phenix2016.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Jupyter notebook for interactive development\n",
    "\n",
    "    url: http://jupyter.org/\n",
    "    \n",
    "![](./main-logo-jupyter.svg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to run this notebook?\n",
    "\n",
    "Click **Cell** and then **Run All**\n",
    "\n",
    "# How to run it online? \n",
    "[mybinder.org/repo/hainm/notebook-pytraj](http://mybinder.org/repo/hainm/notebook-pytraj)\n",
    "\n",
    "# See also\n",
    "[protein viewer example](./3pqr.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install\n",
    "\n",
    "```bash\n",
    "conda install parmed -c ambermd # Python package for topology editing and force field development\n",
    "conda install pytraj-dev -c ambermd # Python interface for cpptraj (MD trajectory data analysis)\n",
    "conda install pysander -c ambermd # Python interface for SANDER\n",
    "# all above will be also available in AMBER16 release (next few months)\n",
    "\n",
    "conda install nglview -c ambermd # Protein/DNA/RAN viewer in notebook\n",
    "\n",
    "# notebook\n",
    "conda install jupyter notebook\n",
    "\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ParmEd: Cross-program parameter and topology file editor and molecular mechanical simulator engine.\n",
    "    \n",
    "    url: https://github.com/ParmEd/ParmEd (AMBER16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SER',\n",
       " 'TRP',\n",
       " 'THR',\n",
       " 'TRP',\n",
       " 'GLU',\n",
       " 'ASN',\n",
       " 'GLY',\n",
       " 'LYS',\n",
       " 'TRP',\n",
       " 'THR',\n",
       " 'TRP',\n",
       " 'LYS',\n",
       " 'NME']"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\", category=DeprecationWarning) \n",
    "warnings.filterwarnings(\"ignore\", category=UserWarning) \n",
    "\n",
    "import parmed as pmd\n",
    "\n",
    "x = pmd.load_file('tz2.pdb')\n",
    "[res.name for res in x.residues]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['N', 'H1', 'H2', 'H3', 'CA', 'HA', 'CB', 'HB2', 'HB3', 'OG', 'HG', 'C', 'O']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[atom.name for atom in x.residues[0]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PYTRAJ: Interactive data analysis for molecular dynamics simulations\n",
    "\n",
    "    url: https://github.com/Amber-MD/pytraj (AMBER 16)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute distances and plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Dis_00000</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19.596637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>17.348018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>17.844973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>15.645448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>14.393131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Dis_00000\n",
       "0  19.596637\n",
       "1  17.348018\n",
       "2  17.844973\n",
       "3  15.645448\n",
       "4  14.393131"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pytraj as pt\n",
    "\n",
    "traj = pt.load('tz2.nc', 'tz2.parm7')\n",
    "distances = pt.distances(traj, ':1 :12', dtype='dataframe')\n",
    "distances.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x2aaac75a1438>]], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAEKCAYAAAACS67iAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaac58bb320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "distances.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Compute multiple dihedrals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>psi_1</th>\n",
       "      <th>phi_2</th>\n",
       "      <th>psi_2</th>\n",
       "      <th>chip_2</th>\n",
       "      <th>omega_2</th>\n",
       "      <th>phi_3</th>\n",
       "      <th>psi_3</th>\n",
       "      <th>omega_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-86.697506</td>\n",
       "      <td>-52.781235</td>\n",
       "      <td>-37.458655</td>\n",
       "      <td>-167.770537</td>\n",
       "      <td>-170.555872</td>\n",
       "      <td>-79.867366</td>\n",
       "      <td>117.355933</td>\n",
       "      <td>-179.707558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-91.159994</td>\n",
       "      <td>-40.620800</td>\n",
       "      <td>-41.199817</td>\n",
       "      <td>-178.620425</td>\n",
       "      <td>177.860923</td>\n",
       "      <td>-120.154899</td>\n",
       "      <td>108.983575</td>\n",
       "      <td>-178.763975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-67.937422</td>\n",
       "      <td>-51.215481</td>\n",
       "      <td>-49.467222</td>\n",
       "      <td>-179.101253</td>\n",
       "      <td>160.530607</td>\n",
       "      <td>-52.189986</td>\n",
       "      <td>132.574742</td>\n",
       "      <td>164.425577</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       psi_1      phi_2      psi_2      chip_2     omega_2       phi_3  \\\n",
       "0 -86.697506 -52.781235 -37.458655 -167.770537 -170.555872  -79.867366   \n",
       "1 -91.159994 -40.620800 -41.199817 -178.620425  177.860923 -120.154899   \n",
       "2 -67.937422 -51.215481 -49.467222 -179.101253  160.530607  -52.189986   \n",
       "\n",
       "        psi_3     omega_3  \n",
       "0  117.355933 -179.707558  \n",
       "1  108.983575 -178.763975  \n",
       "2  132.574742  164.425577  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dihedrals = pt.multidihedral(traj, resrange='1-3', dtype='dataframe')\n",
    "dihedrals.head(3) # show only first 3 snapshots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(-180, 180)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXwAAAD7CAYAAABpJS8eAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aaac58bb518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "plt.plot(dihedrals['phi_2'], dihedrals['psi_2'], '-bo', linewidth=0)\n",
    "plt.xlim([-180, 180])\n",
    "plt.ylim([-180, 180])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### get help?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function calc_multidihedral in module pytraj.all_actions:\n",
      "\n",
      "calc_multidihedral(traj=None, dhtypes=None, resrange=None, define_new_type=None, range360=False, dtype='dataset', top=None, frame_indices=None)\n",
      "    perform dihedral search\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    traj : Trajectory-like object\n",
      "    dhtypes : dihedral type, default None\n",
      "        if None, calculate all supported dihedrals\n",
      "    resrange : str | array-like\n",
      "        residue range for searching. If `resrange` is string, use index starting with 1\n",
      "        (cpptraj convertion)\n",
      "        if `resrange` is array-like, use index starting from 0 (python convention)\n",
      "    define_new_type : str\n",
      "        define new type for searching\n",
      "    range360 : bool, default False\n",
      "        if True: use 0-360\n",
      "    top : Topology | str, optional\n",
      "        only need to have 'top' if can not find it in `traj`\n",
      "    \n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    pytraj.DatasetList (use `values` attribute to get raw `numpy` array)\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "        Dataset lables show residue number in 1-based index\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> import pytraj as pt\n",
      "    >>> traj = pt.datafiles.load_tz2_ortho()\n",
      "    >>> data = pt.multidihedral(traj)\n",
      "    >>> data = pt.multidihedral(traj, 'phi psi')\n",
      "    >>> data = pt.multidihedral(traj, resrange=range(8))\n",
      "    >>> data = pt.multidihedral(traj, range360=True)\n",
      "    >>> data = pt.multidihedral(traj, resrange='1,3,5')\n",
      "    >>> data = pt.multidihedral(traj, dhtypes='phi psi')\n",
      "    >>> data = pt.multidihedral(traj, dhtypes='phi psi', resrange='3-7')\n",
      "    >>> data = pt.multidihedral(traj, dhtypes='phi psi', resrange=[3, 4, 8])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(pt.multidihedral)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Protein/DNA/RNA viewer in notebook\n",
    "\n",
    "- Written in Python/Javascript\n",
    "- super light  (~3 MB)\n",
    "- super easy to install (pip install nglview)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "import nglview as nv\n",
    "view = nv.show_pytraj(traj)\n",
    "view"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "view.representations = []\n",
    "view.add_representation('cartoon', color='residueindex')\n",
    "view.add_representation('licorice')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "t0 = pt.fetch_pdb('3pqr')\n",
    "view0 = pt.view.to_nglview(t0)\n",
    "view0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "view0.representations = []\n",
    "view0.add_representation('cartoon', selection='protein', color='residueindex')\n",
    "view0.add_representation('surface', selection='protein', opacity='0.2')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Not yet ported to Python\n",
    "\n",
    "![](./ngl_cpp4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Phenix wishlist (for developers)\n",
    "\n",
    "- phenix.conda install jupyter notebook\n",
    "- phenix.ipython"
   ]
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}