Docking_positions_evaluation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "01f75b86-d71f-48ee-b3e5-7dbe4fee8232",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import warnings\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from Bio.PDB import PDBParser, PDBIO, Superimposer\n",
    "from Bio.PDB.PDBIO import PDBIO\n",
    "from Bio.PDB.PDBExceptions import PDBConstructionWarning\n",
    "\n",
    "# Modules fix_pdb.py and optimize_ligand.py also support command line interface\n",
    "from fix_pdb import fix_pdb\n",
    "from optimize_ligand import optimize_ligand\n",
    "from lsalign_tools import extract_aligned_ligand, get_alignment_report\n",
    "\n",
    "\n",
    "warnings.simplefilter('ignore', PDBConstructionWarning)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da98d73c-4097-463d-a74d-53d3221655d8",
   "metadata": {},
   "source": [
    "### Setting up and creating working directories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "59e08d2c-3287-4360-a465-5766b2df9338",
   "metadata": {},
   "outputs": [],
   "source": [
    "PROTEIN_PDB = \"./input/2xp2.pdb\"         # Complex of the studied protein\n",
    "LIGANDS_DIR = \"./input/ligands/set_1/\"   # Directory with query ligands\n",
    "\n",
    "INTERMEDIATE_DIR = \"./intermediate_results/\"\n",
    "ENERGY_GRID_DIR = f\"{INTERMEDIATE_DIR}energy_grid_maps/\"\n",
    "OUTPUT_DIR = \"./output/\"\n",
    "LOG_DIR = \"./logs/\"\n",
    "\n",
    "\n",
    "dirs_to_create = (\n",
    "    f'{OUTPUT_DIR}docked_ligands',\n",
    "    f'{OUTPUT_DIR}filtered_docked_poses',\n",
    "    f'{OUTPUT_DIR}reports',\n",
    "    f'{INTERMEDIATE_DIR}ligands',\n",
    "    f'{ENERGY_GRID_DIR}AutoDock'\n",
    ")\n",
    "\n",
    "for _dir in dirs_to_create:\n",
    "    !mkdir -p $_dir"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729e910e-2509-4d98-8d82-5614c828aae3",
   "metadata": {},
   "source": [
    "### Functions of the main metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "697327e5-b9b6-46ee-a169-cc363a3a8728",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_RMSD(target, queries, ignore_hydrogens=True):\n",
    "    '''\n",
    "    Computes RMSD between taeget and every query structure.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    target : Bio.PDB.Structure.Structure\n",
    "        Target structure.\n",
    "    queries : Bio.PDB.Structure.Structure\n",
    "        Query structures (may content multiple models).\n",
    "    ignore_hydrogens: bool, optional\n",
    "        To take into account or not the hydrogen atoms.\n",
    "        The default is False wich means skip hydrogens.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    RMSD : dict\n",
    "        RMSD for each query model.\n",
    "    '''\n",
    "    target_atoms = next(target.get_residues())    \n",
    "    RMSD = {}\n",
    "    for model in queries:\n",
    "        query_atoms = next(model.get_residues())\n",
    "        rmsd = 0\n",
    "        atom_counter = 0\n",
    "        for atom in target_atoms:\n",
    "            if ignore_hydrogens and atom.element == 'H':\n",
    "                continue\n",
    "            t, q = atom.get_coord(), query_atoms[atom.get_name()].get_coord()\n",
    "            rmsd += ((t - q)**2).sum()\n",
    "            atom_counter += 1\n",
    "            \n",
    "        RMSD[model.get_id()] = (rmsd / atom_counter)**0.5\n",
    "        \n",
    "    return RMSD\n",
    "\n",
    "\n",
    "def compute_rotation_trace(target, queries, ignore_hydrogens=True):\n",
    "    '''\n",
    "    Computes rotation trace between taeget and every query structure.\n",
    "    Rotation trace may be considered as measure of angle between \n",
    "    structures.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    target : Bio.PDB.Structure.Structure\n",
    "        Target structure.\n",
    "    queries : Bio.PDB.Structure.Structure\n",
    "        Query structures (may content multiple models).\n",
    "    ignore_hydrogens: bool, optional\n",
    "        To take into account or not the hydrogen atoms.\n",
    "        The default is False wich means skip hydrogens.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    rotation : dict\n",
    "        Rotation trace for each query model.\n",
    "    '''\n",
    "    target_atoms = sorted(atom for atom in target.get_atoms() if atom.element != 'H')\n",
    "    sup = Superimposer()\n",
    "    rotation = {}\n",
    "    for model in queries:\n",
    "        query_atoms = sorted(atom for atom in model.get_atoms() if atom.element != 'H')\n",
    "        sup.set_atoms(target_atoms, query_atoms)\n",
    "        rotation[model.get_id()] = abs(np.trace(sup.rotran[0]))\n",
    "    \n",
    "    return rotation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cba6dc4-32ae-4acd-a889-47103b1c3464",
   "metadata": {},
   "source": [
    "## Preparation of protein model end energy grid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b05c235-17b3-4f6c-ac83-2cecfac222d2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "protein = os.path.basename(PROTEIN_PDB)\n",
    "\n",
    "# Fixing protein structure and extracting native ligand\n",
    "!./build_model \\\n",
    "    -f    $f'{PROTEIN_PDB}' \\\n",
    "    -oref $f'{INTERMEDIATE_DIR}{protein.rstrip('.pdb')}_ligand_reference.pdb' \\\n",
    "    -omm  $f'{INTERMEDIATE_DIR}corrected_{protein}' \\\n",
    "  \n",
    "# # Checking Protein Structure for Potential Problems \n",
    "# # SUPRESSED! (Duplicates the functionality of the previous command)\n",
    "# !./leadfinder \\\n",
    "#     --check-only \\\n",
    "#     --protein $f'{INTERMEDIATE_DIR}corrected_{protein}'\n",
    "\n",
    "# Creating and Saving Energy Grid Maps\n",
    "!./leadfinder \\\n",
    "    --grid-only \\\n",
    "    --save-grid        $f'{ENERGY_GRID_DIR}{protein.rstrip('.pdb')}_energy_grid_map.bin' \\\n",
    "    --protein          $f'{INTERMEDIATE_DIR}corrected_{protein}' \\\n",
    "    --ligand-reference $f'{INTERMEDIATE_DIR}{protein.rstrip('.pdb')}_ligand_reference.pdb'\n",
    "\n",
    "# Converting Energy Grid to AutoDock map format\n",
    "!./leadfinder \\\n",
    "    --grid-only \\\n",
    "    --load-grid $f'{ENERGY_GRID_DIR}{protein.rstrip('.pdb')}_energy_grid_map.bin' \\\n",
    "    --save-grid $f'{ENERGY_GRID_DIR}AutoDock/{protein.rstrip('.pdb')}_energy_grid_map.map'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cab8f5ae-e4bd-4cb1-81cb-dce529ab84a1",
   "metadata": {},
   "source": [
    "## Docking and evaluation of ligands positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bd261599-3313-4872-a363-636c3fb61b0b",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing:  1a.mol\n",
      "Processing:  1b.mol\n",
      "Processing:  1c.mol\n",
      "Processing:  1d.mol\n",
      "Processing:  1e.mol\n",
      "Processing:  1f.mol\n",
      "Processing:  2a.mol\n",
      "Processing:  2b.mol\n",
      "Processing:  2c.mol\n",
      "Processing:  3a.mol\n",
      "Processing:  3b.mol\n",
      "Processing:  3c.mol\n",
      "Processing:  3d.mol\n",
      "Processing:  3e.mol\n",
      "Processing:  3f.mol\n",
      "Processing:  3g.mol\n",
      "Processing:  3h.mol\n",
      "Processing:  3i.mol\n",
      "Processing:  3j.mol\n"
     ]
    }
   ],
   "source": [
    "# Preparation of the native ligand for alignment \n",
    "!obabel \\\n",
    "    $f'{INTERMEDIATE_DIR}{protein.rstrip('.pdb')}_ligand_reference.pdb' \\\n",
    "    -O $f'{INTERMEDIATE_DIR}{protein.rstrip('.pdb')}_ligand_reference.mol2'\n",
    "\n",
    "# Ligands processing\n",
    "docking_poses_evaluation = {}\n",
    "ligands = sorted(ligand for ligand in os.listdir(LIGANDS_DIR) if ligand.endswith('.mol'))\n",
    "for ligand_mol in ligands:\n",
    "    print('Processing: ', ligand_mol)\n",
    "    ligand = ligand_mol.rstrip('.mol')\n",
    "    \n",
    "    # Docking\n",
    "    optimize_ligand(f'{LIGANDS_DIR}{ligand_mol}', f'{INTERMEDIATE_DIR}ligands/{ligand}.pdb')\n",
    "    \n",
    "    !./leadfinder \\\n",
    "        --load-grid $f'{ENERGY_GRID_DIR}{protein.rstrip('.pdb')}_energy_grid_map.bin' \\\n",
    "        --ligand $f'{INTERMEDIATE_DIR}ligands/{ligand}.pdb' \\\n",
    "        --output-poses $f'{OUTPUT_DIR}docked_ligands/{ligand}_docking_poses.pdb' \\\n",
    "        --text-report $f'{OUTPUT_DIR}reports/{ligand}_docking_report.txt'\n",
    "    \n",
    "    # Fixing docking output\n",
    "    fix_pdb(f'{INTERMEDIATE_DIR}ligands/{ligand}.pdb', \n",
    "            f'{OUTPUT_DIR}docked_ligands/{ligand}_docking_poses.pdb')\n",
    "    \n",
    "    \n",
    "    # Alignment of the considered ligand with native ligand using LSalign software\n",
    "    # https://doi.org/10.1093/bioinformatics/bty081\n",
    "    !obabel $f'{LIGANDS_DIR}{ligand_mol}' -O $f'{INTERMEDIATE_DIR}ligands/{ligand}.mol2'\n",
    "\n",
    "    !LSalign \\\n",
    "        $f'{INTERMEDIATE_DIR}ligands/{ligand}.mol2' \\\n",
    "        $f'{INTERMEDIATE_DIR}{protein.rstrip(\".pdb\")}_ligand_reference.mol2' \\\n",
    "        -m $f'{INTERMEDIATE_DIR}ligands/{ligand}_translation_matrix.txt' \\\n",
    "        -a $f'{INTERMEDIATE_DIR}ligands/{ligand}_alignment_information.txt' \\\n",
    "        -o $f'{INTERMEDIATE_DIR}ligands/{ligand}_alignment.pdb' \\\n",
    "        -rf 1 -md 1 -acc 0 \\\n",
    "        > $f'{INTERMEDIATE_DIR}ligands/{ligand}_alignment_report.txt'\n",
    "    \n",
    "    extract_aligned_ligand(f'{INTERMEDIATE_DIR}ligands/{ligand}.pdb',\n",
    "                           f'{INTERMEDIATE_DIR}ligands/{ligand}_alignment.pdb',\n",
    "                           f'{INTERMEDIATE_DIR}ligands/{ligand}_rotated.pdb')\n",
    "    \n",
    "    \n",
    "    # Evaluation of the similarity of the ligand positions with the original structure\n",
    "    parser = PDBParser()\n",
    "    ligand_structure = parser.get_structure('ligand', f'{INTERMEDIATE_DIR}ligands/{ligand}_rotated.pdb')\n",
    "    docked_positions = parser.get_structure('docked', f'{OUTPUT_DIR}docked_ligands/{ligand}_docking_poses.pdb')\n",
    "\n",
    "    # Not working because of LSalign translation matrix generating bug! \n",
    "    # Using extract_aligned_ligand instead.\n",
    "    ##########################################################################################\n",
    "    # M = get_translation_matrix(f'{INTERMEDIATE_DIR}ligands/{ligand}_translation_matrix.txt')\n",
    "    # for atom in ligand_structure.get_atoms():\n",
    "    #     # atom.transform(M, T)\n",
    "\n",
    "    # io = PDBIO()\n",
    "    # io.set_structure(ligand_structure)\n",
    "    # io.save(f'{INTERMEDIATE_DIR}ligands/{ligand}_rotated.pdb')\n",
    "    ##########################################################################################\n",
    "        \n",
    "    docking_poses_evaluation[ligand] = {\n",
    "        'alignment_report': get_alignment_report(f'{INTERMEDIATE_DIR}ligands/{ligand}_alignment_report.txt'),\n",
    "        'RMSD': sorted(compute_RMSD(ligand_structure, docked_positions).items(), key=lambda x: x[1]),\n",
    "        'rotation': sorted(compute_rotation_trace(ligand_structure, docked_positions).items(), key=lambda x: x[1])\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fa931b06-15b6-4841-8d03-db005adecbea",
   "metadata": {},
   "source": [
    "## Plotting the similarity of query ligand positions with a naive ligand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "154259cb-d425-470b-b3ff-7d05d5cb71ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1080 with 20 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "N_COLS = 4\n",
    "TILE_WIDTH = 5\n",
    "TILE_HIGHT = 3\n",
    "THRESHOLD = 3    # Take a look at the charts and set this parameter to see discarded models\n",
    "\n",
    "n_ligands = len(docking_poses_evaluation)\n",
    "n_rows = n_ligands // N_COLS + bool(n_ligands % N_COLS)\n",
    "\n",
    "fig, axes = plt.subplots(n_rows, N_COLS, figsize=(TILE_WIDTH * N_COLS, TILE_HIGHT * n_rows))\n",
    "\n",
    "docking_poses_evaluation_iter = iter(sorted(docking_poses_evaluation.items()))\n",
    "y_limit = (0, 0)\n",
    "x_limit = (0, 0)\n",
    "i = 0\n",
    "for ax in axes.flatten():\n",
    "    if i >= (n_rows - 1)*N_COLS:\n",
    "        ax.set_xlabel('Docked pose', fontsize=13)\n",
    "    if not i % N_COLS:\n",
    "        ax.set_ylabel('RMSD over aligned struct.\\n/ RMSD_lb', fontsize=13)\n",
    "        \n",
    "    try:\n",
    "        ligand, metrics = next(docking_poses_evaluation_iter)\n",
    "    except StopIteration:\n",
    "        # ax.set_axis_off()\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_xticks([])\n",
    "        ax.set_yticks([])\n",
    "        ax.spines['top'].set_visible(False)\n",
    "        ax.spines['right'].set_visible(False)\n",
    "        ax.spines['bottom'].set_visible(False)\n",
    "        ax.spines['left'].set_visible(False)\n",
    "        continue\n",
    "    \n",
    "    ax.set_title(ligand, fontsize=20)\n",
    "    \n",
    "    RMSD_lb = metrics['alignment_report']['RMSD_lb']\n",
    "    box = dict(boxstyle='round', facecolor='w', alpha=0.5)\n",
    "    ax.text(0.03, 0.87, f'RMSD_lb = {RMSD_lb:.2f}', \n",
    "            fontsize=15, transform=ax.transAxes, bbox=box)\n",
    "    \n",
    "    RMSD, rotation = metrics['RMSD'], metrics['rotation']\n",
    "    \n",
    "    n = len(RMSD)\n",
    "    docking_poses = (str(record[0]) for record in RMSD)\n",
    "    docking_rmsd = tuple(record[1] / RMSD_lb for record in RMSD)\n",
    "    ax.bar(range(n), docking_rmsd)\n",
    "    ax.set_xticks(range(n))\n",
    "    ax.set_xticklabels(docking_poses)\n",
    "    \n",
    "    ax.axhline(THRESHOLD, color='r')\n",
    "    \n",
    "    y_limit = max(y_limit, ax.get_ylim())\n",
    "    x_limit = max(x_limit, ax.get_xlim(), key=lambda x: x[1])\n",
    "    i += 1\n",
    "\n",
    "for ax in axes.flatten():\n",
    "    ax.set_ylim(y_limit)\n",
    "    ax.set_xlim(x_limit)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4154ce7-4c9b-444d-975b-66f697eaddf1",
   "metadata": {},
   "source": [
    "The graphs for each position of each ligand show the metric RMSD / RMSD_lb, where\n",
    "- RMSD is a root mean square error between current position and position aligned with native ligand by LSalign tool.\n",
    "- RMSD_lb is a metric for the quality of alignment obtained using LSalign (see documentation for more details). RMSD_lb stands in the denominator because with a low quality of alignment of the ligand with the native one, the metric becomes less representative and the exclusion criterion should be more conservative.\n",
    "\n",
    "The red line indicates the threshold exceeding which leads to the exclusion of the model from consideration. The threshold is set manually after visual analysis of the graphs."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "900f6112-7027-431c-8f48-41d1b05246d8",
   "metadata": {},
   "source": [
    "## Exclusion of inadequate docking positions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd09e1ba-7ae8-47a4-af6b-7a21180138b0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "for ligand_mol in ligands:\n",
    "    ligand = ligand_mol.rstrip('.mol')   \n",
    "    \n",
    "    RMSD_lb = docking_poses_evaluation[ligand]['alignment_report']['RMSD_lb']\n",
    "    bad_models = [model[0] for model in docking_poses_evaluation[ligand]['RMSD'] if model[1] / RMSD_lb > THRESHOLD]\n",
    "    \n",
    "    parser = PDBParser()\n",
    "    docked_positions = parser.get_structure('docked', f'{OUTPUT_DIR}docked_ligands/{ligand}_docking_poses.pdb')\n",
    "    for model in bad_models:\n",
    "        del docked_positions[model]\n",
    "        \n",
    "    io = PDBIO()\n",
    "    io.set_structure(docked_positions)\n",
    "    io.save(f'{OUTPUT_DIR}filtered_docked_poses/{ligand}_filtered_docking_poses.pdb')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
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