PDBbind.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>PDBbind Database</h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4.1-16-gb8cc8d0\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, division, unicode_literals\n",
    "\n",
    "import oddt\n",
    "from oddt.datasets import pdbbind\n",
    "\n",
    "oddt.toolkit.image_size = (400, 400)\n",
    "\n",
    "print(oddt.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Download a dataset from [PDBbind](http://www.pdbbind.org.cn/) and unpack (I used [core-set 2016](http://www.pdbbind.org.cn/download/pdbbind_v2016_core.tar.gz))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "wget -qO- http://www.pdbbind.org.cn/download/pdbbind_v2016_core.tar.gz | tar xz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "directory = './core-set/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use the pdbbind class. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pdbbind_database = pdbbind(home=directory, \n",
    "    version='2016', \n",
    "    default_set='core') # Available sets in wrapper: core, refined, general_PL (general for 2007)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get one target or iterate over all of them. To do it you can use PDB ID of a target or an index from list in INDEX file (INDEX_refined_data.2016)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The class has two properties: ids and activities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of targets: 290\n",
      "First ten targets: ['3ao4', '3gv9', '1uto', '1ps3', '4ddk', '4jsz', '3g2z', '3dxg', '3l7b', '3gr2']\n"
     ]
    }
   ],
   "source": [
    "all_ids = pdbbind_database.ids\n",
    "print('Number of targets:', len(all_ids))\n",
    "print('First ten targets:', all_ids[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First ten activities: [2.07, 2.12, 2.27, 2.28, 2.29, 2.3, 2.36, 2.4, 2.4, 2.52]\n"
     ]
    }
   ],
   "source": [
    "all_activities = pdbbind_database.activities\n",
    "print('First ten activities:', all_activities[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's choose one target."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "target = pdbbind_database[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can always check PDB ID."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3ao4'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "target.id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Target` has three properties: protein, pocket and ligand. All of them are of `oddt.tolkit.Molecule` class."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's find the largest ligand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Target ID: 3ag9 \n",
      "Number of atoms: 140\n"
     ]
    }
   ],
   "source": [
    "max_atoms = 0\n",
    "\n",
    "for target in pdbbind_database:\n",
    "    if max_atoms < len(target.ligand.atoms):\n",
    "        max_atoms = len(target.ligand.atoms)\n",
    "        largest = target\n",
    "\n",
    "print('Target ID:', largest.id, '\\nNumber of atoms:', max_atoms)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
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++efZRazaKTU1NS8vr/11HvbACAuBJQgEFnSg4GA6c4bef58WLGDvxWtoaPD29n7hhRdS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\nOwJLnyGwQEyUSqW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\" 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      ],
      "text/plain": [
       "<img 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\" alt=\"3ag9_ligand\">"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "largest_ligand = largest.ligand\n",
    "largest_ligand.removeh()\n",
    "largest_ligand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to check activity, you can use the `sets` dict."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8.05"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pdbbind_database.sets['core'][largest.id]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}