV\n",
            ">amp5_30_315\n",
            "GRILSFIKGLAEHL\n",
            ">amp5_30_316\n",
            "GVVDTLKNLLMGLL\n",
            ">amp5_30_317\n",
            "HFLGTLVNLAKKIL\n",
            ">amp5_30_318\n",
            "IDWKKLLDAAKQIL\n",
            ">amp5_30_319\n",
            "IDWLKLGKMVIDAL\n",
            ">amp5_30_320\n",
            "IDWLKLGKMVMDVL\n",
            ">amp5_30_321\n",
            "IKWKAILDAVKKVL\n",
            ">amp5_30_322\n",
            "ILPILSLIGGLLGK\n",
            ">amp5_30_323\n",
            "INLKAIAAFAKKLL\n",
            ">amp5_30_324\n",
            "INLKAIAALAKKLL\n",
            ">amp5_30_325\n",
            "INLKALAALAKKIL\n",
            ">amp5_30_326\n",
            "INLLKIAKGIIKSL\n",
            ">amp5_30_327\n",
            "INMKASAAVAKKLL\n",
            ">amp5_30_328\n",
            "INWDILIDTIKDKL\n",
            ">amp5_30_329\n",
            "INWKALLDAAKKVL\n",
            ">amp5_30_330\n",
            "INWKKIFEKVKNLV\n",
            ">amp5_30_331\n",
            "INWLKLGKAIIDAL\n",
            ">amp5_30_332\n",
            "INWLKLGKKILGAL\n",
            ">amp5_30_333\n",
            "INWLKLGKKMMSAL\n",
            ">amp5_30_334\n",
            "KDLHTVVSAILQAL\n",
            ">amp5_30_335\n",
            "KKKKPLFGLFFGLF\n",
            ">amp5_30_336\n",
            "KLAKLAKKLAKLAK\n",
            ">amp5_30_337\n",
            "LKKLLKKLKKLLKK\n",
            ">amp5_30_338\n",
            "LKLKSIVSWAKKVL\n",
            ">amp5_30_339\n",
            "LKLLKKLLKLLKKL\n",
            ">amp5_30_340\n",
            "NFLDTLINLAKKFI\n",
            ">amp5_30_341\n",
            "NFLGTLINLAKKIM\n",
            ">amp5_30_342\n",
            "NFLGTLVNLAKKIL\n",
            ">amp5_30_343\n",
            "NILNTIINLAKKIL\n",
            ">amp5_30_344\n",
            "PRGSPRTEYEAARR\n",
            ">amp5_30_345\n",
            "RCTCTTIISSSSTF\n",
            ">amp5_30_346\n",
            "RILSILRHQNLLKE\n",
            ">amp5_30_347\n",
            "RKFHEKHHSHRGYR\n",
            ">amp5_30_348\n",
            "RPRPNYRPRPIYRP\n",
            ">amp5_30_349\n",
            "SCNCVCGFCCSCSP\n",
            ">amp5_30_350\n",
            "SCNCVCGVCCSCSP\n",
            ">amp5_30_351\n",
            "SCTTCVCTCSCCTT\n",
            ">amp5_30_352\n",
            "SILPTIVSFLSKFL\n",
            ">amp5_30_353\n",
            "SILPTIVSFLSKVF\n",
            ">amp5_30_354\n",
            "SILPTIVSFLTKFL\n",
            ">amp5_30_355\n",
            "STNCFCYPCCSCSS\n",
            ">amp5_30_356\n",
            "TWLKKRRWKKAKPP\n",
            ">amp5_30_357\n",
            "ALKAVPAVMGMIKKE\n",
            ">amp5_30_358\n",
            "ALLHHGLNCAKGVLA\n",
            ">amp5_30_359\n",
            "AVVNGVNYVGETTAA\n",
            ">amp5_30_360\n",
            "DIDITGCSACKYAAG\n",
            ">amp5_30_361\n",
            "DTHISEKIIDCNDIG\n",
            ">amp5_30_362\n",
            "EQPGGDKVNLGYFTN\n",
            ">amp5_30_363\n",
            "ESGINLQGDATLANN\n",
            ">amp5_30_364\n",
            "FASLLGKALKALAKQ\n",
            ">amp5_30_365\n",
            "FFLPSLIGGLISAFK\n",
            ">amp5_30_366\n",
            "FFLPSLIGGLISAIK\n",
            ">amp5_30_367\n",
            "FIFPKKNIINSLFGR\n",
            ">amp5_30_368\n",
            "FLGGLIKWPWWPWRR\n",
            ">amp5_30_369\n",
            "FLPLIGKILGTILGK\n",
            ">amp5_30_370\n",
            "GLLGTLGNLLNGLGL\n",
            ">amp5_30_371\n",
            "IIGHLIKTALGFLGL\n",
            ">amp5_30_372\n",
            "IIGHLIKTALGMLGL\n",
            ">amp5_30_373\n",
            "ILGIITSLLKSLGKK\n",
            ">amp5_30_374\n",
            "INLKALAALAKKIIL\n",
            ">amp5_30_375\n",
            "INWKKIKSIIKAAMN\n",
            ">amp5_30_376\n",
            "INWKKIKSLIKAAMS\n",
            ">amp5_30_377\n",
            "KQQLATEAESAGPIL\n",
            ">amp5_30_378\n",
            "KWKLFKKAVLKVLTT\n",
            ">amp5_30_379\n",
            "KWKLFKKGAVLKVLT\n",
            ">amp5_30_380\n",
            "KWKLFKKGIGAVLKV\n",
            ">amp5_30_381\n",
            "KWKLFKKIGAVLKVL\n",
            ">amp5_30_382\n",
            "KWKLFKKLKVLTTGL\n",
            ">amp5_30_383\n",
            "KWKLFKKVLKVLTTG\n",
            ">amp5_30_384\n",
            "LNLKGIFKKVASLLT\n",
            ">amp5_30_385\n",
            "NSMERVEELRKKLQD\n",
            ">amp5_30_386\n",
            "PEDPQRRYQEEQRRE\n",
            ">amp5_30_387\n",
            "QGIGVGDNDGKRGKR\n",
            ">amp5_30_388\n",
            "QKIQEIDLQTYLQPQ\n",
            ">amp5_30_389\n",
            "SIYERCELARELINR\n",
            ">amp5_30_390\n",
            "SKGKKANKDVELARG\n",
            ">amp5_30_391\n",
            "TSRCYIGYRRKVVCS\n",
            ">amp5_30_392\n",
            "VAGPFRIPPLRREFQ\n",
            ">amp5_30_393\n",
            "VAIALKAAHYHTHKE\n",
            ">amp5_30_394\n",
            "VDKPDYRPRPRPPNM\n",
            ">amp5_30_395\n",
            "VDKPDYRPRPWPRPN\n",
            ">amp5_30_396\n",
            "VGALAVVVWLFLWLW\n",
            ">amp5_30_397\n",
            "VGALAVVVWLWLWLW\n",
            ">amp5_30_398\n",
            "VGALAVVVWLYLWLW\n",
            ">amp5_30_399\n",
            "VNWKKILGKIIKVAK\n",
            ">amp5_30_400\n",
            "VNWKKILGKIIKVVK\n",
            ">amp5_30_401\n",
            "VNWKKVLGKIIKVAK\n",
            ">amp5_30_402\n",
            "VWPLGLVICKALKIC\n",
            ">amp5_30_403\n",
            "YIRDFITRRPPFGNI\n",
            ">amp5_30_404\n",
            "YQEPVLGPVRGPFPI\n",
            ">amp5_30_405\n",
            "AVDLAKIANKVLSSLF\n",
            ">amp5_30_406\n",
            "CVHWMTNTARTACIAP\n",
            ">amp5_30_407\n",
            "CVHWQTNTARTSCIGP\n",
            ">amp5_30_408\n",
            "DCTRWIIGINGRICRD\n",
            ">amp5_30_409\n",
            "EKKPPRPPQWAVGHFM\n",
            ">amp5_30_410\n",
            "ESILIVHQQQSRSSGS\n",
            ">amp5_30_411\n",
            "FFPIVGKLLFGLFGLL\n",
            ">amp5_30_412\n",
            "FFPIVGKLLFGLSGLL\n",
            ">amp5_30_413\n",
            "FFPIVGKLLSGLSGLL\n",
            ">amp5_30_414\n",
            "FGLIPSLIGGLVSAFK\n",
            ">amp5_30_415\n",
            "FGLIPSMMGGLVSAFK\n",
            ">amp5_30_416\n",
            "FIGAIARLLSKIFGKR\n",
            ">amp5_30_417\n",
            "FIGALLRPALKLLAGK\n",
            ">amp5_30_418\n",
            "FIGKLISAASGLLSHL\n",
            ">amp5_30_419\n",
            "FKVQNQHGQVVKIFHH\n",
            ">amp5_30_420\n",
            "FLGGLIKKWPWWPWRR\n",
            ">amp5_30_421\n",
            "FLPFLIPALTSLISSL\n",
            ">amp5_30_422\n",
            "FLPIAGKLLSGLSGLL\n",
            ">amp5_30_423\n",
            "FLPIVGKLLSGLSGLL\n",
            ">amp5_30_424\n",
            "FLPIVGKLLSGLSGLS\n",
            ">amp5_30_425\n",
            "FSLLPSLIGGLVSAIK\n",
            ">amp5_30_426\n",
            "GCSRWIIGIHGQICRD\n",
            ">amp5_30_427\n",
            "GLFDIAKKVIGVIGSL\n",
            ">amp5_30_428\n",
            "GLFDIIKKVASVIGGL\n",
            ">amp5_30_429\n",
            "GLFDIIKKVASVVGGL\n",
            ">amp5_30_430\n",
            "GLFDIVKKVVGAFGSL\n",
            ">amp5_30_431\n",
            "GLFDIVKKVVGAIGSL\n",
            ">amp5_30_432\n",
            "GLFDIVKKVVGALGSL\n",
            ">amp5_30_433\n",
            "GLFDIVKKVVGTLAGL\n",
            ">amp5_30_434\n",
            "GLFDVIKKVASVIGGL\n",
            ">amp5_30_435\n",
            "GLLDFAKHVIGIASKL\n",
            ">amp5_30_436\n",
            "GLLDIVKKVVGAFGSL\n",
            ">amp5_30_437\n",
            "GLLDLAKHVIGIASKL\n",
            ">amp5_30_438\n",
            "GRPNPVNNKPTPHPRL\n",
            ">amp5_30_439\n",
            "GRPNPVNTKPTPYPRL\n",
            ">amp5_30_440\n",
            "IIEKLVNTALGLLSGL\n",
            ">amp5_30_441\n",
            "ILGAIIPLVSGLLSHL\n",
            ">amp5_30_442\n",
            "ILGKLLSTAAGLLSNL\n",
            ">amp5_30_443\n",
            "KGLKKLLKLLKKLLKL\n",
            ">amp5_30_444\n",
            "LIKIVPAMICAVTKKC\n",
            ">amp5_30_445\n",
            "LKLLKKLLKLLKKLGK\n",
            ">amp5_30_446\n",
            "PSCVCSGFETSGIHFC\n",
            ">amp5_30_447\n",
            "RAGLKFPVGRVHRLLR\n",
            ">amp5_30_448\n",
            "RFIYMKGFGKPRFGKR\n",
            ">amp5_30_449\n",
            "RGGRLCYCRRRFCICV\n",
            ">amp5_30_450\n",
            "SDYSRKRDPPQKYEEE\n",
            ">amp5_30_451\n",
            "SFHVFPPWMCKSLKKC\n",
            ">amp5_30_452\n",
            "SFPFFPPGICKRLKRC\n",
            ">amp5_30_453\n",
            "VDKPDYRPRPWPRNMI\n",
            ">amp5_30_454\n",
            "VDKPDYRPRPWPRPNM\n",
            ">amp5_30_455\n",
            "VVKCSYRLGSPDSQCN\n",
            ">amp5_30_456\n",
            "AAFRGCWTKNYSPKPCL\n",
            ">amp5_30_457\n",
            "AAPRGGKGFFCKLFKDC\n",
            ">amp5_30_458\n",
            "ALGGLLADVVKSKEQPA\n",
            ">amp5_30_459\n",
            "ALYKKFKKKLLKSLKRL\n",
            ">amp5_30_460\n",
            "ALYNSEDLYEETSDSDD\n",
            ">amp5_30_461\n",
            "ATITFTNKCTRTVWPGG\n",
            ">amp5_30_462\n",
            "ATNIPFKVHFRCKAAFC\n",
            ">amp5_30_463\n",
            "AVGRHGRRFGLRKHRKH\n",
            ">amp5_30_464\n",
            "AVNIPFKVHFRCKAAFC\n",
            ">amp5_30_465\n",
            "FFPFLLGALGSLLPKIF\n",
            ">amp5_30_466\n",
            "FFPLLFGALSSHLPKLF\n",
            ">amp5_30_467\n",
            "FFPLLFGALSSMMPKLF\n",
            ">amp5_30_468\n",
            "FFPLVLGALGSILPKIF\n",
            ">amp5_30_469\n",
            "FFSLIPSLIGGLVFAIK\n",
            ">amp5_30_470\n",
            "FFSLIPSLIGGLVSAIK\n",
            ">amp5_30_471\n",
            "FFSLLPSLIGGLVSAIK\n",
            ">amp5_30_472\n",
            "FITLLLRKFICSITKKC\n",
            ">amp5_30_473\n",
            "FLGGLIKWKWPWWPWRR\n",
            ">amp5_30_474\n",
            "FLGMIPGLIGGLISAFK\n",
            ">amp5_30_475\n",
            "FLIGMTHGLICLISRKC\n",
            ">amp5_30_476\n",
            "FLIGMTQGLICLITRKC\n",
            ">amp5_30_477\n",
            "FLPAALAGIGGILGKLF\n",
            ">amp5_30_478\n",
            "FLPIALKALGSIFPKIL\n",
            ">amp5_30_479\n",
            "FLPLFLPKIICVITKKC\n",
            ">amp5_30_480\n",
            "FLPLVLGALSGILPKIL\n",
            ">amp5_30_481\n",
            "FLPVILPVIGKLLNGIL\n",
            ">amp5_30_482\n",
            "FLPVILPVIGKLLSGIL\n",
            ">amp5_30_483\n",
            "FLSLIPSLVGGSISAFK\n",
            ">amp5_30_484\n",
            "FVDLKKIANIINSIFGK\n",
            ">amp5_30_485\n",
            "GALRGCWTKSYPPKPCK\n",
            ">amp5_30_486\n",
            "GATAIKQVKKLFKKWGW\n",
            ">amp5_30_487\n",
            "GFGSLLGKALKIGTNLL\n",
            ">amp5_30_488\n",
            "GFGSLLGKALRLGANVL\n",
            ">amp5_30_489\n",
            "GFKRIVQRIKDFLRNLV\n",
            ">amp5_30_490\n",
            "GIGSALAKAAKLVAGIV\n",
            ">amp5_30_491\n",
            "GIGSLLAKAAKLGANLL\n",
            ">amp5_30_492\n",
            "GLFDIVKKIAGHIAGSI\n",
            ">amp5_30_493\n",
            "GLFDIVKKIAGHIASSI\n",
            ">amp5_30_494\n",
            "GLFDIVKKIAGHIVSSI\n",
            ">amp5_30_495\n",
            "GLGSLVGNALRIGAKLL\n",
            ">amp5_30_496\n",
            "GLGSVLGKALKIGANLL\n",
            ">amp5_30_497\n",
            "GLIKIVPAMICAVTKKC\n",
            ">amp5_30_498\n",
            "GLLDFAKHVIGIASKLG\n",
            ">amp5_30_499\n",
            "GLLDFLKAAGKGLVTNL\n",
            ">amp5_30_500\n",
            "GNRPVYIPPPRPPHPRL\n",
            ">amp5_30_501\n",
            "GTGLPMSERRKIMLMMR\n",
            ">amp5_30_502\n",
            "GVGDLIRKAVSVIKNIV\n",
            ">amp5_30_503\n",
            "GVGSFIHKVVSAIKNVA\n",
            ">amp5_30_504\n",
            "GVIDAAKKVVNVLKNLF\n",
            ">amp5_30_505\n",
            "GVIDAAKKVVNVLKNLP\n",
            ">amp5_30_506\n",
            "GVLDAFRKIATVVKNVV\n",
            ">amp5_30_507\n",
            "IASASCTTCICTCSCSS\n",
            ">amp5_30_508\n",
            "IKIMDILAKLGKVLAHV\n",
            ">amp5_30_509\n",
            "IKITTMLAKLGKVLAHV\n",
            ">amp5_30_510\n",
            "ILGAILPLVSGLLSNKL\n",
            ">amp5_30_511\n",
            "ILGAILPLVSGLLSSKL\n",
            ">amp5_30_512\n",
            "ILGPILGLVSNALGGLL\n",
            ">amp5_30_513\n",
            "ILPLLLGKVVCAITKKC\n",
            ">amp5_30_514\n",
            "INIKDILAKLVKVLGHV\n",
            ">amp5_30_515\n",
            "INVLGILGLLGKALSHL\n",
            ">amp5_30_516\n",
            "INWKKIAEIGKQVLSAL\n",
            ">amp5_30_517\n",
            "INWKKIAEVGGKILSSL\n",
            ">amp5_30_518\n",
            "INWKKIASIGKEVLKAL\n",
            ">amp5_30_519\n",
            "KLALKALKALKAALKLA\n",
            ">amp5_30_520\n",
            "KWCFRVCYRGICYRKCR\n",
            ">amp5_30_521\n",
            "KWCFRVCYRGICYRRCR\n",
            ">amp5_30_522\n",
            "KWKLFKKIKFLHSAKKF\n",
            ">amp5_30_523\n",
            "LFGLIPSLIGGLVSAFK\n",
            ">amp5_30_524\n",
            "LKLLKKLLKLLKKLGGK\n",
            ">amp5_30_525\n",
            "LVPLFLSKLICFITKKC\n",
            ">amp5_30_526\n",
            "PAQPFRIKKRQGPFERP\n",
            ">amp5_30_527\n",
            "PDPAKTAPKKKSKKAVT\n",
            ">amp5_30_528\n",
            "RPRCWIKIKFRCKSLKF\n",
            ">amp5_30_529\n",
            "RWCFRVCYRGICYRKCR\n",
            ">amp5_30_530\n",
            "SAPRGCWTKSYPPKPCK\n",
            ">amp5_30_531\n",
            "SKITDILAKLGKVLAHV\n",
            ">amp5_30_532\n",
            "SLIPHAINAVSAIAKHF\n",
            ">amp5_30_533\n",
            "SLSRFLSFLKIVYPPAF\n",
            ">amp5_30_534\n",
            "SNMIEGVFAKGFKKASH\n",
            ">amp5_30_535\n",
            "YQEPVLGPVRGPFPIIV\n",
            ">amp5_30_536\n",
            "ALKAALLAILKIVRVIKK\n",
            ">amp5_30_537\n",
            "DWTAWSALVAAACSVELL\n",
            ">amp5_30_538\n",
            "ECRRLCYKQRCVTYCRGR\n",
            ">amp5_30_539\n",
            "FFGHLFKLATKIIPSLFQ\n",
            ">amp5_30_540\n",
            "FLFSLIPSAISGLISAFK\n",
            ">amp5_30_541\n",
            "FLGGLIGPLMSLIPGLLK\n",
            ">amp5_30_542\n",
            "FLPKLFAKITKKNMAHIR\n",
            ">amp5_30_543\n",
            "GFCRCICTRGFCRCICTR\n",
            ">amp5_30_544\n",
            "GFCRCLCRRGVCRCICTR\n",
            ">amp5_30_545\n",
            "GFLGSLLKTGLKVGSNLL\n",
            ">amp5_30_546\n",
            "GFLSILKKVLPKVMAHMK\n",
            ">amp5_30_547\n",
            "GFVDFLKKVAGTIANVVT\n",
            ">amp5_30_548\n",
            "GGLIKIVPAMICAVTKKC\n",
            ">amp5_30_549\n",
            "GICRCICGRGICRCICGR\n",
            ">amp5_30_550\n",
            "GICRCICGRRICRCICGR\n",
            ">amp5_30_551\n",
            "GICRCICTRGFCRCICVL\n",
            ">amp5_30_552\n",
            "GICRCLCRRGVCRCICVL\n",
            ">amp5_30_553\n",
            "GKLQAFLAKMKEIAAQTL\n",
            ">amp5_30_554\n",
            "GLFDVIKKVASVIGLASP\n",
            ">amp5_30_555\n",
            "GLLGPLLKIAAKVGSNLL\n",
            ">amp5_30_556\n",
            "GLVDVLGKVGGLIKKLLP\n",
            ">amp5_30_557\n",
            "GNNRPIYIPQPRPPHPRL\n",
            ">amp5_30_558\n",
            "GNNRPVYIPQPRPPHPRI\n",
            ">amp5_30_559\n",
            "GNNRPVYIPQPRPPHPRL\n",
            ">amp5_30_560\n",
            "GRLQAFLAKMKEIAAQTL\n",
            ">amp5_30_561\n",
            "GSNKGFNFMVDMINALSN\n",
            ">amp5_30_562\n",
            "GSNKGFNFMVDMIQALSK\n",
            ">amp5_30_563\n",
            "GSNKGFNFMVDMIQALSN\n",
            ">amp5_30_564\n",
            "GSQLVYREWVGHSNVIKP\n",
            ">amp5_30_565\n",
            "GVCRCLCRRGVCRCLCRR\n",
            ">amp5_30_566\n",
            "GWKKWFTKGERLSQRHFA\n",
            ">amp5_30_567\n",
            "IFGAILPLALGALKNLIK\n",
            ">amp5_30_568\n",
            "IGCGGGAVACQNYRQFCR\n",
            ">amp5_30_569\n",
            "IIGPVLGLVGKPLESLLE\n",
            ">amp5_30_570\n",
            "KNLRRITRKIIHIIKKYG\n",
            ">amp5_30_571\n",
            "KQIMTQFFNFARSPAVKD\n",
            ">amp5_30_572\n",
            "KWKLFKKIGIGAVLKVLT\n",
            ">amp5_30_573\n",
            "KWKLFKKIPKFLHLAKKF\n",
            ">amp5_30_574\n",
            "KWKLFKKIPKFLHSAKKF\n",
            ">amp5_30_575\n",
            "KYYGNGVHCGKHSCTVDW\n",
            ">amp5_30_576\n",
            "LKLLKKLLKLLKKLGGGK\n",
            ">amp5_30_577\n",
            "LLGMIPLAISAISALSKL\n",
            ">amp5_30_578\n",
            "LLGMIPLAISAISSLSKL\n",
            ">amp5_30_579\n",
            "LLGMIPVAISAISALSKL\n",
            ">amp5_30_580\n",
            "LSLIPHIVSGVASIAKHF\n",
            ">amp5_30_581\n",
            "LVRGCWTKSYPPKPCFVR\n",
            ">amp5_30_582\n",
            "NALSMPRNKCNRALMCFG\n",
            ">amp5_30_583\n",
            "NALSSPRNKCDRASSCFG\n",
            ">amp5_30_584\n",
            "NLLGSLLKTGLKVGSNLL\n",
            ">amp5_30_585\n",
            "PAAAAQAVAGLAPVAAEQ\n",
            ">amp5_30_586\n",
            "PPEAAYGPGNTNSDSGDK\n",
            ">amp5_30_587\n",
            "RCVCRRGVCRCVCRRGVC\n",
            ">amp5_30_588\n",
            "RCVCRRGVCRCVCTRGFC\n",
            ">amp5_30_589\n",
            "RCVCTRGFCRCFCRRGVC\n",
            ">amp5_30_590\n",
            "RCVCTRGFCRCICLLGIC\n",
            ">amp5_30_591\n",
            "RCVCTRGFCRCVCRRGVC\n",
            ">amp5_30_592\n",
            "RCVCTRGFCRCVCTRGFC\n",
            ">amp5_30_593\n",
            "RGGGLCYCRRRFCVCVGR\n",
            ">amp5_30_594\n",
            "RGGRLCYCRGWICFCVGR\n",
            ">amp5_30_595\n",
            "RGGRLCYCRPRFCVCVGR\n",
            ">amp5_30_596\n",
            "RGGRLCYCRRRFCVCVGR\n",
            ">amp5_30_597\n",
            "RICRCICGRRICRCICGR\n",
            ">amp5_30_598\n",
            "RRWCFRVCYKGFCYRKCR\n",
            ">amp5_30_599\n",
            "RRWCFRVCYRGFCYRKCR\n",
            ">amp5_30_600\n",
            "RSNKGFNFMVDMIQALSK\n",
            ">amp5_30_601\n",
            "SAVGRHGRRFGLRKHRKH\n",
            ">amp5_30_602\n",
            "SPPNQPSIMTFDYAKTNK\n",
            ">amp5_30_603\n",
            "WLNALLHHGLNCAKGVLA\n",
            ">amp5_30_604\n",
            "AGIGKIGDFIKKAIAKYKN\n",
            ">amp5_30_605\n",
            "AKKVFKRLEKLFSKIQNDK\n",
            ">amp5_30_606\n",
            "CANSCSYGPLTWSCDGNTK\n",
            ">amp5_30_607\n",
            "CKQSCSFGPFTFVCDGNTK\n",
            ">amp5_30_608\n",
            "CLRFGMRGRELMGGVGKTM\n",
            ">amp5_30_609\n",
            "CLRIGMRGRELMGGIGKTM\n",
            ">amp5_30_610\n",
            "CPQTPRCTNYAEKGQCPPN\n",
            ">amp5_30_611\n",
            "CRQSCSFGPFTFVCDGNTK\n",
            ">amp5_30_612\n",
            "CRQSCSFGPLTFVCDGNTK\n",
            ">amp5_30_613\n",
            "CVISAGWNHKIRCKLTGNC\n",
            ">amp5_30_614\n",
            "FFPTIAGLTKLFCAITKKC\n",
            ">amp5_30_615\n",
            "FFRLLFHGVHHGGGYLNAA\n",
            ">amp5_30_616\n",
            "FFRLLFHGVHHVGKIKPRA\n",
            ">amp5_30_617\n",
            "FKTWKRPPFQTSCWGIIKE\n",
            ">amp5_30_618\n",
            "FLGGLIKIVPAMICAVTKK\n",
            ">amp5_30_619\n",
            "FLSLIPHAINAVGVHAKHF\n",
            ">amp5_30_620\n",
            "FLSLIPHAINAVSAIAKHF\n",
            ">amp5_30_621\n",
            "FLSLIPHAINAVSAIAKHN\n",
            ">amp5_30_622\n",
            "FLSLIPHAINAVSAIAKHS\n",
            ">amp5_30_623\n",
            "FLSLIPHAINAVSALANHG\n",
            ">amp5_30_624\n",
            "FLSLIPHAINAVSTLVHHF\n",
            ">amp5_30_625\n",
            "FLSLIPHAINAVSTLVHHS\n",
            ">amp5_30_626\n",
            "FLSLIPHIVSGVAALAKHL\n",
            ">amp5_30_627\n",
            "FLSLIPHIVSGVASIAKHF\n",
            ">amp5_30_628\n",
            "FLSLLPSIVSGAVSLAKKL\n",
            ">amp5_30_629\n",
            "FLWGLIPGAISAVTSLIKK\n",
            ">amp5_30_630\n",
            "FVGLAKVAAHVVPAIAEHF\n",
            ">amp5_30_631\n",
            "GFFDLAKKVVGGIRNALGI\n",
            ">amp5_30_632\n",
            "GFGKAFHSVSNFAKKHKTA\n",
            ">amp5_30_633\n",
            "GFGPAFHSVSNFAKKHKTA\n",
            ">amp5_30_634\n",
            "GFKDLLKGAAKALVKTVLF\n",
            ">amp5_30_635\n",
            "GFVDLAKKVVGGIRNALGI\n",
            ">amp5_30_636\n",
            "GIGGKPVQTAFVDNDGIYD\n",
            ">amp5_30_637\n",
            "GIIDIAKKLVGGIRNVLGI\n",
            ">amp5_30_638\n",
            "GILDFAKTVVGGIRNALGI\n",
            ">amp5_30_639\n",
            "GILDVAKTLVGKLRNVLGI\n",
            ">amp5_30_640\n",
            "GIVDFAKGVLGKIKNVLGI\n",
            ">amp5_30_641\n",
            "GIVDFAKKVVGGIRNALGI\n",
            ">amp5_30_642\n",
            "GKPRPYSPRPTSHPRPIRV\n",
            ">amp5_30_643\n",
            "GLADFLNKAVGKVVDFVKS\n",
            ">amp5_30_644\n",
            "GLKKLLGKLLKKLGKLLLK\n",
            ">amp5_30_645\n",
            "GLVDVLGKVGGLIKKLLPG\n",
            ">amp5_30_646\n",
            "GPVGLLSSPGSLPPVGGAP\n",
            ">amp5_30_647\n",
            "GTPGFQTPDARVISRFGFN\n",
            ">amp5_30_648\n",
            "GVIKSVLKGVAKTVALGML\n",
            ">amp5_30_649\n",
            "GWKKWLRKGAKHLGQAAIK\n",
            ">amp5_30_650\n",
            "GWMSKIASGIGTFLSGMQQ\n",
            ">amp5_30_651\n",
            "HVINLEESFQEPEYENHLA\n",
            ">amp5_30_652\n",
            "KAYSMPRCKGGFRAVMCWL\n",
            ">amp5_30_653\n",
            "KAYSMPRCKYLFRAVLCWL\n",
            ">amp5_30_654\n",
            "KAYSTPRCKGLFRALMCWL\n",
            ">amp5_30_655\n",
            "KGAAKGLLEVASCKLSKSC\n",
            ">amp5_30_656\n",
            "KGRGKQGGKVRAKAKTRSS\n",
            ">amp5_30_657\n",
            "LGGLIKIVPAMICAVTKKC\n",
            ">amp5_30_658\n",
            "LSLALAALPKLFCLIFKKC\n",
            ">amp5_30_659\n",
            "PDPAKTAPKKGSKKAVTKA\n",
            ">amp5_30_660\n",
            "PQRGEGGRAGNLLREEQEI\n",
            ">amp5_30_661\n",
            "PVVDTTGNNPLQQQEEYYV\n",
            ">amp5_30_662\n",
            "RAIGGGLSSVGGGSSTIKY\n",
            ">amp5_30_663\n",
            "RNCESLSHRFKGPCTRDSN\n",
            ">amp5_30_664\n",
            "RPDKPRPYLPRPRPPRPVR\n",
            ">amp5_30_665\n",
            "RRRTPSPRRRRSQSPRRRR\n",
            ">amp5_30_666\n",
            "SALVGCGTKSYPPKPCFGR\n",
            ">amp5_30_667\n",
            "SALVGCWTKSWPPKPCFGR\n",
            ">amp5_30_668\n",
            "SALVGCWTKSYPPKPCFGR\n",
            ">amp5_30_669\n",
            "SCLRIGMRGRELMGGVGKT\n",
            ">amp5_30_670\n",
            "SSGWVCTLTIECGTVICAC\n",
            ">amp5_30_671\n",
            "YVPPVQKPHPNGPKFPTFP\n",
            ">amp5_30_672\n",
            "AALKGCWTKSIPPKPCFGKR\n",
            ">amp5_30_673\n",
            "AALKGCWTKSIPPKPCSGKR\n",
            ">amp5_30_674\n",
            "AALRGCWTKSIPPKPCPGKR\n",
            ">amp5_30_675\n",
            "AALRGCWTKSIPPKPCSGKR\n",
            ">amp5_30_676\n",
            "ACAAHCLLRGNRGGYCNGKG\n",
            ">amp5_30_677\n",
            "AHDRNPLEEYFRETDYEEFL\n",
            ">amp5_30_678\n",
            "ARCENFADSYRQPPISSSQT\n",
            ">amp5_30_679\n",
            "CRFCCRCCPRMRGCGLCCRF\n",
            ">amp5_30_680\n",
            "CTFTLPGGGGVCTLTSECIC\n",
            ">amp5_30_681\n",
            "EGPVGLADPDGPASAPLGAP\n",
            ">amp5_30_682\n",
            "EWEPVQNGGSSYYMVPRIWA\n",
            ">amp5_30_683\n",
            "FFGAIAAALPHVISAIKNAL\n",
            ">amp5_30_684\n",
            "FFPIIAGMAAKVICAITKKC\n",
            ">amp5_30_685\n",
            "FIGLLISAGKAIHDLIRRRH\n",
            ">amp5_30_686\n",
            "FIGTALGIASAIPAIVKLFK\n",
            ">amp5_30_687\n",
            "FLGAIAAALPHVINAVTNAL\n",
            ">amp5_30_688\n",
            "FLGGLIKIVPAMICAVRKKC\n",
            ">amp5_30_689\n",
            "FLGGLIKIVPAMICAVTKKC\n",
            ">amp5_30_690\n",
            "FLGGLIKPVPAMICAVRKKC\n",
            ">amp5_30_691\n",
            "FLGGLIKRPPAMICAVRKKC\n",
            ">amp5_30_692\n",
            "FLGGLIKRVPAMICAVRKKC\n",
            ">amp5_30_693\n",
            "FLGGLMKAFPAIICAVTKKC\n",
            ">amp5_30_694\n",
            "FLGGLMKAFPALICAVTKKC\n",
            ">amp5_30_695\n",
            "FLGGLMKIIPAAFCAVTKKC\n",
            ">amp5_30_696\n",
            "FLGVVFKLASKVFPAVFGKV\n",
            ">amp5_30_697\n",
            "FLPIIAGMAAKVICAITKKC\n",
            ">amp5_30_698\n",
            "FLPLLLAGLPKLLCLFFKKC\n",
            ">amp5_30_699\n",
            "FLPLLLAGLPLKLCFLFKKC\n",
            ">amp5_30_700\n",
            "FLSLALAALPKFLCLVFKKC\n",
            ">amp5_30_701\n",
            "FLSLALAALPKLFCLIFKKC\n",
            ">amp5_30_702\n",
            "FLSLIPHAINAVSAIAKHFG\n",
            ">amp5_30_703\n",
            "FLSLIPHAINAVSTLVHHSG\n",
            ">amp5_30_704\n",
            "FLSLLPSIVSGAVSLAKKLG\n",
            ">amp5_30_705\n",
            "FPPPGESAVDMSFFYALSNP\n",
            ">amp5_30_706\n",
            "FVGAIAAALPHVISAIKNAL\n",
            ">amp5_30_707\n",
            "GADFQECMKEHSQKQHQHQG\n",
            ">amp5_30_708\n",
            "GELDELVYLLDGPGYDPIHS\n",
            ">amp5_30_709\n",
            "GKLNLFLSRLEILKLFVGAL\n",
            ">amp5_30_710\n",
            "GLFSKFAGKGIVNFLIEGVE\n",
            ">amp5_30_711\n",
            "GLFTLIKCAYQLIAPTVACN\n",
            ">amp5_30_712\n",
            "GLFTLIKGAYKLDAPTVACN\n",
            ">amp5_30_713\n",
            "GLFTLIKGAYKNDAPTVACN\n",
            ">amp5_30_714\n",
            "GLLASLGKVLGGYLAEKLKP\n",
            ">amp5_30_715\n",
            "GLLGSIGNAIGAFIANKLKP\n",
            ">amp5_30_716\n",
            "GLMSVLGHAVGNVLGGLFKS\n",
            ">amp5_30_717\n",
            "GLRKRLRKFRNKIKEKLKKI\n",
            ">amp5_30_718\n",
            "GSQLVYREWVGHSNVIKGPP\n",
            ">amp5_30_719\n",
            "GVGYKVVVTTTAAADDDDVV\n",
            ">amp5_30_720\n",
            "GWANTLKNVAGGLCKITGAA\n",
            ">amp5_30_721\n",
            "ICIFCCGCCHRSKCGMCCKT\n",
            ">amp5_30_722\n",
            "IIGAIAAALPHVINAIKNTF\n",
            ">amp5_30_723\n",
            "IIGPVLGLVGSALGGLLKKI\n",
            ">amp5_30_724\n",
            "IIGPVLGMVGSALGGLLKKI\n",
            ">amp5_30_725\n",
            "IKELLPHLSGIIDSVANAIK\n",
            ">amp5_30_726\n",
            "ILGPVIKTIGGVIGGLLKNL\n",
            ">amp5_30_727\n",
            "ILGPVISKIGGVLGGLLKNL\n",
            ">amp5_30_728\n",
            "ILGPVISTIGGVLGGLLKNL\n",
            ">amp5_30_729\n",
            "ILGPVLGLVGNALGGLIKKI\n",
            ">amp5_30_730\n",
            "ILGPVLGLVGNALGGLIKKL\n",
            ">amp5_30_731\n",
            "ILGPVLGLVGNALGGLIKNE\n",
            ">amp5_30_732\n",
            "ILGPVLGLVSDTLDDVLGIL\n",
            ">amp5_30_733\n",
            "ILGPVLSLVGNALGGLLKNE\n",
            ">amp5_30_734\n",
            "ILGPVLSLVGSALGGLIKKI\n",
            ">amp5_30_735\n",
            "ILGPVLSMVGSALGGLIKKI\n",
            ">amp5_30_736\n",
            "KAKLFKKIGIGKFLHSAKKF\n",
            ">amp5_30_737\n",
            "KIFTKCELARKLRAEGMDGF\n",
            ">amp5_30_738\n",
            "KIKIPWGKVKDFLVGGMKAV\n",
            ">amp5_30_739\n",
            "KLKLFKKIGIGKFLHSAKKF\n",
            ">amp5_30_740\n",
            "KRIVQRIKDFLRNLVPRTES\n",
            ">amp5_30_741\n",
            "KSCCRNTTARNCYNVCRIPG\n",
            ">amp5_30_742\n",
            "KSLRPRCWIKIKFRCKSLKF\n",
            ">amp5_30_743\n",
            "KTCENLADTFRGPCFATSNC\n",
            ">amp5_30_744\n",
            "KWKLFKKIGIGAVLKVLTTG\n",
            ">amp5_30_745\n",
            "KWKLFKKIGIGKFLHSAKKF\n",
            ">amp5_30_746\n",
            "LAHQKPFIRKSYKCLHKRCR\n",
            ">amp5_30_747\n",
            "LFCKRGTCHFGRCPSHLIKV\n",
            ">amp5_30_748\n",
            "LIGPVLGLVGSALGGLLKKI\n",
            ">amp5_30_749\n",
            "LLSLALAALPKLFCLIFKKC\n",
            ">amp5_30_750\n",
            "LPSDATLVLDQTGKELDARL\n",
            ">amp5_30_751\n",
            "PAQPFRFPKHPQGPQTRPPI\n",
            ">amp5_30_752\n",
            "QSHLSLCRWCCNCCRSNKGC\n",
            ">amp5_30_753\n",
            "QWGRRCCGWGPGRRYCRRWC\n",
            ">amp5_30_754\n",
            "QWGRRCCGWGPGRRYCVRWC\n",
            ">amp5_30_755\n",
            "QYGRRCCNWGPGRRYCKRWC\n",
            ">amp5_30_756\n",
            "QYRHRCCAWGPGRKYCKRWC\n",
            ">amp5_30_757\n",
            "RILSILRHQNLLKELQDLAL\n",
            ">amp5_30_758\n",
            "RNCESLSHRFKGPCTRDSNC\n",
            ">amp5_30_759\n",
            "SALVGCWTKSWPPKPCFGRG\n",
            ">amp5_30_760\n",
            "SALVGCWTKSYPPNPCFGRG\n",
            ">amp5_30_761\n",
            "SWASMAKKLKEYMEKLKQRA\n",
            ">amp5_30_762\n",
            "SWKSMAKKLKEYMEKLKQRA\n",
            ">amp5_30_763\n",
            "TLRGDERILSILRHQNLLKE\n",
            ">amp5_30_764\n",
            "VDKGSYLPRPTPPRPIYNRN\n",
            ">amp5_30_765\n",
            "VDKPPYLPRPPPPRRIYNNR\n",
            ">amp5_30_766\n",
            "VGKTWIKVIRGIGKSKIKWQ\n",
            ">amp5_30_767\n",
            "VKSTGRADDDLAVKTKYLPP\n",
            ">amp5_30_768\n",
            "VNPIILGVLPKFVCLITKKC\n",
            ">amp5_30_769\n",
            "VRLEFKLQQTSCRKRDWKKP\n",
            ">amp5_30_770\n",
            "WLRRIGKGVKIIGGAALDHL\n",
            ">amp5_30_771\n",
            "AGYLLGKINLKALAALAKKIL\n",
            ">amp5_30_772\n",
            "APPGARPPPGPPPPGPPPPGP\n",
            ">amp5_30_773\n",
            "ASSGWVCTLTIECGTVICACR\n",
            ">amp5_30_774\n",
            "AVLDILKDVGKGLLSHFMEKV\n",
            ">amp5_30_775\n",
            "CLGIGSCNDFAGCGYAIVCFW\n",
            ">amp5_30_776\n",
            "CLGIGSCNDFAGCGYAVVCFW\n",
            ">amp5_30_777\n",
            "CLGVGSCNDFAGCGYAIVCFW\n",
            ">amp5_30_778\n",
            "CLGVGSCNDFAGCGYAVVCFW\n",
            ">amp5_30_779\n",
            "CLGVGSCNNFAGCGYAIVCFW\n",
            ">amp5_30_780\n",
            "CPPCPSCPSCPWCPMCPRCPS\n",
            ">amp5_30_781\n",
            "FALGAVTKLLPSLLCMITRKC\n",
            ">amp5_30_782\n",
            "FALGAVTKRLPSLFCLITRKC\n",
            ">amp5_30_783\n",
            "FGLPMLSILPKALCILLKRKC\n",
            ">amp5_30_784\n",
            "FLFSLIPSAISGLISAFKGRR\n",
            ">amp5_30_785\n",
            "FLGSLIGAAIPAIKQLLGLKK\n",
            ">amp5_30_786\n",
            "FLHHIVGLIHHGLSLFGDRAD\n",
            ">amp5_30_787\n",
            "FLNALKNFAKTAGKRLKSLLN\n",
            ">amp5_30_788\n",
            "FLPKMSTKLRVPYRRGTKDYH\n",
            ">amp5_30_789\n",
            "GCRFCCNCCPNMSGCGVCCRF\n",
            ">amp5_30_790\n",
            "GELGRLVYLLDGPGYDPIHCD\n",
            ">amp5_30_791\n",
            "GFIFHIIKGLFHAGKMIHGLV\n",
            ">amp5_30_792\n",
            "GFMKYIGPLIPHAVKAISDLI\n",
            ">amp5_30_793\n",
            "GFWGKLFKLGLHGIGLLHLHL\n",
            ">amp5_30_794\n",
            "GGAGHVPEYFVGIGTPISFYG\n",
            ">amp5_30_795\n",
            "GHHPHGHHPHGHHPHGHHHPH\n",
            ">amp5_30_796\n",
            "GIFGKILGAGKKVLCGLSGLC\n",
            ">amp5_30_797\n",
            "GIFGKILGVGKKTLCELSGMC\n",
            ">amp5_30_798\n",
            "GIFGKILGVGKKVLCGLSGMC\n",
            ">amp5_30_799\n",
            "GIFGKILGVGKKVLCGLSGWC\n",
            ">amp5_30_800\n",
            "GIFLKVLGVGKKVLCGVSGLC\n",
            ">amp5_30_801\n",
            "GIFSTVFKAGKGIVCGLTGLC\n",
            ">amp5_30_802\n",
            "GILGNIVGMGKKIVCGLSGLC\n",
            ">amp5_30_803\n",
            "GILGNIVGMGKKVVCGLSGLC\n",
            ">amp5_30_804\n",
            "GILGNIVGMGKQVVCGLSGLC\n",
            ">amp5_30_805\n",
            "GILGTVFKAGKGIVCGLTGLC\n",
            ">amp5_30_806\n",
            "GILSGILGAGKSLVCGLSGLC\n",
            ">amp5_30_807\n",
            "GILSGILGMGKKLVCGLSGLC\n",
            ">amp5_30_808\n",
            "GILSGVLGMGKKIVCGLRGLC\n",
            ">amp5_30_809\n",
            "GILSGVLGMGKKIVCGLSGLC\n",
            ">amp5_30_810\n",
            "GILSNVLGMGKKIVCGLSGLC\n",
            ">amp5_30_811\n",
            "GILSTVFKAGKGIVCGLSGLC\n",
            ">amp5_30_812\n",
            "GIWDTIKSMGKVFAGKILQNL\n",
            ">amp5_30_813\n",
            "GLFGKILGVGKKVLCGLSGMC\n",
            ">amp5_30_814\n",
            "GLFGVLAKVAAHVVPAIAEHF\n",
            ">amp5_30_815\n",
            "GLISGILGVGKKLVCGLSGLC\n",
            ">amp5_30_816\n",
            "GLISGILGVGKMLVCGLSGLC\n",
            ">amp5_30_817\n",
            "GLISGLLGVGKMLVCGLSGLC\n",
            ">amp5_30_818\n",
            "GLLASLGKVFGGYLAEKLKPK\n",
            ">amp5_30_819\n",
            "GLLASLGKVLGGYLAEKLKPK\n",
            ">amp5_30_820\n",
            "GLLDFVTGVGKDIFAQLIKQI\n",
            ">amp5_30_821\n",
            "GLLGLLGSVVSHVLPAITQHL\n",
            ">amp5_30_822\n",
            "GLLGLLGSVVSHVVPAIVGHF\n",
            ">amp5_30_823\n",
            "GLLGSIFGAGKKIACALSGLC\n",
            ">amp5_30_824\n",
            "GLLGSIGKVLGGYLAEKLKPK\n",
            ">amp5_30_825\n",
            "GLLGSIGNAIGAFIANKLKPK\n",
            ">amp5_30_826\n",
            "GLLGSLFGAGKKVACALSGLC\n",
            ">amp5_30_827\n",
            "GLLSFLPKVIGVIGHLIHPPS\n",
            ">amp5_30_828\n",
            "GLLSGILGAGKHIICGLSGLC\n",
            ">amp5_30_829\n",
            "GLLSGILGAGKHIVCGLSGLC\n",
            ">amp5_30_830\n",
            "GLLSGILGAGKHIVCGLSGLK\n",
            ">amp5_30_831\n",
            "GLLSGILGAGKNIVCGLSGLC\n",
            ">amp5_30_832\n",
            "GLLSGILGAGKQKVCGLSGLC\n",
            ">amp5_30_833\n",
            "GLLSGVLGVGKKIVCGLSGLC\n",
            ">amp5_30_834\n",
            "GLLSGVLGVGKKVDCGLSGLC\n",
            ">amp5_30_835\n",
            "GLLSKVLGVGKKVLCGVSGLC\n",
            ">amp5_30_836\n",
            "GLWSTIKNVAAAAGKAALGAL\n",
            ">amp5_30_837\n",
            "GMASKAGAIAGKIAKVALKAL\n",
            ">amp5_30_838\n",
            "GNGVLKTISHECNMNTWQFLF\n",
            ">amp5_30_839\n",
            "GSKKPVPIIYCNRRTGKCQRM\n",
            ">amp5_30_840\n",
            "GVLDILKNAAKNILAHAAEQI\n",
            ">amp5_30_841\n",
            "IIGAIAAALPHVINAIKNTFG\n",
            ">amp5_30_842\n",
            "IIGPVLGMVGSALGGLLKKIG\n",
            ">amp5_30_843\n",
            "ITPATPFTPAIITEITAAVIA\n",
            ">amp5_30_844\n",
            "KSWSLCTPGCARTGSFNSYCC\n",
            ">amp5_30_845\n",
            "KTCENLADTYKGPCFTTGSCD\n",
            ">amp5_30_846\n",
            "KYALMKKIAELIPNLKSRQVK\n",
            ">amp5_30_847\n",
            "LIGPVLGLVGSALGGLLKKIG\n",
            ">amp5_30_848\n",
            "MIASHLAFEKLSKLGSKHTML\n",
            ">amp5_30_849\n",
            "PRRRTPSPRRRRSQSPRRRRS\n",
            ">amp5_30_850\n",
            "QRFSQPTFKLPQGRLTLSRKF\n",
            ">amp5_30_851\n",
            "RCRFCCRCCPRMRGCGICCRF\n",
            ">amp5_30_852\n",
            "RKFHEKHHSHRGYRSNYLYDN\n",
            ">amp5_30_853\n",
            "RQRVEELSKFSKKGAAARRRK\n",
            ">amp5_30_854\n",
            "RWCVYAYVRIRGVLVRYRRCW\n",
            ">amp5_30_855\n",
            "RWCVYAYVRVRGVLVRYRRCW\n",
            ">amp5_30_856\n",
            "SILSGNFGVGKKIVCGLSGLC\n",
            ">amp5_30_857\n",
            "SLWETIKNAGKGFIQNLDKIR\n",
            ">amp5_30_858\n",
            "SPRRRTPSPRRRRSQSPRRRR\n",
            ">amp5_30_859\n",
            "SSMKLSFRARAYGFRGPGPQL\n",
            ">amp5_30_860\n",
            "SSSGWLCTLTIECGTIICACR\n",
            ">amp5_30_861\n",
            "TCESPSHKFKGPCATNRNCES\n",
            ">amp5_30_862\n",
            "TRSSRAGLQFPVGRVHRLLRK\n",
            ">amp5_30_863\n",
            "TTPACFTIGLGVGALFSAKFC\n",
            ">amp5_30_864\n",
            "TVVRRRGRSPRRRTPSPRRRR\n",
            ">amp5_30_865\n",
            "VGIGTPIFSYGGGAGHVPEYF\n",
            ">amp5_30_866\n",
            "YGQSTHAVIYAQGYTYSSDWR\n",
            ">amp5_30_867\n",
            "ALPKKLKYLNLFNDGFNYMGVV\n",
            ">amp5_30_868\n",
            "ATPATPTVAQFVIQGSTICLVC\n",
            ">amp5_30_869\n",
            "AVLDFIKAAGKGLVTNIMEKVG\n",
            ">amp5_30_870\n",
            "CKHQCMSTRGDQARKICESVCM\n",
            ">amp5_30_871\n",
            "CPAIQRCCQQLRNIQPPCRCCQ\n",
            ">amp5_30_872\n",
            "DSIRDVSPTFNKIRRWFDGLFK\n",
            ">amp5_30_873\n",
            "FFGHLYRGITSVVKHVHGLLSG\n",
            ">amp5_30_874\n",
            "FFHHIFRGIVHVGKTIHKLVTG\n",
            ">amp5_30_875\n",
            "FFHHIFRGIVHVGKTIHRLVTG\n",
            ">amp5_30_876\n",
            "FIHHIFRGIVHAGRSIGRFLTG\n",
            ">amp5_30_877\n",
            "FIHHIIGWISHGVRAIHRAIHG\n",
            ">amp5_30_878\n",
            "FKSWSFCTPGCAKTGSFNSYCC\n",
            ">amp5_30_879\n",
            "FKSWSLCTPGCARTGSFNSYCC\n",
            ">amp5_30_880\n",
            "FLGSIVGALASALPSLISKIRN\n",
            ">amp5_30_881\n",
            "FTSKKSMLLFFFLGTISLSLCQ\n",
            ">amp5_30_882\n",
            "GAARKSIRLHRLYTWKATIYTR\n",
            ">amp5_30_883\n",
            "GIGGALLSAGKAALKGLAKVLV\n",
            ">amp5_30_884\n",
            "GIGKFLKKAKKFGKAFVKILKK\n",
            ">amp5_30_885\n",
            "GIKCRFCCGCCTPGICGVCCRF\n",
            ">amp5_30_886\n",
            "GLFLDTLKKFAKAGMEAVINPK\n",
            ">amp5_30_887\n",
            "GLFVGVLAKVAAHVVPAIAEHF\n",
            ">amp5_30_888\n",
            "GLIGSIGKALGGLLVDVLKPKL\n",
            ">amp5_30_889\n",
            "GLLGGLLGPLLGGGGGGGGGLL\n",
            ">amp5_30_890\n",
            "GLWEKIKEKANELVSGIVEGVK\n",
            ">amp5_30_891\n",
            "GLWEKIKEKASELVSGIVEGVK\n",
            ">amp5_30_892\n",
            "GLWEKIREKANELVSGIVEGVK\n",
            ">amp5_30_893\n",
            "GLWEKVKEKANELVSGIVEGVK\n",
            ">amp5_30_894\n",
            "GLWQKIKDKASELVSGIVEGVK\n",
            ">amp5_30_895\n",
            "GMASKAGSVLGKVAKVALKAAL\n",
            ">amp5_30_896\n",
            "GMATKAGTAFGKAAKAIIGAAL\n",
            ">amp5_30_897\n",
            "GMATKAGTALGKVAKAVIGAAL\n",
            ">amp5_30_898\n",
            "GSKILHSAGKFGKAFLGEINKS\n",
            ">amp5_30_899\n",
            "GSRYLCTPGSCWKLVCFTTTVK\n",
            ">amp5_30_900\n",
            "GVFLDALKKFAKGGMNAVLNPK\n",
            ">amp5_30_901\n",
            "GVLDIFKDAAKQILAHAAEKQI\n",
            ">amp5_30_902\n",
            "IASKFICTPGCAKTGSFNSYCC\n",
            ">amp5_30_903\n",
            "IASKFLCTPGCAKTGSFNSYCC\n",
            ">amp5_30_904\n",
            "IFHHIFKGIVHVGKTIHRLVTG\n",
            ">amp5_30_905\n",
            "ITSFIGCTPGCGKTGSFNSFCC\n",
            ">amp5_30_906\n",
            "KRGSGWIATITDDCPNSVFVCC\n",
            ">amp5_30_907\n",
            "KRKKHRCRVYNNGMPTGMYRWC\n",
            ">amp5_30_908\n",
            "LGAWLAGKVAGTVATYAWNRYV\n",
            ">amp5_30_909\n",
            "MQFITDLIKKAVDFFKGLFGNK\n",
            ">amp5_30_910\n",
            "QRFIHPTYRPPPQPRRPVIMRA\n",
            ">amp5_30_911\n",
            "RPWAGNGSVHRYTVLSPRLKTQ\n",
            ">amp5_30_912\n",
            "SVLGKSVAKHLPHVVPVIAEKT\n",
            ">amp5_30_913\n",
            "SVLGSVAKHVLPHVVPVIAEHL\n",
            ">amp5_30_914\n",
            "TPALAVVTTVLPAAAVTTAKSV\n",
            ">amp5_30_915\n",
            "YPGPQAKEDSEGPSQGPASREK\n",
            ">amp5_30_916\n",
            "YSLQMGATAIKQVKKLFKKKGG\n",
            ">amp5_30_917\n",
            "YSLQMGATAIKQVKKLFKKWGW\n",
            ">amp5_30_918\n",
            "AFKLLGRIIHHVGNFVYGFSHVF\n",
            ">amp5_30_919\n",
            "AIGSILGALAKGLPTLISWIKNR\n",
            ">amp5_30_920\n",
            "ALWKDILKNAGKAALNEINQIVQ\n",
            ">amp5_30_921\n",
            "CYSAAKYPGFQEFINRKYKSSRF\n",
            ">amp5_30_922\n",
            "FALALKALKKALKKLKKALKKAL\n",
            ">amp5_30_923\n",
            "FFHHIFRGIVHVGKTIHRLVTGG\n",
            ">amp5_30_924\n",
            "FHPSLWVLIPQYIQLIRKILKSG\n",
            ">amp5_30_925\n",
            "FIHHIIGGLFSVGKHIHGLIHGH\n",
            ">amp5_30_926\n",
            "FIHHIIGGLFSVGKHIHSLIHGH\n",
            ">amp5_30_927\n",
            "FLPLVRGAAKLIPSVVCAISKRC\n",
            ">amp5_30_928\n",
            "FLRFIGSVIHGIGHLVHHIGVAL\n",
            ">amp5_30_929\n",
            "FLSGILKLAFKIPSVLCAVLKNC\n",
            ">amp5_30_930\n",
            "FRSGILKLASKIPSVLCAVLKNC\n",
            ">amp5_30_931\n",
            "GFRDVLKGAAKAFVKTVAGHIAN\n",
            ">amp5_30_932\n",
            "GIGKFLHSAGKFGKAFVGEIMKS\n",
            ">amp5_30_933\n",
            "GIGKFLHSAKKFGKAFVGEIMNS\n",
            ">amp5_30_934\n",
            "GIGKHVGKALKGLKGLLKGLGES\n",
            ">amp5_30_935\n",
            "GINTLKKVIQGLHEVIKLVSNHA\n",
            ">amp5_30_936\n",
            "GINTLKKVIQGLHEVIKLVSNHE\n",
            ">amp5_30_937\n",
            "GLFGKSSVVGRKYYVDLAGCAKA\n",
            ">amp5_30_938\n",
            "GLFGLAKGSVAKPHVVPVISQLV\n",
            ">amp5_30_939\n",
            "GLFGVLAKVASHVVPAIAEHFQA\n",
            ">amp5_30_940\n",
            "GLFVGLAKVAAHNNPAIAEHFQA\n",
            ">amp5_30_941\n",
            "GLKDIFKAGLGSLVKGIAAHVAN\n",
            ">amp5_30_942\n",
            "GLKDIFKAGLGSLVKNIAAHVAN\n",
            ">amp5_30_943\n",
            "GLKEIFKAGLGSLVKGIAAHVAN\n",
            ">amp5_30_944\n",
            "GLKEIFKAGLGSLVKGIAAHVAS\n",
            ">amp5_30_945\n",
            "GLKEVAHSAKKFAKGFISGLTGS\n",
            ">amp5_30_946\n",
            "GLLRASSVWGRKYYVDLAGCAKA\n",
            ">amp5_30_947\n",
            "GLLSGILGAGKHIVCGLTGCAKA\n",
            ">amp5_30_948\n",
            "GLSQGVEPDIGQTYFEESRINQD\n",
            ">amp5_30_949\n",
            "GLVTGLLKTAGKLLGDLFGSLTG\n",
            ">amp5_30_950\n",
            "GLVTSLIKGAGKLLGGLFGSVTG\n",
            ">amp5_30_951\n",
            "GLWQFIKDKFKDAATGLVTGIQS\n",
            ">amp5_30_952\n",
            "GLWQFIKDKLKDAATGLVTGIQS\n",
            ">amp5_30_953\n",
            "GLWQKIKNAAGDLASGIVEGIKS\n",
            ">amp5_30_954\n",
            "GLWQKIKSAAGDLASGIVEAIKS\n",
            ">amp5_30_955\n",
            "GLWQKIKSAAGDLASGIVEGIKS\n",
            ">amp5_30_956\n",
            "GLWQLIKDKIKDAATGFVTGIQS\n",
            ">amp5_30_957\n",
            "GRSPRRRTPSPRRRRSQSPRRRR\n",
            ">amp5_30_958\n",
            "GVSKILHSAGKFGKAFLGEIMKS\n",
            ">amp5_30_959\n",
            "GVVTDLLKTAGKLLGNLFGSLSG\n",
            ">amp5_30_960\n",
            "GVVTDLLKTAGKLLGNLVGSLSG\n",
            ">amp5_30_961\n",
            "GVVTDLLNTAGGLLGNLVGSLSG\n",
            ">amp5_30_962\n",
            "GWKKWLRKGAKHLGQAAIKGLAS\n",
            ">amp5_30_963\n",
            "GWRLLLKKAEVKTVGKLALKHYL\n",
            ">amp5_30_964\n",
            "GWRTLLKKAEVKTVGKLALKHYL\n",
            ">amp5_30_965\n",
            "IRPRPTARPPYINRPPNPFKPRW\n",
            ">amp5_30_966\n",
            "ITCQQVTSELGPCVPYLTGQGIP\n",
            ">amp5_30_967\n",
            "ITIPPIVKDTLKKFIKGAISGVM\n",
            ">amp5_30_968\n",
            "ITIPPIVKDTLKKFIKGAISSVM\n",
            ">amp5_30_969\n",
            "LAFVAGVAAEMMQHVYCAASKKC\n",
            ">amp5_30_970\n",
            "LFKLLGKIIHHVGNFVHGFSHVF\n",
            ">amp5_30_971\n",
            "LLGDFFRKSKEKIGKEFKRIVQR\n",
            ">amp5_30_972\n",
            "LPFVAGVAAEMMQHVYCAASKKC\n",
            ">amp5_30_973\n",
            "LPLIASLAANFVPKIFCKITKKC\n",
            ">amp5_30_974\n",
            "MFFSSKKCKTVSKTFRGPCVRNA\n",
            ">amp5_30_975\n",
            "NPKVAHCASQIGRSTAWGAVSGA\n",
            ">amp5_30_976\n",
            "QKLCERPSGTWSGVCGNNNACKN\n",
            ">amp5_30_977\n",
            "QRPYTQPLIYYPPPPTPPRIYRA\n",
            ">amp5_30_978\n",
            "RIGVLLARLPKLFSLFKLMGKKV\n",
            ">amp5_30_979\n",
            "RIIDLLWRVRRPQKPKFVTVWVR\n",
            ">amp5_30_980\n",
            "RPKHPIKHQGLPQEVLNENLLRF\n",
            ">amp5_30_981\n",
            "SNMIEGVFAKGFKKASHLFKGIG\n",
            ">amp5_30_982\n",
            "VFHLLGKIIHHVGNFVYGFSHVF\n",
            ">amp5_30_983\n",
            "VFQFLGKIIHHVGNFVHGFSHVF\n",
            ">amp5_30_984\n",
            "VFQFLGRIIHHVGNFVHGFSHVF\n",
            ">amp5_30_985\n",
            "VGSRYLCTPGSCWKLVCFTTTVK\n",
            ">amp5_30_986\n",
            "VIGSILGALASGLPTLISWIKNR\n",
            ">amp5_30_987\n",
            "VIPFVASVAAEMQHVYCAASRKC\n",
            ">amp5_30_988\n",
            "WATLAKGALKLIPTIANAFSSKS\n",
            ">amp5_30_989\n",
            "ADNKNPLEECFRETNYEEFLEIAR\n",
            ">amp5_30_990\n",
            "ALWKDILKNAGKAALNEINQLVNQ\n",
            ">amp5_30_991\n",
            "DSHAKRHHGYKRKFHEKHHSHRGY\n",
            ">amp5_30_992\n",
            "DSHEKRHHEHRRKFHEKHHSHRGY\n",
            ">amp5_30_993\n",
            "DSMGAVKLAKLLIDKMKCEVTKAC\n",
            ">amp5_30_994\n",
            "FFGSVLKVAAKVLPAALCQIFKKC\n",
            ">amp5_30_995\n",
            "FFGTALKIAANILPTAICKILKKC\n",
            ">amp5_30_996\n",
            "FFGTALKIAANVLPTAICKILKKC\n",
            ">amp5_30_997\n",
            "FFPIIAGMAAKLIPSLFCKITKKC\n",
            ">amp5_30_998\n",
            "FFPIVAGVAGQVLKKIYCTISKKC\n",
            ">amp5_30_999\n",
            "FFPMLAGVAARVVPKVICLITKKC\n",
            ">amp5_30_1000\n",
            "FIGPVLKIAAGILPTAICKIFKKC\n",
            ">amp5_30_1001\n",
            "FIGPVLKMATSILPTAICKGFKKC\n",
            ">amp5_30_1002\n",
            "FLGALFKVASKVLPSVFCAITKKC\n",
            ">amp5_30_1003\n",
            "FLGALIKGAIHGGRFIHGMIQNHH\n",
            ">amp5_30_1004\n",
            "FLGLLFHGVHHVGKWIHGLIHGHH\n",
            ">amp5_30_1005\n",
            "FLGPIIKIATGILPTAICKFLKKC\n",
            ">amp5_30_1006\n",
            "FLGPIIKMATGILPTAICKGLKKC\n",
            ">amp5_30_1007\n",
            "FLGSLLGLVGKVVPTLFCKISKKC\n",
            ">amp5_30_1008\n",
            "FLPAIAGMAAKFLPKIFCAISKKC\n",
            ">amp5_30_1009\n",
            "FLPAIAGVAAKFLPKIFCAISKKC\n",
            ">amp5_30_1010\n",
            "FLPAIFRMAAKVVPTIICSITKKC\n",
            ">amp5_30_1011\n",
            "FLPAIVGAAAKFLPKIFCAISKKC\n",
            ">amp5_30_1012\n",
            "FLPAIVGAAAKFLPKIFCVISKKC\n",
            ">amp5_30_1013\n",
            "FLPAIVGAAGKFLPKIFCAISKKC\n",
            ">amp5_30_1014\n",
            "FLPAIVGAAGQFLPKIFCAISKKC\n",
            ">amp5_30_1015\n",
            "FLPAVIRVAANVLPTAFCAISKKC\n",
            ">amp5_30_1016\n",
            "FLPAVIRVAANVLPTVFCAISKKC\n",
            ">amp5_30_1017\n",
            "FLPAVLKVAAHILPTAICAISRRC\n",
            ">amp5_30_1018\n",
            "FLPAVLLVATHVLPTVFCAITRKC\n",
            ">amp5_30_1019\n",
            "FLPAVLRVAAKIVPTVFCAISKKC\n",
            ">amp5_30_1020\n",
            "FLPAVLRVAAKVVPTVFCLISKKC\n",
            ">amp5_30_1021\n",
            "FLPAVLRVAAQVVPTVFCAISKKC\n",
            ">amp5_30_1022\n",
            "FLPFIAGMAAKFLPKIFCAISKKC\n",
            ">amp5_30_1023\n",
            "FLPFIAGMAANFLPKIFCAISKKC\n",
            ">amp5_30_1024\n",
            "FLPFIAGVAAKFLPKIFCAISKKC\n",
            ">amp5_30_1025\n",
            "FLPFIARLAAKVFPSIICSVTKKC\n",
            ">amp5_30_1026\n",
            "FLPGVLRLVTKVGPAVVCAITRNC\n",
            ">amp5_30_1027\n",
            "FLPIIAGAAAKVVEKIFCAISKKC\n",
            ">amp5_30_1028\n",
            "FLPIIAGAAAKVVQKIFCAISKKC\n",
            ">amp5_30_1029\n",
            "FLPIIAGIAAKFLPKIFCTISKKC\n",
            ">amp5_30_1030\n",
            "FLPIIAGIAAKVFPKIFCAISKKC\n",
            ">amp5_30_1031\n",
            "FLPIIAGVAAKVFPKIFCAISKKC\n",
            ">amp5_30_1032\n",
            "FLPIIAGVAAKVLPKIFCAISKKC\n",
            ">amp5_30_1033\n",
            "FLPIIAGVAAKVLPKLFCAITKKC\n",
            ">amp5_30_1034\n",
            "FLPIIASVAAKVFPKIFCAISKKC\n",
            ">amp5_30_1035\n",
            "FLPIIASVAAKVFSKIFCAISKKC\n",
            ">amp5_30_1036\n",
            "FLPIIASVAANVFSKIFCAISKKC\n",
            ">amp5_30_1037\n",
            "FLPILAGLAAKIVPKLFCLATKKC\n",
            ">amp5_30_1038\n",
            "FLPILAGLAAKLVPKVFCSITKKC\n",
            ">amp5_30_1039\n",
            "FLPILAGLAANILPKVFCSITKKC\n",
            ">amp5_30_1040\n",
            "FLPILASLAAKFGPKLFCLVTKKC\n",
            ">amp5_30_1041\n",
            "FLPILASLAAKLGPKLFCLVTKKC\n",
            ">amp5_30_1042\n",
            "FLPILASLAATLGPKLLCLITKKC\n",
            ">amp5_30_1043\n",
            "FLPIVAGLAANFLPKIVCKITKKC\n",
            ">amp5_30_1044\n",
            "FLPLAVSLAANFLPKLFCKITKKC\n",
            ">amp5_30_1045\n",
            "FLPLIAGLAANFLPKIFCAITKKC\n",
            ">amp5_30_1046\n",
            "FLPLIASLAANFVPKIFCKITKKC\n",
            ">amp5_30_1047\n",
            "FLPLIASVAANLVPKIFCKITKKC\n",
            ">amp5_30_1048\n",
            "FLPLLAGLAANFFPKIFCKITRKC\n",
            ">amp5_30_1049\n",
            "FLPLLAGLAANFLPKIFCKITKKC\n",
            ">amp5_30_1050\n",
            "FLPLLAGLAANFLPKIFCKITRKC\n",
            ">amp5_30_1051\n",
            "FLPLLAGLAANFLPTIICKISYKC\n",
            ">amp5_30_1052\n",
            "FLPLLAGVVANFLPQIICKIARKC\n",
            ">amp5_30_1053\n",
            "FLPMLAGLAANFLPKIVCKITKKC\n",
            ">amp5_30_1054\n",
            "FLPMLAGLAASMVPKFVCLITKKC\n",
            ">amp5_30_1055\n",
            "FLPMLAGLAASMVPKLVCLITKKC\n",
            ">amp5_30_1056\n",
            "FLPVIAGLAAKVLPKLFCAITKKC\n",
            ">amp5_30_1057\n",
            "FLPVIAGVAAKFLPKIFCAITKKC\n",
            ">amp5_30_1058\n",
            "FLPVIAGVAANFLPKLFCAISKKC\n",
            ">amp5_30_1059\n",
            "FLPVLAGIAAKVVPALFCKITKKC\n",
            ">amp5_30_1060\n",
            "FLPVLAGLTPSIVPKLVCLLTKKC\n",
            ">amp5_30_1061\n",
            "FLPVLTGLTPSIVPKLVCLLTKKC\n",
            ">amp5_30_1062\n",
            "FLPVVAGLAAKVLPSIICAVTKKC\n",
            ">amp5_30_1063\n",
            "FLSTALKVAANVVPTLFCKITKKC\n",
            ">amp5_30_1064\n",
            "FLSTLLKVAFKVVPTLFCPITKKC\n",
            ">amp5_30_1065\n",
            "FLSTLLNVASKVVPTLFCKITKKC\n",
            ">amp5_30_1066\n",
            "FLSTLLNVASNVVPTLICKITKKC\n",
            ">amp5_30_1067\n",
            "FMGGLIKAATKALPAAFCAITKKC\n",
            ">amp5_30_1068\n",
            "FMGGLIKAATKIVPAAYCAITKKC\n",
            ">amp5_30_1069\n",
            "FMGSALRIAAKVLPAALCQIFKKC\n",
            ">amp5_30_1070\n",
            "FMGTALKIAANVLPAAFCKIFKKC\n",
            ">amp5_30_1071\n",
            "FVGPVLKIAAGILPTAICKIYKKC\n",
            ">amp5_30_1072\n",
            "FWGALAKGALKLIGSLFSSFSKKD\n",
            ">amp5_30_1073\n",
            "FWGALAKGALKLIPSLFSSFSKKD\n",
            ">amp5_30_1074\n",
            "FWGALIKGAAKLIPSVVGLFKKKQ\n",
            ">amp5_30_1075\n",
            "GFKDWIKGAAKKLIKTVASSIANQ\n",
            ">amp5_30_1076\n",
            "GFRDVLKGAAKAFVKTVAGHIANI\n",
            ">amp5_30_1077\n",
            "GIFSNMYARTPAGYFRGPAGYAAN\n",
            ">amp5_30_1078\n",
            "GIGALSAKGALKGLAKGLAEHFAN\n",
            ">amp5_30_1079\n",
            "GIKDWIKGAAKKLIKTVASNIANQ\n",
            ">amp5_30_1080\n",
            "GKGRWLERIGKAGGIIIGGALDHL\n",
            ">amp5_30_1081\n",
            "GLFGVLGSIAKHVLPHVVPVIAEK\n",
            ">amp5_30_1082\n",
            "GLFKVLGSVAKHLLPHVAPVIAEK\n",
            ">amp5_30_1083\n",
            "GLFKVLGSVAKHLLPHVVPVIAEK\n",
            ">amp5_30_1084\n",
            "GLFSVLGAVAKHVLPHVVPVIAEK\n",
            ">amp5_30_1085\n",
            "GLKDKFKSMGEKLKQYIQTWKAKF\n",
            ">amp5_30_1086\n",
            "GLLNGLALRLGKRALKKIIKRLCR\n",
            ">amp5_30_1087\n",
            "GLLSVLGSLKLIVPHVVPLIAEHL\n",
            ">amp5_30_1088\n",
            "GLNALKKVFQGIHEAIKLINNHVQ\n",
            ">amp5_30_1089\n",
            "GLNTLKKVFQGLHEAIKLINNHVQ\n",
            ">amp5_30_1090\n",
            "GLRSKIWLWVLLMIWQESNKFKKM\n",
            ">amp5_30_1091\n",
            "GNGVLKTISHECNMNTWQFLFTCC\n",
            ">amp5_30_1092\n",
            "GNGVVLTLTHECNLATWTKKLKCC\n",
            ">amp5_30_1093\n",
            "GRSPRRRTPSPRRRRSQSPRRRRS\n",
            ">amp5_30_1094\n",
            "GVLSNVIGYLKKLGTGALNAVLKQ\n",
            ">amp5_30_1095\n",
            "GWLPTFGKILRKAMQLGPKLIQPI\n",
            ">amp5_30_1096\n",
            "IDPFVAGVAAEMMQHVYCAASKKC\n",
            ">amp5_30_1097\n",
            "IIGLVSKGTCVLVKTVCKKVLKQG\n",
            ">amp5_30_1098\n",
            "IIPFVAGVAAEMMEHVYCAASKKC\n",
            ">amp5_30_1099\n",
            "ILPFVAGVAAEMMEHVYCAASKKC\n",
            ">amp5_30_1100\n",
            "ILPFVAGVAAEMMKHVYCAASKKC\n",
            ">amp5_30_1101\n",
            "ILPFVAGVAAEMMQHVYCAASKKC\n",
            ">amp5_30_1102\n",
            "ILPFVAGVAAMEMEHVYCAASKKC\n",
            ">amp5_30_1103\n",
            "INPFVAGVAAEMMQHVYCAASKKC\n",
            ">amp5_30_1104\n",
            "IPWKLPATFRPVERPFSKPFCRKD\n",
            ">amp5_30_1105\n",
            "ITIPPIVKNTLKKFIKGAVSALMS\n",
            ">amp5_30_1106\n",
            "ITSVSWCTPGCTSEGGGSGCSHCC\n",
            ">amp5_30_1107\n",
            "KLGFENFLVKALKTVMHVPTSPLL\n",
            ">amp5_30_1108\n",
            "KRKCPKTPFDNTPGAWFAHLILGC\n",
            ">amp5_30_1109\n",
            "LFGFLIPLLPHIIGAIPQVIGAIR\n",
            ">amp5_30_1110\n",
            "LFGFLIPLLPHLIGAIPQVIGAIR\n",
            ">amp5_30_1111\n",
            "LLGDLLGQTSKLVNDLTDTVGSIV\n",
            ">amp5_30_1112\n",
            "LLKELWTKIKGAGKAVLGKIKGLL\n",
            ">amp5_30_1113\n",
            "LLKELWTKMKGAGKAVLGKIKGLL\n",
            ">amp5_30_1114\n",
            "LLSVLGSVAKHVLPHVVPVIAEHL\n",
            ">amp5_30_1115\n",
            "LVSSIGRALGGLLADVVKSKGQPA\n",
            ">amp5_30_1116\n",
            "MFFSSKKCKTVSKTFRGPCVRNAN\n",
            ">amp5_30_1117\n",
            "NCIQQCVSKGAQGGYCTNEKCTCY\n",
            ">amp5_30_1118\n",
            "QLPFVAGVACEMCQCVYCAASKKC\n",
            ">amp5_30_1119\n",
            "RILSILRHQNLLKELQDLALQGAK\n",
            ">amp5_30_1120\n",
            "RKGWFKAMKSIAKFIAKEKLKEHL\n",
            ">amp5_30_1121\n",
            "RRWVRRVRRWVRRVVRVVRRWVRR\n",
            ">amp5_30_1122\n",
            "SLKDKVKSMGEKLKQYIQTWKAKF\n",
            ">amp5_30_1123\n",
            "SPAGCRFCCGCCPNMRGCGVCCRF\n",
            ">amp5_30_1124\n",
            "VIPFVASVAAEMMHHVYCAASKRC\n",
            ">amp5_30_1125\n",
            "VIPFVASVAAEMMPHVYCAASRKC\n",
            ">amp5_30_1126\n",
            "VIPFVASVAAEMMQHVYCAASKKC\n",
            ">amp5_30_1127\n",
            "VIVFVASVAAEMMQHVYCAASKKC\n",
            ">amp5_30_1128\n",
            "VTSWSLCTPGCTSPGGGSNCSFCC\n",
            ">amp5_30_1129\n",
            "VVDILKGAAKDLAGHLASKVMNKI\n",
            ">amp5_30_1130\n",
            "WRSLGRTLLRLSHALKPLARRSGW\n",
            ">amp5_30_1131\n",
            "ADPTFGFTPLGLSEKANLQIMKAYD\n",
            ">amp5_30_1132\n",
            "APAHRSSTFPKWVTKTERGRQPLRS\n",
            ">amp5_30_1133\n",
            "APAKCTPYCYPTHDGVFCGVRCDFQ\n",
            ">amp5_30_1134\n",
            "ATRVVYCNRRSGSVVGGDDTVYYEG\n",
            ">amp5_30_1135\n",
            "DSHAKRHHGYKRKFHEKHHSHRGYR\n",
            ">amp5_30_1136\n",
            "DTHFPICIFCCGCCHRSKCGMCCKT\n",
            ">amp5_30_1137\n",
            "FFGSLLSLGSKLLPSVFKLFQRKKE\n",
            ">amp5_30_1138\n",
            "FFGWLIKGAIHAGKAIHGLIHRRRH\n",
            ">amp5_30_1139\n",
            "FFGWLIRGAIHAGKAIHGLIHRRRH\n",
            ">amp5_30_1140\n",
            "FFPNVASVPGQVLLKKIFCAISKKC\n",
            ">amp5_30_1141\n",
            "FIHHIIGGLFSAGKAIHRLIRRRRR\n",
            ">amp5_30_1142\n",
            "FKCRRWQWRMKKLGAPSITCVRRAF\n",
            ">amp5_30_1143\n",
            "FKIPPIVKDTLKKFFKGGIAGVMGQ\n",
            ">amp5_30_1144\n",
            "FLGLIFHGLVHAGKLIHGLIHRNRG\n",
            ">amp5_30_1145\n",
            "GAWKNFWSSLRKGFYDGEAGRAIRR\n",
            ">amp5_30_1146\n",
            "GCWSTVLGGLKKFAKGGLEAIVNPK\n",
            ">amp5_30_1147\n",
            "GFMSKVANFAKKFAKGGVNAIMNQK\n",
            ">amp5_30_1148\n",
            "GFWTTAAEGLKKFAKAGLASILNPK\n",
            ">amp5_30_1149\n",
            "GGGVIQTISHECRMNSWQFLFTCCS\n",
            ">amp5_30_1150\n",
            "GIFSSRKCKTVSKTFRGICTRNANC\n",
            ">amp5_30_1151\n",
            "GIGAAILSAGKSALKGLAKGLAEHF\n",
            ">amp5_30_1152\n",
            "GIGAAILSAGKSIIKGLANGLAEHF\n",
            ">amp5_30_1153\n",
            "GIGGALLSAGKSALKGLAKGLAEHF\n",
            ">amp5_30_1154\n",
            "GIGGALLSAGKSALKGLAKGLAEHL\n",
            ">amp5_30_1155\n",
            "GIGGALLSVGKSALKGLTKGLAEHF\n",
            ">amp5_30_1156\n",
            "GILKKFMLHRGTKVYKMRTLSKRSH\n",
            ">amp5_30_1157\n",
            "GLASTLGSFLGKFAKGGAQAFLQPK\n",
            ">amp5_30_1158\n",
            "GLFGILGSVAKHVLPHVIPVVAEHL\n",
            ">amp5_30_1159\n",
            "GLFGILGSVAKHVLPHVVPVIAEHS\n",
            ">amp5_30_1160\n",
            "GLFGVLGSIAKHVLPHVVPVIAEKL\n",
            ">amp5_30_1161\n",
            "GLFKVLGSVAKHLLPHVAPIIAEKL\n",
            ">amp5_30_1162\n",
            "GLFKVLGSVAKHLLPHVAPVIAEKL\n",
            ">amp5_30_1163\n",
            "GLFSVLGAVAKHVLPHVVPVIAEKL\n",
            ">amp5_30_1164\n",
            "GLFSVLGSVAKHLLPHVAPIIAEKL\n",
            ">amp5_30_1165\n",
            "GLFSVLGSVAKHLLPHVVPVIAEKL\n",
            ">amp5_30_1166\n",
            "GLLDTLKGAAKNVVGSLASKVMEKL\n",
            ">amp5_30_1167\n",
            "GLLGAMFKVASKVLPHVVPAITEHF\n",
            ">amp5_30_1168\n",
            "GLLGVLGSVAKHVLPHVVPVIAEHL\n",
            ">amp5_30_1169\n",
            "GLLSNVAGLLKQFAKGGVNAVLNPK\n",
            ">amp5_30_1170\n",
            "GLLSSLSSVAKHVLPHVVPVIAEHL\n",
            ">amp5_30_1171\n",
            "GLLSVLGSVAKHVLPHVVPVIAEHL\n",
            ">amp5_30_1172\n",
            "GLLSVLGSVAKHVLPHVVPVIAEKL\n",
            ">amp5_30_1173\n",
            "GLLSVLGSVAQHVLPHVVPVIAEHL\n",
            ">amp5_30_1174\n",
            "GLLSVLGSVVKHVIPHVVPVIAEHL\n",
            ">amp5_30_1175\n",
            "GLMSSIGKALGGLIVDVLKPKTPAS\n",
            ">amp5_30_1176\n",
            "GLVASIGRALGGLLADVVKSKEQPA\n",
            ">amp5_30_1177\n",
            "GLVSSIGKALGGLLADVVKTKEQPA\n",
            ">amp5_30_1178\n",
            "GLVSSIGKALGGLLVDVVKSKGQPA\n",
            ">amp5_30_1179\n",
            "GLVSSIGKVLGGLLADVVKSKGQPA\n",
            ">amp5_30_1180\n",
            "GLVSSIGRALGGLLADVVKSKEQPA\n",
            ">amp5_30_1181\n",
            "GLVSSIGRALGGLLADVVKSKGQPA\n",
            ">amp5_30_1182\n",
            "GLWSKIKEAAKTAGLMAMGFVNDMV\n",
            ">amp5_30_1183\n",
            "GLWSTIKNVGKEAAIAAGKAALGAL\n",
            ">amp5_30_1184\n",
            "GLWSTIKNVGKEAAIAAGKAVLGSL\n",
            ">amp5_30_1185\n",
            "GSGVIPTISHECHMNSFQFVFTCCS\n",
            ">amp5_30_1186\n",
            "GVLDILKGAAKDLAGHVATKVINKI\n",
            ">amp5_30_1187\n",
            "GVLDILKGAGKDLLAHALSKISEKV\n",
            ">amp5_30_1188\n",
            "GVLDILTGAGKDLLAHALSKLSEKV\n",
            ">amp5_30_1189\n",
            "GVVDILKGAAKDIAGHLASKVMNKL\n",
            ">amp5_30_1190\n",
            "GVVDILKGAAKDLAGHLATKVMNKL\n",
            ">amp5_30_1191\n",
            "GVVDILKGAGKDLLAHALSKLSEKV\n",
            ">amp5_30_1192\n",
            "GVVDILKGAGKDLLAHLVGKISEKV\n",
            ">amp5_30_1193\n",
            "GVWSTILGGLKKFAKGGLDAIVNPK\n",
            ">amp5_30_1194\n",
            "GVWSTVLGGLKKFAKGGLEAIVNPK\n",
            ">amp5_30_1195\n",
            "GVWTTILGGLKKFAKGGLEALTNPK\n",
            ">amp5_30_1196\n",
            "GWASKIGQTLGKIAKVGLKELIQPK\n",
            ">amp5_30_1197\n",
            "GWASSIGSILGKFAKGGAQAFLQPK\n",
            ">amp5_30_1198\n",
            "GWGSFFKKAAHVGKHVGKAALTHYL\n",
            ">amp5_30_1199\n",
            "GWGSIFKHGRHAAKHIGHAAVNHYL\n",
            ">amp5_30_1200\n",
            "GWKKWFNRAKKVGKTVGGLAVDHYL\n",
            ">amp5_30_1201\n",
            "GWKSVFRKAKKVGKTVGGLALDHYL\n",
            ">amp5_30_1202\n",
            "HVDKKVADKVLLLKQLRIMRLLTRL\n",
            ">amp5_30_1203\n",
            "ISCQDVKQSLAPCLPYVTGRAPKPA\n",
            ">amp5_30_1204\n",
            "ITIPPIVKDTLKKFFKGGIAGVMGQ\n",
            ">amp5_30_1205\n",
            "IWLTALKFLGKHAAKHLAKQQLSKL\n",
            ">amp5_30_1206\n",
            "KLCGETCFKFKCYTPGCSCSYPFCK\n",
            ">amp5_30_1207\n",
            "KRRGSVTTRYQFLMIHLLRPKKLFA\n",
            ">amp5_30_1208\n",
            "LDVKKIICVACKIKPNPACKKICPK\n",
            ">amp5_30_1209\n",
            "LDVKKIICVACKIRPNPACKKICPK\n",
            ">amp5_30_1210\n",
            "LFGFLIKLIPSLFGALSNIGRNRNQ\n",
            ">amp5_30_1211\n",
            "LSCKRGTCHFGRCPSHLIKGSCSGG\n",
            ">amp5_30_1212\n",
            "MAADIISTIGDLVKLIINTVKKFQK\n",
            ">amp5_30_1213\n",
            "NLVSGLIEARKYLEQLHRKLKNCKV\n",
            ">amp5_30_1214\n",
            "NWRKILGQIASVGAGLLGSLLAGYE\n",
            ">amp5_30_1215\n",
            "PNPKVFFDMTIGGQSAGRIVMEEYA\n",
            ">amp5_30_1216\n",
            "QSERFEQQMQGQDFSHDERFLSQAA\n",
            ">amp5_30_1217\n",
            "RSVCRQIKICRRRGGCYYKCTNRPY\n",
            ">amp5_30_1218\n",
            "RWGKWFKKATHVGKHVGKAALTAYL\n",
            ">amp5_30_1219\n",
            "SMISVLKNLGKVGLGFVACKVNKQC\n",
            ">amp5_30_1220\n",
            "SMLSVLKNLGKVGLGFVACKINKQC\n",
            ">amp5_30_1221\n",
            "SMLSVLKNLGKVGLGLVACKINKQC\n",
            ">amp5_30_1222\n",
            "TLQPPSALRRRHYHHALPPSRHYPG\n",
            ">amp5_30_1223\n",
            "WLGSALKIGAKLLPSVVGLFKKKKQ\n",
            ">amp5_30_1224\n",
            "WLGSALKIGAKLLPSVVGLFQKKKK\n",
            ">amp5_30_1225\n",
            "ACHAHCQSVGRRGGYCGNFRMTCYCY\n",
            ">amp5_30_1226\n",
            "AGWGSIFKHIFKAGKFIHGAIQAHND\n",
            ">amp5_30_1227\n",
            "AKAWGIPPHVIPQIVPVRIRPLCGNV\n",
            ">amp5_30_1228\n",
            "FIPGLRRLFATVVPTVVCAINKLPPG\n",
            ">amp5_30_1229\n",
            "FWGALAKGALKLIGPGSLFSSFSKKD\n",
            ">amp5_30_1230\n",
            "FWGHIWNAVKRVGANALHGAVTGALS\n",
            ">amp5_30_1231\n",
            "GFLGILFHGVHHGRKKALHMNSERRS\n",
            ">amp5_30_1232\n",
            "GIGAVLKVLTTGLPALISWIKRKRQQ\n",
            ">amp5_30_1233\n",
            "GIWGTALKWGVKLLPKLVGMAQTKKQ\n",
            ">amp5_30_1234\n",
            "GIWGTLAKIGIKAVPRVISMLKKKKQ\n",
            ">amp5_30_1235\n",
            "GKNGVFKTISHECHLNTWAFLATCCS\n",
            ">amp5_30_1236\n",
            "GLFGKLIKKFGRKAISYAVKKARGKH\n",
            ">amp5_30_1237\n",
            "GLFSVLGSVAKHVVPRVVPVIAEHLG\n",
            ">amp5_30_1238\n",
            "GLIGSIGKALGGLLVDVLKPKLQAAS\n",
            ">amp5_30_1239\n",
            "GLLQTIKEKLESLESLAKGIVSGIQA\n",
            ">amp5_30_1240\n",
            "GLVSGLLNTAGGLLGDLLGSLGSLSG\n",
            ">amp5_30_1241\n",
            "GLVTSLIKGAGKLLGGLFGSVTGGQS\n",
            ">amp5_30_1242\n",
            "GLWNKIKEAASKAAGKAALGFVNEMV\n",
            ">amp5_30_1243\n",
            "GMFTNMLKGIGKLAGQAALGAVKTLA\n",
            ">amp5_30_1244\n",
            "GRRKRKWLRRIGKGVKIIGGAALDHL\n",
            ">amp5_30_1245\n",
            "HRHQGPIFDTRPSPFNPNQPRPGPIY\n",
            ">amp5_30_1246\n",
            "KKSGVIPTVSHDCHMNTFQFMFTCCS\n",
            ">amp5_30_1247\n",
            "KRFGRLAKSFLRMRILLPRRKILLAS\n",
            ">amp5_30_1248\n",
            "KSSAYSLQMGATAIKQVKKLFKKWGW\n",
            ">amp5_30_1249\n",
            "KTHYPTNAWKSLWKGFWESLRYTDGF\n",
            ">amp5_30_1250\n",
            "KWKLFKKIGIGAVLKVLTTGLPALIS\n",
            ">amp5_30_1251\n",
            "NKLAYNMGHYAGKATIFGLAAWALLA\n",
            ">amp5_30_1252\n",
            "NWRKILGKIAKVAAGLLGSMLAGYQV\n",
            ">amp5_30_1253\n",
            "QCVGTITLDQSDDLFDLNCNELQSVR\n",
            ">amp5_30_1254\n",
            "RFRLPFRRPPIRIHPPPFYPPFRRFL\n",
            ">amp5_30_1255\n",
            "RIKRFWPVVIRTVVAGYNLYRAIKKK\n",
            ">amp5_30_1256\n",
            "RILTMTKRVKMPQLYKQIVCRLFKTC\n",
            ">amp5_30_1257\n",
            "RVKRVWPLVIRTVIAGYNLYRAIKKK\n",
            ">amp5_30_1258\n",
            "SIITMTKEAKLPQLWKQIACRLYNTC\n",
            ">amp5_30_1259\n",
            "SIITMTKEAKLPQSWKQIACRLYNTC\n",
            ">amp5_30_1260\n",
            "SMWSGMWRRKLKKLRNALKKKLKGEK\n",
            ">amp5_30_1261\n",
            "VIDDLKKVAKKVRRELLCKKHHKKLN\n",
            ">amp5_30_1262\n",
            "ADDKNPLEEAFREADYEVFLEIAKNGL\n",
            ">amp5_30_1263\n",
            "AGETHTVMINHAGRGAPKLVVGGKKLS\n",
            ">amp5_30_1264\n",
            "ALSILKGLEKLAKMGIALTNCKATKKC\n",
            ">amp5_30_1265\n",
            "ALSILRGLEKLAKMGIALTNCKATKKC\n",
            ">amp5_30_1266\n",
            "ALWMTLLKKVLKAAAKALNAVLVGANA\n",
            ">amp5_30_1267\n",
            "GFGSFLGKALKAALKIGADVLGGAPQQ\n",
            ">amp5_30_1268\n",
            "GFGSFLGKALKAALKIGANVLGGAPEQ\n",
            ">amp5_30_1269\n",
            "GFGSFLGKALKAALKIGANVLGGAPQQ\n",
            ">amp5_30_1270\n",
            "GFLGPLLKLAAKGVAKVIPHLIPSRQQ\n",
            ">amp5_30_1271\n",
            "GFLGPLLKLGLKGAAKLLPQLLPSRQQ\n",
            ">amp5_30_1272\n",
            "GFLGPLLKLGLKGVAKVIPHLIPSRQQ\n",
            ">amp5_30_1273\n",
            "GGLRSLGRKILRAWKKYGPQATPATRQ\n",
            ">amp5_30_1274\n",
            "GIFPIFAKLLGKVIKVASSLISKGRTE\n",
            ">amp5_30_1275\n",
            "GIGASILSAGKSALKGFAKGLAEHFAN\n",
            ">amp5_30_1276\n",
            "GIGASILSAGKSALKGLAKGLAEHFAN\n",
            ">amp5_30_1277\n",
            "GIGGALLSAAKVGLKGLAKGLAEHFAN\n",
            ">amp5_30_1278\n",
            "GIGGALLSAGKSALKGLAKGLAEHFAN\n",
            ">amp5_30_1279\n",
            "GIGGALLSVGKLALKGLANVLADKFAN\n",
            ">amp5_30_1280\n",
            "GIGGKILGGLKTALKGAAKELASTYLH\n",
            ">amp5_30_1281\n",
            "GIGGKILGGLRTALKGAAKELAATYLH\n",
            ">amp5_30_1282\n",
            "GIGGKILSGLKTALKGAAKELASTYLH\n",
            ">amp5_30_1283\n",
            "GIGGVLLGAGKATLKGLAKVLAEKYAN\n",
            ">amp5_30_1284\n",
            "GIGGVLLSAGKAALKGLAKVLAEKYAN\n",
            ">amp5_30_1285\n",
            "GIGGVLLSAGKAALKGLTKVLAEKYAN\n",
            ">amp5_30_1286\n",
            "GIGRKFLGGVKTTFRCGVKDFASKHLY\n",
            ">amp5_30_1287\n",
            "GIGSAILSAGKSALKGLAKGLAEHFAN\n",
            ">amp5_30_1288\n",
            "GIGTKFLGGVKTALKGALKELAFTYVN\n",
            ">amp5_30_1289\n",
            "GIGTKFLGGVKTALKGALKELASTYVN\n",
            ">amp5_30_1290\n",
            "GIGTKILGGVKAALKGALKELASTYVN\n",
            ">amp5_30_1291\n",
            "GIGTKILGGVKTALKGALKELASTYAN\n",
            ">amp5_30_1292\n",
            "GIGTKILGGVKTALKGALKELASTYVN\n",
            ">amp5_30_1293\n",
            "GIMDTVKNAAKDLAGQLDKLKCRITGC\n",
            ">amp5_30_1294\n",
            "GLASFLGKALKAGLKIGSHLLGGAPQQ\n",
            ">amp5_30_1295\n",
            "GLFLDTLKGLAGKLLQGLKCIKAGCKP\n",
            ">amp5_30_1296\n",
            "GLPRKILCAIAKKKGKCKGPLKLVCKC\n",
            ">amp5_30_1297\n",
            "GMWGSLLKGVATVVKHVLPHALSSQQS\n",
            ">amp5_30_1298\n",
            "GRFKRFRKKFKKLFKKLSPVIPLLHLG\n",
            ">amp5_30_1299\n",
            "GVFGLLAKAALKGASKLIPHLLPSRQQ\n",
            ">amp5_30_1300\n",
            "IDWKKVDWKKVSKKTCKVMLKACKFLG\n",
            ">amp5_30_1301\n",
            "ITIPPIIKDTLKKFFKGGIAGVMGKSQ\n",
            ">amp5_30_1302\n",
            "ITIPPIVKDTLKKFFKGGIAGVMGKSQ\n",
            ">amp5_30_1303\n",
            "KGGSGVIHTISHECNMNSWQFVFTCCS\n",
            ">amp5_30_1304\n",
            "KGGSGVIHTISHEVIYNSWNFVFTCCS\n",
            ">amp5_30_1305\n",
            "KIKWFKTMKSIAKFIAKEQMKKHLGGE\n",
            ">amp5_30_1306\n",
            "KKKSGVIPTVSHDCHMNSFQFVFTCCS\n",
            ">amp5_30_1307\n",
            "KRRHWFPLSFQEFLEQLRRFRDQLPFP\n",
            ">amp5_30_1308\n",
            "KSKEKIGKEFKRIVQRIKDFLRNLVPR\n",
            ">amp5_30_1309\n",
            "LMDTVKNVAKNLAGHMLDKLKCKITGC\n",
            ">amp5_30_1310\n",
            "MRKEFHNVLSSGQLLADKRPARDYNRK\n",
            ">amp5_30_1311\n",
            "NRWWQGVVPTVSYECRMNSWQHVFTCC\n",
            ">amp5_30_1312\n",
            "QKLCERPSGTWSGVCGNNNACKNQCIN\n",
            ">amp5_30_1313\n",
            "RDWERREFERRQNELRREQEQRREELL\n",
            ">amp5_30_1314\n",
            "RFGRFLRKIRRFRPKVTITIQGSARFG\n",
            ">amp5_30_1315\n",
            "RKFHEKHHSHREFPFYGDYGSNYLYDN\n",
            ">amp5_30_1316\n",
            "SIGAKILGGVKTFFKGALKELAFTYLQ\n",
            ">amp5_30_1317\n",
            "SIGAKILGGVKTFFKGALKELASTYLQ\n",
            ">amp5_30_1318\n",
            "SLFGTFAKMALKGASKLIPHLLPSRQQ\n",
            ">amp5_30_1319\n",
            "SLWSKIKEMAATAGKAALNAVTGMVNQ\n",
            ">amp5_30_1320\n",
            "STIVCVSLRICNWSLRFCPSFKVRCPM\n",
            ">amp5_30_1321\n",
            "TPGGIDFISGGPHVAQDVLNAIKNFFK\n",
            ">amp5_30_1322\n",
            "VRDICKKEAERQDLSSCENYITQRRGY\n",
            ">amp5_30_1323\n",
            "AIMDTIKDTAKTVAVGLLNKLKCKITGC\n",
            ">amp5_30_1324\n",
            "ALFSILRGLKKLGKMGQAFVNCEIYKKC\n",
            ">amp5_30_1325\n",
            "ALFSILRGLKKLGNMGQAFVNCKIYKKC\n",
            ">amp5_30_1326\n",
            "ALLDKLKSLGKVVGKVAIGVAQHYLNPQ\n",
            ">amp5_30_1327\n",
            "ALWKDILKNVGKAAGKAVLNTVTDMVNQ\n",
            ">amp5_30_1328\n",
            "ALWKNMLKGIGKLAGQAALGAVKTLVGA\n",
            ">amp5_30_1329\n",
            "ALWKTLLKNVGKAAGKAALNAVTDMVNQ\n",
            ">amp5_30_1330\n",
            "ATAVDFGPHGLLPIRPIRIRPLCGKDKS\n",
            ">amp5_30_1331\n",
            "AWLDKLKSIGKVVGKVAIGVAKNLLNPQ\n",
            ">amp5_30_1332\n",
            "CAWYNISCRLGNKGAYCTLTVECMPSCN\n",
            ">amp5_30_1333\n",
            "GFFGKMKEYFKKFGASFKRRFANLKKRL\n",
            ">amp5_30_1334\n",
            "GGLRSLGRKILRAWKKYGPIIVPIIRIG\n",
            ">amp5_30_1335\n",
            "GIACGESCVFLGCFIPGCSCKSKVCYFN\n",
            ">amp5_30_1336\n",
            "GILDTIKNAAKTVAVGLLEKIKCKMTGC\n",
            ">amp5_30_1337\n",
            "GIMDSVKGLAKNLAGKLLDSLKCKITGC\n",
            ">amp5_30_1338\n",
            "GIMDSVKGVAKNLAAKLLEKLKCKITGC\n",
            ">amp5_30_1339\n",
            "GIMDSVKNAAKNLAGQLLDTIKCKITAC\n",
            ">amp5_30_1340\n",
            "GIMDSVKNVAKNIAGQLLDKLKCKITGC\n",
            ">amp5_30_1341\n",
            "GIMDTIKDTAKTVAVGLLNKLKCKITGC\n",
            ">amp5_30_1342\n",
            "GIMDTVKGVAKTVAASLLDKLKCKITGC\n",
            ">amp5_30_1343\n",
            "GIMDTVKNAAKDLAGQLLDKLKCRITGC\n",
            ">amp5_30_1344\n",
            "GIMDTVKNVAKNLAGQLLDKLKCKITAC\n",
            ">amp5_30_1345\n",
            "GKQYFPKVGGRLSGKAPLAAKTHRRLKP\n",
            ">amp5_30_1346\n",
            "GLGSLLGKAFKIGLKTVGKMMGGAPREQ\n",
            ">amp5_30_1347\n",
            "GLKDWWNKHKDKIIAVAKEMGKAGLQAA\n",
            ">amp5_30_1348\n",
            "GLKDWWNKHKDKIIDVVKEMGKAGLQAA\n",
            ">amp5_30_1349\n",
            "GLKDWWNKHKDKIVEVVKDSGKAGLNAA\n",
            ">amp5_30_1350\n",
            "GLKDWWNKHKDKIVEVVKEMGKAGLNAA\n",
            ">amp5_30_1351\n",
            "GLLDAIKDTAQNLFANVLDKIKCKFTKC\n",
            ">amp5_30_1352\n",
            "GLLDTIKGVAKTVAASMLDKLKCKISGC\n",
            ">amp5_30_1353\n",
            "GLLDTIKNTAKNLAVGLLDKIKCKMTGC\n",
            ">amp5_30_1354\n",
            "GLLDVVKGAAKNLLASALDKLKCKVTGC\n",
            ">amp5_30_1355\n",
            "GLMDMVKGAAKNLFASALDTLKCKITGC\n",
            ">amp5_30_1356\n",
            "GLMDTIKGVAKNVAASLLEKLKCKVTGC\n",
            ">amp5_30_1357\n",
            "GLMDTIKGVAKTVAASWLDKLKCKITGC\n",
            ">amp5_30_1358\n",
            "GLMDTVKNAAKNLAGQLLDRLKCKITGC\n",
            ">amp5_30_1359\n",
            "GLMDTVKNAAKNLAGQLLDTIKCKMTGC\n",
            ">amp5_30_1360\n",
            "GLMDTVKNVAKNLAGHMLDKLKCKITGC\n",
            ">amp5_30_1361\n",
            "GLMDVFKGAAKNLLASALDKIRCKVTKC\n",
            ">amp5_30_1362\n",
            "GLWSKIKDVAAAAGKAALGAVNEALGEQ\n",
            ">amp5_30_1363\n",
            "GLWSTIKNVGKEAAIAAGKAVLGSLGEQ\n",
            ">amp5_30_1364\n",
            "GTACGESCYVLPCFTVGCTCTSSQCFKN\n",
            ">amp5_30_1365\n",
            "GYGGHGGHGGHGGHGGHGGHGHGGGGHG\n",
            ">amp5_30_1366\n",
            "HSDAVFTDNYTRLRKQMAVKKYLNSILN\n",
            ">amp5_30_1367\n",
            "IKIPAFVKDTLKKVAKGVISAVAGALTQ\n",
            ">amp5_30_1368\n",
            "IKIPAVVKDTLKKVAKGVLSAVAGALTQ\n",
            ">amp5_30_1369\n",
            "IKIPPIVKDTLKKVAKGVLSTIAGALST\n",
            ">amp5_30_1370\n",
            "IPCGESCVWIPCISGMFGCSCKDKVCYS\n",
            ">amp5_30_1371\n",
            "IPCGESCVWIPCITAIAGCSCKNKVCYT\n",
            ">amp5_30_1372\n",
            "LTTKLWSSWGYYLGKKARWNLKHPYVQF\n",
            ">amp5_30_1373\n",
            "NPQSCRWNMGVCIPISCPGNMRQIGTCS\n",
            ">amp5_30_1374\n",
            "QLPICGETCVLGGCYTPNCRCQYPICVR\n",
            ">amp5_30_1375\n",
            "WWRELLKKLAFTAAGHLGSVLAAKQSGW\n",
            ">amp5_30_1376\n",
            "AIPCGESCVWIPCISAAIGCSCKNKVCYR\n",
            ">amp5_30_1377\n",
            "AIPCGESCVWIPCISTVIGCSCSNKVCYR\n",
            ">amp5_30_1378\n",
            "ALLDKLKSLGKVVGKVAIGVAQHYLNPQQ\n",
            ">amp5_30_1379\n",
            "ALLDKLKSLGKVVGKVALGVAQHYLNPQQ\n",
            ">amp5_30_1380\n",
            "ALLDKLKSLGKVVGKVALGVVQNYLNPRQ\n",
            ">amp5_30_1381\n",
            "ALWKNMLKGIGKLAGQAALGAVKTLVGAE\n",
            ">amp5_30_1382\n",
            "AWLDKLKNLGKVVGKVALGVVQNYLNPRQ\n",
            ">amp5_30_1383\n",
            "AWLDKLKSLGKVVGKVAIGVAQHYLNPQQ\n",
            ">amp5_30_1384\n",
            "AWLDKLKSLGKVVGKVALGVAQNYLNPQQ\n",
            ">amp5_30_1385\n",
            "AWLDKLKSLGKVVGKVGLGVVQNYLNPRQ\n",
            ">amp5_30_1386\n",
            "CSCRTSSCRFGERLSGACRLNGRIYRLCC\n",
            ">amp5_30_1387\n",
            "CYCRIPACIAGERRYGTCIYQGRLWAFCC\n",
            ">amp5_30_1388\n",
            "DFKDWMKTAGEWLKKKGPGILKAAMAAAT\n",
            ">amp5_30_1389\n",
            "FAEPLPSEEEGESYSKEPPEMEKRYGGFM\n",
            ">amp5_30_1390\n",
            "FAEPLPSEEEGESYSKEVPEMEKRYGGFM\n",
            ">amp5_30_1391\n",
            "FLSFPTTKTYFPHFDLSHGSAQVKGHGAK\n",
            ">amp5_30_1392\n",
            "GASCGETCFTGICFTAGCSCNPWPTCTRN\n",
            ">amp5_30_1393\n",
            "GDACGETCFTGICFTAGCSCNPWPTCTRN\n",
            ">amp5_30_1394\n",
            "GEYCGESCYLIPCFTPGCYCVSRQCVNKN\n",
            ">amp5_30_1395\n",
            "GFISTVKNLATNVAGTVIDTIKCKVTGGC\n",
            ">amp5_30_1396\n",
            "GFLSTVKNLATNVAGTVIDTIKCKVTGGC\n",
            ">amp5_30_1397\n",
            "GFMATAKNVAKNMDVTLLDNLKCKITKAC\n",
            ">amp5_30_1398\n",
            "GFMDTAKNVAKNEAGNLLDNLKCKITKAC\n",
            ">amp5_30_1399\n",
            "GFMDTAKNVAKNMAGNLLDNLKCKITKAC\n",
            ">amp5_30_1400\n",
            "GFMDTAKNVAKNMAVTLLDNLKCKITKAC\n",
            ">amp5_30_1401\n",
            "GFMDTAKNVAKNVAATLLDKLKCKITGGC\n",
            ">amp5_30_1402\n",
            "GFMDTAKNVAKNVAVTLIDKLRCKVTGGC\n",
            ">amp5_30_1403\n",
            "GFMDTAKNVAKNVAVTLIDNLKCKITKAC\n",
            ">amp5_30_1404\n",
            "GFMDTAKNVAKNVAVTLLDKLKCKITGGC\n",
            ">amp5_30_1405\n",
            "GFMDTAKQVAKNVAVTLIDKLRCKVTGGC\n",
            ">amp5_30_1406\n",
            "GFMNTAKNVAKNVAVTLLDNLKCKITGGC\n",
            ">amp5_30_1407\n",
            "GIFDKLKNFAKGVAQSLLNKASCKLSGQC\n",
            ">amp5_30_1408\n",
            "GILDSLKNLAKNAGQILLNKASCKLSGQC\n",
            ">amp5_30_1409\n",
            "GLPICGETCVGGTCNTPGCSCSWPVCTRN\n",
            ">amp5_30_1410\n",
            "GLPTCGETCTLGTCYVPDCSCSWPICMKN\n",
            ">amp5_30_1411\n",
            "GLPVCGETCAGGTCNTPGCSCSWPICTRN\n",
            ">amp5_30_1412\n",
            "GLPVCGETCFGGTCNTPGCSCTWPICTRD\n",
            ">amp5_30_1413\n",
            "GLPVCGETCVGGTCNTPGCACSWPVCTRN\n",
            ">amp5_30_1414\n",
            "GLPVCGETCVGGTCNTPGCGCSWPVCTRN\n",
            ">amp5_30_1415\n",
            "GLPVCGETCVGGTCNTPGCSCSWPVCTRN\n",
            ">amp5_30_1416\n",
            "GLPVCGETCVGGTCNTPGCTCSWPVCTRN\n",
            ">amp5_30_1417\n",
            "GLWETIKNFGKKFTLNILHKLKCKIGGGC\n",
            ">amp5_30_1418\n",
            "GLWETIKNFGKKFTLNILHNLKCKIGGGC\n",
            ">amp5_30_1419\n",
            "GLWSKIKETGKEAAKAAGKAALNKIAEAV\n",
            ">amp5_30_1420\n",
            "GLWSKIKTAGKSVAKAAAKAAVKAVTNAV\n",
            ">amp5_30_1421\n",
            "GLWSTIKQKGKEAAIAAAKAAGQAALGAL\n",
            ">amp5_30_1422\n",
            "GVPICGETCTLGTCYTAGCSCSWPVCTRN\n",
            ">amp5_30_1423\n",
            "HNKQEGRDHDKSKGHFHRVVIHHKGGKAH\n",
            ">amp5_30_1424\n",
            "IKIPSFFRNILKKVGKEAVSLIAGALKQS\n",
            ">amp5_30_1425\n",
            "IKLSPETKDNLKKVLKGAIKGAIAVAKMV\n",
            ">amp5_30_1426\n",
            "IPAMEPAARVKRSPGYGGCSPRWACGGYG\n",
            ">amp5_30_1427\n",
            "IRCPKSWKCKAFKQRVLKRLLAMLRQHAF\n",
            ">amp5_30_1428\n",
            "KCKWWNISCDLGNNGHVCTLSHECQVSCN\n",
            ">amp5_30_1429\n",
            "KQEGRDHDKSKGHFHMIVIHHKGGQAHHG\n",
            ">amp5_30_1430\n",
            "KVCYRAIPCGESCVWIPCISAAIGCSCKN\n",
            ">amp5_30_1431\n",
            "LKIPGFVKDTLKKVAKGIFSAVAGAMTPS\n",
            ">amp5_30_1432\n",
            "LKLSPETKDTLKKVLKGAIKGAIAIASLA\n",
            ">amp5_30_1433\n",
            "LKLSPKTKDTLKKVLKGAIKGAIAIASMA\n",
            ">amp5_30_1434\n",
            "LLGDFFRKSKEKIGKEFKRIVQRIKDFLR\n",
            ">amp5_30_1435\n",
            "RGLRRLGRKIAHGVKKYGPTVLRIIRIAG\n",
            ">amp5_30_1436\n",
            "RKCNFLCKLKEKLRTVITSHIDKVLRPQG\n",
            ">amp5_30_1437\n",
            "RVKRFWPLVPVAINTVAAGINLYKAIRRK\n",
            ">amp5_30_1438\n",
            "RVRRFWPLVPVAINTVAAGINLYKAIRRK\n",
            ">amp5_30_1439\n",
            "SGTPEKERESGRLLGVVKRYIVCVRNPCP\n",
            ">amp5_30_1440\n",
            "SGTSEKERESERLLGVVNPLIKCFRSPCP\n",
            ">amp5_30_1441\n",
            "SGTSEKERESGRLLGVVKRLIVCFRSPFP\n",
            ">amp5_30_1442\n",
            "SGTSEKERESGRLLGVVKRLIVGFRSPFR\n",
            ">amp5_30_1443\n",
            "SIGSAFKKALPVAKKIGKAALPIAKAALP\n",
            ">amp5_30_1444\n",
            "SIGSALKKALPVAKKIGKIALPIAKAALP\n",
            ">amp5_30_1445\n",
            "TTPATPAISILSAYISTNTCPTTKCTRAC\n",
            ">amp5_30_1446\n",
            "VGECVRGRCPSGMCCSQFGYCGKGPKYCG\n",
            ">amp5_30_1447\n",
            "VLSKSLCTPGCITGPLQTCYLCFPTFAKC\n",
            ">amp5_30_1448\n",
            "VNHALCAAHCIARRYRGGYCNSKAVCVCR\n",
            ">amp5_30_1449\n",
            "VNHSACAAHCILRGKTGGRCNSNAVCVCR\n",
            ">amp5_30_1450\n",
            "WNHTLCAAHCIARRYRGGYCNSKAVCVCR\n",
            ">amp5_30_1451\n",
            "ACYCRIPACFAGERRYGTCFYLGRVWAFCC\n",
            ">amp5_30_1452\n",
            "ACYCRIPACIAGERRYGTCIYQGRLWAFCC\n",
            ">amp5_30_1453\n",
            "ACYCRIPACLAGERRYGTCFYLGRVWAFCC\n",
            ">amp5_30_1454\n",
            "ACYCRIPACLAGERRYGTCFYMGRVWAFCC\n",
            ">amp5_30_1455\n",
            "ACYCRIPACLAGERRYGTCFYRRRVWAFCC\n",
            ">amp5_30_1456\n",
            "AEVAPAPAAAAPAKAPKKKAAAKPKKAGPS\n",
            ">amp5_30_1457\n",
            "AGECVQGRCPSGMCCSQFGYCGRGPKYCGR\n",
            ">amp5_30_1458\n",
            "AISCGQVSSAIGPCLSYARGQGSAPSAGCC\n",
            ">amp5_30_1459\n",
            "AKEFGIPAAVAGTVLNVVEAGGWVTTIVSI\n",
            ">amp5_30_1460\n",
            "ALWKNMLKGIGKLAGKAALGAVKKLVGAES\n",
            ">amp5_30_1461\n",
            "ALWKNMLKGIGKLAGQAALGAVKTLVGAES\n",
            ">amp5_30_1462\n",
            "ALWKTIIKGAGKMIGSLAKNLLGSQAQPES\n",
            ">amp5_30_1463\n",
            "ANFEIVNNCPYTVWAAASPGGGRRLDRGQT\n",
            ">amp5_30_1464\n",
            "ASIIKTTIKVSKAVCKTLTCICTGSCSNCK\n",
            ">amp5_30_1465\n",
            "ASIVKTTIKASKKLCRGFTLTCGCHFTGKK\n",
            ">amp5_30_1466\n",
            "AYPGNGVHCGKYSCTVDKQTAIGNIGNNAA\n",
            ">amp5_30_1467\n",
            "CGESCVFIPCITSVAGCSCKSKVCYRNGIP\n",
            ">amp5_30_1468\n",
            "CSTNTFSLSDYWGNKGNWCTATHECMSWCK\n",
            ">amp5_30_1469\n",
            "CSTNTFSLSDYWGNNGAWCTLTHECMAWCK\n",
            ">amp5_30_1470\n",
            "DCYCRIPACIAGERRYGTCIYQGRLWAFCC\n",
            ">amp5_30_1471\n",
            "DSHEERRQGRHGHHEYGRKFHEKHHSHRGY\n",
            ">amp5_30_1472\n",
            "FNRGGYNFGKSVRHVVDAIGSVAGILKSIR\n",
            ">amp5_30_1473\n",
            "FVYGNGVTSILVQAQFLVNGQRRFFYTPDK\n",
            ">amp5_30_1474\n",
            "GAFGDLLKGVAKEAGLKLLNMAQCKLSGNC\n",
            ">amp5_30_1475\n",
            "GAFGDLLKGVAKEAGMKLLNMAQCKLSGKC\n",
            ">amp5_30_1476\n",
            "GAFGNFLKGVAKKAGLKILSIAQCKLFGTC\n",
            ">amp5_30_1477\n",
            "GAFGNFLKGVAKKAGLKILSIAQCKLSGTC\n",
            ">amp5_30_1478\n",
            "GAFGNLLKGVAKKAGLKILSIAQCKLSGTC\n",
            ">amp5_30_1479\n",
            "GAGSQEERMQGQMEGQDFSHEERFLSMVRE\n",
            ">amp5_30_1480\n",
            "GAIKDALKGAAKTVAVELLKKAQCKLEKTC\n",
            ">amp5_30_1481\n",
            "GEFLKCGESCVQGECYTPGCSCDWPICKKN\n",
            ">amp5_30_1482\n",
            "GFFDRIKALTKNVTLELLNTITCKLPVTPP\n",
            ">amp5_30_1483\n",
            "GFPCGESCVFIPCISAAIGCSCKNKVCYRN\n",
            ">amp5_30_1484\n",
            "GIFLDKLKNFAKGVAQSLLNKASCKLSGQC\n",
            ">amp5_30_1485\n",
            "GIPCAESCVWIPCTVTAIVGCSCSDKVCYN\n",
            ">amp5_30_1486\n",
            "GIPCAESCVWIPCTVTALIGCGCSNKVCYN\n",
            ">amp5_30_1487\n",
            "GIPCAESCVWIPCTVTALVGCSCSDKVCYN\n",
            ">amp5_30_1488\n",
            "GIPCGESCVFIPCISSVIGCSCSSKVCYRN\n",
            ">amp5_30_1489\n",
            "GIPCGESCVFIPCITAAIGCSCKSKVCYRN\n",
            ">amp5_30_1490\n",
            "GIPCGESCVFIPCITGAIGCSCKSKVCYRN\n",
            ">amp5_30_1491\n",
            "GIPCGESCVFIPCITSVAGCSCKSKVCYRN\n",
            ">amp5_30_1492\n",
            "GIPCGESCVFIPCLTSAIDCSCKSKVCYRN\n",
            ">amp5_30_1493\n",
            "GIPCGESCVFIPCLTTVAGCSCKNKVCYRN\n",
            ">amp5_30_1494\n",
            "GIPCGESCVWIPCISAAIGCSCKSKVCYRN\n",
            ">amp5_30_1495\n",
            "GIPCGESCVWIPCISAALGCSCKNKVCYRN\n",
            ">amp5_30_1496\n",
            "GIPCGESCVWIPCISSAIGCSCKSKVCYRN\n",
            ">amp5_30_1497\n",
            "GIRCPKSWKCKAFKQRVLKRLLAMLRQHAF\n",
            ">amp5_30_1498\n",
            "GKCNVLCQLKQKLRSIGSGSHIGSVVLPRG\n",
            ">amp5_30_1499\n",
            "GLKDWVKIAGGWLKKKGPGILKAAMAAATQ\n",
            ">amp5_30_1500\n",
            "GLLLDTVKGAAKNVAGILLNKLKCKMTGDC\n",
            ">amp5_30_1501\n",
            "GLPTCGETCFGGTCNTPGCTCDPWPVCTHN\n",
            ">amp5_30_1502\n",
            "GLPVCGETCFGGTCNTPGCTCDPWPVCTRN\n",
            ">amp5_30_1503\n",
            "GLPVCGETCFTGTCYTNGCTCDPWPVCTRN\n",
            ">amp5_30_1504\n",
            "GNAACVIGCIGSCVISEGIGSLVGTAFTLG\n",
            ">amp5_30_1505\n",
            "GSVIKCGESCLLGKCYTPGCTCSRPICKKD\n",
            ">amp5_30_1506\n",
            "GTRCGETCFVLPCWSAKFGCYCQKGFCYRN\n",
            ">amp5_30_1507\n",
            "GVITDALKGAAKTVAAELLRKAHCKLTNSC\n",
            ">amp5_30_1508\n",
            "GVPCGESCVFIPCITGVIGCSCSSNVCYLN\n",
            ">amp5_30_1509\n",
            "GWFKKAWRKVKNAGRRVLKGVGIHYGVGLI\n",
            ">amp5_30_1510\n",
            "GWFKKTFHKVSHAVKSGIHAGQRGCSALGF\n",
            ">amp5_30_1511\n",
            "GWKDWAKKAGGWLKKKGPGMAKAALKAAMQ\n",
            ">amp5_30_1512\n",
            "GWKDWLKKGKEWLKAKGPGIVKAALQAATQ\n",
            ">amp5_30_1513\n",
            "GWKDWLNKGKEWLKKKGPGIMKAALKAATQ\n",
            ">amp5_30_1514\n",
            "ILQKAVLDCLKAAGSSLSKAAITAIYNKIT\n",
            ">amp5_30_1515\n",
            "IRNSLTCRFNFGICLPKRCPGRMRQIGTCF\n",
            ">amp5_30_1516\n",
            "KFFRKLKKSVKKRAKEFFKKPRVIGVSIPF\n",
            ">amp5_30_1517\n",
            "KIPCGESCVWIPCLTSVFNCKCENKVCYHD\n",
            ">amp5_30_1518\n",
            "KIPCGESCVWIPCVTSIFNCKCENKVCYHD\n",
            ">amp5_30_1519\n",
            "KSKEKIGKEFKRIVQRIKDFLRNLVPRTES\n",
            ">amp5_30_1520\n",
            "MKTILRFVAGYDIASHKKKTGGYPWERGKA\n",
            ">amp5_30_1521\n",
            "PPCRGIFCRRVGSSSAIARPGKTLSTFITV\n",
            ">amp5_30_1522\n",
            "QDYCAQEGQQEVQRKDLSDLERYLRQSRQR\n",
            ">amp5_30_1523\n",
            "RCICTTRTCRFPYRRLGTCLFQNRVYTFCC\n",
            ">amp5_30_1524\n",
            "SLGPAIKATRQVCPKATRFVTVSCKKSDCQ\n",
            ">amp5_30_1525\n",
            "SLGSFMKGVGKGLATVGKIVADQFGKLLEA\n",
            ">amp5_30_1526\n",
            "SWFSRTVHNVGNAVRKGIHAGQGVCSGLGL\n",
            ">amp5_30_1527\n",
            "TLQPPSALRRRHYHHALPPSRHYPGREAQA\n",
            ">amp5_30_1528\n",
            "TLRGDERILSILRHQNLLKELQDLALQGAK\n",
            ">amp5_30_1529\n",
            "VGECVRGRCPSGMCCSQFGYCGKGPKYCGR\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In **amp5_30_1 ACSAG**:\n",
        "\n",
        "* **amp5_30_1** is the peptide's identification name. **amp** indicates that there is an antimicrobial activity, while **nonamp** denotes that there isn't.\n",
        "\n",
        "* **ACSAG** is the peptide sequence, where each letter denotes a single amino acid. The twenty common amino acids are depicted in the below figure, along with their acronyms and chemical structures."
      ],
      "metadata": {
        "id": "Nk_4AL5mUB6h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "![20-Common-Amino-Acids-v3.webp](data:image/webp;base64,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)"
      ],
      "metadata": {
        "id": "d73pqst2WcUY"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Source: https://www.compoundchem.com/2014/09/16/aminoacids/"
      ],
      "metadata": {
        "id": "Zv-e_heSYS8v"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Copy training and testing sets to the same folder"
      ],
      "metadata": {
        "id": "JTq2tQKXv60F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cp -v train_po.fasta train_ne.fasta .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WD0QJTU4KJ5D",
        "outputId": "58bc01df-e4b9-41c6-d781-9cdafe1c3073"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'train_po.fasta' -> '../train_po.fasta'\n",
            "'train_ne.fasta' -> '../train_ne.fasta'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ..\n",
        "%cd test_set\n",
        "!ls -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Iwb9TkIJNw3p",
        "outputId": "85952512-c429-4927-d244-39d0b7d1131d"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Pfeature\n",
            "/content/Pfeature/test_set\n",
            "total 16\n",
            "-rw-r--r-- 1 root root 8659 Jan  5  2020 test_ne.fasta\n",
            "-rw-r--r-- 1 root root 3946 Jan  5  2020 test_po.fasta\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cp -v test_ne.fasta test_po.fasta .."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hoB0rDA7PBz4",
        "outputId": "a777639c-6cc3-466d-8854-8edaddde997e"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'test_ne.fasta' -> '../test_ne.fasta'\n",
            "'test_po.fasta' -> '../test_po.fasta'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd ..\n",
        "!ls -l"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uDBsQWBYPROB",
        "outputId": "578d3455-b1aa-4972-99fc-ca5f2a744bfb"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/Pfeature\n",
            "total 572\n",
            "drwxr-xr-x 4 root root   4096 Nov 27 18:20 build\n",
            "-rw-r--r-- 1 root root   4332 Nov 10 06:36 cuda-keyring_1.0-1_all.deb\n",
            "-rw-r--r-- 1 root root   3326 Jan 18  2020 deep-ampep30-test_set.zip\n",
            "-rw-r--r-- 1 root root  77275 Jan 18  2020 deep-ampep30-train_set.zip\n",
            "drwxr-xr-x 2 root root   4096 Nov 27 18:20 dist\n",
            "-rw-r--r-- 1 root root  86079 Sep 29  2020 Functions_Tables.pdf\n",
            "-rw-r--r-- 1 root root  17294 Nov 10 06:36 NGC-DL-CONTAINER-LICENSE\n",
            "drwxr-xr-x 3 root root   4096 Nov 27 18:22 Pfeature\n",
            "-rw-r--r-- 1 root root 221146 Sep 29  2020 Pfeature_Descriptors.pdf\n",
            "drwxr-xr-x 2 root root   4096 Nov 27 18:20 Pfeature.egg-info\n",
            "-rw-r--r-- 1 root root    971 Sep 28  2020 PKG-INFO\n",
            "-rw-r--r-- 1 root root   1265 Sep 29  2020 README\n",
            "-rw-r--r-- 1 root root   1267 Sep 29  2020 setup.py\n",
            "-rw-r--r-- 1 root root   8659 Nov 27 18:27 test_ne.fasta\n",
            "-rw-r--r-- 1 root root   3946 Nov 27 18:27 test_po.fasta\n",
            "drwxr-xr-x 2 root root   4096 Jan  8  2020 test_set\n",
            "-rw-r--r-- 1 root root  57206 Nov 27 18:27 train_ne.fasta\n",
            "-rw-r--r-- 1 root root  52619 Nov 27 18:27 train_po.fasta\n",
            "drwxr-xr-x 2 root root   4096 Jan  6  2020 train_set\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "All training and testing sets are now under the same folder."
      ],
      "metadata": {
        "id": "wV_Us12_0F7T"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Remove redundant sequences using CD-HIT"
      ],
      "metadata": {
        "id": "ZZKTELyCQJ1_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will remove duplicated sequences (i.e., peptide sequences with a peptide similarity of 100%) from the training dataset, using the CD-HIT program. Unique peptide sequences will be saved in text files, under the name original_filename_cdhit.txt."
      ],
      "metadata": {
        "id": "WO4yGXMqwmJW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! cd-hit -i train_po.fasta -o train_po_cdhit.txt -c 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6bO-uZvQSAh",
        "outputId": "b278a2fb-f0c4-4ca6-b1c5-efaead99e7cb"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================\n",
            "Program: CD-HIT, V4.8.1 (+OpenMP), May 15 2023, 22:49:31\n",
            "Command: cd-hit -i train_po.fasta -o train_po_cdhit.txt -c 1\n",
            "\n",
            "Started: Mon Nov 27 18:27:43 2023\n",
            "================================================================\n",
            "                            Output                              \n",
            "----------------------------------------------------------------\n",
            "total seq: 1445\n",
            "longest and shortest : 30 and 11\n",
            "Total letters: 30071\n",
            "Sequences have been sorted\n",
            "\n",
            "Approximated minimal memory consumption:\n",
            "Sequence        : 0M\n",
            "Buffer          : 1 X 16M = 16M\n",
            "Table           : 1 X 65M = 65M\n",
            "Miscellaneous   : 0M\n",
            "Total           : 81M\n",
            "\n",
            "Table limit with the given memory limit:\n",
            "Max number of representatives: 4000000\n",
            "Max number of word counting entries: 89796593\n",
            "\n",
            "\rcomparing sequences from          0  to       1445\n",
            ".\n",
            "     1445  finished       1345  clusters\n",
            "\n",
            "Approximated maximum memory consumption: 81M\n",
            "writing new database\n",
            "writing clustering information\n",
            "program completed !\n",
            "\n",
            "Total CPU time 0.13\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "! cd-hit -i train_ne.fasta -o train_ne_cdhit.txt -c 1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VxPWpYQr6ofX",
        "outputId": "4c5d2e9f-8a13-4df8-9f5b-152c438c7ea7"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "================================================================\n",
            "Program: CD-HIT, V4.8.1 (+OpenMP), May 15 2023, 22:49:31\n",
            "Command: cd-hit -i train_ne.fasta -o train_ne_cdhit.txt -c 1\n",
            "\n",
            "Started: Mon Nov 27 18:29:53 2023\n",
            "================================================================\n",
            "                            Output                              \n",
            "----------------------------------------------------------------\n",
            "total seq: 1445\n",
            "longest and shortest : 30 and 11\n",
            "Total letters: 30071\n",
            "Sequences have been sorted\n",
            "\n",
            "Approximated minimal memory consumption:\n",
            "Sequence        : 0M\n",
            "Buffer          : 1 X 16M = 16M\n",
            "Table           : 1 X 65M = 65M\n",
            "Miscellaneous   : 0M\n",
            "Total           : 81M\n",
            "\n",
            "Table limit with the given memory limit:\n",
            "Max number of representatives: 4000000\n",
            "Max number of word counting entries: 89796051\n",
            "\n",
            "\rcomparing sequences from          0  to       1445\n",
            ".\n",
            "     1445  finished       1424  clusters\n",
            "\n",
            "Approximated maximum memory consumption: 81M\n",
            "writing new database\n",
            "writing clustering information\n",
            "program completed !\n",
            "\n",
            "Total CPU time 0.17\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls -l"
      ],
      "metadata": {
        "id": "LAY0IcP00sR8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14da3149-70b1-468a-aa20-87f238e20256"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "total 792\n",
            "drwxr-xr-x 4 root root   4096 Nov 27 18:20 \u001b[0m\u001b[01;34mbuild\u001b[0m/\n",
            "-rw-r--r-- 1 root root   4332 Nov 10 06:36 cuda-keyring_1.0-1_all.deb\n",
            "-rw-r--r-- 1 root root   3326 Jan 18  2020 deep-ampep30-test_set.zip\n",
            "-rw-r--r-- 1 root root  77275 Jan 18  2020 deep-ampep30-train_set.zip\n",
            "drwxr-xr-x 2 root root   4096 Nov 27 18:20 \u001b[01;34mdist\u001b[0m/\n",
            "-rw-r--r-- 1 root root  86079 Sep 29  2020 Functions_Tables.pdf\n",
            "-rw-r--r-- 1 root root  17294 Nov 10 06:36 NGC-DL-CONTAINER-LICENSE\n",
            "drwxr-xr-x 3 root root   4096 Nov 27 18:22 \u001b[01;34mPfeature\u001b[0m/\n",
            "-rw-r--r-- 1 root root 221146 Sep 29  2020 Pfeature_Descriptors.pdf\n",
            "drwxr-xr-x 2 root root   4096 Nov 27 18:20 \u001b[01;34mPfeature.egg-info\u001b[0m/\n",
            "-rw-r--r-- 1 root root    971 Sep 28  2020 PKG-INFO\n",
            "-rw-r--r-- 1 root root   1265 Sep 29  2020 README\n",
            "-rw-r--r-- 1 root root   1267 Sep 29  2020 setup.py\n",
            "-rw-r--r-- 1 root root   8659 Nov 27 18:27 test_ne.fasta\n",
            "-rw-r--r-- 1 root root   3946 Nov 27 18:27 test_po.fasta\n",
            "drwxr-xr-x 2 root root   4096 Jan  8  2020 \u001b[01;34mtest_set\u001b[0m/\n",
            "-rw-r--r-- 1 root root  54416 Nov 27 18:29 train_ne_cdhit.txt\n",
            "-rw-r--r-- 1 root root  61435 Nov 27 18:29 train_ne_cdhit.txt.clstr\n",
            "-rw-r--r-- 1 root root  57206 Nov 27 18:27 train_ne.fasta\n",
            "-rw-r--r-- 1 root root  47501 Nov 27 18:27 train_po_cdhit.txt\n",
            "-rw-r--r-- 1 root root  56705 Nov 27 18:27 train_po_cdhit.txt.clstr\n",
            "-rw-r--r-- 1 root root  52619 Nov 27 18:27 train_po.fasta\n",
            "drwxr-xr-x 2 root root   4096 Jan  6  2020 \u001b[01;34mtrain_set\u001b[0m/\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Initial number of positive peptide sequences (amp)\n",
        "! grep \">\" train_po.fasta | wc -l\n",
        "# Number of unique positive peptide sequences\n",
        "! grep \">\" train_po_cdhit.txt | wc -l\n",
        "# Initial number of negative peptide sequences (amp)\n",
        "! grep \">\" train_ne.fasta | wc -l\n",
        "# Number of unique negative peptide sequences\n",
        "! grep \">\" train_ne_cdhit.txt | wc -l"
      ],
      "metadata": {
        "id": "y9x2kz_OR33h",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "915abc78-dc0c-4e7b-9be3-564a0d8e6c9e"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1529\n",
            "1345\n",
            "1529\n",
            "1424\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We went from 1529 to 1345 amp/positive peptide sequences and 1529 to 1424 nonamp/negative peptide sequences, i.e., a total of 289 sequences were eliminated."
      ],
      "metadata": {
        "id": "aOTq77sfxE-t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Calculate features of whole peptides (WP)"
      ],
      "metadata": {
        "id": "bHeou5m7b_3s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Amino Acid Composition (AAC)"
      ],
      "metadata": {
        "id": "l9iOAMbje66n"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The AAC function will compute the composition of each of the 20 amino acids, in each peptide sequence (i.e., the frequence of each AA in each sequence)."
      ],
      "metadata": {
        "id": "IjJ4Qd_If5yf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def aac(input):\n",
        "  output = input.split(\".\")[0] + \"_aac.csv\"\n",
        "  aac_wp(input, output)\n",
        "  aac_df = pd.read_csv(output)\n",
        "  return aac_df\n",
        "\n",
        "# Example\n",
        "aac(\"train_po_cdhit.txt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "4ULitaHrXS2I",
        "outputId": "52644b06-8f69-4215-a566-4273c7c7165a"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      AAC_A  AAC_C  AAC_D  AAC_E  AAC_F  AAC_G  AAC_H  AAC_I  AAC_K  AAC_L  \\\n",
              "0     27.27   0.00   9.09   0.00   9.09   9.09   0.00   0.00   0.00   0.00   \n",
              "1      0.00  54.55   0.00   9.09   0.00   0.00   0.00   0.00   0.00   0.00   \n",
              "2      0.00   0.00   9.09  18.18   9.09   9.09   9.09   0.00   0.00   9.09   \n",
              "3      0.00   0.00   0.00  18.18   9.09   9.09   9.09   0.00   0.00   9.09   \n",
              "4      0.00   0.00   9.09  18.18  18.18   9.09   0.00   0.00   0.00   9.09   \n",
              "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "1340  10.00   0.00   3.33   3.33   6.67  20.00   0.00   3.33  13.33  13.33   \n",
              "1341   6.67   3.33   0.00   0.00   3.33  20.00   6.67   3.33   3.33   6.67   \n",
              "1342  13.33   0.00   0.00   3.33   0.00   3.33  13.33   0.00   0.00  10.00   \n",
              "1343   6.67   0.00   6.67   6.67   0.00   6.67   3.33   6.67   6.67  26.67   \n",
              "1344   0.00  20.00   0.00   3.33   3.33  23.33   0.00   0.00   6.67   0.00   \n",
              "\n",
              "      AAC_M  AAC_N  AAC_P  AAC_Q  AAC_R  AAC_S  AAC_T  AAC_V  AAC_W  AAC_Y  \n",
              "0      0.00   9.09   9.09   0.00   0.00   0.00   0.00  18.18   0.00   9.09  \n",
              "1      0.00   0.00   0.00   0.00   0.00  18.18  18.18   0.00   0.00   0.00  \n",
              "2      9.09   0.00  18.18   0.00   0.00   0.00   0.00   9.09   0.00   0.00  \n",
              "3      9.09   9.09  18.18   0.00   0.00   0.00   0.00   9.09   0.00   0.00  \n",
              "4      9.09   9.09  18.18   0.00   0.00   0.00   0.00   0.00   0.00   0.00  \n",
              "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...  \n",
              "1340   3.33   0.00   0.00   3.33   0.00   6.67   3.33  10.00   0.00   0.00  \n",
              "1341   0.00   6.67   0.00   3.33   6.67  10.00   3.33  13.33   3.33   0.00  \n",
              "1342   0.00   0.00  16.67   6.67  16.67   6.67   3.33   0.00   0.00   6.67  \n",
              "1343   0.00   3.33   0.00  10.00  10.00   3.33   3.33   0.00   0.00   0.00  \n",
              "1344   3.33   0.00   6.67   3.33  10.00   6.67   0.00   6.67   0.00   6.67  \n",
              "\n",
              "[1345 rows x 20 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d1a2a33-582c-40e4-9147-a5aa1ab1ee72\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>AAC_A</th>\n",
              "      <th>AAC_C</th>\n",
              "      <th>AAC_D</th>\n",
              "      <th>AAC_E</th>\n",
              "      <th>AAC_F</th>\n",
              "      <th>AAC_G</th>\n",
              "      <th>AAC_H</th>\n",
              "      <th>AAC_I</th>\n",
              "      <th>AAC_K</th>\n",
              "      <th>AAC_L</th>\n",
              "      <th>AAC_M</th>\n",
              "      <th>AAC_N</th>\n",
              "      <th>AAC_P</th>\n",
              "      <th>AAC_Q</th>\n",
              "      <th>AAC_R</th>\n",
              "      <th>AAC_S</th>\n",
              "      <th>AAC_T</th>\n",
              "      <th>AAC_V</th>\n",
              "      <th>AAC_W</th>\n",
              "      <th>AAC_Y</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>27.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>18.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.00</td>\n",
              "      <td>54.55</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>18.18</td>\n",
              "      <td>18.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>18.18</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>18.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>18.18</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>18.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>18.18</td>\n",
              "      <td>18.18</td>\n",
              "      <td>9.09</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>9.09</td>\n",
              "      <td>18.18</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>6.67</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>13.33</td>\n",
              "      <td>13.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
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              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1341</th>\n",
              "      <td>6.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>20.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>6.67</td>\n",
              "      <td>10.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>13.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1342</th>\n",
              "      <td>13.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>13.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>16.67</td>\n",
              "      <td>6.67</td>\n",
              "      <td>16.67</td>\n",
              "      <td>6.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1343</th>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>6.67</td>\n",
              "      <td>6.67</td>\n",
              "      <td>26.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>10.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1344</th>\n",
              "      <td>0.00</td>\n",
              "      <td>20.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>23.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>3.33</td>\n",
              "      <td>10.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1345 rows × 20 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Dipeptide composition (DPC)"
      ],
      "metadata": {
        "id": "4XFO2clzjjKy"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "DPC will compute the percentage composition of all possible dipeptides (i.e., 20^2=400 different dipeptides) in each peptide sequence."
      ],
      "metadata": {
        "id": "LGfK7qfXkVmi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def dpc(input):\n",
        "  output = input.split(\".\")[0] + \"_dpc.csv\"\n",
        "  dpc_wp(input, output, 1)\n",
        "  dpc_df = pd.read_csv(output)\n",
        "  return dpc_df\n",
        "\n",
        "# Example\n",
        "dpc(\"train_po_cdhit.txt\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "VIdwR3YAj2bI",
        "outputId": "7dc71add-91c0-4ff5-f072-62e60312adb5"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      DPC1_AA  DPC1_AC  DPC1_AD  DPC1_AE  DPC1_AF  DPC1_AG  DPC1_AH  DPC1_AI  \\\n",
              "0        0.00     0.00     0.00     0.00    10.00     0.00     0.00     0.00   \n",
              "1        0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "2        0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "3        0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "4        0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "...       ...      ...      ...      ...      ...      ...      ...      ...   \n",
              "1340     0.00     0.00     3.45     0.00     0.00     0.00     0.00     0.00   \n",
              "1341     0.00     0.00     0.00     0.00     0.00     3.45     0.00     0.00   \n",
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              "1344     0.00     0.00     0.00     0.00     0.00     0.00     0.00     0.00   \n",
              "\n",
              "      DPC1_AK  DPC1_AL  ...  DPC1_YM  DPC1_YN  DPC1_YP  DPC1_YQ  DPC1_YR  \\\n",
              "0        0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "1        0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "2        0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "3        0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "4        0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "...       ...      ...  ...      ...      ...      ...      ...      ...   \n",
              "1340     0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "1341     0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "1342     0.00     6.90  ...     0.00     0.00     3.45     0.00     0.00   \n",
              "1343     3.45     3.45  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "1344     0.00     0.00  ...     0.00     0.00     0.00     0.00     0.00   \n",
              "\n",
              "      DPC1_YS  DPC1_YT  DPC1_YV  DPC1_YW  DPC1_YY  \n",
              "0        0.00     0.00     0.00     0.00     0.00  \n",
              "1        0.00     0.00     0.00     0.00     0.00  \n",
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              "3        0.00     0.00     0.00     0.00     0.00  \n",
              "4        0.00     0.00     0.00     0.00     0.00  \n",
              "...       ...      ...      ...      ...      ...  \n",
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              "1341     0.00     0.00     0.00     0.00     0.00  \n",
              "1342     0.00     0.00     0.00     0.00     0.00  \n",
              "1343     0.00     0.00     0.00     0.00     0.00  \n",
              "1344     0.00     0.00     0.00     0.00     0.00  \n",
              "\n",
              "[1345 rows x 400 columns]"
            ],
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              "      <th></th>\n",
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              "      <th>DPC1_YP</th>\n",
              "      <th>DPC1_YQ</th>\n",
              "      <th>DPC1_YR</th>\n",
              "      <th>DPC1_YS</th>\n",
              "      <th>DPC1_YT</th>\n",
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              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1340</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1341</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1342</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>6.90</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1343</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>3.45</td>\n",
              "      <td>3.45</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1344</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>...</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1345 rows × 400 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a102dbf-1b2c-4649-86a4-ce8ede22eb56')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-8a102dbf-1b2c-4649-86a4-ce8ede22eb56 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-8a102dbf-1b2c-4649-86a4-ce8ede22eb56');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-f84b82ca-6293-4f82-be8b-0d6daac7d38d\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-f84b82ca-6293-4f82-be8b-0d6daac7d38d')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f84b82ca-6293-4f82-be8b-0d6daac7d38d button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tripeptide Composition (TPC)"
      ],
      "metadata": {
        "id": "f1TJftLjAdGm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "TPC will compute the percentage composition of all possible tripeptides (i.e., 20^3=8000 different tripeptides) in each peptide sequence."
      ],
      "metadata": {
        "id": "mrCHctOcEtZ_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def tpc(input):\n",
        "  input_name = input.rstrip(\".txt\")\n",
        "  output_name = input_name + \"_tpc.csv\"\n",
        "  tpc_wp(input, output_name)\n",
        "  tpc_df = pd.read_csv(output_name)\n",
        "  return tpc_df\n",
        "\n",
        "# Example\n",
        "#tpc(\"train_po_cdhit.txt\")"
      ],
      "metadata": {
        "id": "DtoVH8I__2Pi"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## General feature calculation function"
      ],
      "metadata": {
        "id": "JfZjljXg_5Dx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The **feature_calc** function will calculate any peptide feature, given its name."
      ],
      "metadata": {
        "id": "KpQ6agtvFhE8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def feature_calc(po_data, ne_data, feature_name):\n",
        "  # Calculate feature values\n",
        "  po_feature = feature_name(po_data)\n",
        "  ne_feature = feature_name(ne_data)\n",
        "\n",
        "  # Create class labels (0 for negative/nonamp and 1 for positive/amp)\n",
        "  po_class = pd.Series([1 for i in range(len(po_feature))])\n",
        "  ne_class = pd.Series([0 for i in range(len(ne_feature))])\n",
        "\n",
        "  # Combine po and ne datasets\n",
        "  po_ne_class = pd.concat([po_class, ne_class], axis=0)\n",
        "  po_ne_class.name = 'class'\n",
        "  po_ne_feature = pd.concat([po_feature, ne_feature], axis=0)\n",
        "\n",
        "  # Combine feature and class\n",
        "  df = pd.concat([po_ne_feature, po_ne_class], axis=1)\n",
        "  return df\n",
        "\n",
        "# Example\n",
        "feature_calc(\"train_po_cdhit.txt\", \"train_ne_cdhit.txt\", aac)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 443
        },
        "id": "R4Yf0jVQyyf2",
        "outputId": "96a86072-26b5-4af9-a757-0e5ae0bf779a"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      AAC_A  AAC_C  AAC_D  AAC_E  AAC_F  AAC_G  AAC_H  AAC_I  AAC_K  AAC_L  \\\n",
              "0     27.27   0.00   9.09   0.00   9.09   9.09   0.00   0.00   0.00   0.00   \n",
              "1      0.00  54.55   0.00   9.09   0.00   0.00   0.00   0.00   0.00   0.00   \n",
              "2      0.00   0.00   9.09  18.18   9.09   9.09   9.09   0.00   0.00   9.09   \n",
              "3      0.00   0.00   0.00  18.18   9.09   9.09   9.09   0.00   0.00   9.09   \n",
              "4      0.00   0.00   9.09  18.18  18.18   9.09   0.00   0.00   0.00   9.09   \n",
              "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "1419  13.33   0.00   3.33   3.33   0.00  13.33   0.00   3.33  26.67  10.00   \n",
              "1420   6.67   0.00   0.00   3.33   6.67  20.00   3.33   3.33  13.33  13.33   \n",
              "1421   6.67  20.00   0.00   3.33   0.00  10.00   0.00   6.67  10.00   3.33   \n",
              "1422   6.67   0.00   3.33   3.33   6.67   0.00   3.33   3.33   3.33   6.67   \n",
              "1423   0.00  20.00   6.67   6.67   0.00   6.67   6.67   6.67   6.67  10.00   \n",
              "\n",
              "      ...  AAC_N  AAC_P  AAC_Q  AAC_R  AAC_S  AAC_T  AAC_V  AAC_W  AAC_Y  \\\n",
              "0     ...   9.09   9.09   0.00   0.00   0.00   0.00  18.18   0.00   9.09   \n",
              "1     ...   0.00   0.00   0.00   0.00  18.18  18.18   0.00   0.00   0.00   \n",
              "2     ...   0.00  18.18   0.00   0.00   0.00   0.00   9.09   0.00   0.00   \n",
              "3     ...   9.09  18.18   0.00   0.00   0.00   0.00   9.09   0.00   0.00   \n",
              "4     ...   9.09  18.18   0.00   0.00   0.00   0.00   0.00   0.00   0.00   \n",
              "...   ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
              "1419  ...   3.33   3.33   3.33   0.00   0.00   3.33   0.00  10.00   0.00   \n",
              "1420  ...   0.00   3.33  10.00   0.00   6.67   3.33   3.33   0.00   0.00   \n",
              "1421  ...   3.33   6.67   0.00   0.00   6.67   6.67  10.00   0.00   6.67   \n",
              "1422  ...  16.67   3.33   6.67  10.00   6.67   0.00  13.33   0.00   3.33   \n",
              "1423  ...   0.00   6.67   0.00   3.33   3.33   0.00   3.33   0.00   3.33   \n",
              "\n",
              "      class  \n",
              "0         1  \n",
              "1         1  \n",
              "2         1  \n",
              "3         1  \n",
              "4         1  \n",
              "...     ...  \n",
              "1419      0  \n",
              "1420      0  \n",
              "1421      0  \n",
              "1422      0  \n",
              "1423      0  \n",
              "\n",
              "[2769 rows x 21 columns]"
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              "      <th></th>\n",
              "      <th>AAC_A</th>\n",
              "      <th>AAC_C</th>\n",
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              "      <th>0</th>\n",
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              "      <td>18.18</td>\n",
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              "      <td>9.09</td>\n",
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              "      <th>3</th>\n",
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              "      <td>18.18</td>\n",
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              "      <td>9.09</td>\n",
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              "      <td>0.00</td>\n",
              "      <td>9.09</td>\n",
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              "      <th>1420</th>\n",
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              "      <td>13.33</td>\n",
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              "      <th>1421</th>\n",
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              "      <td>3.33</td>\n",
              "      <td>3.33</td>\n",
              "      <td>6.67</td>\n",
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              "      <td>0.00</td>\n",
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              "<p>2769 rows × 21 columns</p>\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-c4578e71-70da-4aac-9b3c-11d8653539f7 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# AMP Activity Prediction Model"
      ],
      "metadata": {
        "id": "HCONsEcp-qIA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Training and testing datasets preprocessing"
      ],
      "metadata": {
        "id": "6CUVjlSEv-Jh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature calculation\n",
        "train_set = feature_calc(\"train_po_cdhit.txt\", \"train_ne_cdhit.txt\", aac)\n",
        "test_set = feature_calc(\"test_po.fasta\", \"test_ne.fasta\", aac)"
      ],
      "metadata": {
        "id": "76mGQXCwrHFc"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Split datasets into input features and class feature\n",
        "X_train = train_set.drop(\"class\", axis=1)\n",
        "y_train = train_set['class']\n",
        "\n",
        "X_test = test_set.drop(\"class\", axis=1)\n",
        "y_test = test_set[\"class\"]"
      ],
      "metadata": {
        "id": "xBybNyvaqCkg"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature selection"
      ],
      "metadata": {
        "id": "uDF5sK9kv5Fv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Feature names\n",
        "X_train.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TyHMnbeMy0pS",
        "outputId": "1c3445cb-bef9-40c1-c626-330498ce94a9"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['AAC_A', 'AAC_C', 'AAC_D', 'AAC_E', 'AAC_F', 'AAC_G', 'AAC_H', 'AAC_I',\n",
              "       'AAC_K', 'AAC_L', 'AAC_M', 'AAC_N', 'AAC_P', 'AAC_Q', 'AAC_R', 'AAC_S',\n",
              "       'AAC_T', 'AAC_V', 'AAC_W', 'AAC_Y'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Remove features with no variance"
      ],
      "metadata": {
        "id": "KAPaTsiIGtxZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Select features with 0 variance\n",
        "selector = VarianceThreshold()\n",
        "selector.fit_transform(X_train)\n",
        "selector.get_support()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Ujm2Xu4rzFV",
        "outputId": "b85e5d03-5d82-420c-a54c-ecb1bcd2643d"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True,  True,  True,  True,  True,  True,  True,  True,\n",
              "        True,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "There are no features with 0 variance."
      ],
      "metadata": {
        "id": "KPquFSm5GeuW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove features with 0 variance\n",
        "#X_train = X_train.loc[:, selector.get_support()]\n",
        "#X_test = X_test.loc[:, selector.get_support()]"
      ],
      "metadata": {
        "id": "gPGSOM8n0Ulm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model selection"
      ],
      "metadata": {
        "id": "Jg0YWiXD2dHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "clf = LazyClassifier(verbose=0, custom_metric=matthews_corrcoef)\n",
        "models_train, _ = clf.fit(X_train, X_train, y_train, y_train)\n",
        "models_train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "iRxAjHvf2kD9",
        "outputId": "63bc94d0-8d97-4c7a-b427-7b33573099bb"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:14<00:00,  2.07it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
              "Model                                                                           \n",
              "RandomForestClassifier             0.91               0.91     0.91      0.91   \n",
              "XGBClassifier                      0.91               0.91     0.91      0.91   \n",
              "LabelSpreading                     0.91               0.91     0.91      0.91   \n",
              "LabelPropagation                   0.91               0.91     0.91      0.91   \n",
              "DecisionTreeClassifier             0.91               0.91     0.91      0.91   \n",
              "ExtraTreeClassifier                0.91               0.91     0.91      0.91   \n",
              "ExtraTreesClassifier               0.91               0.91     0.91      0.91   \n",
              "BaggingClassifier                  0.91               0.91     0.91      0.91   \n",
              "LGBMClassifier                     0.90               0.90     0.90      0.90   \n",
              "NuSVC                              0.85               0.85     0.85      0.85   \n",
              "SVC                                0.82               0.82     0.82      0.82   \n",
              "KNeighborsClassifier               0.82               0.82     0.82      0.82   \n",
              "QuadraticDiscriminantAnalysis      0.75               0.75     0.75      0.74   \n",
              "AdaBoostClassifier                 0.74               0.75     0.75      0.74   \n",
              "LinearDiscriminantAnalysis         0.73               0.73     0.73      0.73   \n",
              "RidgeClassifierCV                  0.73               0.73     0.73      0.73   \n",
              "RidgeClassifier                    0.73               0.73     0.73      0.73   \n",
              "LinearSVC                          0.73               0.73     0.73      0.73   \n",
              "LogisticRegression                 0.73               0.73     0.73      0.73   \n",
              "CalibratedClassifierCV             0.72               0.73     0.73      0.72   \n",
              "NearestCentroid                    0.72               0.72     0.72      0.72   \n",
              "SGDClassifier                      0.71               0.72     0.72      0.71   \n",
              "GaussianNB                         0.70               0.71     0.71      0.70   \n",
              "BernoulliNB                        0.71               0.71     0.71      0.71   \n",
              "Perceptron                         0.69               0.69     0.69      0.69   \n",
              "PassiveAggressiveClassifier        0.65               0.65     0.65      0.65   \n",
              "DummyClassifier                    0.51               0.50     0.50      0.35   \n",
              "\n",
              "                               matthews_corrcoef  Time Taken  \n",
              "Model                                                         \n",
              "RandomForestClassifier                      0.83        1.22  \n",
              "XGBClassifier                               0.82        3.98  \n",
              "LabelSpreading                              0.82        1.56  \n",
              "LabelPropagation                            0.83        1.29  \n",
              "DecisionTreeClassifier                      0.83        0.08  \n",
              "ExtraTreeClassifier                         0.83        0.03  \n",
              "ExtraTreesClassifier                        0.83        0.66  \n",
              "BaggingClassifier                           0.81        0.27  \n",
              "LGBMClassifier                              0.80        0.18  \n",
              "NuSVC                                       0.71        1.33  \n",
              "SVC                                         0.65        1.18  \n",
              "KNeighborsClassifier                        0.64        0.34  \n",
              "QuadraticDiscriminantAnalysis               0.50        0.09  \n",
              "AdaBoostClassifier                          0.49        0.31  \n",
              "LinearDiscriminantAnalysis                  0.46        0.13  \n",
              "RidgeClassifierCV                           0.46        0.13  \n",
              "RidgeClassifier                             0.46        0.07  \n",
              "LinearSVC                                   0.45        0.44  \n",
              "LogisticRegression                          0.45        0.05  \n",
              "CalibratedClassifierCV                      0.45        0.11  \n",
              "NearestCentroid                             0.44        0.03  \n",
              "SGDClassifier                               0.44        0.12  \n",
              "GaussianNB                                  0.42        0.05  \n",
              "BernoulliNB                                 0.41        0.03  \n",
              "Perceptron                                  0.37        0.04  \n",
              "PassiveAggressiveClassifier                 0.30        0.04  \n",
              "DummyClassifier                             0.00        0.02  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9f8499f5-3035-43f0-a884-e12a1b429a74\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>matthews_corrcoef</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.83</td>\n",
              "      <td>1.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.82</td>\n",
              "      <td>3.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.82</td>\n",
              "      <td>1.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.83</td>\n",
              "      <td>1.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.66</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.91</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.90</td>\n",
              "      <td>0.80</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.71</td>\n",
              "      <td>1.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.82</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.65</td>\n",
              "      <td>1.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.82</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.45</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.44</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.42</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.71</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.71</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.69</td>\n",
              "      <td>0.37</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.65</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.51</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.02</td>\n",
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          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "models_test, _ = clf.fit(X_train, X_test, y_train, y_test)\n",
        "models_test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "HWZyFfAz-Fgd",
        "outputId": "ae41ec1e-2378-491e-9381-bf993b31ca50"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 29/29 [00:12<00:00,  2.41it/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               Accuracy  Balanced Accuracy  ROC AUC  F1 Score  \\\n",
              "Model                                                                           \n",
              "BernoulliNB                        0.81               0.81     0.81      0.81   \n",
              "KNeighborsClassifier               0.79               0.79     0.79      0.79   \n",
              "PassiveAggressiveClassifier        0.78               0.78     0.78      0.78   \n",
              "RandomForestClassifier             0.78               0.78     0.78      0.78   \n",
              "AdaBoostClassifier                 0.77               0.77     0.77      0.77   \n",
              "LinearDiscriminantAnalysis         0.77               0.77     0.77      0.77   \n",
              "RidgeClassifierCV                  0.77               0.77     0.77      0.77   \n",
              "RidgeClassifier                    0.77               0.77     0.77      0.77   \n",
              "GaussianNB                         0.77               0.77     0.77      0.77   \n",
              "QuadraticDiscriminantAnalysis      0.77               0.77     0.77      0.77   \n",
              "LogisticRegression                 0.77               0.77     0.77      0.77   \n",
              "LinearSVC                          0.77               0.77     0.77      0.77   \n",
              "CalibratedClassifierCV             0.77               0.77     0.77      0.77   \n",
              "ExtraTreesClassifier               0.76               0.76     0.76      0.76   \n",
              "NearestCentroid                    0.76               0.76     0.76      0.76   \n",
              "NuSVC                              0.75               0.75     0.75      0.75   \n",
              "SGDClassifier                      0.75               0.75     0.75      0.75   \n",
              "LGBMClassifier                     0.75               0.75     0.75      0.75   \n",
              "SVC                                0.74               0.74     0.74      0.74   \n",
              "XGBClassifier                      0.74               0.74     0.74      0.74   \n",
              "BaggingClassifier                  0.73               0.73     0.73      0.73   \n",
              "LabelSpreading                     0.70               0.70     0.70      0.70   \n",
              "LabelPropagation                   0.70               0.70     0.70      0.70   \n",
              "Perceptron                         0.70               0.70     0.70      0.70   \n",
              "DecisionTreeClassifier             0.64               0.64     0.64      0.64   \n",
              "ExtraTreeClassifier                0.62               0.62     0.62      0.62   \n",
              "DummyClassifier                    0.50               0.50     0.50      0.33   \n",
              "\n",
              "                               matthews_corrcoef  Time Taken  \n",
              "Model                                                         \n",
              "BernoulliNB                                 0.62        0.03  \n",
              "KNeighborsClassifier                        0.59        0.05  \n",
              "PassiveAggressiveClassifier                 0.57        0.04  \n",
              "RandomForestClassifier                      0.55        1.12  \n",
              "AdaBoostClassifier                          0.55        0.35  \n",
              "LinearDiscriminantAnalysis                  0.54        0.12  \n",
              "RidgeClassifierCV                           0.54        0.08  \n",
              "RidgeClassifier                             0.54        0.06  \n",
              "GaussianNB                                  0.55        0.03  \n",
              "QuadraticDiscriminantAnalysis               0.53        0.06  \n",
              "LogisticRegression                          0.53        0.04  \n",
              "LinearSVC                                   0.53        0.45  \n",
              "CalibratedClassifierCV                      0.53        0.22  \n",
              "ExtraTreesClassifier                        0.52        0.78  \n",
              "NearestCentroid                             0.51        0.07  \n",
              "NuSVC                                       0.50        0.79  \n",
              "SGDClassifier                               0.50        0.11  \n",
              "LGBMClassifier                              0.50        0.25  \n",
              "SVC                                         0.49        0.61  \n",
              "XGBClassifier                               0.49        4.37  \n",
              "BaggingClassifier                           0.46        0.37  \n",
              "LabelSpreading                              0.40        0.94  \n",
              "LabelPropagation                            0.40        0.69  \n",
              "Perceptron                                  0.39        0.04  \n",
              "DecisionTreeClassifier                      0.28        0.10  \n",
              "ExtraTreeClassifier                         0.24        0.03  \n",
              "DummyClassifier                             0.00        0.04  "
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Accuracy</th>\n",
              "      <th>Balanced Accuracy</th>\n",
              "      <th>ROC AUC</th>\n",
              "      <th>F1 Score</th>\n",
              "      <th>matthews_corrcoef</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>BernoulliNB</th>\n",
              "      <td>0.81</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.81</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsClassifier</th>\n",
              "      <td>0.79</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.79</td>\n",
              "      <td>0.59</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveClassifier</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestClassifier</th>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.78</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostClassifier</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearDiscriminantAnalysis</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifierCV</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeClassifier</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.54</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianNB</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.55</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>QuadraticDiscriminantAnalysis</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LogisticRegression</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVC</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CalibratedClassifierCV</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.77</td>\n",
              "      <td>0.53</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesClassifier</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.52</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NearestCentroid</th>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.76</td>\n",
              "      <td>0.51</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVC</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDClassifier</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMClassifier</th>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.75</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.49</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBClassifier</th>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.74</td>\n",
              "      <td>0.49</td>\n",
              "      <td>4.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingClassifier</th>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.73</td>\n",
              "      <td>0.46</td>\n",
              "      <td>0.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelSpreading</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LabelPropagation</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.40</td>\n",
              "      <td>0.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Perceptron</th>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.70</td>\n",
              "      <td>0.39</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeClassifier</th>\n",
              "      <td>0.64</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.64</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeClassifier</th>\n",
              "      <td>0.62</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.24</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyClassifier</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.50</td>\n",
              "      <td>0.33</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-60b7d86f-681b-4dbc-b105-04ec98846a0b')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-60b7d86f-681b-4dbc-b105-04ec98846a0b button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-60b7d86f-681b-4dbc-b105-04ec98846a0b');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-8b44a091-aba6-446d-9d72-ed0ef61362cd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-8b44a091-aba6-446d-9d72-ed0ef61362cd')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-8b44a091-aba6-446d-9d72-ed0ef61362cd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model training"
      ],
      "metadata": {
        "id": "vDKiL30SC-ha"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rf_model = RandomForestClassifier(n_estimators=500)\n",
        "rf_model.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 74
        },
        "id": "SJgPicxbCQIt",
        "outputId": "c3d85b95-4df4-41b7-e8f9-50e7e2d9ec58"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(n_estimators=500)"
            ],
            "text/html": [
              "<style>#sk-container-id-1 {color: black;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(n_estimators=500)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(n_estimators=500)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Model evaluation"
      ],
      "metadata": {
        "id": "7szT_5vgC0gk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training Accuracy\n",
        "rf_model.score(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h42_yEG5z3xU",
        "outputId": "7b0e5f4c-b2b9-40c1-a7e8-734ddce1e265"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.911520404478151"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Testing Accuracy\n",
        "rf_model.score(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UaA9Ub3uvxDZ",
        "outputId": "d4918811-fcef-429b-e938-18ca4aee4bc6"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7712765957446809"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicted training and testing outputs\n",
        "y_train_pred = rf_model.predict(X_train)\n",
        "y_test_pred = rf_model.predict(X_test)"
      ],
      "metadata": {
        "id": "mXxfToOsDBu_"
      },
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Confusion matrix\n",
        "confusion_matrix(y_test, y_test_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fFCxM2wx0Jo8",
        "outputId": "e3e6f33c-3510-40a0-8e9f-5b1a9a8d1860"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[74, 20],\n",
              "       [23, 71]])"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model performance report\n",
        "rf_model_report = classification_report(y_test, y_test_pred, target_names=[\"positive\",\"negative\"])\n",
        "\n",
        "f = open('rf_model_report.txt','w')\n",
        "f.writelines(rf_model_report)\n",
        "f.close()"
      ],
      "metadata": {
        "id": "lsG0AR440OxH"
      },
      "execution_count": 44,
      "outputs": []
    }
  ]
}