Sequence_aligment.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "import os\n",
    "import glob\n",
    "import linecache\n",
    "import itertools\n",
    "import shutil\n",
    "from pprint import pprint\n",
    "import itertools\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib as mt\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import pylab as pl\n",
    "import matplotlib.gridspec as gridspec\n",
    "import numpy as np\n",
    "import scipy.stats as stats\n",
    "\n",
    "from rdkit import Chem\n",
    "import rdkit.Chem.Recap as Recap\n",
    "from rdkit.Chem import AllChem\n",
    "\n",
    "import mmtf\n",
    "\n",
    "from Bio import pairwise2\n",
    "from Bio.pairwise2 import format_alignment\n",
    "import itertools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "files='../CD44_crystal_structures/Crystals/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "structures=[]\n",
    "for file in os.listdir(files):\n",
    "    if '.pdb' in file:\n",
    "        structures.append (file.split('_')[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "unq=list(set(structures))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "human=['1uuh','4pz3','4pz4']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "new=list(set(unq)-set(human))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_order=human+sorted(new)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets=pd.DataFrame()\n",
    "pdb_id=[]\n",
    "title=[]\n",
    "classification=[]\n",
    "sequence=[]\n",
    "method=[]\n",
    "resolution=[]\n",
    "date=[]\n",
    "for pdb_code in new_order:\n",
    "    data=mmtf.fetch (pdb_code)\n",
    "    pdb_id.append (data.structure_id)\n",
    "    title.append (data.title)\n",
    "    classification.append (data.entity_list[0]['description'])\n",
    "    sequence.append (data.entity_list[0]['sequence'])\n",
    "    method.append (data.experimental_methods[0])\n",
    "    resolution.append (data.resolution)\n",
    "    date.append (data.release_date)\n",
    "\n",
    "targets['PDB_id'] = pd.Series(pdb_id)\n",
    "targets['Classification'] = pd.Series(classification)\n",
    "targets['Title'] = pd.Series(title)\n",
    "targets['Sequence'] = pd.Series(sequence)\n",
    "targets['Method'] = pd.Series(method)\n",
    "targets['Resolution'] = pd.Series(resolution)\n",
    "targets['Release_date'] = pd.Series(date)\n",
    "o=[x.lower() for x in targets['Classification']]\n",
    "targets['Classification']=pd.Series(o,targets.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>PDB_id</th>\n",
       "      <th>Classification</th>\n",
       "      <th>Title</th>\n",
       "      <th>Sequence</th>\n",
       "      <th>Method</th>\n",
       "      <th>Resolution</th>\n",
       "      <th>Release_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>1UUH</td>\n",
       "      <td>cd44 antigen</td>\n",
       "      <td>Hyaluronan binding domain of human CD44</td>\n",
       "      <td>AQIDLNITCRFAGVFHVEKNGRYSISRTEAADLCKAFNSTLPTMAQ...</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>2.200</td>\n",
       "      <td>2004-03-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>4PZ3</td>\n",
       "      <td>cd44 antigen</td>\n",
       "      <td>High-resolution crystal structure of the human...</td>\n",
       "      <td>AMAQIDLNITCRFAGVFHVEKNGRYSISRTEAADLCKAFNSTLPTM...</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>1.083</td>\n",
       "      <td>2014-09-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4PZ4</td>\n",
       "      <td>cd44 antigen</td>\n",
       "      <td>High-resolution crystal structure of the human...</td>\n",
       "      <td>AMAQIDLNITCRFAGVFHVEKNGRYSISRTEAADLCKAFNSTLPTM...</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>1.600</td>\n",
       "      <td>2014-09-17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>2JCP</td>\n",
       "      <td>cd44 antigen</td>\n",
       "      <td>The hyaluronan binding domain of murine CD44</td>\n",
       "      <td>MQQIDLNVTCRYAGVFHVEKNGRYSISRTEAADLCQAFNSTLPTMD...</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>1.300</td>\n",
       "      <td>2007-01-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>2JCQ</td>\n",
       "      <td>cd44 antigen</td>\n",
       "      <td>The hyaluronan binding domain of murine CD44 i...</td>\n",
       "      <td>MNQIDLNVTCRYAGVFHVEKNGRYSISRTEAADLCQAFNSTLPTMD...</td>\n",
       "      <td>X-RAY DIFFRACTION</td>\n",
       "      <td>1.250</td>\n",
       "      <td>2007-01-30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  PDB_id Classification                                              Title  \\\n",
       "0   1UUH   cd44 antigen            Hyaluronan binding domain of human CD44   \n",
       "1   4PZ3   cd44 antigen  High-resolution crystal structure of the human...   \n",
       "2   4PZ4   cd44 antigen  High-resolution crystal structure of the human...   \n",
       "3   2JCP   cd44 antigen       The hyaluronan binding domain of murine CD44   \n",
       "4   2JCQ   cd44 antigen  The hyaluronan binding domain of murine CD44 i...   \n",
       "\n",
       "                                            Sequence             Method  \\\n",
       "0  AQIDLNITCRFAGVFHVEKNGRYSISRTEAADLCKAFNSTLPTMAQ...  X-RAY DIFFRACTION   \n",
       "1  AMAQIDLNITCRFAGVFHVEKNGRYSISRTEAADLCKAFNSTLPTM...  X-RAY DIFFRACTION   \n",
       "2  AMAQIDLNITCRFAGVFHVEKNGRYSISRTEAADLCKAFNSTLPTM...  X-RAY DIFFRACTION   \n",
       "3  MQQIDLNVTCRYAGVFHVEKNGRYSISRTEAADLCQAFNSTLPTMD...  X-RAY DIFFRACTION   \n",
       "4  MNQIDLNVTCRYAGVFHVEKNGRYSISRTEAADLCQAFNSTLPTMD...  X-RAY DIFFRACTION   \n",
       "\n",
       "   Resolution Release_date  \n",
       "0       2.200   2004-03-04  \n",
       "1       1.083   2014-09-17  \n",
       "2       1.600   2014-09-17  \n",
       "3       1.300   2007-01-30  \n",
       "4       1.250   2007-01-30  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "targets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "targets.to_csv('../CD44_crystal_structures/aligment.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "f= open('../CD44_crystal_structures/sequence_aligment.txt',\"w+\")\n",
    "size=len(list(targets.index))\n",
    "hmap=np.empty(shape=(size,size))\n",
    "for i in targets.index:\n",
    "    for j in targets.index:\n",
    "        a=targets.loc[i].at['Sequence']\n",
    "        b=targets.loc[j].at['Sequence']\n",
    "        alignment= pairwise2.align.globalxx(a, b,score_only=True)\n",
    "        identity=((alignment*100)/len(b))\n",
    "        hmap[i,j]=identity\n",
    "        line= (targets.loc[i].at['PDB_id'].split('_')[0]+' vs '+targets.loc[j].at['PDB_id'].split('_')[0]+' : '+str(identity)+'\\n')\n",
    "        f.write (line)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,12))\n",
    "plt.imshow (hmap,cmap='Blues', interpolation='nearest')\n",
    "plt.xticks (targets.index,targets['PDB_id'],rotation=90)\n",
    "plt.yticks (targets.index,targets['PDB_id'])\n",
    "ax.xaxis.tick_top()\n",
    "m=plt.colorbar(shrink=0.7,orientation='horizontal',spacing='uniform',pad=0.02)\n",
    "m.set_label ('% of Identity')\n",
    "plt.tick_params ('both',width=2,labelsize=10)\n",
    "plt.savefig('../CD44_crystal_structures/aligment_hmap.svg',quality=95,dpi=600,format='svg',transparent=False)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Chem_3",
   "language": "python",
   "name": "chem_3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}