analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d02eadc",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/mk/ptch0465/softwaresrc/anaconda3/envs/test/lib/python3.9/site-packages/MDAnalysis/coordinates/chemfiles.py:108: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n",
      "  MIN_CHEMFILES_VERSION = LooseVersion(\"0.9\")\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "2fde1f7c1a614afca861a16365848fbd",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": []
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using MDAnalysis version 2.0.0\n",
      "Using NGLView version 3.0.3\n"
     ]
    }
   ],
   "source": [
    "import MDAnalysis\n",
    "import numpy as np\n",
    "import nglview as nv\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"Using MDAnalysis version\", MDAnalysis.__version__)\n",
    "print(\"Using NGLView version\", nv.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "9cac29e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200874"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#load a trajectory\n",
    "u = MDAnalysis.Universe('final/C36dT.tpr','final/C36dT.xtc')\n",
    "len(u.trajectory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "076987b5",
   "metadata": {},
   "outputs": [],
   "source": [
    "v = nv.show_mdanalysis(u)\n",
    "system = v.component_0\n",
    "system.clear()\n",
    "v.camera='orthographic'\n",
    "v.center()\n",
    "v.add_unitcell()\n",
    "v.parameters = {\"fogNear\": 20}\n",
    "sel=u.select_atoms('all')\n",
    "system.add_licorice(selection = sel.atoms.indices)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "12b1f7f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c1ee44691bf04819a08b1255024ab542",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget(max_frame=200873)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "v.display()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d52a80bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# radius of gyration\n",
    "sel=u.select_atoms('all')\n",
    "Rgs=[]\n",
    "skipframes= 10000 #skip 100ns\n",
    "for ts in u.trajectory[skipframes::]:\n",
    "    \n",
    "    coordinates = sel.atoms.positions\n",
    "    center = np.sum(coordinates,axis=0)/len(coordinates)\n",
    "    \n",
    "    # get squared distance from center\n",
    "    ri = np.linalg.norm(coordinates-center,axis=1) #0.852 oxdna conversion factor?\n",
    "    # sum the unweighted positions\n",
    "    sq = np.sum(ri*ri)/len(coordinates)\n",
    "\n",
    "    Rgs.append(np.sqrt(sq))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "8b282fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#end to end distance\n",
    "sel1=u.select_atoms('resindex 0')\n",
    "sel2=u.select_atoms('resindex 29')\n",
    "Rs=[]\n",
    "\n",
    "for ts in u.trajectory[skipframes::]:\n",
    "\n",
    "    coordinates1 = sel1.atoms.positions \n",
    "    coordinates2 = sel2.atoms.positions \n",
    "    center1 = np.sum(coordinates1,axis=0)/len(coordinates1)\n",
    "    center2 = np.sum(coordinates2,axis=0)/len(coordinates2)\n",
    "    Rs.append(np.linalg.norm(center1-center2))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dfb3041b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.DataFrame({'x' : Rgs, 'y' : Rs})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "34e27a8d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/u/mk/ptch0465/softwaresrc/anaconda3/envs/test/lib/python3.9/site-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variable as a keyword arg: y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f792653b670>]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(2, 2),dpi=200)\n",
    "import seaborn as sns\n",
    "ax = sns.kdeplot(df.x, df.y, cmap='Blues',levels=8) #cmap=\"viridis\"\n",
    "\n",
    "\n",
    "for path in ax.collections[-2].get_paths():\n",
    "    x1, y1 = path.vertices.mean(axis=0);\n",
    "    \n",
    "innerx1 = ax.collections[-3].get_paths()[0].vertices[:,0] #inner path x coords\n",
    "innery1 = ax.collections[-3].get_paths()[0].vertices[:,1]\n",
    "    \n",
    "ax.plot(x1, y1, \"x\",color='black')\n",
    "ax.plot(innerx1[np.argmin(innery1)], innery1[np.argmin(innery1)], \"x\",color='black')\n",
    "ax.plot(innerx1[np.argmax(innery1)], innery1[np.argmax(innery1)], \"x\",color='black')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "b9fb5171",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_nearest2(array, value, array2, value2):\n",
    "    array = np.asarray(array)\n",
    "    array2 = np.asarray(array2)\n",
    "    idx = (np.abs(array - value)+np.abs(array2 - value2)).argmin()\n",
    "    return idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "e2a177c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200591"
      ]
     },
     "execution_count": 260,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = find_nearest2(Rgs,x1,Rs,y1) #find a frame of the trajectory with a given Rg, R\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "fd311cf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "myRg=np.round(Rgs[frame],2)\n",
    "myR=np.round(Rs[frame],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "53982d08",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: the result of the automatic normal detection ( 1 ) is not reliable\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_238276/3119592392.py:18: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n",
      "  radii[atom] = 0.5 * np.float(data[5]) *(2**(1/6)) #vdw radii, same as pdb2pqr\n",
      "/u/mk/ptch0465/softwaresrc/anaconda3/envs/test/lib/python3.9/site-packages/MDAnalysis/core/selection.py:907: SelectionWarning: Using float equality to select atoms is not recommended because of inherent limitations in representing numbers on computers (see https://docs.python.org/3.8/tutorial/floatingpoint.html for more). Instead, we recommend using a range to select, e.g. 'mass 0.008000000000000007 to 2.008'. If you still want to compare floats, use the `atol` and `rtol` keywords to modify the tolerance for `np.isclose`.\n",
      "  warnings.warn(msg, category=SelectionWarning)\n"
     ]
    }
   ],
   "source": [
    "#pqr writer\n",
    "import pytim\n",
    "from pytim.datafiles import *\n",
    "from MDAnalysis import transformations as trans\n",
    "charmm = pytim_data.vdwradii(CHARMM36_TOP) \n",
    "interface = pytim.ITIM(u, radii_dict = charmm)\n",
    "u.trajectory[frame+skipframes] # frames discarded remember!\n",
    "\n",
    "radii = dict() #make dictionary from gmx .itp\n",
    "#with open('ffamberbsc1.ff') as f:\n",
    "with open('ffcharmm36.nonbonded.itp') as f:\n",
    "#with open('hbcufix.itp') as f:\n",
    "#with open('ffDES-AMBER.nonbonded.itp') as f:\n",
    "#with open('DES_RNA.itp') as f:\n",
    "    for line in f:\n",
    "        data = (line.split(\";\")[0]).split()\n",
    "        atom = data[0]\n",
    "        radii[atom] = 0.5 * np.float(data[5]) *(2**(1/6)) #vdw radii, same as pdb2pqr\n",
    "        \n",
    "radii_array = np.zeros(len(u.atoms))\n",
    "for i in range(len(u.atoms)):\n",
    "    radii_array[i]=radii[u.atoms.types[i]] \n",
    "    \n",
    "radii_array = radii_array*10 #convert to angstrom\n",
    "        \n",
    "hydrogens=u.select_atoms('mass 1.008').atoms.indices\n",
    "heavyatoms=np.setdiff1d(u.atoms.indices,u.select_atoms('mass 1.008').atoms.indices) #indices\n",
    "\n",
    "for j in heavyatoms:\n",
    "    radii_array[j] = radii_array[j] + 2 # puff out, Ali w1.4\n",
    "    \n",
    "u.atoms.radii=radii_array #convert to angstrom\n",
    "\n",
    "workflow = [trans.unwrap(u.atoms)]\n",
    "u.trajectory.add_transformations(*workflow)\n",
    "\n",
    "pa = u.atoms.principal_axes()\n",
    "u.atoms.align_principal_axis(2, [1,0,0])\n",
    "\n",
    "#Patoms=u.select_atoms('name P').atoms.indices #DES RNA only\n",
    "#u.atoms[Patoms].charges= u.atoms[Patoms].charges -0.1\n",
    "    \n",
    "u.atoms[0].charge=u.atoms[0].charge -1\n",
    "u.atoms[-1].charge=u.atoms[-1].charge -1 #terminal charges\n",
    "    \n",
    "a = MDAnalysis.coordinates.PQR.PQRWriter('C360Rg{}_R{}'.format(myRg,myR))\n",
    "\n",
    "a.write(u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "394eccf1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# orientational correlation analysis\n",
    "backbone=u.select_atoms(\"name P\")\n",
    "n = len(backbone) #number of atoms formed to make chain of vectors\n",
    "\n",
    "bs = []\n",
    "for i in range(n-1):\n",
    "    b = backbone.atoms.positions[i+1] - backbone.atoms.positions[i]\n",
    "    bs.append(np.linalg.norm(b))\n",
    "\n",
    "lb = np.mean(bs) #mean bond length for chain of vectors\n",
    "\n",
    "results = np.zeros(n - 1, dtype=np.float32)\n",
    "\n",
    "skipframes=10000\n",
    "\n",
    "for step in u.trajectory[skipframes::]:\n",
    "    # Vector from each atom to next\n",
    "    vecs = backbone.positions[1:] - backbone.positions[:-1]\n",
    "    #bcs.append(np.linalg.norm(backbone.positions[1:] - backbone.positions[:-1],axis=1))\n",
    "    # Normalized to unit vectors\n",
    "    vecs /= np.sqrt((vecs * vecs).sum(axis=1))[:, None]\n",
    "\n",
    "    inner_pr = np.inner(vecs, vecs)\n",
    "    for i in range(n-1):\n",
    "            results[:(n-1)-i] += inner_pr[i, i:]\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfb1def2",
   "metadata": {},
   "outputs": [],
   "source": [
    "norm = np.linspace(n - 1, 1, n - 1)\n",
    "norm *= (len(u.trajectory)-skipframes)\n",
    "\n",
    "results = results / norm\n",
    "x = lb *np.arange(len(results))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "976135b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7926410df0>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 400x400 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize=(2, 2),dpi=200)\n",
    "\n",
    "dx=x[3]-x[2];\n",
    "ax.plot((x/dx),results,'^--',label='dT',markersize=7,markerfacecolor='none',color='tab:blue')\n",
    "\n",
    "ax.set_xlabel(\"|i-j|\")\n",
    "ax.set_ylabel(\"OCF\")\n",
    "\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "de1d6366",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}