interatomic_distances_MD_traj_graph.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Script to calculate distances between atoms with pytraj\n",
    "\n",
    "Made by XFF\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Calculate distances between the desired residues, atoms, etc\n",
    "%matplotlib inline\n",
    "import pytraj as pt\n",
    "import os\n",
    "import numpy as np\n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.decomposition import PCA\n",
    "import time\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "w_dir = '/media/xavi/99e463f2-6cec-40d5-85bb-b6aa84b4b648/gh3'\n",
    "save_dir = '/media/xavi/99e463f2-6cec-40d5-85bb-b6aa84b4b648/HRMOVA_PAPER/'\n",
    "\n",
    "target = 'WA'\n",
    "prod = '3'\n",
    "\n",
    "prod3 = ' G3OG3OG'\n",
    "prod6 = ' G3OG3OG3OG3OG3OG'\n",
    "gamd = ' GaMD'\n",
    "md = ' MD'\n",
    "\n",
    "\n",
    "tag = str(target) + prod3 + md\n",
    "dcd_path = 'w434a_mdcrd/dcd/w434a-nowat-fix-v2.dcd'\n",
    "prmtop_path = 'w434a_mdcrd/dcd/w434a-nowat.prmtop'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/media/xavi/99e463f2-6cec-40d5-85bb-b6aa84b4b648/gh3/w434a_mdcrd/dcd/w434a-nowat-fix-v2.dcd\n"
     ]
    }
   ],
   "source": [
    "TRAJECTORY_FILENAME = os.path.join(w_dir, dcd_path )\n",
    "TOPOLOGY_FILENAME = os.path.join(w_dir, prmtop_path)\n",
    "TRAJECTORY_LENGTH = 1000 #Length of the trajectory in ns (for correct axis labels in plots)\n",
    "print(TRAJECTORY_FILENAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pytraj.Trajectory, 5000 frames: \n",
      "Size: 1.027398 (GB)\n",
      "<Topology: 9193 atoms, 610 residues, 5 mols, PBC with box type = ortho>\n",
      "           \n"
     ]
    }
   ],
   "source": [
    "trajectory = pt.iterload(TRAJECTORY_FILENAME, TOPOLOGY_FILENAME)\n",
    "trajectory = trajectory[:]\n",
    "trajectory = pt.align(trajectory)\n",
    "print(trajectory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "if target == 'WT' and prod == '6':\n",
    "    head_trp = '@9292 @4437'\n",
    "    tail_trp = '@9208 @4437'\n",
    "\n",
    "if target == 'WH' and prod == '6':\n",
    "    head_trp = '@9241 @4393'\n",
    "    tail_trp = '@9157 @4393 '\n",
    "\n",
    "if target == 'WA' and prod == '6':\n",
    "\n",
    "    head_trp = '@9233 @4393'\n",
    "    tail_trp = '@9149 @4393'\n",
    "    \n",
    "if target == 'WT' and prod == '3':\n",
    "    head_trp = '@9204 @4437'\n",
    "    tail_trp = '@9183 @4437'\n",
    "\n",
    "if target == 'WH' and prod == '3':\n",
    "    head_trp = '@9153 @4393'\n",
    "    tail_trp = '@9132 @4393 '\n",
    "\n",
    "if target == 'WA' and prod == '3':\n",
    "\n",
    "    head_trp = '@9146 @4393'\n",
    "    tail_trp = '@9125 @4393'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "dist = pt.distance(trajectory, mask=head_trp)\n",
    "dist2 = pt.distance(trajectory, mask=tail_trp)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WA_WA G3OG3OG MD.pdf\n"
     ]
    }
   ],
   "source": [
    "output_graph = (str(target)+'_'+str(tag)+'.pdf') #Output filename for the plot\n",
    "print(output_graph)\n",
    "\n",
    "#DIST_COLOR = 'forestgreen' #Color of the line\n",
    "#MAX_DIST = math.ceil(dist.max()) #It can be modified if you want to change the scale of the plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/media/xavi/99e463f2-6cec-40d5-85bb-b6aa84b4b648/HRMOVA_PAPER/WA_WA G3OG3OG MD.pdf\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot([TRAJECTORY_LENGTH*a/len(dist) for a in range(len(dist))], dist, linewidth=0.2, label='non-reducing end')#, color=DIST_COLOR)\n",
    "plt.plot([TRAJECTORY_LENGTH*a/len(dist2) for a in range(len(dist2))], dist2, linewidth=0.2, label='reducing end')\n",
    "#plt.ylim(0, MAX_DIST)\n",
    "plt.xlabel('Time (ns)')\n",
    "plt.ylabel('Distance (Å)')\n",
    "plt.title(str(tag))\n",
    "plt.legend()\n",
    "plt.savefig(str(save_dir+output_graph), bbox_inches='tight')\n",
    "\n",
    "print(save_dir+output_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "md_analysis",
   "language": "python",
   "name": "md_analysis"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}