H-bond_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Analysis script using MDAnalysis to perform h-bond analysis in between the Latrophilin and the Teneurin proteins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing MDAnalysis\n",
    "import MDAnalysis as mda\n",
    "\n",
    "# importing hbonds analysis from mda\n",
    "from MDAnalysis.analysis import hbonds\n",
    "\n",
    "#importing numpy\n",
    "import numpy as np\n",
    "\n",
    "# importing matplotlib to perform figures\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load a pdb file (last frame of the trajectory)\n",
    "u = mda.Universe(\"nowat.gro\",\"nowat.xtc\")\n",
    "\n",
    "# define prot1 (index, resid, etc ..) see MDAnalysis language selection: https://www.mdanalysis.org/docs/documentation_pages/selections.html\n",
    "sel_prot1 = \"bynum 1:4074\"\n",
    "\n",
    "# define prot2 (index, resid, etc ..) see MDAnalysis language selection: https://www.mdanalysis.org/docs/documentation_pages/selections.html\n",
    "sel_prot2 = \"bynum 4075:8148\"\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "HBonds frame  5000:  5001/5001 [100.0%]\n"
     ]
    }
   ],
   "source": [
    "#use MDanalysis HydrogenBondAnalysis to calculate the hbonds in between the 2 selections\n",
    "hbonds_calc= hbonds.HydrogenBondAnalysis(u, sel_prot1, sel_prot2, distance=3.0, angle=120.0)\n",
    "hbonds_calc.run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import defaultdict\n",
    "\n",
    "# create a numpy array with the data extracted from hbond calculations - see block above\n",
    "hbond_timeseries = np.array(hbonds_calc.timeseries)\n",
    "\n",
    "percentage_traj_unused=0.2\n",
    "nb_frames_unused=int(percentage_traj_unused*len(hbond_timeseries))\n",
    "nb_frames_used=len(hbond_timeseries)-nb_frames_unused\n",
    "\n",
    "\n",
    "# create dictionnaries for which the values are lists. \n",
    "# These will be filled with the name of atoms creating hbonds\n",
    "dict_lect_res_hbond=defaultdict(list)\n",
    "dict_ten_res_hbond=defaultdict(list)  \n",
    "\n",
    "\n",
    "\n",
    "# iterate on the array to extract the res. as key and the atoms which can create hbonds as values \n",
    "for elt in hbond_timeseries[nb_frames_unused:]:\n",
    "    list_res = elt\n",
    "    \n",
    "    for i in list_res:\n",
    "        resA= int(i[2].split(':')[0][3:])\n",
    "        resB= int(i[3].split(':')[0][3:])\n",
    "        #print i\n",
    "        #print (str(resA)+\" create hbonds with \"+str(resB))\n",
    "\n",
    "        if int(i[0])<4074:\n",
    "            dict_lect_res_hbond[resA].append(i[2].split(':')[1])\n",
    "        else:\n",
    "            dict_ten_res_hbond[resA].append(i[2].split(':')[1]) \n",
    "            \n",
    "        if int(i[1])<4074:\n",
    "            dict_lect_res_hbond[resB].append(i[3].split(':')[1])\n",
    "        else:\n",
    "            dict_ten_res_hbond[resB].append(i[3].split(':')[1]) \n",
    "       \n",
    "# sort the 2 dictionnaries in function of the res nb\n",
    "dict_lect_res_hbond_sorted= sorted({k: list(set(v)) for k, v in dict_lect_res_hbond.iteritems()}.iteritems(),key=lambda x: int(x[0]))\n",
    "dict_ten_res_hbond_sorted= sorted({k: list(set(v)) for k, v in dict_ten_res_hbond.iteritems()}.iteritems(),key=lambda x: int(x[0]))\n",
    "\n",
    "# create empty lists of atoms to be used to create the matrix\n",
    "list_hbond_lect=[]\n",
    "list_hbond_ten=[]\n",
    "  \n",
    "# concatenate res. nb and atom names to create a list unique identifiers    \n",
    "for list_res_hbond in dict_lect_res_hbond_sorted:\n",
    "    #print list_res_hbond[0]\n",
    "    #print list_res_hbond[1]\n",
    "    for atom_hbond in list_res_hbond[1]:\n",
    "        atom_name=str(list_res_hbond[0])+\":\"+atom_hbond \n",
    "        list_hbond_lect.append(atom_name)\n",
    "\n",
    "# concatenate res. nb and atom names to create a list unique identifiers       \n",
    "for list_res_hbond in dict_ten_res_hbond_sorted:\n",
    "    #print list_res_hbond[0]\n",
    "    #print list_res_hbond[1]\n",
    "    for atom_hbond in list_res_hbond[1]:\n",
    "        atom_name=str(list_res_hbond[0])+\":\"+atom_hbond \n",
    "        list_hbond_ten.append(atom_name)        \n",
    "        \n",
    "        \n",
    "#print list_hbond_lect\n",
    "#print list_hbond_ten\n",
    "\n",
    "# Create the interaction matrix for all the hbonds\n",
    "w = len(list_hbond_ten)\n",
    "h = len(list_hbond_lect)\n",
    "Matrix_inter_hbond = np.zeros((w,h))\n",
    "\n",
    "# fill the matrix \n",
    "for elt in hbond_timeseries[nb_frames_unused:]:\n",
    "    list_res = elt\n",
    "    \n",
    "    for i in list_res:\n",
    "        resA= i[2][3:]\n",
    "        resB= i[3][3:]\n",
    "        \n",
    "        for j in range(0,len(list_hbond_ten)):\n",
    "            for k in range(0,len(list_hbond_lect)):\n",
    "                #print list_res_lect_int01[j]\n",
    "                #print(list_res_ten_int01[j])   \n",
    "                if(list_hbond_ten[j]==resA and list_hbond_lect[k]==resB):\n",
    "                    #print(list_res_ten_int01_sorted[j])\n",
    "                    #print(list_res_lect_int01_sorted[k])\n",
    "                    Matrix_inter_hbond[j][k] =  Matrix_inter_hbond[j][k]+1\n",
    "                    break\n",
    "                if(list_hbond_ten[j]==resB and list_hbond_lect[k]==resA):\n",
    "                    #print(list_res_ten_int01_sorted[j])\n",
    "                    #print(list_res_lect_int01_sorted[k])\n",
    "                    Matrix_inter_hbond[j][k] =  Matrix_inter_hbond[j][k]+1\n",
    "                    break\n",
    "                                "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1656x1080 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# normalize the matrix to display the stability of hbond during the trjectory analysed\n",
    "Matrix_inter_hbond_norm = np.around(Matrix_inter_hbond/float(nb_frames_used),decimals=3)\n",
    "#replace zeros by nan in order to only display colored area only for atoms in interaction \n",
    "Matrix_inter_hbond_nan=np.empty(Matrix_inter_hbond_norm.shape)\n",
    "Matrix_inter_hbond_nan[Matrix_inter_hbond_norm == 0] = 'nan'\n",
    "\n",
    "fig = plt.figure(figsize=(23, 15))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "heatmap= ax.pcolor(Matrix_inter_hbond_nan ,cmap='viridis_r')  \n",
    "\n",
    "# put the major ticks at the middle of each cell\n",
    "ax.set_xticks(np.arange(Matrix_inter_hbond.shape[1])+0.5, minor=False)\n",
    "ax.set_yticks(np.arange(Matrix_inter_hbond.shape[0])+0.5, minor=False)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "#legend and labels\n",
    "plt.colorbar(heatmap)\n",
    "column_labels = list_hbond_lect\n",
    "row_labels = list_hbond_ten\n",
    "ax.set_xticklabels(column_labels, minor=False, rotation=50,ha=\"right\",rotation_mode='anchor')\n",
    "ax.set_yticklabels(row_labels, minor=False)\n",
    "\n",
    "fig.savefig(\"hbonds_last_avg_inter.svg\", dpi=200)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "503:HG1 - 776:OE2 : 0.347\n",
      "503:HG1 - 776:OE1 : 0.341\n",
      "504:H - 776:OE2 : 0.838\n",
      "504:H - 776:OE1 : 0.737\n",
      "682:HH22 - 776:OE2 : 0.386\n",
      "682:HH12 - 776:OE2 : 0.746\n",
      "776:OE2 - 504:H : 0.838\n",
      "776:OE1 - 504:H : 0.737\n"
     ]
    }
   ],
   "source": [
    "list_stable_hbond_lect=[]\n",
    "list_stable_hbond_ten=[]\n",
    "\n",
    "for i in range(len(list_hbond_lect)):\n",
    "    for j in range(0,len(list_hbond_ten)):\n",
    "        if Matrix_inter_hbond_norm[j][i] > 0.3:\n",
    "            print(list_hbond_lect[i]+\" - \"+list_hbond_ten[j]+\" : \"+str(Matrix_inter_hbond_norm[j][i]))\n",
    "            list_stable_hbond_lect.append(list_hbond_lect[i])\n",
    "            list_stable_hbond_ten.append(list_hbond_ten[j])\n",
    "            \n",
    "list_stable_hbond_lect = sorted(set(list_stable_hbond_lect), key=lambda student: int(student.split(':')[0]))\n",
    "list_stable_hbond_ten = sorted(set(list_stable_hbond_ten), key=lambda student: int(student.split(':')[0]))\n",
    "\n",
    "#list_stable_hbond_ten = set(list_stable_hbond_ten)\n",
    "\n",
    "#print list_stable_hbond_lect\n",
    "#print list_stable_hbond_ten"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Creates a matrix\n",
    "w = len(list_stable_hbond_ten)\n",
    "h = len(list_stable_hbond_lect)\n",
    "Matrix_inter_hbond_stable = np.zeros((w,h))\n",
    "\n",
    "for elt in hbond_timeseries[nb_frames_unused:]:\n",
    "    list_res = elt\n",
    "    \n",
    "    for i in list_res:\n",
    "        resA= i[2][3:]\n",
    "        resB= i[3][3:]\n",
    "        \n",
    "        for j in range(0,len(list_stable_hbond_ten)):\n",
    "            for k in range(0,len(list_stable_hbond_lect)):\n",
    "                #print list_res_lect_int01[j]\n",
    "                #print(list_res_ten_int01[j])   \n",
    "                if(list_stable_hbond_ten[j]==resA and list_stable_hbond_lect[k]==resB):\n",
    "                    #print(list_res_ten_int01_sorted[j])\n",
    "                    #print(list_res_lect_int01_sorted[k])\n",
    "                    Matrix_inter_hbond_stable[j][k] =  Matrix_inter_hbond_stable[j][k]+1\n",
    "                    break\n",
    "                if(list_stable_hbond_ten[j]==resB and list_stable_hbond_lect[k]==resA):\n",
    "                    #print(list_res_ten_int01_sorted[j])\n",
    "                    #print(list_res_lect_int01_sorted[k])\n",
    "                    Matrix_inter_hbond_stable[j][k] =  Matrix_inter_hbond_stable[j][k]+1\n",
    "                    break\n",
    "                                \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Matrix_inter_hbond_stable_norm = np.around(Matrix_inter_hbond_stable/float(nb_frames_used),decimals=3)\n",
    "Matrix_inter_hbond_stable_nan=np.around(Matrix_inter_hbond_stable/float(nb_frames_used),decimals=3)\n",
    "Matrix_inter_hbond_stable_nan[Matrix_inter_hbond_stable_norm == 0] = 'nan'\n",
    "\n",
    "fig = plt.figure(figsize=(7, 3.5))\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "heatmap= ax.pcolor(Matrix_inter_hbond_stable_nan ,cmap='viridis_r')  \n",
    "\n",
    "# put the major ticks at the middle of each cell\n",
    "ax.set_xticks(np.arange(Matrix_inter_hbond_stable_norm.shape[1])+0.5, minor=False)\n",
    "ax.set_yticks(np.arange(Matrix_inter_hbond_stable_norm.shape[0])+0.5, minor=False)\n",
    "ax.invert_yaxis()\n",
    "\n",
    "#legend and labels\n",
    "plt.colorbar(heatmap)\n",
    "column_labels = list_stable_hbond_lect\n",
    "row_labels = list_stable_hbond_ten\n",
    "ax.set_xticklabels(column_labels, minor=False, rotation=50,ha=\"right\",rotation_mode='anchor')\n",
    "ax.set_yticklabels(row_labels, minor=False)\n",
    "\n",
    "fig.savefig(\"hbonds_last_stable_avg_inter.svg\", dpi=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{504: 0.945, 776: 0.945, 682: 1.0, 503: 0.413}\n",
      "{504: 0.401, 776: 1.0}\n"
     ]
    }
   ],
   "source": [
    "list_res_lect=[]\n",
    "for i in list_stable_hbond_lect:\n",
    "    list_res_lect.append(int(i.split(':')[0]))\n",
    "list_res_lect=sorted(set(list_res_lect))\n",
    "\n",
    "list_res_ten=[]\n",
    "for i in list_stable_hbond_ten:\n",
    "    list_res_ten.append(int(i.split(':')[0]))\n",
    "list_res_ten=sorted(set(list_res_ten))\n",
    "\n",
    "\n",
    "h_bond_stable_avg_dict_lect = {}\n",
    "for i in list_res_lect:\n",
    "    h_bond_stable_avg_dict_lect[i]=0 \n",
    "\n",
    "for i in range(0,len(list_stable_hbond_lect)):\n",
    "    res_nb = int(list_stable_hbond_lect[i].split(':')[0])\n",
    "    inter_value = round((Matrix_inter_hbond_stable_norm).sum(axis=0)[i],3)\n",
    "    h_bond_stable_avg_dict_lect[res_nb]= inter_value+h_bond_stable_avg_dict_lect[res_nb]\n",
    "\n",
    "\n",
    "h_bond_stable_avg_dict_lect_norm= {k: round(v / max(h_bond_stable_avg_dict_lect.values()),3) for k, v in h_bond_stable_avg_dict_lect.iteritems()} \n",
    "print(h_bond_stable_avg_dict_lect_norm)\n",
    "\n",
    "file_lect_stable = open('inter_prot1_stable.dat','w')\n",
    "\n",
    "for i in h_bond_stable_avg_dict_lect_norm:\n",
    "    file_lect_stable.write(str(i)+\"\\t\"+str(h_bond_stable_avg_dict_lect_norm[i])+\"\\t\"+str(h_bond_stable_avg_dict_lect[i])+\"\\n\") \n",
    "file_lect_stable.close()\n",
    "\n",
    "h_bond_stable_avg_dict_ten = {}\n",
    "for i in list_res_ten:\n",
    "    h_bond_stable_avg_dict_ten[i]=0 \n",
    "\n",
    "for i in range(0,len(list_stable_hbond_ten)):\n",
    "    res_nb = int(list_stable_hbond_ten[i].split(':')[0])\n",
    "    inter_value = round((Matrix_inter_hbond_stable_norm).sum(axis=1)[i],3)\n",
    "    h_bond_stable_avg_dict_ten[res_nb]= inter_value+h_bond_stable_avg_dict_ten[res_nb]\n",
    "\n",
    "h_bond_stable_avg_dict_ten_norm= {k: round(v / max(h_bond_stable_avg_dict_ten.values()),3) for k, v in h_bond_stable_avg_dict_ten.iteritems()} \n",
    "print(h_bond_stable_avg_dict_ten_norm)\n",
    "\n",
    "file_ten_stable = open('inter_prot2_stable.dat','w')\n",
    "\n",
    "for i in h_bond_stable_avg_dict_ten_norm:\n",
    "    file_ten_stable.write(str(i)+\"\\t\"+str(h_bond_stable_avg_dict_ten_norm[i])+\"\\t\"+str(h_bond_stable_avg_dict_ten[i])+\"\\n\") \n",
    "file_ten_stable.close()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "frame: 1000 +overlap: 8\n",
      "frame: 1001 +overlap: 8\n",
      "frame: 1005 +overlap: 8\n",
      "frame: 1007 +overlap: 8\n",
      "frame: 1011 +overlap: 8\n",
      "frame: 1017 +overlap: 8\n",
      "frame: 1018 +overlap: 8\n",
      "frame: 1021 +overlap: 8\n",
      "frame: 1022 +overlap: 8\n",
      "frame: 1023 +overlap: 8\n",
      "frame: 1024 +overlap: 8\n",
      "frame: 1025 +overlap: 8\n",
      "frame: 1026 +overlap: 8\n",
      "frame: 1027 +overlap: 8\n",
      "frame: 1028 +overlap: 8\n",
      "frame: 1029 +overlap: 8\n",
      "frame: 1030 +overlap: 8\n",
      "frame: 1031 +overlap: 8\n",
      "frame: 1032 +overlap: 8\n",
      "frame: 1033 +overlap: 8\n",
      "frame: 1034 +overlap: 8\n",
      "frame: 1035 +overlap: 8\n",
      "frame: 1036 +overlap: 8\n",
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      "frame: 1039 +overlap: 8\n",
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      "frame: 1064 +overlap: 8\n",
      "frame: 1065 +overlap: 8\n",
      "frame: 1066 +overlap: 8\n",
      "frame: 1067 +overlap: 8\n",
      "frame: 1068 +overlap: 8\n",
      "frame: 1069 +overlap: 8\n",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "max_val_overlap =0\n",
    "frame=0\n",
    "counter=nb_frames_unused\n",
    "for elt in hbond_timeseries[nb_frames_unused:]:\n",
    "    list_res = elt\n",
    "\n",
    "    b=[]\n",
    "\n",
    "    for i in list_res:\n",
    "        resA= i[2][3:]\n",
    "        resB= i[3][3:]\n",
    "        b.append(resA)\n",
    "        b.append(resB) \n",
    "\n",
    "    list_res_hbonds =sorted(set(b))\n",
    "    overlapping_ten= set(list_res_hbonds) & set(list_stable_hbond_ten)\n",
    "    overlapping_lect= set(list_res_hbonds) & set(list_stable_hbond_lect)\n",
    "    overlap_nb= len(overlapping_ten) + len(overlapping_lect) \n",
    "    if overlap_nb>=max_val_overlap:\n",
    "        max_val_overlap=overlap_nb\n",
    "        frame=counter\n",
    "        print(\"frame: \"+str(frame)+\" +overlap: \"+str(max_val_overlap))\n",
    "    counter=counter+1\n",
    "    #print list_lect_res_rep\n",
    "    #print list_ten_res_rep    \n",
    "    #print list_res_hbonds    \n",
    "    #print len(overlapping_ten) \n",
    "    #print len(overlapping_lect)\n",
    "    #print(\"\\n\\n\")\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['THR503:HG1', 'GLU776:OE1']\n",
      "['THR503:HG1', 'GLU776:OE2']\n",
      "['LEU504:H', 'GLU776:OE2']\n",
      "['ARG682:HH12', 'GLU776:OE2']\n",
      "['ARG682:HH22', 'GLU776:OE2']\n",
      "['LYS751:HZ2', 'GLU776:OE1']\n",
      "['LYS751:HZ2', 'GLU776:OE2']\n"
     ]
    }
   ],
   "source": [
    "for i in hbond_timeseries[5000]:\n",
    "    print(i[2:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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