MDTraj_exam_4.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Identificar puentes de H (Baker-Hubbard)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import print_function\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import itertools\n",
    "import mdtraj as md\n",
    "import mdtraj.testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cargar un pequeño péptido de 28 residuos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<mdtraj.Trajectory with 20 frames, 423 atoms, 28 residues, without unitcells>\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/liliana/Myopt/Miniconda/miniconda3/envs/UIBCDF_lab/lib/python3.7/site-packages/mdtraj/formats/pdb/pdbfile.py:198: UserWarning: Unlikely unit cell vectors detected in PDB file likely resulting from a dummy CRYST1 record. Discarding unit cell vectors.\n",
      "  'cell vectors.', category=UserWarning)\n"
     ]
    }
   ],
   "source": [
    "t = md.load_pdb('http://www.rcsb.org/pdb/files/2EQQ.pdb')\n",
    "\n",
    "print(t)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**md.baker_hubbard** identifica enlaces de hidrógeno en los puntos de corte para el donante-H, distancia y ángulo del aceptor.\n",
    "\n",
    "El valor de retorno es una lista de los índices de los átomos (donante --H-- aceptor) que satisfacen este criterio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "GLU1-N -- GLU1-OE2\n",
      "GLU1-N -- GLU1-OE1\n",
      "GLY6-N -- SER4-O\n",
      "CYS7-N -- GLY5-O\n",
      "TYR11-N -- VAL8-O\n",
      "MET12-N -- LYS20-O\n",
      "ARG13-NH1 -- TYR11-O\n",
      "THR14-N -- ARG18-O\n",
      "ASP16-N -- ASP16-OD1\n",
      "GLY17-N -- THR14-O\n",
      "ARG18-N -- THR14-OG1\n",
      "ARG18-NE -- ASP16-OD2\n",
      "LYS20-N -- MET12-O\n",
      "THR22-N -- GLY10-O\n",
      "THR14-OG1 -- ASP16-OD1\n",
      "THR28-OG1 -- ILE27-O\n"
     ]
    }
   ],
   "source": [
    "hbonds = md.baker_hubbard(t, periodic=False)\n",
    "label = lambda hbond : '%s -- %s' % (t.topology.atom(hbond[0]), t.topology.atom(hbond[2]))\n",
    "for hbond in hbonds:\n",
    "    print(label(hbond))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calcular las distancias reales entre donantes y aceptores."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "da_distances = md.compute_distances(t, hbonds[:, [0,2]], periodic = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Calculando un histograma"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Donor-acceptor distance [nm]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WrWLmzJlcd911BbabN28ejzzyCO+++y4ffvghDz/8MH369OGrr74q34QIOeDNbJOZrTWzTDOr5lcwiUhBWrZsSZcuXRg/fjx/+tOf6NevH82bN8/Xrm3btnz++efxxxdddBFJSUn52p1zzjmkpKSUqO/09PQKubIV4IwzzuD4449n6dKl8WXPP/88vXr1AmKFwWbNmsW0adN48803yc7OLnJ7Zhavb7Nz585Ci5mNHz+eiRMnxv9SadOmDf3798/1y7CsjsQefAd3Ty3sSisRqf7uvPNOnn32WebPn8+IESPyPX/w4EEWLlxI165dK7Tftm3bUrNmTRYtWlQh2+vdu3f8y0yWLFlC/fr1Of300wF47733aNq0Kc2bN6d9+/bFlljIyMjg6aefplGjRnTu3Jm//e1vBbbLysrKV4a5osoI6xCNiJRb3bp1ufLKK+nbt2+ubyf64YcfSE1NpX79+nzzzTd06tSpwvsePXp0he3F9+rVi1mzZnHo0KF8ZYxnzJgR35vv1atXsYdpZsyYwTXXXMPWrVt57bXX6Nu3b7yCZXEqqoxw2AHvwJtmtsLMBhXUwMwGmdlyM1u+ffv2kIcjImEpqJTu4WPwmzdvZt++feU67HDJJZeQmprKgAEDci3v2LEj2dnZLFmypMD1Ro0aFS8dXFQ5YoBTTz2VlJQU/v73vzN79mx69uwJxP4CmT17NmPGjCElJYWhQ4cyf/58vvvuu0LH+/jjj8fXb9u2LdnZ2Xz99de5xgOx8sV5yzBXVBnhsAP+fHdvA1wGXG9mF+Zt4O5T3T3N3dMOf2mtiETLT37yE+6//34mTZrE/v37y7SNN954g8zMTB577LF8z40aNYoJEyYUuN7YsWPjHw4fLkecmZnJmDFjCmzfu3dvhg8fTvPmzeMVJRcsWEDr1q357LPP2LRpE5s3b6ZHjx65atvn1bhx4/iXm6xfv57s7GySk5NzjQdgxIgRjBw5kh07dgCxM3emTZtW6IeypRHqaZLu/kXw7zYzmwP8EngnzD5FjhrVrFLpOeecQ+vWrZk5cyZ9+/blggsu4MMPP2T37t00atSIxx9/nEsuuYT777+fCRMm8NVXX9GqVSs6d+5cYKjn1LlzZypqB/GKK67gxhtvzHXMfMaMGXTv3j1Xux49ejBlyhT69u3Lnj17cpUXvummm7jnnnsYOHAgf/3rXzEzpk2bVuBhl65du/L555/z61//GjMjKSmJp59+Ol5jvjxCKxdsZnWBBHf/Lrj/FjDG3V8vbB2VCxYpnMoFS2nLBYe5B38SMCf4jXUM8GxR4S4iIhUrtIB3941A67C2LyIiRdNpkiLVSFX6BjY5ssry3ivgRaqJWrVqsWPHDoX8Ucjd2bFjR6m/v1XFxkSqiUaNGrF161Z0vcjRqVatWrnO1CkJBbxINVGjRg2aNm1a2cOQakSHaEREIkoBLyISUQp4EZGIUsCLiESUAl5EJKIU8CIiEaWAFxGJKAW8iEhEKeBFRCJKAS8iElEKeBGRiFLAi4hElAJeRCSiFPAiIhGlgBcRiSgFvIhIRCngRUQiSgEvIhJRCngRkYhSwIuIRJQCXkQkohTwIiIRFXrAm1mima0ys3lh9yUiIj86EnvwNwLrj0A/IiKSQ6gBb2aNgH8HHguzHxERye+YkLc/GRgBJBXWwMwGAYMAGjduHPJwSiAjo3zrt1ycZ0H73A/X5Xlc3v5EpNSK/W+3eHE4/bYvZLsh5UBoe/Bm9jtgm7uvKKqdu0919zR3T0tOTg5rOCIiR50wD9GcD3Q1s03ATKCjmT0dYn8iIpJDaAHv7re5eyN3TwF6AW+7+9Vh9SciIrnpPHgRkYgK+0NWANx9MbD4SPQlIiIx2oMXEYkoBbyISEQp4EVEIkoBLyISUQp4EZGIUsCLiESUAl5EJKIU8CIiEaWAFxGJKAW8iEhEKeBFRCJKAS8iElEKeBGRiFLAi4hElAJeRCSiSlQP3sxuKup5d7+3YoYjIiIVpaRf+JEGnAe8HDzuArwDfBbGoEREpPxKGvANgDbu/h2AmWUAL7j7gLAGJiIi5VPSY/CNgX05Hu8DUip8NCIiUmFKugc/HfjAzOYADnQHngptVCIiUm4lCnh3H2tm84ELgkV/cPdV4Q1LRETKqzSnSdYBdrn7fcBWM2sa0phERKQClCjgzexOYCRwW7CoBvB0WIMSEZHyK+kefHegK/A9gLt/ASSFNSgRESm/kgb8Pnd3Yh+wYmZ1wxuSiIhUhJIG/PNm9ghwvJkNBBYAjxa1gpnVMrMPzGy1mWWZ2Z/KO1gRESm5kp5FM8nMOgG7gDOAO9z9rWJW2wt0dPfdZlYDeNfM5rv7kvINWURESqLYgDezROANd78YKC7U44JDOruDhzWCm5dlkCIiUnrFBry7HzSzPWb2E3ffWZqNB78cVgCnAQ+6+9IC2gwCBgE0bty4NJvPJSOjzKvmtrh96fodUo6OWy6G7TnWTy7Hto6ACnuNj0S/FTDYjFL+LNC+lO0L6zcjrMYVoLJ+CKRMSnolazaw1szeIjiTBsDdbyhqJXc/CKSa2fHAHDNr6e7r8rSZCkwFSEtL0x6+iEgFKWnAvxrcysTdvzWzxcClwLpimouISAUoMuDNrLG7b3H3/ynths0sGdgfhHtt4GJgfBnHKSIipVTcaZJzD98xs9ml3PbPgEVmtgZYBrzl7vNKuQ0RESmj4g7RWI77zUqzYXdfA5xT6hGJiEiFKG4P3gu5LyIiVVxxe/CtzWwXsT352sF9gsfu7vVCHZ2IiJRZkQHv7olHaiAiIlKxSlMPXkREqhEFvIhIRCngRUQiSgEvIhJRCngRkYhSwIuIRJQCXkQkohTwIiIRpYAXEYkoBbyISEQp4EVEIkoBLyISUQp4EZGIUsCLiESUAl5EJKIU8CIiEaWAFxGJKAW8iEhEKeBFRCJKAS8iElEKeBGRiFLAi4hElAJeRCSiQgt4MzvVzBaZ2XozyzKzG8PqS0RE8jsmxG0fAG5295VmlgSsMLO33P2fIfYpIiKB0Pbg3f1Ld18Z3P8OWA+cElZ/IiKSW5h78HFmlgKcAywt4LlBwCCAxo0bH4nhVHsZGZU9AinQ4sUVspmM9qVpXarGxfS7uASNMiqsP4CMxe2Lb9S+BG2kQKF/yGpmxwGzgWHuvivv8+4+1d3T3D0tOTk57OGIiBw1Qg14M6tBLNyfcfcXw+xLRERyC/MsGgMeB9a7+71h9SMiIgULcw/+fKAv0NHMMoNb5xD7ExGRHEL7kNXd3wUsrO2LiEjRdCWriEhEKeBFRCJKAS8iElEKeBGRiFLAi4hElAJeRCSiFPAiIhGlgBcRiSgFvIhIRCngRUQiSgEvIhJRCngRkYhSwIuIRJQCXkQkohTwIiIRpYAXEYkoBbyISEQp4EVEIkoBLyISUQp4EZGIUsCLiESUAl5EJKIU8CIiEaWAFxGJKAW8iEhEhRbwZvaEmW0zs3Vh9SEiIoULcw9+GnBpiNsXEZEihBbw7v4O8E1Y2xcRkaIdU9kDMLNBwCCAxo0bH7F+27fMiN9fvC6j0HYA7X83reI6brm44rZVVosXH/k+27c/8n1KqWQsbl/ZQyhYZfy8RkSlf8jq7lPdPc3d05KTkyt7OCIikVHpAS8iIuFQwIuIRFSYp0nOAN4HzjCzrWb2x7D6EhGR/EL7kNXde4e1bRERKZ4O0YiIRJQCXkQkohTwIiIRpYAXEYkoBbyISEQp4EVEIkoBLyISUQp4EZGIUsCLiESUAl5EJKIU8CIiEaWAFxGJKAW8iEhEKeBFRCJKAS8iElEKeBGRiFLAi4hElAJeRCSiFPAiIhGlgBcRiSgFvIhIRCngRUQiSgEvIhJRCngRkYhSwIuIRJQCXkQkokINeDO71Mw+MrNPzOzWMPsSEZHcQgt4M0sEHgQuA84CepvZWWH1JyIiuYW5B/9L4BN33+ju+4CZwO9D7E9ERHIwdw9nw2aXA5e6+4DgcV/gV+4+JE+7QcCg4OEZwEehDKjyNAC+ruxBVALN++iieVeeJu6eXNATx4TYqRWwLN9vE3efCkwNcRyVysyWu3taZY/jSNO8jy6ad9UU5iGarcCpOR43Ar4IsT8REckhzIBfBpxuZk3N7FigF/ByiP2JiEgOoR2icfcDZjYEeANIBJ5w96yw+qvCInv4qRia99FF866CQvuQVUREKpeuZBURiSgFvIhIRCngy6G4Ugxm9nszW2NmmWa23MzalXTdqqqcc95kZmsPP3dkR14+JX2/zOw8MzsYXAdSqnWronLOO7Lvt5m1N7OdwdwyzeyOkq57RLm7bmW4EfvgeAPQDDgWWA2clafNcfz4OUcr4MOSrlsVb+WZc/B4E9CgsucRxrxztHsbeA24vDq/1+Wdd9Tfb6A9MK+sr9mRumkPvuyKLcXg7rs9eNeBuvx4oVd1LeNQnjlXZyV9v4YCs4FtZVi3KirPvKuz8rxnVer9VsCX3SnAZzkebw2W5WJm3c3sQ+BV4D9Ls24VVJ45Qyzs3zSzFUGJiuqi2Hmb2SlAd+Dh0q5bhZVn3hDh9zvQ1sxWm9l8M2tRynWPCAV82ZW0FMMcdz8T6Ab8uTTrVkHlmTPA+e7ehliF0evN7MJwhlnhSjLvycBIdz9YhnWrqvLMG6L9fq8kVgOmNfA3YG4p1j1iwqxFE3WlKsXg7u+YWXMza1DadauQMs/Z3b929y+C5dvMbA6xP2ffCXXEFaMk804DZpoZxApQdTazAyVct6oq87zdfW6U329335Xj/mtm9lCV/L9d2R9oVNcbsV+OG4Gm/PhhSos8bU7jxw8c2wCfE/sNX+y6VfFWzjnXBZKC5XWB/yVWbbTS51UR887Tfho/fshaLd/rCph3pN9v4OQcP+e/BLZUxf/b2oMvIy+kFIOZDQ6efxjoAfQzs/3AD8CVHvuJqJZlHMozZzM7CZgT7OkdAzzr7q9XykRKqYTzLtW6R2Lc5VWeeQNRf78vB64N/kr7AehVFf9vq1SBiEhE6UNWEZGIUsCLiESUAl5EJKIU8CIiEaWAFxGJKAX8US6oAJhpZlnBZdc3mdlR8XNhZteYWcMK3ubu4N+GZjariHbHm9l1Fdl3aeR43ytk/ma2yMx2m1mV/QLqo9FR8R9ZivSDu6e6ewugE9AZuDPMDs0sMcztl8I1QKkCrqRjd/cv3P3yIpocD1RawPPj+14hV1m6ewegWpUEPhoo4CXO3bcBg4AhFlPLzJ4ManqvMrMOEN/zfdHMXjezj81swuFtmFnvoP06MxufY/luMxtjZkuBtjn7NbPjzGyhma0M1v19juf6Way+/Gozmx4sO8nM5gTLVpvZr4PlV5vZB8Ge6SOHwzjo+55g+wvNLNlidcvTgGeC9rXN7KJgnmvN7Akzqxmsv8nM7jCzd4Er8oy9qZm9b2bLzOzPOZanmNm64H6LHONaY2anA+OA5sGyiYW9BsF21pvZo8FfWW+aWe3gudPMbEHwGqw0s+bB8vRgPGvM7E8lee+D12hssK0lwYVpmNk0M5sS7KFvNLPfBK/NejObVpJtSyWq7MuCdavcG7C7gGX/n9iViDcDTwbLziR2OXYtYnu+G4GfBI83E6u/0TBok0zs6sW3gW7B+g70LGQMxwD1gvsNgE+IXfbdAviIoKY4cELw73PAsOB+YjCOXwCvADWC5Q8B/XL0fVVw/w7ggeD+YiAtuF+LWBXAnwePn8rRxyZgRCFjfzlHP9cffj2BFGBdcP9vOfo/Fqid8/liXoMU4ACQGjz3PHB1cH8p0D3H+OsAvyX2RdBGbAduHnBhce978Bp1Ce5PAEYH96cRK3lrxMre7gLODra94vC48r6eulWNm/bgpSCHK+K1A6YDuPuHxIL858FzC919p7tnA/8EmgDnAYvdfbu7HwCeAQ5XEDxIrGZ4Yf3dZWZrgAXEyqueBHQEZrn718EYvgnadwSmBMsOuvtO4CLgXGCZmWUGj5sF7Q8R+6UA8HQwr7zOAD519/8XPP6fHGMnx/p5nQ/MCO5PL6TN+8DtZjaSWAXCHwpoU9hrQDCuzOD+CiDFzJKAU9x9DoC7Z7v7HmIB/1tgFbGKh2cCpxcyrpz2EftlEO8jx3OveAS6pfcAAAJXSURBVCzB1wL/cve17n4IyMrTTqoY1aKRXMysGbEw3kbBpU8P25vj/kFiP0tFtc/2oKSsmf0KeCRYfgdwArG9/nPdfb+ZbSK2R2qUvNSqAf/j7reVoG1B2yxq7ADfl3J7Pz7p/mxwaOrfgTfMbACxv4ByuoqCXwPI/1rXLmK8Btzt7o8U8nxh9gchfriPnNlwuP9DecZyCGVIlaY9eIkzs2RiX9zwQPCf/R1iwYOZ/RxoTOyQSWGWAr8xswbB8e/ewN/zNnL3pR77gC/V3V8mdohlWxBsHYj9NQCwEOhpZvWDMZyQY/m1wbJEM6sXLLvczE483NbMDm8ngVhxKIA+wLvB/e+ApOD+h8T2jE8LHvctaOwFeA/oFdy/qqAGwS/Nje5+P7FDOq3y9E0Rr0GBPFaudquZdQv6qGlmdYgVufpPMzsuWH7K4ddEjj4KeKkdfNCXRezQwJvA4Q/mHgISzWwtsUMU17j73kK2g7t/CdwGLCJWJnWlu79UgjE8A6RZ7IuZryIWtnisCt9Y4O9mthq4N2h/I9AhGNcKYuVY/wmMJvYNQmuAt4CfBe2/B1qY2Qpih3fGBMunAQ8Hh3QM+APwQrDdQxT8LUV53UjsyyyWEQvpglwJrAv6ORN4yt13AO9Z7MPoiYW9BsXoC9wQzPd/gZPd/U3gWeD9YB6zyP2LRI4iqiYpkWdmu939uMoeR1USxmtiZouBW9xdp0tWEdqDFzk67bIKvtCJ2Ifa+ytie1IxtAcvIhJR2oMXEYkoBbyISEQp4EVEIkoBLyISUQp4EZGI+j+qF+rXMgWGMAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "color = itertools.cycle(['r', 'b', 'gold'])\n",
    "for i in [2, 3, 4]:\n",
    "    plt.hist(da_distances[:, i], color = next(color), label = label (hbonds[i]), alpha = 0.5)\n",
    "plt.legend()\n",
    "plt.ylabel('Freq');\n",
    "plt.xlabel('Donor-acceptor distance [nm]')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}