docs/tutorials/05-tutorial-cb6-but-plumed.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pAPRika tutorial 5 - APR/Amber with Plumed restraints\n",
    "\n",
    "In this tutorial, we will perform APR calculations for the butane (BUT)--cucurbit[6]uril (CB6) host-guest system. This is a repeat of [Tutorial 1](01-tutorial-cb6-but.ipynb) using `Plumed`-based restraints and the `AMBER` MD engine. `Plumed` is a plugin for MD codes that can analyze trajectories and perform free-energy calculations on collective variables. It is a versatile plugin that can interface with a number of MD engines. Here, we will go through the process of converting APR restraints constructed with pAPRika to a `Plumed` file and run a short calculation with `sander`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "## Initialize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <ins>Before you start</ins>\n",
    "<a id='start'></a>\n",
    "We will run the simulations in this tutorial using `sander` (*Ambertools*) and `Plumed`. Both of these should be installed in your `conda` environment if you installed *pAPRika* though the `conda` route. However, for `Plumed` to work with `sander` we first need to make sure the `PLUMED_KERNEL` environment variable is loaded (the library is called `libplumedKernel.so`). *pAPRika* should load the `Plumed` kernel automatically but let's make sure it is loaded and run the cell below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "'PLUMED_KERNEL' in os.environ.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If it does not exists we will load the environment in this Jupyter Notebook. Since `Plumed` is installed through `conda` the kernel will be located in your conda environment library folder. If you are running this on a Mac replace the kernel library in the cell below to `libplumedKernel.dylib`. If you compiled `Plumed` yourself and then you will need to change the path below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.environ['PLUMED_KERNEL'] = f\"{os.environ['CONDA_PREFIX']}/lib/libplumedKernel.so\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For running `Plumed` with Amber outside of this notebook it might be better to export the `PLUMED_KERNEL` variable into your `.bashrc` file.\n",
    "\n",
    "<font size=\"4\"><ins>**Note:**</ins></font> we can run `Plumed` with `AMBER` versions 18 and 20, but version 18 requires you to patch the source code first and recompile. Older versions of Amber are not supported. See the `Plumed` documentation for more details https://www.plumed.org/doc-v2.6/user-doc/html/index.html.\n",
    "\n",
    "🔵 Since we have a prepared the host-guest-dummy setup from the first tutorial, we will skip the initial tleap steps and go right into initializing the restraints."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define names\n",
    "We will store the files created in this tutorial in a folder called `plumed` so we don't mix files with the previous tutorial."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_name = \"cb6-but-dum\"\n",
    "work_dir = \"plumed\"\n",
    "complex_dir = \"complex\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure APR Restraints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define anchor atoms\n",
    "See [tutorial 1](01-tutorial-cb6-but.ipynb) for the choice of selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Guest atoms\n",
    "G1 = \":BUT@C\"\n",
    "G2 = \":BUT@C3\"\n",
    "\n",
    "# Host atoms\n",
    "H1 = \":CB6@C\"\n",
    "H2 = \":CB6@C31\"\n",
    "H3 = \":CB6@C18\"\n",
    "\n",
    "# Dummy atoms\n",
    "D1 = \":DM1\"\n",
    "D2 = \":DM2\"\n",
    "D3 = \":DM3\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Determine the number of windows\n",
    "Before we add the restraints, it is helpful to set the $\\lambda$ fractions that control the strength of the force constants during attach and release, and to define the distances for the pulling phase.\n",
    "\n",
    "The attach fractions go from 0 to 1 and we place more points at the bottom of the range to sample the curvature of $dU/d \\lambda$. Next, we generally apply a distance restraint until the guest is ~18 Angstroms away from the host, in increments of 0.4 Angstroms. This distance should be at least twice the Lennard-Jones cutoff in the system. These values have worked well for us, but this is one aspect that should be carefully checked for new systems."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are [15, 18, 0] windows in this attach-pull-release calculation.\n"
     ]
    }
   ],
   "source": [
    "attach_string = \"0.00 0.40 0.80 1.60 2.40 4.00 5.50 8.65 11.80 18.10 24.40 37.00 49.60 74.80 100.00\"\n",
    "attach_fractions = [float(i) / 100 for i in attach_string.split()]\n",
    "\n",
    "initial_distance = 6.0\n",
    "pull_distances = np.arange(0.0 + initial_distance, 18.0 + initial_distance, 1.0)\n",
    "\n",
    "release_fractions = []\n",
    "\n",
    "windows = [len(attach_fractions), len(pull_distances), len(release_fractions)]\n",
    "print(f\"There are {windows} windows in this attach-pull-release calculation.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import parmed as pmd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Load complex structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "structure = pmd.load_file(\n",
    "    os.path.join(complex_dir, f\"{base_name}.prmtop\"),\n",
    "    os.path.join(complex_dir, f\"{base_name}.rst7\"),\n",
    "    structure = True,\n",
    ") "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Host Static Restraints\n",
    "See [tutorial 1](01-tutorial-cb6-but.ipynb#host_static) for an explanation of the static restraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paprika.restraints as restraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "static_restraints = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.static_DAT_restraint(restraint_mask_list = [D1, H1],\n",
    "                                    num_window_list = windows,\n",
    "                                    ref_structure = structure,\n",
    "                                    force_constant = 5.0,\n",
    "                                    amber_index=True)\n",
    "\n",
    "static_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.static_DAT_restraint(restraint_mask_list = [D2, D1, H1],\n",
    "                                    num_window_list = windows,\n",
    "                                    ref_structure = structure,\n",
    "                                    force_constant = 100.0,\n",
    "                                    amber_index=True)\n",
    "\n",
    "static_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.static_DAT_restraint(restraint_mask_list = [D3, D2, D1, H1],\n",
    "                                    num_window_list = windows,\n",
    "                                    ref_structure = structure,\n",
    "                                    force_constant = 100.0,\n",
    "                                    amber_index=True)\n",
    "\n",
    "static_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.static_DAT_restraint(restraint_mask_list = [D1, H1, H2],\n",
    "                                    num_window_list = windows,\n",
    "                                    ref_structure = structure,\n",
    "                                    force_constant = 100.0,\n",
    "                                    amber_index=True)\n",
    "\n",
    "static_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.static_DAT_restraint(restraint_mask_list = [D2, D1, H1, H2],\n",
    "                                    num_window_list = windows,\n",
    "                                    ref_structure = structure,\n",
    "                                    force_constant = 100.0,\n",
    "                                    amber_index=True)\n",
    "\n",
    "static_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.static_DAT_restraint(restraint_mask_list = [D1, H1, H2, H3],\n",
    "                                    num_window_list = windows,\n",
    "                                    ref_structure = structure,\n",
    "                                    force_constant = 100.0,\n",
    "                                    amber_index=True)\n",
    "\n",
    "static_restraints.append(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Guest translational and rotational restraints\n",
    "See [tutorial 1](01-tutorial-cb6-but.ipynb#guest) for an explanation of the guest restraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "guest_restraints = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.DAT_restraint()\n",
    "r.mask1 = D1\n",
    "r.mask2 = G1\n",
    "r.topology = structure\n",
    "r.auto_apr = True\n",
    "r.continuous_apr = True\n",
    "r.amber_index = True\n",
    "\n",
    "r.attach[\"target\"] = pull_distances[0]          # Angstroms\n",
    "r.attach[\"fraction_list\"] = attach_fractions\n",
    "r.attach[\"fc_final\"] = 5.0                      # kcal/mol/Angstroms**2\n",
    "\n",
    "r.pull[\"target_final\"] = 24.0                   # Angstroms\n",
    "r.pull[\"num_windows\"] = windows[1]\n",
    "\n",
    "r.initialize()\n",
    "guest_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.DAT_restraint()\n",
    "r.mask1 = D2\n",
    "r.mask2 = D1\n",
    "r.mask3 = G1\n",
    "r.topology = structure\n",
    "r.auto_apr = True\n",
    "r.continuous_apr = True\n",
    "r.amber_index = True\n",
    "\n",
    "r.attach[\"target\"] = 180.0                      # Degrees\n",
    "r.attach[\"fraction_list\"] = attach_fractions\n",
    "r.attach[\"fc_final\"] = 100.0                    # kcal/mol/radian**2\n",
    "\n",
    "r.pull[\"target_final\"] = 180.0                  # Degrees\n",
    "r.pull[\"num_windows\"] = windows[1]\n",
    "\n",
    "r.initialize()\n",
    "guest_restraints.append(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "r = restraints.DAT_restraint()\n",
    "r.mask1 = D1\n",
    "r.mask2 = G1\n",
    "r.mask3 = G2\n",
    "r.topology = structure\n",
    "r.auto_apr = True\n",
    "r.continuous_apr = True\n",
    "r.amber_index = True\n",
    "\n",
    "r.attach[\"target\"] = 180.0                      # Degrees\n",
    "r.attach[\"fraction_list\"] = attach_fractions\n",
    "r.attach[\"fc_final\"] = 100.0                    # kcal/mol/radian**2\n",
    "\n",
    "r.pull[\"target_final\"] = 180.0                  # Degrees\n",
    "r.pull[\"num_windows\"] = windows[1]\n",
    "\n",
    "r.initialize()\n",
    "guest_restraints.append(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Create APR windows\n",
    "We use the guest restraints to create a list of windows with the appropriate names and then create the directories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from paprika.restraints.utils import create_window_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "window_list = create_window_list(guest_restraints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not os.path.isdir(work_dir):\n",
    "    os.makedirs(work_dir)\n",
    "    \n",
    "for window in window_list:\n",
    "    folder = os.path.join(work_dir, window)\n",
    "    if not os.path.isdir(folder):\n",
    "        os.makedirs(os.path.join(work_dir, window))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Write APR restraints to Plumed format\n",
    "In this section we create an instance of `Plumed()` from `paprika.restraints.plumed`, which is a class to generate the `Plumed` restraint files. We need to specify the list of restraints used throughout the APR calculations and the corresponding windows list. In this tutorial we will print the **host static** restraints and the **guest** restraints. The `Plumed` class includes a method to add restraints to dummy atoms (`add_dummy_atoms_to_file`) but we will not do that here. Instead we will use the built-in position restraints feature in Amber (see [Simulation](#simulate) section below).\n",
    "\n",
    "<font size='4'><ins>**Note**</ins></font>: be careful when specifiying the force constants in `DAT_restraints`. We follow the Amber (and CHARMM) convention where the force constant is already multiplied by a factor of 1/2 but `Plumed` requires the user to specify the force constant without this factor, i.e.\n",
    "\n",
    "$$\n",
    "\\begin{align}\n",
    "U_{amber} &= K_{amber} (r-r_{0})^2 \\\\\n",
    "U_{plumed} &= \\frac{1}{2} k_{plumed} (r - r_{0})^2 \n",
    "\\end{align}\n",
    "$$\n",
    "\n",
    "thus $k_{plumed} = 2 \\times  K_{amber}$. If Amber force constants was used in generating the `DAT_restraints` (the case in this tutorial) we need to set the variable `uses_legacy_k` to `True` (this is on by default)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "from paprika.restraints.plumed import Plumed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "restraints_list = (static_restraints + guest_restraints)\n",
    "\n",
    "plumed = Plumed()\n",
    "plumed.file_name = 'plumed.dat'\n",
    "plumed.path = work_dir\n",
    "plumed.window_list = window_list\n",
    "plumed.restraint_list = restraints_list\n",
    "plumed.uses_legacy_k = True\n",
    "\n",
    "plumed.dump_to_file()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare host-guest system"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {
     "name": "#%% md\n"
    }
   },
   "source": [
    "### Translate guest molecule\n",
    "For the attach windows, we will use the initial, bound coordinates for the host-guest complex. Only the force constants change during this phase, so a single set of coordinates is sufficient. For the pull windows, we will translate the guest to the target value of the restraint before solvation, and for the release windows, we will use the coordinates from the final pull window."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "import shutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In window p000 we will translate the guest 0.0 Angstroms.\n",
      "In window p001 we will translate the guest 1.1 Angstroms.\n",
      "In window p002 we will translate the guest 2.1 Angstroms.\n",
      "In window p003 we will translate the guest 3.2 Angstroms.\n",
      "In window p004 we will translate the guest 4.2 Angstroms.\n",
      "In window p005 we will translate the guest 5.3 Angstroms.\n",
      "In window p006 we will translate the guest 6.4 Angstroms.\n",
      "In window p007 we will translate the guest 7.4 Angstroms.\n",
      "In window p008 we will translate the guest 8.5 Angstroms.\n",
      "In window p009 we will translate the guest 9.5 Angstroms.\n",
      "In window p010 we will translate the guest 10.6 Angstroms.\n",
      "In window p011 we will translate the guest 11.6 Angstroms.\n",
      "In window p012 we will translate the guest 12.7 Angstroms.\n",
      "In window p013 we will translate the guest 13.8 Angstroms.\n",
      "In window p014 we will translate the guest 14.8 Angstroms.\n",
      "In window p015 we will translate the guest 15.9 Angstroms.\n",
      "In window p016 we will translate the guest 16.9 Angstroms.\n",
      "In window p017 we will translate the guest 18.0 Angstroms.\n"
     ]
    }
   ],
   "source": [
    "for window in window_list:\n",
    "    if window[0] == \"a\":\n",
    "        shutil.copy(os.path.join(complex_dir, f\"{base_name}.prmtop\"),\n",
    "                    os.path.join(work_dir, window, f\"{base_name}.prmtop\"))\n",
    "        shutil.copy(os.path.join(complex_dir, f\"{base_name}.rst7\"),\n",
    "                    os.path.join(work_dir, window, f\"{base_name}.rst7\"))\n",
    "\n",
    "    elif window[0] == \"p\":\n",
    "        structure = pmd.load_file(\n",
    "            os.path.join(complex_dir, f\"{base_name}.prmtop\"), \n",
    "            os.path.join(complex_dir, f\"{base_name}.rst7\"), \n",
    "            structure = True\n",
    "        )\n",
    "        target_difference = guest_restraints[0].phase['pull']['targets'][int(window[1:])] -\\\n",
    "                            guest_restraints[0].pull['target_initial']\n",
    "        print(f\"In window {window} we will translate the guest {target_difference.magnitude:0.1f}.\")\n",
    "        \n",
    "        for atom in structure.atoms:\n",
    "            if atom.residue.name == \"BUT\":\n",
    "                atom.xz += target_difference.magnitude\n",
    "                \n",
    "        structure.save(os.path.join(work_dir, window, f\"{base_name}.prmtop\"), overwrite=True)\n",
    "        structure.save(os.path.join(work_dir, window, f\"{base_name}.rst7\"), overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulation\n",
    "<a id='simulate'></a>\n",
    "Since we are going to run an implicit solvent simulation, we have everything ready to go. **pAPRika** has an `AMBER` module that can help setting default parameters for the simulation. There are some high level options that we set directly, like `simulation.path`, and then we call the function `config_gb_min()` to setup reasonable default simulation parameters for a minimization in the Generalized-Born ensemble. After that, we directly modify the simulation `cntrl` section to apply the positional restraints on the dummy atoms. \n",
    "\n",
    "We will run the simulations with `sander` but it is also possible and faster to run this with `pmemd` or `pmemd.cuda` if you have them installed.\n",
    "\n",
    "<font size='4'><ins>**Note**</ins></font>: The difference here compared to [Tutorial 1](01-tutorial-cb6-but.ipynb#simulate) is that instead of specifying a `simulation.restraint_file` we will specify `simulation.plumed_file`.\n",
    "\n",
    "<font size='4'><ins>**Note**</ins></font>: as explained at the [start](#start) of this tutorial, make sure that the `PLUMED_KERNEL` environment variable is set otherwise the simulation will fail to run."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [],
   "source": [
    "from paprika.simulate import AMBER"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize logger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "from importlib import reload\n",
    "reload(logging)\n",
    "\n",
    "logger = logging.getLogger()\n",
    "logging.basicConfig(\n",
    "    format='%(asctime)s %(message)s',\n",
    "    datefmt='%Y-%m-%d %I:%M:%S %p',\n",
    "    level=logging.INFO\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Energy Minimization\n",
    "Run a quick minimization in every window. Note that we need to specify `simulation.cntrl[\"ntr\"] = 1` to enable the positional restraints on the dummy atoms."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:18:20 AM Running minimization in window a000...\n",
      "2020-10-01 11:18:31 AM Running minimization in window a001...\n",
      "2020-10-01 11:18:42 AM Running minimization in window a002...\n",
      "2020-10-01 11:18:54 AM Running minimization in window a003...\n",
      "2020-10-01 11:19:05 AM Running minimization in window a004...\n",
      "2020-10-01 11:19:17 AM Running minimization in window a005...\n",
      "2020-10-01 11:19:28 AM Running minimization in window a006...\n",
      "2020-10-01 11:19:42 AM Running minimization in window a007...\n",
      "2020-10-01 11:19:54 AM Running minimization in window a008...\n",
      "2020-10-01 11:20:06 AM Running minimization in window a009...\n",
      "2020-10-01 11:20:18 AM Running minimization in window a010...\n",
      "2020-10-01 11:20:30 AM Running minimization in window a011...\n",
      "2020-10-01 11:20:42 AM Running minimization in window a012...\n",
      "2020-10-01 11:20:53 AM Running minimization in window a013...\n",
      "2020-10-01 11:21:04 AM Running minimization in window p000...\n",
      "2020-10-01 11:21:14 AM Running minimization in window p001...\n",
      "2020-10-01 11:21:26 AM Running minimization in window p002...\n",
      "2020-10-01 11:21:38 AM Running minimization in window p003...\n",
      "2020-10-01 11:21:50 AM Running minimization in window p004...\n",
      "2020-10-01 11:22:01 AM Running minimization in window p005...\n",
      "2020-10-01 11:22:15 AM Running minimization in window p006...\n",
      "2020-10-01 11:22:28 AM Running minimization in window p007...\n",
      "2020-10-01 11:22:40 AM Running minimization in window p008...\n",
      "2020-10-01 11:22:54 AM Running minimization in window p009...\n",
      "2020-10-01 11:23:06 AM Running minimization in window p010...\n",
      "2020-10-01 11:23:19 AM Running minimization in window p011...\n",
      "2020-10-01 11:23:31 AM Running minimization in window p012...\n",
      "2020-10-01 11:23:44 AM Running minimization in window p013...\n",
      "2020-10-01 11:23:55 AM Running minimization in window p014...\n",
      "2020-10-01 11:24:07 AM Running minimization in window p015...\n",
      "2020-10-01 11:24:19 AM Running minimization in window p016...\n",
      "2020-10-01 11:24:30 AM Running minimization in window p017...\n"
     ]
    }
   ],
   "source": [
    "for window in window_list:\n",
    "    simulation = AMBER()\n",
    "    simulation.executable = \"sander\"\n",
    "\n",
    "    simulation.path = f\"{work_dir}/{window}/\"\n",
    "    simulation.prefix = \"minimize\"\n",
    "\n",
    "    simulation.topology = \"cb6-but-dum.prmtop\"\n",
    "    simulation.coordinates = \"cb6-but-dum.rst7\"\n",
    "    simulation.ref = \"cb6-but-dum.rst7\"\n",
    "    simulation.plumed_file = \"plumed.dat\"\n",
    "\n",
    "    simulation.config_gb_min()\n",
    "    simulation.cntrl[\"ntr\"] = 1\n",
    "    simulation.cntrl[\"restraint_wt\"] = 50.0\n",
    "    simulation.cntrl[\"restraintmask\"] = \"'@DUM'\"\n",
    "\n",
    "    logger.info(f\"Running minimization in window {window}...\")\n",
    "    simulation.run(overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Production Run\n",
    "Here we will skip the equilibration step and go straight to production!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2020-10-01 11:12:15 AM Running production in window a000...\n",
      "2020-10-01 11:12:25 AM Running production in window a001...\n",
      "2020-10-01 11:12:34 AM Running production in window a002...\n",
      "2020-10-01 11:12:44 AM Running production in window a003...\n",
      "2020-10-01 11:12:55 AM Running production in window a004...\n",
      "2020-10-01 11:13:06 AM Running production in window a005...\n",
      "2020-10-01 11:13:19 AM Running production in window a006...\n",
      "2020-10-01 11:13:29 AM Running production in window a007...\n",
      "2020-10-01 11:13:39 AM Running production in window a008...\n",
      "2020-10-01 11:13:49 AM Running production in window a009...\n",
      "2020-10-01 11:13:59 AM Running production in window a010...\n",
      "2020-10-01 11:14:10 AM Running production in window a011...\n",
      "2020-10-01 11:14:19 AM Running production in window a012...\n",
      "2020-10-01 11:14:29 AM Running production in window a013...\n",
      "2020-10-01 11:14:39 AM Running production in window p000...\n",
      "2020-10-01 11:14:49 AM Running production in window p001...\n",
      "2020-10-01 11:14:59 AM Running production in window p002...\n",
      "2020-10-01 11:15:10 AM Running production in window p003...\n",
      "2020-10-01 11:15:21 AM Running production in window p004...\n",
      "2020-10-01 11:15:33 AM Running production in window p005...\n",
      "2020-10-01 11:15:43 AM Running production in window p006...\n",
      "2020-10-01 11:15:55 AM Running production in window p007...\n",
      "2020-10-01 11:16:05 AM Running production in window p008...\n",
      "2020-10-01 11:16:18 AM Running production in window p009...\n",
      "2020-10-01 11:16:31 AM Running production in window p010...\n",
      "2020-10-01 11:16:45 AM Running production in window p011...\n",
      "2020-10-01 11:16:56 AM Running production in window p012...\n",
      "2020-10-01 11:17:05 AM Running production in window p013...\n",
      "2020-10-01 11:17:16 AM Running production in window p014...\n",
      "2020-10-01 11:17:26 AM Running production in window p015...\n",
      "2020-10-01 11:17:37 AM Running production in window p016...\n",
      "2020-10-01 11:17:49 AM Running production in window p017...\n"
     ]
    }
   ],
   "source": [
    "for window in window_list:\n",
    "    simulation = AMBER()\n",
    "    simulation.executable = \"sander\"\n",
    "    \n",
    "    simulation.path = f\"{work_dir}/{window}/\"\n",
    "    simulation.prefix = \"production\"\n",
    "\n",
    "    simulation.topology = \"cb6-but-dum.prmtop\"\n",
    "    simulation.coordinates = \"minimize.rst7\"\n",
    "    simulation.ref = \"cb6-but-dum.rst7\"\n",
    "    simulation.plumed_file = \"plumed.dat\"\n",
    "\n",
    "    simulation.config_gb_md()\n",
    "    simulation.cntrl[\"ntr\"] = 1\n",
    "    simulation.cntrl[\"restraint_wt\"] = 50.0\n",
    "    simulation.cntrl[\"restraintmask\"] = \"'@DUM'\"\n",
    "    \n",
    "    logger.info(f\"Running production in window {window}...\")\n",
    "    simulation.run(overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once the simulation is completed, we can using the `analysis` module to determine the binding free energy. We supply the location of the parameter information, a string or list for the file names (wildcards supported), the location of the windows, and the restraints on the guest.\n",
    "\n",
    "In this example, we use the method `ti-block` which determines the free energy using **t**hermodynamic **i**integration and then estimates the standard error of the mean at each data point using blocking analysis. Bootstrapping it used to determine the uncertainty of the  full thermodynamic integral for each phase.\n",
    "\n",
    "After running `compute_free_energy()`, a dictionary called `results` will be populated, that contains the free energy and SEM for each phase of the simulation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "import paprika.analysis as analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "free_energy = analysis.fe_calc()\n",
    "free_energy.topology = \"cb6-but-dum.prmtop\"\n",
    "free_energy.trajectory = 'production*.nc'\n",
    "free_energy.path = work_dir\n",
    "free_energy.restraint_list = guest_restraints\n",
    "free_energy.collect_data()\n",
    "free_energy.methods = ['ti-block']\n",
    "free_energy.ti_matrix = \"full\"\n",
    "free_energy.boot_cycles = 1000\n",
    "free_energy.compute_free_energy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also need to calculate the free-energy cost of releasing the restraints on the guest molecule."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "free_energy.compute_ref_state_work([\n",
    "    guest_restraints[0], guest_restraints[1], None, None,\n",
    "    guest_restraints[2], None\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we add the free-energies together and combine the uncertainties to get the binding-free energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "binding_affinity = -1 * (\n",
    "    free_energy.results[\"attach\"][\"ti-block\"][\"fe\"] + \\\n",
    "    free_energy.results[\"pull\"][\"ti-block\"][\"fe\"] + \\\n",
    "    free_energy.results[\"ref_state_work\"]\n",
    ")\n",
    "\n",
    "sem = np.sqrt(\n",
    "    free_energy.results[\"attach\"][\"ti-block\"][\"sem\"]**2 + \\\n",
    "    free_energy.results[\"pull\"][\"ti-block\"][\"sem\"]**2\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The binding affinity of butane to cucurbit[6]uril = -7.24 +/- 6.52 kcal/mol\n"
     ]
    }
   ],
   "source": [
    "print(f\"The binding affinity of butane to cucurbit[6]uril = {binding_affinity.magnitude:0.2f} +/- {sem.magnitude:0.2f} kcal/mol\")"
   ]
  }
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