IFP_generation_examples_PDB.ipynb

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    "# A collection of examples showing how to carry out computations of \n",
    "# Protein-Ligand  Interaction Fingerprits, IFPs,  using MD-IFP tools but without using a trajectory object.\n",
    "\n",
    "# IFPs can be generated for \n",
    "\n",
    "## (i) a PDB structure\n",
    "## (ii) a single MD trajectory\n",
    "\n",
    "    1. Computing interaction fingerprints (IFP) for\n",
    "        -- a single structure prepared for MD simulations (HSP90; PDB ID 6EI5, dcd format)\n",
    "        -- PDB structure\n",
    "    2. Visualizing protein residues that are involved in protein-ligand interactions, including water-bridges\n",
    "  \n",
    "  \n",
    "\n",
    "### Input data required:\n",
    "    MD trajectory file of a complex\n",
    "    pdb file of a complex (for example, generated from the first frame)\n",
    "    pdb and mol2 file of a ligand separately \n",
    "    Data can be downloaded from Zenodo  https://zenodo.org/record/3981155#.XzQEUCgzaUk   - version 3.1\n",
    "    \n",
    "        Data directory must look like this (otherwise you have to edit file location in the code)\n",
    "    \n",
    "    Data\n",
    "    ├── 2YKI\n",
    "    │   ├── 2YKI.pse\n",
    "    │   ├── 2yki.pdb\n",
    "    │   ├── 2yki_MOE.pdb\n",
    "    │   ├── ligand_2yki_MOE.mol2\n",
    "    │   └── ligand_2yki_MOE.pdb\n",
    "    └── 6EI5\n",
    "    │   ├── INH.pdb\n",
    "    │   ├── amber2namd2.dcd\n",
    "    │   ├── moe.mol2\n",
    "    │   ├── ref-min.pdb\n",
    "    │   └── ref.prmtop\n",
    "   \n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "### Packages required:\n",
    "    numpy\n",
    "    matplotlib\n",
    "    MDAnalysis v. 20.1 or above\n",
    "    pandas\n",
    "    seaborn\n",
    "    RDkit\n",
    "    nglview\n",
    "    code is written on Python 3.x and tested on the version 3.7\n",
    "\n",
    "\n",
    "\n",
    "### v 1.0\n",
    "    06.06.2020\n",
    "    Copyright (c) 2020\n",
    "    Released under the EUPL Licence, v1.2 or any higher version\n",
    "    \n",
    "### Authors: Daria Kokh & Fabian Ormersbach \n",
    "    Daria.Kokh@h-its.org\n",
    "    Heidelberg Institute of Theoretical Studies (HITS, www.h-its.org)\n",
    "    Schloss-Wolfsbrunnenweg 35\n",
    "    69118 Heidelberg, Germany\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Application example \n",
    "\n",
    "    Heat Shock Protein 90 (HSP90)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import all of the required packages and scripts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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      "text/plain": [
       "_ColormakerRegistry()"
      ]
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     "output_type": "display_data"
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    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "RDKit WARNING: [09:00:26] Enabling RDKit 2019.09.3 jupyter extensions\n"
     ]
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   ],
   "source": [
    "import glob, os\n",
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import nglview as nv\n",
    "import MDAnalysis as mda\n",
    "import warnings\n",
    "from rdkit import Chem, RDConfig\n",
    "from rdkit.Chem.rdMolChemicalFeatures import MolChemicalFeatureFactory\n",
    "from Scripts.IFP_generation import *\n",
    "from Scripts.Trajectories import *\n",
    "#from Scripts.Complex_structure import *\n",
    "#from Scripts.Clustering import *\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Generation IFPs for a single HSP90 structure (PDB ID 6EI5) prepared for MD simulations \n",
    "### 1.1 find chemical properties of the ligand using RDKit "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before we start with the IFP-Analysis we first need to extract the chemical information of the ligand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Molecular properties of the compound:\n",
      "\n",
      "{'Donor': ['N3', 'N14', 'N30'], 'Acceptor': ['O4', 'N8', 'N10'], 'PosIonizable': ['N30'], 'Aromatic': ['C1', 'C7', 'C11', 'C12', 'C9', 'C6', 'C1', 'C6', 'N10', 'C13', 'N8', 'C5', 'C15', 'C24', 'C25', 'C26', 'C27', 'C28', 'C18', 'C23', 'C22', 'C21', 'C20', 'C19'], 'Hydrophobe': ['C1', 'C7', 'C9', 'C11', 'C12', 'C15', 'C18', 'C19', 'C20', 'C21', 'C22', 'C23', 'C24', 'C25', 'C26', 'C27', 'C28'], 'LumpedHydrophobe': ['C1', 'C7', 'C11', 'C12', 'C9', 'C6', 'C15', 'C24', 'C25', 'C26', 'C27', 'C28', 'C18', 'C23', 'C22', 'C21', 'C20', 'C19']}\n"
     ]
    }
   ],
   "source": [
    "# Define the path to the Ligand data, pdb file and mol2 file are needed\n",
    "ligand_pdb = \"Data/6EI5//INH.pdb\"\n",
    "ligand_mol2 = \"Data/6EI5//moe.mol2\"\n",
    "\n",
    "# Get the atom names of the ligand\n",
    "with open(ligand_pdb, 'r') as fasta_file: #Does ff stand for fasta file? \n",
    "    list_labels = [line.split()[2] for line in fasta_file.readlines() if line.split()[0]=='ATOM']\n",
    "\n",
    "# Get the ligand features using RDKit feature factory\n",
    "mol = Chem.rdmolfiles.MolFromMol2File(ligand_mol2, removeHs=False)\n",
    "factory_name = os.path.join(RDConfig.RDDataDir, 'BaseFeatures.fdef')\n",
    "factory = Chem.ChemicalFeatures.BuildFeatureFactory(factory_name)\n",
    "features = factory.GetFeaturesForMol(mol)\n",
    "\n",
    "# Create dictionary that maps Atoms to their features\n",
    "properties = {feature.GetFamily():[] for feature in features}\n",
    "for feature in features:\n",
    "    current_atoms = [list_labels[atom_id] for atom_id in list(feature.GetAtomIds())]\n",
    "    for atom in current_atoms:\n",
    "        properties[feature.GetFamily()].append(atom)\n",
    "print(\"Molecular properties of the compound:\\n\")\n",
    "print(properties)\n",
    "# Additionally we need to load our protein/ligand complex into a MDAnalysis universe\n",
    "ref_pdb = 'Data/6EI5/ref-min.pdb'\n",
    "u = mda.Universe(ref_pdb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Generate PL IFPs \n",
    "Now that we have all the chemical information for our ligand, we can start the actual IFP analysis.  \n",
    "The possible interactions with their corresponding abbreviations are shown in the table below.  \n",
    "  \n",
    "| Abbreviation |    Type of interaction   |\n",
    "|:---------------|:---------------------------|\n",
    "|   HY  | Hydrophobic |\n",
    "|  IP  |       Salt bridge (positive charge) |\n",
    "|  IN  |       Salt bridge (negative charge) |\n",
    "|  AR  |   $\\pi$-stacking  |\n",
    "|    |  Cation-$\\pi$  |\n",
    "|    |  $\\pi$-Cation  |\n",
    "|    |  $\\pi$-Amide  |\n",
    "|  HL  |  Halogen-$\\pi$  |\n",
    "|    |  Halogen-carboxyl  |\n",
    "|    |  Halogen-Amide  |\n",
    "|    |  Halogen-Sulfur  |\n",
    "|  HD  |  Hydrogen-bond donor  |\n",
    "|  HA  |  Hydrogen-bond acceptor  |\n",
    "|  WB  |  Water bridge  |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this we use the IFP and table_combine functions contained in the IFP_generation script.\\\n",
    "The IFP function takes a variety of inputs.\\\n",
    "We need to supply our universe,  the ligand residue name and the properties of our ligand and set RE to false. The other settings we'll leave on the default for now.\\\n",
    "The IFP function returns three data frames: all IFPs properties except hydrogen bonds, hydrogen bonds only, and a waterbridges.\n",
    "Then we can combine all three tables in one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start HB analysis 09:00:31.010103\n",
      "Start WB analysis 09:00:31.453852\n",
      "Start collecting IFPs:  09:00:33.115897\n",
      "Start building IFP table:  09:00:38.396874\n",
      "IFP database is ready  09:00:38.400871\n"
     ]
    }
   ],
   "source": [
    "df_prop,df_HB,df_WB = IFP(u,\"INH\",properties,RE=False)\n",
    "df_prop_complete = table_combine(df_HB,df_WB,df_prop,\"INH\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the complete IFP database.\\\n",
    "It contains Waterbridges between the ligand and THR168, ASP77 and GLY81.\\\n",
    "Hydrogen bonds and aromatic contact with the ligand are constituted with ASP77/168, and PHE122/TRP146 respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>WB_LEU32</th>\n",
       "      <th>WB_SER36</th>\n",
       "      <th>HY_ALA39</th>\n",
       "      <th>HA_ASP77</th>\n",
       "      <th>WB_ASP77</th>\n",
       "      <th>HY_ILE80</th>\n",
       "      <th>WB_GLY81</th>\n",
       "      <th>HY_MET82</th>\n",
       "      <th>HY_LEU91</th>\n",
       "      <th>HY_ILE94</th>\n",
       "      <th>AR_PHE122</th>\n",
       "      <th>HY_PHE122</th>\n",
       "      <th>AR_TRP146</th>\n",
       "      <th>HD_THR168</th>\n",
       "      <th>WAT</th>\n",
       "    </tr>\n",
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       "   time  WB_LEU32  WB_SER36  HY_ALA39  HA_ASP77  WB_ASP77  HY_ILE80  WB_GLY81  \\\n",
       "0     0         1         1         1         1         1         1         1   \n",
       "\n",
       "   HY_MET82  HY_LEU91  HY_ILE94  AR_PHE122  HY_PHE122  AR_TRP146  HD_THR168  \\\n",
       "0         1         1         1          1          1          1          1   \n",
       "\n",
       "   WAT  \n",
       "0    4  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prop_complete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.4 Visualization of protein residues involved in the PL interactions\n",
    "For the visualization in jp notebook we'll use NGLWidget.\\\n",
    "Let's first look at all residues participating in protein-ligand water bridges and H-bonds (taken from df_HB and df_WB)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f5d6cc6f0fd941a8bdc51058633d6a56",
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       "version_minor": 0
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      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "water_bridges = df_WB[df_WB.sele1_resnm == \"WAT\"].sele1_resid.values\n",
    "view = nv.NGLWidget()\n",
    "\n",
    "# We only want to see the protein and the ligand\n",
    "u_reduced = u.select_atoms('protein or (resname INH WAT)')\n",
    "w=nv.show_mdanalysis(u_reduced)\n",
    "residues = \"32 36  77 81 \" + ''.join([\" \"+ str(w) for w in water_bridges])\n",
    "w.add_licorice(residues)\n",
    "w"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Next, let us look at aromatic interactions. The following can be adapted to other interactions by changing \"AR\" to one of \"HD\", \"HA\", \"IP\", \"IN\", \"WB\", \"HA\".\n",
    "Note: if you don't see aromatic contacts, please update you 6EI5.tar.gz data from Zenodo achive version 3.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing aromatic residues  122 146\n"
     ]
    },
    {
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      "text/plain": [
       "NGLWidget()"
      ]
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.NGLWidget()\n",
    "u_reduced = u.select_atoms('protein or (resname INH )')\n",
    "w = nv.show_mdanalysis(u_reduced)\n",
    "\n",
    "residues = ''.join([' ' + str(ifp[6:]) for ifp in df_prop_complete.columns.tolist() \n",
    "                    if (ifp[:2] == 'AR')])\n",
    "print('Showing aromatic residues', residues)\n",
    "w.add_licorice(residues)\n",
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Generation of PL IFPs for a single MD trajectory\n",
    "### 2.1 Read a trajectory, compute PL IFPs and show results\n",
    "This time we'll calculate the IFPs not for a static situation, but over a trajectory. The procedure stays mainly the same as the above, with a few tweaks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Start HB analysis 09:01:04.490783\n",
      "Start WB analysis 09:01:07.829875\n",
      "Start collecting IFPs:  09:01:35.889706\n",
      "Start building IFP table:  09:03:28.889941\n",
      "IFP database is ready  09:03:28.940912\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1152x432 with 5 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Set the file paths for all of the needed data\n",
    "ref_pdb = \"Data/6EI5/ref-min.pdb\"\n",
    "ligand_pdb = \"Data/6EI5//INH.pdb\"\n",
    "ligand_mol2 = \"Data/6EI5//moe.mol2\"\n",
    "traj = \"Data/6EI5/amber2namd2.dcd\"\n",
    "\n",
    "# Initialize the MDAnalysis Universes (static and with trajectory) and calculate the radius of gyration \n",
    "ref = mda.Universe(ref_pdb)\n",
    "Rgyr_t0 = ref.select_atoms(\"protein\").radius_of_gyration()\n",
    "\n",
    "u = mda.Universe(ref_pdb,traj) \n",
    "u_length = len(u.trajectory)\n",
    "\n",
    "# Load part of the trajectory in the memory\n",
    "u_reduced = u.select_atoms('protein or (resname INH WAT)')    \n",
    "stepsize = 50\n",
    "u_mem = mda.Merge(u_reduced).load_new(AnalysisFromFunction(\n",
    "            lambda ag: ag.positions.copy(), u_reduced).run(\n",
    "                start=0,stop=-1,step=stepsize).results,format=MemoryReader)\n",
    "\n",
    "# Bring the trajectory into the right format\n",
    "# One can save selected snapshots in the pdb format\n",
    "save_pdb = False \n",
    "\n",
    "RMSDl = []\n",
    "RMSDp = []\n",
    "for i in range(0,len(u_mem.trajectory),1):\n",
    "    u_mem.trajectory[i]  \n",
    "    u_mem.dimensions = u.dimensions\n",
    "    \n",
    "    # Wrap all atoms back to the box, use radius of gyration for checking whether the procedure was successful\n",
    "    Rgyr = pbc(u_mem,Rgyr_t0)\n",
    "\n",
    "    # Calculate Root-mean-square deviation\n",
    "    rmsd = superimpose_traj(ref,u_mem,[\"protein\",\"resname INH\"])\n",
    "    RMSDp.append(rmsd[0])\n",
    "    RMSDl.append(rmsd[1])\n",
    "    \n",
    "#    print(i,\"RMSD (protein , ligand):\",rmsd,\"traj length:\",u_length)\n",
    "    \n",
    "    if save_pdb == True:\n",
    "        all_write = u.select_atoms(\"all\")\n",
    "        all_write.write(\"/hits/fast/mcm/kokhda/TMP/\"+\"/namd_protein-\"+str(i)+\".pdb\")\n",
    "\n",
    "# Compute IFP:\n",
    "# parameters used: \n",
    "#  WB_analysis=True  - find water-mediated protein-ligand contacts; \n",
    "#  RE = False - don't store unspecific prorein-ligand contacts\n",
    "df_prop,df_HB,df_WB = IFP(u_mem, \"INH\", properties, WB_analysis=True, RE=True, Lipids=[])\n",
    "\n",
    "# Build IFP table\n",
    "df_prop_complete = table_combine(df_HB, df_WB, df_prop, \"INH\")\n",
    "\n",
    "# Plot IFPs\n",
    "Plot_IFP(df_prop_complete, contact_collection=None)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Explanation for the plots:  \n",
    "The first plot shows the devlopment of detected IFPs over the time of the trajetorie.  \n",
    "The second plot shows all contacts between ligand and protein over the time of the trajectorie.  \n",
    "The third plot shows the amount of molecules participating in the water shell of the ligand.\n",
    "\n",
    "Additionally, we can plot the variation of RMSD for the protein and ligand heavy atoms (relative to the reference structure ref_pdb):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YyPxhl+eJp7Wth9iaid0HN/eLrRMDI/FH1rQ3+PUGJ7/KC35+hlgPyacfvs5AcV7ZfDJDUYRMHwrpo5fqvY34+a5sB+uG4o806PnqtYrlD2DXm3B2vfjjHbIarP+xGkRZAseXwoEPRDv+8xU071f1MquLIIj1kJdaWhBNbavdQ7icmku/rw6zZGQrngku66ZXhqL78NenYv0YW0K3dyB8YpmHjzIznlPbvsQz6VfcZJkoLZwwaDUKWo0BJzXi/DQEQfzeZ8eL3/eseMhKePx/TrIozA8pFgx5YNkIWzc/sXWbeUvsWXX6L7R4rmpDH0+h2qLZsWNHevfuTWJiIiUlJaxevZq0tDTCwsJITS3v6akftCKaJQqxeR93SBTJxBMPn4IysRvTpKPYfUg7DynnxKeb8HAXTlsPUTwbPhLTkIrH5QpzIP3qY3FMvyK+8tMf5zGxBkfvh4LYuKw4WjiqF78ShTgDHbsHru8VbQXxqe3XW+zOekaWHV8qkcOdSw/FMVp8UGQ+jOwtMwSXAHEs0b212CopzBU/T9HDv+reF+WCsQV0eFUcazLRYjCFv3+E318Trzl4NXh3Fo9nxcMvL0HiMWj+DPznS91NzNQCdl9MY+p3Z/n9lUgCG5W/EKVc7l6HXW+IY6UugY9n2a/thDPrxeNAuktHPkhtzVmzCJaNiaClh45dkkrkkJNMdmosX/+8jwDzLJ7zUmCQHQ9GZqK3Q7N+Oh2OqrZonj9/nmnTpmFiYsK6devw9PRkw4YNHDhwgHXr1mnbXo2pkmgqlWJXLe6w2JpMOPa4BeXcQnR1adIJPNuXP+tWnA9pf0PKWXFGN/Ws+CMFQCaOOzUKA5cW4tjOI3F8smtmbAkNmoGzPzRoLpbr3Fz7QwDZiaLbTuxe8bMqCkUB8+4ivnJTRJFMOfu4u2TpLI51uYeLItmwVfWETlkiPmQMjav/ecoj/YrYXc+IhS5viS3O3W+BzAD6fgoth9cqf0pd8M2hm3y86yoX3u+FjVkl61kQxF7A7rchN1lslRfmiA/pVqNFbwY7D66k5TJpQzTpeUV8PCiIQaHuuvkwTzDt+7Psu3yHna9G4utcQWNER1RbNGsrlRJNQYCtE8SZzgdZ4jFHX1EgvTqKr3LGdjQiP0Mcq3skoilnxPFGI3No0FTsSjg3f/jXX/xC6nvSprhAnO18JKI5SWBgLLam3VuLIunRRrStrolM0X2xxRmzRXzv1VH09bRrXLN26Ym3tsWw+2Ia597TcGKsPIrzRW+GrDgIGgo+XcvMsGfmFzPt+7Mcv5XBpI5NeLNPc4wMdfM9ftR6fuo4rY5Qpy0aDQb89ddf5R43NTXF09MTV1f9RBepFo+EoFn/h63JjmCjwfiPJlg6gl8P8QWPx2zM7Sp07dA7JhbiGGfT3qJ92Ylg5VJ9l5vagKkVDFopzk7LCyB0bO33JNAiiZniDpTVwsQSur6lNouDpQkbJrZhwY4rfHs4jqu381gyIhRbC+32IrILinn310sENrJhcicdrASrJhqJZvfu3VEqlaXWmz/pn9m5c2e+//772r/s8Xk9rTKSydQ6Edc4Mpnmbj51BZkMWg6raStqhISMAsI8dR9HEsDY0ID3BwTg72bNu79e5NmlR/h2TDh+LtrrPs///TLZBcVsmNAGYx21ZKuDRhatWbOG559/nhs3biCXy7lx4wbDhg1j9erVXL58GXNzc2bOnKlrWyUkJP5BsUJJavYDPPW8emdY68b8OLkt94tKeG7pUX6/oJ0J4T+vprPtbAovd/GhRcPauXGjxvE0Y2JisLISHVC9vb1ZsWIFwcHBxMXFsWbNGlq2VLMUTUJCQickZxWgFKh+97wKhHk68NsrHXj5+7NM/+Ecp+Iyeae/P6ZGVRuSyiuU8/YvMfg5WzGtm/7HMTVFo5Zmbm4uRUVFpY4VFRWRk5MDQIMGDSgoKNC+dRISEmp5tJlaVULCaQM3W3O2TGnHpI5N2HA8gcHLj1V5z6GPdl3lTm4hnw4JrrLw6gONRLN///4MHDiQQ4cOERcXx8GDBxkyZAjPPPMMACdPnsTTs56NkUlI1AE03bZXlxgbGvBO/xZ8OyacxIwCnvnqCLti0ip1jWM37/HDyURe7OhNKz3s81MdNBLNZcuW4evrS+/evfHx8aFPnz54e3uzbNkyQNwDaMuWLTo1VEJCoiwJGQVYmBjSQIdb1mpKzxYu7JjREW9nK176/izvb79EkeLpwZ0LihX8388xeDla8FqPpnqwtHpoNKZpZWXFmjVrWL16NXfv3qVBgwalZs+9vLx0ZZ+EhIQaEjLyaexgUaPRxp7Ew8GCn6a045PdV1l9JI6ziVksHRmqNszcwj3XScwsYPPktpib1N5u+SMqNZ8vk8lwdnauNTdIQuLfTkJmAY1rUdxLABMjA+Y804IVL4QRdy+ffl8dZvfF2+XmPZOQydpjcYxp50mEdy1203sCjUTz+vXr9O7dG0dHR0xMTEq9JCQkagalUtyBsibHM9XRO8CVnTM60sTJkqnfnWH+b5cpVihV6YXyEmZvvUBDW3Nm92leg5ZWDo265+PGjcPd3Z2NGzdiaal/1wYJCYmy3MkrpFihrPQOlPrEw8GCn6a246OdV1lzNI4ziVksGdEKDwcLvtofy827+WyY0AarWri9RkVoZOnFixc5dOgQxsY6CrogISFRaRJqwcy5JpgaGfL+gAAimjgwe+sF+n91mJe7+rLir1s8H+ZOp6Z1a1dZjbrnzZs3Jz09/ekZJSQk9MYjf0hPh9rb0nySvkFu/D4jksaOFny86yqOlia8279FTZtVaTRqaY4fP57Bgwcze/bsMsE52rdvrxPDJCQk1JOQUYCRgYyGdnUn6IqnoyU/v9SeVYfjaOvtqPVgH/pAI9GcNm0aAEOGDCl1XCaTUVLydD8sCQkJ7ZOQWYC7vbnOwrPpClMjwxoJ96YtNBJNpVL59EwSEhJ6JTGjoEbWnP/bqfIj6tKlS8yYMUObtkhISGiIIAjEZ+TrPbqRRCVFs6ioiA0bNhAZGUlQUBBnz57V6LwJEybg7OxMYGBguekHDx7E1taWkJAQQkJCmD9/fmXMkpD415FdICevUFHrZ87rIxp1zy9fvszKlSvZuHEjBQUFKJVK9uzZQ8+ePTUqZNy4cUyfPp0xY8ZUmKdjx478/vvvmlktIfEPBEFg+qZzGMhkfD2iVU2bo3MeRzeSuuf6Rm1L87vvvqNjx44EBgZy6NAh3n//fVJSUnBwcKhU/MxOnTrh4OBQbWMlJCpiZ8xtdlxIY2dMGpn5xTVtjs5RuRtJLU29o1Y0x4wZw9WrV9mxYwfnzp3jlVde0Zn4HT9+nJYtW9K3b18uXbqkkzIk6ie5hXLe/+0SDW3NKFEK7Ltc/jrn+sSjkHC1bd35vwG1ojl//nysra157rnnGDhwIL/99ptOZtJDQ0NJSEjg77//5pVXXuG5556rMO/KlSsJDw8nPDycu3fvat0WibrHwj3XyLhfxDcvhNHYwYKdMfVfNOMzCnCxMcXMuPZHBapvqB3TfPfdd3nnnXfYvXs3K1euZPDgwTg6OpKdnU1qairOzs5aMcLG5vFeIP369ePll1/m3r17ODk5lck7efJkJk+eDIjbbP7buZGex/pjCRjIwNTYEDMjA0yNDTE1MhBfqv8NMTU2wOzhXytTI/ycrep8xKpziVlsPJHA2HZeBLvb0TfIldWH48gpkNdJx2lNSczMl8Yza4inTgTJZDL69u1L3759SUtLY9WqVaxevZrWrVszcOBArQQfvn37Ni4uLshkMk6dOoVSqcTRsW6EiapJ8grlTFgXzZ3cQsyMDSlSlFCkUKLpTvafDA5iWOu6uze4okTJ279cxMXajNd7icFr+wW6seLQLfZducOQMPcatlB3JGQU0LmOrdmuL1QqtIibmxtz5szh3XffZdeuXaxcuVKj80aMGMHBgwe5d+8e7u7uzJs3D7lcDsDUqVPZunUry5cvx8jICHNzc3788cc63wLSNYIg8PYvF0nJfsCWKW0J83RQHZeXCBQqSiiSKylSlFD48G+RQkmRXEmhooQPd1zhh5OJdVo01x6N50paLt+MDsPaTGxVBrvb0sjOnN0X0+qtaBYUK0jPK5ImgWoItaLZokULBg4cyMCBA0t1hWUyGf369aNfv34aFbJp0ya16dOnT2f69OkaXUtC5KfoZH77O5U3ejdTCSaI98bESIaJkQGoWZIcdzef+b9f5urtXJq71s6tUtWRnFXAF/uu08Pfmd4BLqrjMpmMPoGubDyeQF6hXCWm9YnEh+5G0mqgmkHtRNDKlSspKipixIgReHh4MH36dPbv3y+tN69hbqTnMXf7Jdr7ODK1s0+VrjGwVSNMDA3YfDpJy9bpHkEQmBt1CZkM5j0bWKZX0i/IleISJQeu1s/IXKqQcNLMeY2gVjQjIyNZuHAhsbGx7Ny5ExcXF2bPno2zszNjxoxh27Zt0ta9eqZQXsL0H85hYWLIomEhGBpUbRjD3tKEngEu/HIuRaPNr2oTey7dZv/VdF7r0ZRGduZl0lt52ONiY8rOSu6IWFd45G7kJbU0awSNl1EGBQUxZ84czpw5w7lz5wgPD+frr7/G1dWVNWvW6NJGiSdYsOMKV2/nsXBoS1xsqhcSbFi4B9kFcvZdvqMl63RPXqGc97dfxt/NhvEdvMrNY2Ago0+AKwev3SW/SKFfA/VAQmY+tubG9do7oDZTpYAdjRs3ZsaMGfz555/Ex8fTsWNHbdtV5xAEgWu381hx6CYjVp4gZP5evjl0E0HTqWwN2H3xNhtPJDCpYxO6Nqu+u1cHXyca2pqxJTpZC9bph8/3XudOXiEfDQpSGxKtb5AbRQolB6/VP1/ehIzauy/Qv4Fqb8zh4ODwr10imVco5+iNexy8dpdD1++SllMIQHNXa/xdbfh411WSMguYNyCg2jEPU7IfMHvr3wS72/JGb+1sQmVoIGNIuAdfH4glJftBuV3d2sSF5GzWH4/nhbaehHjYqc3b2ssBJysTdl5Mo3+wm34M1BMJGQUEu9vWtBn/WurObka1AEEQuHo7j4PX7nLwWjpnErJQKAWsTY2I9HNiZo8GdG7qjKutGUqlwGd7r7H84E1Ssx+wZGQollXcPEpRouTVTedQCvD1iFbizLiWeD7Mna8PxLI1OplXe/hp7braRvTJjKGBlSn/7d3sqfkNDWT0CnDl13MpFMpL6s3KGXmJkpTsBwxo2bCmTfnXIonmU5CXKPnj8h1Va/J2rtia9HezYVInb7o0bUCopz3G/2hJGhjIeLNPczzsLZgTdZGhK46zZlzrKo1Dfrk/luiELL4cHqL1VSAeDhZ08HHipzNJvNLNF4MqTizpmvXHE7iYksvSkaHYaOhG1C/QjR9OJnLo+l16B7g+/YQ6QGr2A0qUAo0r2T2Xy+UkJydTWFioI8vqJmZmZri7u1dq00hJNJ/C3O2X+OFkItZmRnT0c6JLU2c6N2ugsfiNjGiMm50Z074/y8ClR1k7vg3NXK01Lv/YjXss+fMGz4e582xIo6p+DLUMbe3BjE3nOHYzg0i/sktXa5rU7Ad8sfcaXZs1oF+Q5uIX4e2AvYUxu2LS6o1oVtXdKDk5GWtra7y8vKSFIw8RBIGMjAySk5Np0qSJxuc9VTQVCgXr169n//79ZGRk4OjoSPfu3RkzZky939L3Rvp9fjyVyKiIxtUal+zazJktU9oxYd1phiw/xjcvhNHB9+nilHG/iJmbz9PEyZJ5zwZUqWxN6NXCBVtzYzZHJ9VK0Xx/+yVKBIH55fhkqsPY0ICeLVzYGXObIkUJpkZ1v4v+OCRc5XochYWFkmD+A5lMhqOjY6UD/6hVgeLiYjp16sT06dNJTEzE3t6exMREpk+fTufOnSkurt9xCz/bcxULEyNm9Wxa7YmcwEa2/DKtAw3tzBm75hRbz6ifsVYqBf77099kP5CzZEQoFia66xSYGRvyXEhD9ly6TXZB7bqney/dZu/lO8zs0RSPKjhz9w1y436RgiOx93Rgnf5JyCjAzNgAZ2vTSp8rCWZZqlInapVg6dKl5OXlERMTw5EjR/jxxx85cuQIMTEx5Ofn8/XXX83ycfoAACAASURBVFfZ2NrO2cQs9ly6w+RO3jhaVf4LWh6N7Mz56aV2tPV25L8//c2ifdcrdElaczSOP6/d5d3+/rRoqPtljkNbe1CsUBJ1PlXnZWnK/SIFc7dformrNRMjNe8+PUkHHyeszYzYdbF+hItLyCygsYNFrR17VoeVlRUAqampZXa21QVeXl7cu6f9h6Va0dy2bRuLFy/G17f0dpu+vr588cUXbNu2TesG1QYEQeDjXVdxsjKt8o+1ImzMjFkzrjVDwtz5cn8s//3pAsWK0jFKLyRn88nuq/Rq4cILbT21Wn5FBDS0JbCRTa1aVrlo33XScgpZMDCozESbppgYiV30vZdul6nnukhiRgGNHer2SqCGDRuydevWmjajyqj9Jl69erVCx/WOHTty7do1nRhV0xy8dpdTcZm82t23ym5C6jAxMuCzIcG81qMpP59NZtzaU+Q8EKM+5RXKeWXTORpYmfLpkGC9dqmGhXtwOS2Xiyk5eiuzIi6m5LD2aByjIhoT5mlfrWv1DXQjt1DB8VsZWrKuZhAEgYTM/Drv2B4fH6/aZLGgoIChQ4cSHBzMsGHDiIiIIDo6GoCXXnqJ8PBwAgICmDt3rup8Ly8v5s6dS2hoKEFBQVy9ehWAjIwMevXqRatWrZgyZYpWF5Y8iVpFkMvlmJiYlJtmYmKCQlH/lqiVKAU+2X0VT0cLhrfRXdg0mUzGqz38cLc3582fL/D8N8dYO74Nn+4WHeI3T2mHnUX5da8rBrRsxAc7rrD5dBKBjWrOebpEKfDOLzE4WJoyu0/1Hfk7+jlhaWLIrpi0Oh2DMj2viEK5stqiOe+3S1xOzdWSVSItGtow9z+Vn6xctmwZ9vb2XLhwgYsXLxISEqJKW7BgAQ4ODpSUlNC9e3cuXLhAcHAwAE5OTpw9e5Zly5axcOFCVq1axbx584iMjOS9995jx44dGoeurCxqW5pP29pCF1tf1DRR51O4ejuP//ZqVuUuYWUYHObOhgltSMsppM+iv4g6n8rMHk1p7aX/VVa2Fsb0DXQl6rzoEF5TRJ1P4e/kHN7t74+tefU9NMyMDenu78Ley3dQlNTd76zK3ageBeo4cuQIw4cPByAwMFAligBbtmwhNDSUVq1acenSJS5fvqxKGzRoEABhYWHEx8cD8NdffzF69GgA+vfvj7199XooFaG2pXn//v0KW5pAvQsRV6Qo4fO91wlsZEP/IP0tvWvv68TPL7Vn/NrThDS2Y1pX36efpCOGhXsQdT6VPZdu68wvVB2FcvEeBDWy1eqql35Brmz/O5VTcZm018DdqzaicjeqZki4qrQIdUVFXei4uDgWLlzI6dOnsbe3Z9y4caUc801NxclZQ0PDUj1efQxnqRXNP//8U+cG1Ca+O5FISvYDPhkcrPfZyaYu1hx6owsymazK4d60QVtvRzwczNl8OqlGRPO7EwmkZD/g0yHavQedmzpjbmzIzotpdVY0EzMLMDSQ0ci+dscIqAyRkZFs2bKFrl27cvnyZWJiYgDIzc3F0tISW1tb7ty5w65du+jSpYvaa3Xq1Invv/9etbNEVlaWTmxWK5qdO3fWSaG1kdxCOUsOxNLRz6nGHLyr6wuqDQwMZAwN8+DzfdfFmVo9TjrkPJCz5M8bdPRz0sj5vzKYmxjStXkD9ly6w7wBgTX6YKoq8RkFNLQz08uwkb54+eWXGTt2LMHBwbRq1Yrg4GBsbW3x8/OjVatWBAQE4O3tTYcOHZ56rblz5zJixAhCQ0Pp3LkzjRvraE5CUMPt27eFO3fuqN4XFRUJc+fOFZ599lnh888/V3eqXggLC9PatRbuuSp4vvm7EJOcrbVr1lVSsgoEr//7XVi456pey/141xXB883fhYspurkH28+nCJ5v/i6cvJWhk+vrmgFfHxZGrzpRpXMvX76sZWu0g0KhEB48eCAIgiDcuHFD8PT0FIqKivRqQ3l1o05b1D6yJkyYwN69e1Xv33zzTRYvXoyRkRELFizgk08+0Y2S65n03EJWHY7jPy0b1uiscW2hoZ05nfwasPVMMiVK3bht/JPbOYWsORLHcyENCWiom3vQtbkzJkYGdTai+yPH9vpEQUEBkZGRtGzZkoEDB7J8+XK18yi1AbWief78efr06QOIM+Xr1q3ju+++Y+vWrfz8889s3LhRL0bqmq8OxCIvUfJ6z6Y1bUqtYVhrD9JyCjkcq58gvov/uI4gwOu9nh72rapYmRrRuWkDdl+8jVJPDwNtkfNATnaBvM77aP4Ta2troqOj+fvvv7lw4QJ9+/ataZOeilrRzMvLw8lJHFu6cOECxcXFKhHt3LkzKSkpurdQx8Tdy2fTqSRGRjTGy6n+uHJUlx7+LjhYmrAlWvcrhGLv5LElOonRbT2rtL68MvQLcuV2biHnk7N1Wo62ebQvUF1fDVQfUCuaNjY2ZGSIqyhOnz5NSEgIRkbi3FFxcbHOPO71ycK91zA1MuCVbrU3AG9NYGJkwHMhjdh3+Q4Z94t0Wtane65hYWLE9G66d7Xq7u+CsaGMXXruop9NzOL5b44xZPkxcgvllT4//qG7kZdT/Wpp1kXUimafPn2YNm0au3fv5quvvmLAgAGqtJiYGN3NTumJC8nZ7LiQxosdvWlQhagx9Z1hrT2Qlwj8ck53PYro+Ez2Xb7D1M7eOFjqfizLxsyYSF8ndsbc1stDPzmrgFc2nWPQsmPE3SvgfFI2L66L5kFx5XycVXud17MxzbqIWtH8+OOPycrKYujQoXh5efHKK6+o0jZt2kT37t01KmTChAk4Ozur1pv+E0EQmDFjBr6+vgQHB3P27NlKfISqITwMyuFgacKkjtoNylFfaOZqTUsPO7ZEJ+lEYARB4KNdV3G2NmWClgOjqKNvkBsp2Q+I0eEa+7xCOZ/uvkq3zw+x7/JtZnTz5dAbXVg0LITTCZm8/P2ZSgUQScjIp4G1qU5DBEpohlrRdHJyYs+ePeTm5vLbb79hYfH4Kff555+zaNEijQoZN24cu3fvrjB9165dxMbGEhsby8qVK3nppZc0NL/qHI69x7GbGbzSzRdrDbdP+DcyLNyD63fu83ey9gVm3+U7nEnIYmaPpnoVg14tXDAykOkkXFyJUuCHk4l0XXiQZQdv0j/IjQOvd2FWr2ZYmhrxn5YN+XBgEH9eu8usLec19k5IyCio9kqgusy6detITX162ML33nuPP/74Q6e26MVLtlOnTmp3rIyKimLMmDHIZDLatm1LdnY2aWm6G3NSPgzK4W5vzsiIuj3EoGv+09INM2MDrYeMU5Qo+XTPNbwbWDI03F2r134adhYmtPNxZFdMmlZb0Idj79L/q8O8/UsMXo6WRE3rwKJhITT8xy6fI9o05q2+zfn9Qhrv/npRIxsS9LzQoCZQtyxbU9GcP38+PXr00KZZZVArmt7e3k99aYOUlBQ8PDxU793d3XU6M/97TBqXUnP5b69m9WILBF1ibWZMvyA3fvs7lYJi7UW12nommRvp95ndu3mNrITqG+hGfEYBV9Lyqn2tG+l5jF97ihdWnyK/WMGyUaH8NLUdLdVsMzylsw8vd/Fh06lEPtmtPsRiobyE27mFeNXhQB3x8fE0b95ctfpnyJAhFBQU4OXlxfz584mMjOSnn37i/PnztG3bluDgYAYOHEhWVhZbt24lOjqaUaNGERISwoMHDzhz5gydO3cmLCyM3r17qxpZ48aNU8XqrCiEXHVR2yeKj4+nRYsWjB8/HldX3W1MVd6TtqKF9ytXrlSFfKrs3h4AxQolC/dcw9/NRtoGVUOGhXuw7WwKO2NuMySs+q3CB8UlLPrjOq0a29E7wEULFlaeXgEuvPtrDLsuplU5Mn5mfjGL/7jO9ycTsTA25K2+zRnb3kvj7YLf6N2M3EI53xy6iY25ES93Kd97ICnzUXQjLbU0d/0f3I7RzrUe4RoEfT9Wm+XatWusXr2aDh06MGHCBJYtWwaIO0IeOXIEgODgYL7++ms6d+7Me++9x7x581i8eDFLlixh4cKFhIeHI5fLeeWVV4iKiqJBgwZs3ryZd955hzVr1pQps7wQctVFrWieOHGCb7/9lgULFtClSxcmTZpEnz59tB5JxN3dnaSkx92/5ORkGjYsX9AmT57M5MmTAQgPD690WT+eTiQxs4B141vXyS0DaoI2TRxo4mTJltNJWhHNNUfjuJNbxNcjQmts3xonK1Mimjiy6+LtSjnUlygFrt7O5c+r6az46xb5RQpGRjTmtR5NK70tikwmY/6AQPIKFXy6+xo2ZsaMLidSf0JG/Zg59/DwUK0hHz16NF999RUAw4YNAyAnJ4fs7GxVzIuxY8fy/PPPl7nOtWvXuHjxIj179gTEbr2bW/lRyZ4MIaetnSbUimabNm1o06YNixYtYtOmTbz33ntMnTqVF198kRkzZmBrq53lbgMGDGDJkiUMHz6ckydPYmtrW2ElVIf8IgVf7Y+lrbdDnQ5Gq29kMhnPh7vz6e5r3Lp7H+8GVlW+VlZ+Md8cvEkPf2faNNF/zNAn6RvkyntRl4i9k4efS/nbKitKlFxMzeVUXAYnb2VyOj6T3EJxmKJz0wa809+fphWcqwkGBjIWPt+S+4UK5kRdxNrMqEx0qYRMLcfRfEqLUFf88wH56L2lZeU+lyAIBAQEcPz48afmrSiEXHXQaDDJysqKSZMmceLECcaPH8+8efM4c+aMxoWMGDGCdu3ace3aNdzd3Vm9ejXffPMN33zzDQD9+vXD29sbX19fJk2apGq2a5tVh+O4d7+Y/+vrL+3MV0mGhLpjaCBj2cGb1QpQvPTPG+QXK3ijd/UjsleX3gGuyGSwM+bxLHqRooTT8Zks/fMGL6w+Sct5e3lu6VE+3HmVuHv59AtyY9Gwlhz9v26sn9CmWoL5CGNDA5aOCqWNlwOvb/mb/VfulEpPyMjH2swIe4u67eWRmJioErpNmzYRGRlZKt3W1hZ7e3sOHz4MwMaNG1WtTmtra/LyxPHnZs2acffuXdW15HI5ly5d0tfHePq+5yCOba5atYp169bh6enJqlWrNArV9IhNmzapTZfJZCxdulTj61WFYoWS708m0DfQlRA1A/QS5eNsY8aw1h78cDKRI7H3eLmrD8Nae1RqIi0ps4ANxxMYHOpOM9fqi011cbExI9zTnqjzKSgFgZNxGZxLzKboof9kMxdrBoW6E+HtQBsvB5xtzHRmi5mxIavGhjNq1Ule/v4s6ye0oa23I/DQ3cjRos4/6P39/Vm/fj1TpkzBz8+Pl156qcyOtuvXr2fq1KkUFBTg7e3N2rVrAXGCZ+rUqZibm3P8+HG2bt3KjBkzyMnJQaFQMHPmTAIC9BNcWSao8XfYunUr3377LefOnWPUqFG8+OKLejNME8LDw1WbMGnCvftFyEuUuNnWnyCu+kQQBI7dzGDRvutEJ2ThZmvGy118GKqheM7afJ4dMWn8+d8uZdxwaop1R+N4/7fLGMjEfW4imjjSpokDrb0c9LJC6Z9k5hczbMVx0nIK+WFSBMHudnRdeJAWbjYsHRVa5eteuXIFf39/LVpaOeLj43nmmWe4ePFijdlQEeXVjTptUdvSHDp0KC1atGDq1KmYmZkRFRVFVFRUqTxvv/12NU3WH05a2r/834pMJqODrxPtfRxV4jkn6hLLDt7k5a6+DA13r1A8L6fm8sv5FCZ38q41ggkwqq0nAY1saeZqjU0tWOTgYGnCxokRDPnmGGPXnGLT5LYkZxXQJ1B33isSlUNtS7NLly5quwQymYwDBw7oxDBNqGxLU0K7CILA0RsZLPrjOmcSsmhoa/ZQPD0wMSo9XD52zSnOJ2Xz1xtdsa3jY3P6ICEjnyHfHKdYoSTngZxPBgcxrHXVF2LUdEuzNqPVlubBgwe1ZphE/UMmkxHp50QHX0eO3LjHon3XeffXiyw/eJNpXX0ZEuaOiZEBx27c49D1u7zdr7kkmBri6WjJxoltGLbiBCCFhKtNVGspxoYNG7Rlh0QdRiaT0dGvAT+/1J4NE9rgbGPK27/E0HXhQTadSuTj3VdpaGvGmHZeNW1qnaK5qw3rJ7ShT4Arwe7Vd++rD6EctU1V6uSponnr1i22bdtWagD3t99+IzAwkFmzZlW6QIn6i0wmo1PTBmx7qT3rJ7ShgbUpb22L4UJyDq/1bKrxShmJx4R42PHNC2FYmlYvoImZmRkZGRmScD6BIAhkZGRgZlY5rwi1d2Lr1q2MHDkShUKBTCZj1apVHDhwgB07djBr1ixeffXVahktUT+RyWR0btqATn5OHLp+l4spOQwK1W9QDonSuLu7k5ycXKWlx/UZMzMz3N0r991UK5oLFizgs88+UzmcT5s2jX79+nHz5k3s7e2rZaxE/Ucmk9GlmTNdmjnXtCn/eoyNjWnSRIobqw3Uzp7b29uTkZGBgYEBxcXFWFhYkJGRobXlk9VFmj2XkJDQBeq0Re2YZklJCQYGYhYTExNsbGxqjWBKSEhI1ARqu+fFxcV8+OGHqvdFRUWl3kPdcm6XkJCQqC512rndyckJLy+vSp1z9+5dGjSo2QhHtcGG2mKHZEPtskOyQSQ+Pp579+6Vm6ZWNOsjtWEctDbYUFvskGyoXXZINjwd/e8zICEhIVGHkURTQkJCohIYvv/+++/XtBH6JiwsrKZNqBU2QO2wQ7LhMbXBDskG9fzrxjQlJCQkqoPUPZeQkJCoBPVWNHfv3k2zZs3w9fXl44/LbiRVVFTEsGHD8PX1JSIigvj4eK2Wn5SURNeuXfH39ycgIIAvv/yyTJ6DBw9ia2tLSEgIISEhzJ8/X6s2PMLLy4ugoCBCQkLK3cFTEARmzJiBr68vwcHBnD17VqvlX7t2TfUZQ0JCsLGxYfHixaXy6KIuJkyYgLOzM4GBgapjmZmZ9OzZEz8/P3r27ElWVla5565fvx4/Pz/8/PxYv3691u144403aN68uWp/7+zs7HLPfdq9q44N77//Po0aNVLV+c6dO8s992m/perYMGzYMFX5Xl5ehISElHuutupBKwj1EIVCIXh7ews3b94UioqKhODgYOHSpUul8ixdulSYMmWKIAiCsGnTJmHo0KFatSE1NVU4c+aMIAiCkJubK/j5+ZWx4c8//xT69++v1XLLw9PTU7h7926F6Tt27BD69OkjKJVK4fjx40KbNm10ZotCoRBcXFyE+Pj4Usd1UReHDh0Szpw5IwQEBKiOvfHGG8JHH30kCIIgfPTRR8Ls2bPLnJeRkSE0adJEyMjIEDIzM4UmTZoImZmZWrVjz549glwuFwRBEGbPnl2uHYLw9HtXHRvmzp0rfPbZZ2rP0+S3VB0bnmTWrFnCvHnzyk3TVj1og3rZ0jx16hS+vr54e3tjYmLC8OHDy2zTERUVxdixYwEYMmQI+/fv12rYLDc3N0JDxT1drK2t8ff3JyUlRWvX1yZRUVGMGTMGmUxG27Ztyc7OJi0tTSdl7d+/Hx8fHzw9y+7vrW06deqEg0PpbYKfvO9jx47l119/LXPenj176NmzJw4ODtjb29OzZ092796tVTt69eqFkZG4IK9t27YkJydX+fpVtUETNPktacMGQRDYsmULI0aMqNK19Um9FM2UlBQ8PDxU793d3csI1pN5jIyMsLW1JSMjQyf2xMfHc+7cOSIiIsqkHT9+nJYtW9K3b1+dbUMqk8no1asXYWFhrFy5sky6JvWlLX788ccKfxj6qIs7d+7g5uYGiA+29PT0Mnn0WR8Aa9asoW/fvuWmPe3eVZclS5YQHBzMhAkTyh2q0FddHD58GBcXF/z8/MpN13U9VIbqRTatpZTXYvznclBN8miD+/fvM3jwYBYvXoyNjU2ptNDQUBISErCysmLnzp0899xzxMbGat2Go0eP0rBhQ9LT0+nZsyfNmzenU6dOqnR91UVxcTHbt2/no48+KpOmr7rQBH3VB4jhF42MjBg1alS56U+7d9XhpZdeYs6cOchkMubMmcPrr7/OmjVrSuXRV11s2rRJbStTl/VQWeplS9Pd3Z2kpCTV++TkZBo2bFhhHoVCQU5OTpW6L+qQy+UMHjyYUaNGMWjQoDLpNjY2WFlZAdCvXz/kcnmF612rw6PP7uzszMCBAzl16lSpdE3qSxvs2rWL0NBQXFxcyqTpqy5cXFxUQw9paWk4O5eN9amv+li/fj2///4733//fYVC9LR7Vx1cXFwwNDTEwMCASZMmlXttfdSFQqFg27ZtDBs2rMI8uqyHylIvRbN169bExsYSFxdHcXExP/74IwMGDCiVZ8CAAapZ0a1bt9KtWzetPkEFQWDixIn4+/tXuC3I7du3VU/yU6dOoVQqcXR01JoNAPn5+eTl5an+37t3b6nZSxDrYsOGDQiCwIkTJ7C1tVV1YbWJutaEPuoCSt/39evX8+yzz5bJ07t3b/bu3UtWVhZZWVns3buX3r17a9WO3bt388knn7B9+3YsLCzKzaPJvasOT45b//LLL+VeW5PfUnX5448/aN68eYUR1HVdD5WmhiagdM6OHTsEPz8/wdvbW/jf//4nCIIgzJkzR4iKihIEQRAePHggDBkyRPDx8RFat24t3Lx5U6vlHz58WACEoKAgoWXLlkLLli2FHTt2CMuXLxeWL18uCIIgfP3110KLFi2E4OBgISIiQjh69KhWbRAEQbh586YQHBwsBAcHCy1atFDVxZN2KJVK4eWXXxa8vb2FwMBA4fTp01q3Iz8/X3BwcBCys7NVx3RdF8OHDxdcXV0FIyMjoVGjRsKqVauEe/fuCd26dRN8fX2Fbt26CRkZGYIgCMLp06eFiRMnqs5dvXq14OPjI/j4+Ahr1qzRuh0+Pj6Cu7u76rvxyJMjJSVF6Nu3ryAIFd87bdkwevRoITAwUAgKChL+85//CKmpqWVsEITyf0vaskEQBGHs2LGq78EjdFUP2kBaESQhISFRCepl91xCQkJCV0iiKSEhIVEJJNGUkJCQqASSaEpISEhUAkk0JSQkJCqBJJoSEtWgS5cu/O9//6tpMyT0iCSaEtXm0a6lW7ZsKXX85MmTyGSyUjuGjhs3DmNjY6ysrLCxscHDw4PBgwezf/9+PVtdO1i3bh2+vr41bYZEJZBEU0Ir+Pv78+2335Y69u233+Lv718m79ixY7l//z65ublER0fToUMH+vfvz1dffaUvcyUkqowkmhJaYdCgQZw7d45bt24BkJeXx88//8z48ePVnufi4sKsWbN45513eOuttyoMxvvHH3/QqlUrbGxscHJyokePHqo0Ly8vPvzwQ7p3746VlRWBgYEcO3ZMlb5//34iIiKwt7enQYMGDB8+vFR0oy5dujBz5kyeeeYZrKysCAgIYNeuXar0c+fOERkZia2tLQ4ODrRv375URKCsrCwGDx6MtbU1Pj4+ZUKnLV++nGbNmmFra0vbtm05fPgwIEZ1mjp1Krdu3cLKygorKysOHjz4lJqWqHFqdD2SRL2gc+fOwgcffCDMmDFDePvttwVBEIQVK1YIzz77rLBx40bB09NTlXfs2LGllis+4vr16wIg7Nq1q9wy3NzchDVr1ghKpVIoLCwUDhw4oErz9PQUfHx8hIsXLwoKhUKYOXOm4Ovrq0o/fPiwcOrUKUEulwtpaWlCx44dheHDh5ey38rKSti7d68gl8uF7777TjA1NRXi4uIEQRCEdu3aCfPmzRMUCoVQXFwsHD9+XLh//77qXEdHR+HIkSNCSUmJ8MUXXwi2trZCfn6+IAiC8MMPPwiOjo7CiRMnBLlcLqxatUqwsLBQBWFeu3at4OPjU4Val6gppJamhNaYNGkSa9euRaFQsHLlSiZNmqTxuY+CNVQU09TExISbN29y584dTE1N6dq1a6n0KVOmEBAQgKGhIS+++CI3btwgJycHgMjISFq3bo2RkRGurq7Mnj27zBjqc889R8+ePVVh2sLDw/nhhx9UZScmJpKUlISxsTFt27bF0tJSde6wYcPo0KEDBgYGTJ48mZycHFVYu7Vr1zJlyhQiIiIwMjJi4sSJBAcHq64tUfeQRFNCawQGBuLp6ckHH3zAnTt36NOnj8bnPopcXlFko6ioKGJjYwkKCqJFixZl9hh6MirTI0F7FBnnzJkz9O7dG1dXV2xsbBgxYgR3794tdf6Tk1WP3j+yae3atSiVSiIjI2nSpAlz5sxBoVBoVHZSUhLe3t6lru3j41Mq3JpE3UISTQmtMnnyZD744AMmTpyIoaGhxudt3rwZc3Nz2rZtW256y5Yt2bx5M+np6axYsYK33nqLAwcOaHTt4cOHExoayvXr18nNzWXTpk1l8vxzY734+HhV67dJkyasWbOG5ORktm/fzqpVq9iwYYNGZXt4eBAXF1fq2K1bt1TR0A0MpJ9gXUO6YxJaZcSIEezdu5dXX31Vo/zp6el8+eWXLFiwgAULFmBnZ1cmT3FxMevXr+fevXvIZDLs7e0xMDBQ7bHzNHJzc7G1tcXa2prExMRyd1T89ddf2b9/PyUlJWzatInTp08zfPhwQIy7mZqaCoCdnR1GRkYalz1u3DhWrFjBqVOnUCgUrFu3jvPnz6viirq6upKenk5ubq5G15OoeSTRlNAqZmZm9OjRA3t7+wrzrF+/XuWnGRoaysGDB4mKiuK1116r8JzNmzfTvHlzrKysGDBgAPPmzdN4u4OVK1eyatUqrK2tGTRoEM8//3yZPBMnTuSLL77A1taW+fPns23bNlW3+sCBA4SFhWFlZUW7du0YOXJkhdtT/JORI0cyd+5cRo8ejaOjI8uWLWPnzp2q4YBu3brRs2dPmjRpgp2dHYcOHdLouhI1hxRPU+JfT5cuXejRowfvvvtuTZsiUQeQWpoSEhISlUASTQkJCYlKIHXPJSQkJCqB1NKUkJCQqASSaEpISEhUAkk0JSQkJCqBJJoSEhISlUASTQkJCYlKIImmxcJEuwAAAAtJREFUhISERCX4fwr9h9YEhM+rAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x144 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (5,2),facecolor='w')\n",
    "plt.plot(RMSDl,label=\"ligand\")\n",
    "plt.plot(RMSDp,label=\"protein\")\n",
    "plt.xlabel(\"MD snapshot\", fontsize=13)\n",
    "plt.ylabel(\"RMSD /Angstrom\", fontsize=13)\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 PL IFP table\n",
    "Now we can take a look at the calculated tables.\\\n",
    "First we will look at water molecules participating in protein-ligand water bridges.\n",
    "Not all frames are included in this table - only those where at least one water bridge found\n",
    "\"sele1*\" is either ligand or water molecule that mediate ligand-protein interaction\n",
    "\"sele2*\" is either protein or water"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>sele1_index</th>\n",
       "      <th>sele2_index</th>\n",
       "      <th>sele1_resnm</th>\n",
       "      <th>sele1_resid</th>\n",
       "      <th>sele1_atom</th>\n",
       "      <th>sele2_resnm</th>\n",
       "      <th>sele2_resid</th>\n",
       "      <th>sele2_atom</th>\n",
       "      <th>distance</th>\n",
       "      <th>angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3326</td>\n",
       "      <td>1248</td>\n",
       "      <td>WAT</td>\n",
       "      <td>267</td>\n",
       "      <td>H2</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>1.700639</td>\n",
       "      <td>163.627926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3326</td>\n",
       "      <td>1249</td>\n",
       "      <td>WAT</td>\n",
       "      <td>267</td>\n",
       "      <td>H2</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>3.167911</td>\n",
       "      <td>127.500696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.0</td>\n",
       "      <td>19784</td>\n",
       "      <td>574</td>\n",
       "      <td>WAT</td>\n",
       "      <td>5753</td>\n",
       "      <td>H2</td>\n",
       "      <td>ASN</td>\n",
       "      <td>35</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.093062</td>\n",
       "      <td>115.841596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3270</td>\n",
       "      <td>3325</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>WAT</td>\n",
       "      <td>267</td>\n",
       "      <td>H1</td>\n",
       "      <td>2.014190</td>\n",
       "      <td>167.031486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3264</td>\n",
       "      <td>19783</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>O4</td>\n",
       "      <td>WAT</td>\n",
       "      <td>5753</td>\n",
       "      <td>H1</td>\n",
       "      <td>2.121748</td>\n",
       "      <td>148.085671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3264</td>\n",
       "      <td>4684</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>O4</td>\n",
       "      <td>WAT</td>\n",
       "      <td>720</td>\n",
       "      <td>H1</td>\n",
       "      <td>2.410882</td>\n",
       "      <td>146.094608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>545</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3315</td>\n",
       "      <td>577</td>\n",
       "      <td>WAT</td>\n",
       "      <td>264</td>\n",
       "      <td>O</td>\n",
       "      <td>ASN</td>\n",
       "      <td>35</td>\n",
       "      <td>HD22</td>\n",
       "      <td>2.108906</td>\n",
       "      <td>168.484768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>559</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3326</td>\n",
       "      <td>1248</td>\n",
       "      <td>WAT</td>\n",
       "      <td>267</td>\n",
       "      <td>H2</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>1.729018</td>\n",
       "      <td>161.655372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3326</td>\n",
       "      <td>1249</td>\n",
       "      <td>WAT</td>\n",
       "      <td>267</td>\n",
       "      <td>H2</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.569712</td>\n",
       "      <td>137.049037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3270</td>\n",
       "      <td>3325</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>WAT</td>\n",
       "      <td>267</td>\n",
       "      <td>H1</td>\n",
       "      <td>2.423445</td>\n",
       "      <td>125.716732</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>139 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     time  sele1_index  sele2_index sele1_resnm  sele1_resid sele1_atom  \\\n",
       "7     0.0         3326         1248         WAT          267         H2   \n",
       "8     0.0         3326         1249         WAT          267         H2   \n",
       "13    0.0        19784          574         WAT         5753         H2   \n",
       "3     0.0         3270         3325         INH          208        N10   \n",
       "4     0.0         3264        19783         INH          208         O4   \n",
       "..    ...          ...          ...         ...          ...        ...   \n",
       "129  18.0         3264         4684         INH          208         O4   \n",
       "545  18.0         3315          577         WAT          264          O   \n",
       "559  19.0         3326         1248         WAT          267         H2   \n",
       "560  19.0         3326         1249         WAT          267         H2   \n",
       "136  19.0         3270         3325         INH          208        N10   \n",
       "\n",
       "    sele2_resnm  sele2_resid sele2_atom  distance       angle  \n",
       "7           ASP           77        OD1  1.700639  163.627926  \n",
       "8           ASP           77        OD2  3.167911  127.500696  \n",
       "13          ASN           35        OD1  3.093062  115.841596  \n",
       "3           WAT          267         H1  2.014190  167.031486  \n",
       "4           WAT         5753         H1  2.121748  148.085671  \n",
       "..          ...          ...        ...       ...         ...  \n",
       "129         WAT          720         H1  2.410882  146.094608  \n",
       "545         ASN           35       HD22  2.108906  168.484768  \n",
       "559         ASP           77        OD1  1.729018  161.655372  \n",
       "560         ASP           77        OD2  2.569712  137.049037  \n",
       "136         WAT          267         H1  2.423445  125.716732  \n",
       "\n",
       "[139 rows x 11 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_WB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can open the table with all hydrogen bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>donor_index</th>\n",
       "      <th>acceptor_index</th>\n",
       "      <th>donor_resnm</th>\n",
       "      <th>donor_resid</th>\n",
       "      <th>donor_atom</th>\n",
       "      <th>acceptor_resnm</th>\n",
       "      <th>acceptor_resid</th>\n",
       "      <th>acceptor_atom</th>\n",
       "      <th>distance</th>\n",
       "      <th>angle</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.182179</td>\n",
       "      <td>103.037945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.205784</td>\n",
       "      <td>101.580293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.258567</td>\n",
       "      <td>163.623818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.803541</td>\n",
       "      <td>154.625633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.038169</td>\n",
       "      <td>115.269321</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>2.891189</td>\n",
       "      <td>114.748865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.157190</td>\n",
       "      <td>168.674191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.371952</td>\n",
       "      <td>126.736667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.224654</td>\n",
       "      <td>102.368431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.019610</td>\n",
       "      <td>155.945045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.173056</td>\n",
       "      <td>115.665265</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>579</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>ASN</td>\n",
       "      <td>35</td>\n",
       "      <td>O</td>\n",
       "      <td>2.915921</td>\n",
       "      <td>113.312414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.041866</td>\n",
       "      <td>100.943558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>3.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.895512</td>\n",
       "      <td>149.233665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.103106</td>\n",
       "      <td>113.393589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.158808</td>\n",
       "      <td>114.905724</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>4.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.863811</td>\n",
       "      <td>140.028612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>4.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.287283</td>\n",
       "      <td>113.328105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.241878</td>\n",
       "      <td>145.081042</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.987322</td>\n",
       "      <td>145.411107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>6.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.272448</td>\n",
       "      <td>124.684570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>7.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.222442</td>\n",
       "      <td>133.095543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>7.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.138708</td>\n",
       "      <td>114.552107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.091957</td>\n",
       "      <td>147.532695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>8.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.055262</td>\n",
       "      <td>136.643889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.215903</td>\n",
       "      <td>169.986705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>9.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.970461</td>\n",
       "      <td>149.574867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.005095</td>\n",
       "      <td>150.443708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>2.948751</td>\n",
       "      <td>101.589009</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>2.858104</td>\n",
       "      <td>101.623939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>11.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.290197</td>\n",
       "      <td>159.474309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>12.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.965952</td>\n",
       "      <td>140.113100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>2.583211</td>\n",
       "      <td>124.333575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.070763</td>\n",
       "      <td>173.425064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>13.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.034159</td>\n",
       "      <td>130.644446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>13.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>2.329812</td>\n",
       "      <td>141.672537</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.160490</td>\n",
       "      <td>122.589163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.292735</td>\n",
       "      <td>174.503736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>14.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.076226</td>\n",
       "      <td>142.740750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>14.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.051742</td>\n",
       "      <td>106.403728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.200961</td>\n",
       "      <td>105.213283</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.229009</td>\n",
       "      <td>167.491041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>15.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.999679</td>\n",
       "      <td>134.152312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>15.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.143314</td>\n",
       "      <td>107.886087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.111675</td>\n",
       "      <td>115.712575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.268957</td>\n",
       "      <td>146.136861</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>16.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>1.798330</td>\n",
       "      <td>161.190577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>16.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>2.928649</td>\n",
       "      <td>110.596198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>17.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>2.932509</td>\n",
       "      <td>113.356088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>17.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.113228</td>\n",
       "      <td>145.545118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>17.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.066359</td>\n",
       "      <td>105.387469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.242350</td>\n",
       "      <td>109.782059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1248</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD1</td>\n",
       "      <td>3.124256</td>\n",
       "      <td>154.575154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>18.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.000345</td>\n",
       "      <td>143.533660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>18.0</td>\n",
       "      <td>2568</td>\n",
       "      <td>3270</td>\n",
       "      <td>THR</td>\n",
       "      <td>168</td>\n",
       "      <td>HG1</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>N10</td>\n",
       "      <td>3.057405</td>\n",
       "      <td>103.107157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3295</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H141</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.085821</td>\n",
       "      <td>104.985161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>587</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>SER</td>\n",
       "      <td>36</td>\n",
       "      <td>OG</td>\n",
       "      <td>3.031413</td>\n",
       "      <td>108.502708</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>19.0</td>\n",
       "      <td>3296</td>\n",
       "      <td>1249</td>\n",
       "      <td>INH</td>\n",
       "      <td>208</td>\n",
       "      <td>H142</td>\n",
       "      <td>ASP</td>\n",
       "      <td>77</td>\n",
       "      <td>OD2</td>\n",
       "      <td>2.203371</td>\n",
       "      <td>151.976434</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  donor_index  acceptor_index donor_resnm  donor_resid donor_atom  \\\n",
       "0    0.0         3295             587         INH          208       H141   \n",
       "1    0.0         3296             587         INH          208       H142   \n",
       "2    0.0         3296            1248         INH          208       H142   \n",
       "3    0.0         3296            1249         INH          208       H142   \n",
       "4    0.0         2568            3270         THR          168        HG1   \n",
       "5    1.0         3295             587         INH          208       H141   \n",
       "6    1.0         3296            1248         INH          208       H142   \n",
       "7    1.0         3296            1249         INH          208       H142   \n",
       "8    2.0         3296             587         INH          208       H142   \n",
       "9    2.0         3296            1249         INH          208       H142   \n",
       "10   2.0         2568            3270         THR          168        HG1   \n",
       "11   3.0         3295             579         INH          208       H141   \n",
       "12   3.0         3296             587         INH          208       H142   \n",
       "13   3.0         3296            1249         INH          208       H142   \n",
       "14   3.0         2568            3270         THR          168        HG1   \n",
       "15   4.0         3295             587         INH          208       H141   \n",
       "16   4.0         3296            1249         INH          208       H142   \n",
       "17   4.0         2568            3270         THR          168        HG1   \n",
       "18   5.0         3296            1249         INH          208       H142   \n",
       "19   6.0         3296            1249         INH          208       H142   \n",
       "20   6.0         2568            3270         THR          168        HG1   \n",
       "21   7.0         3296            1249         INH          208       H142   \n",
       "22   7.0         2568            3270         THR          168        HG1   \n",
       "23   8.0         3296            1248         INH          208       H142   \n",
       "24   8.0         3296            1249         INH          208       H142   \n",
       "25   9.0         3296            1248         INH          208       H142   \n",
       "26   9.0         3296            1249         INH          208       H142   \n",
       "27  10.0         3296            1249         INH          208       H142   \n",
       "28  10.0         2568            3270         THR          168        HG1   \n",
       "29  11.0         3295             587         INH          208       H141   \n",
       "30  11.0         3296            1249         INH          208       H142   \n",
       "31  12.0         3296            1249         INH          208       H142   \n",
       "32  13.0         3295             587         INH          208       H141   \n",
       "33  13.0         3296            1248         INH          208       H142   \n",
       "34  13.0         3296            1249         INH          208       H142   \n",
       "35  13.0         2568            3270         THR          168        HG1   \n",
       "36  14.0         3295             587         INH          208       H141   \n",
       "37  14.0         3296            1248         INH          208       H142   \n",
       "38  14.0         3296            1249         INH          208       H142   \n",
       "39  14.0         2568            3270         THR          168        HG1   \n",
       "40  15.0         3295             587         INH          208       H141   \n",
       "41  15.0         3296            1248         INH          208       H142   \n",
       "42  15.0         3296            1249         INH          208       H142   \n",
       "43  15.0         2568            3270         THR          168        HG1   \n",
       "44  16.0         3296             587         INH          208       H142   \n",
       "45  16.0         3296            1248         INH          208       H142   \n",
       "46  16.0         3296            1249         INH          208       H142   \n",
       "47  16.0         2568            3270         THR          168        HG1   \n",
       "48  17.0         3295             587         INH          208       H141   \n",
       "49  17.0         3296            1249         INH          208       H142   \n",
       "50  17.0         2568            3270         THR          168        HG1   \n",
       "51  18.0         3296             587         INH          208       H142   \n",
       "52  18.0         3296            1248         INH          208       H142   \n",
       "53  18.0         3296            1249         INH          208       H142   \n",
       "54  18.0         2568            3270         THR          168        HG1   \n",
       "55  19.0         3295             587         INH          208       H141   \n",
       "56  19.0         3296             587         INH          208       H142   \n",
       "57  19.0         3296            1249         INH          208       H142   \n",
       "\n",
       "   acceptor_resnm  acceptor_resid acceptor_atom  distance       angle  \n",
       "0             SER              36            OG  3.182179  103.037945  \n",
       "1             SER              36            OG  3.205784  101.580293  \n",
       "2             ASP              77           OD1  3.258567  163.623818  \n",
       "3             ASP              77           OD2  1.803541  154.625633  \n",
       "4             INH             208           N10  3.038169  115.269321  \n",
       "5             SER              36            OG  2.891189  114.748865  \n",
       "6             ASP              77           OD1  3.157190  168.674191  \n",
       "7             ASP              77           OD2  2.371952  126.736667  \n",
       "8             SER              36            OG  3.224654  102.368431  \n",
       "9             ASP              77           OD2  2.019610  155.945045  \n",
       "10            INH             208           N10  3.173056  115.665265  \n",
       "11            ASN              35             O  2.915921  113.312414  \n",
       "12            SER              36            OG  3.041866  100.943558  \n",
       "13            ASP              77           OD2  1.895512  149.233665  \n",
       "14            INH             208           N10  3.103106  113.393589  \n",
       "15            SER              36            OG  3.158808  114.905724  \n",
       "16            ASP              77           OD2  1.863811  140.028612  \n",
       "17            INH             208           N10  3.287283  113.328105  \n",
       "18            ASP              77           OD2  2.241878  145.081042  \n",
       "19            ASP              77           OD2  1.987322  145.411107  \n",
       "20            INH             208           N10  3.272448  124.684570  \n",
       "21            ASP              77           OD2  2.222442  133.095543  \n",
       "22            INH             208           N10  3.138708  114.552107  \n",
       "23            ASP              77           OD1  3.091957  147.532695  \n",
       "24            ASP              77           OD2  2.055262  136.643889  \n",
       "25            ASP              77           OD1  3.215903  169.986705  \n",
       "26            ASP              77           OD2  1.970461  149.574867  \n",
       "27            ASP              77           OD2  2.005095  150.443708  \n",
       "28            INH             208           N10  2.948751  101.589009  \n",
       "29            SER              36            OG  2.858104  101.623939  \n",
       "30            ASP              77           OD2  2.290197  159.474309  \n",
       "31            ASP              77           OD2  1.965952  140.113100  \n",
       "32            SER              36            OG  2.583211  124.333575  \n",
       "33            ASP              77           OD1  3.070763  173.425064  \n",
       "34            ASP              77           OD2  2.034159  130.644446  \n",
       "35            INH             208           N10  2.329812  141.672537  \n",
       "36            SER              36            OG  3.160490  122.589163  \n",
       "37            ASP              77           OD1  3.292735  174.503736  \n",
       "38            ASP              77           OD2  2.076226  142.740750  \n",
       "39            INH             208           N10  3.051742  106.403728  \n",
       "40            SER              36            OG  3.200961  105.213283  \n",
       "41            ASP              77           OD1  3.229009  167.491041  \n",
       "42            ASP              77           OD2  1.999679  134.152312  \n",
       "43            INH             208           N10  3.143314  107.886087  \n",
       "44            SER              36            OG  3.111675  115.712575  \n",
       "45            ASP              77           OD1  3.268957  146.136861  \n",
       "46            ASP              77           OD2  1.798330  161.190577  \n",
       "47            INH             208           N10  2.928649  110.596198  \n",
       "48            SER              36            OG  2.932509  113.356088  \n",
       "49            ASP              77           OD2  2.113228  145.545118  \n",
       "50            INH             208           N10  3.066359  105.387469  \n",
       "51            SER              36            OG  3.242350  109.782059  \n",
       "52            ASP              77           OD1  3.124256  154.575154  \n",
       "53            ASP              77           OD2  2.000345  143.533660  \n",
       "54            INH             208           N10  3.057405  103.107157  \n",
       "55            SER              36            OG  3.085821  104.985161  \n",
       "56            SER              36            OG  3.031413  108.502708  \n",
       "57            ASP              77           OD2  2.203371  151.976434  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_HB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, we can look at the combined table - it comprise all IFPs as well as protein resideus that participate in protein-ligand water bridges\n",
    "This is a binary table with 1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 54 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    time  RE_LEU32  WB_LEU32  HA_ASN35  RE_ASN35  WB_ASN35  HA_SER36  \\\n",
       "0      0         0         0         0         1         1         1   \n",
       "1      1         0         0         0         1         1         1   \n",
       "2      2         0         0         0         1         0         1   \n",
       "3      3         0         0         1         1         1         1   \n",
       "4      4         0         0         0         1         1         1   \n",
       "5      5         0         0         0         1         1         0   \n",
       "6      6         0         0         0         1         1         0   \n",
       "7      7         0         0         0         1         1         0   \n",
       "8      8         0         1         0         1         0         0   \n",
       "9      9         0         0         0         1         1         0   \n",
       "10    10         0         1         0         1         1         0   \n",
       "11    11         1         1         0         1         0         1   \n",
       "12    12         0         0         0         1         1         0   \n",
       "13    13         0         1         0         1         1         1   \n",
       "14    14         0         0         0         1         1         1   \n",
       "15    15         0         0         0         1         1         1   \n",
       "16    16         0         1         0         1         1         1   \n",
       "17    17         0         0         0         1         1         1   \n",
       "18    18         0         0         0         1         1         1   \n",
       "19    19         0         0         0         1         0         1   \n",
       "\n",
       "    RE_SER36  WB_SER36  RE_ASP38  ...  HY_TYR123  RE_TYR123  HY_VAL134  \\\n",
       "0          1         0         0  ...          0          1          0   \n",
       "1          1         0         0  ...          0          1          0   \n",
       "2          1         0         0  ...          0          1          0   \n",
       "3          1         0         0  ...          0          1          0   \n",
       "4          1         0         0  ...          0          1          0   \n",
       "5          1         0         0  ...          0          1          0   \n",
       "6          1         0         0  ...          0          1          0   \n",
       "7          1         0         0  ...          0          1          0   \n",
       "8          1         1         0  ...          0          1          0   \n",
       "9          1         0         0  ...          0          0          1   \n",
       "10         1         1         0  ...          1          1          0   \n",
       "11         1         1         0  ...          1          1          0   \n",
       "12         1         0         0  ...          0          1          0   \n",
       "13         1         1         0  ...          0          1          0   \n",
       "14         1         0         1  ...          0          1          0   \n",
       "15         1         0         1  ...          0          1          0   \n",
       "16         1         0         1  ...          0          1          0   \n",
       "17         1         0         1  ...          0          1          0   \n",
       "18         1         0         1  ...          0          1          0   \n",
       "19         1         0         1  ...          0          1          0   \n",
       "\n",
       "    RE_VAL134  AR_TRP146  HY_TRP146  RE_TRP146  HD_THR168  RE_THR168  WAT  \n",
       "0           1          1          0          1          1          1    8  \n",
       "1           1          1          1          1          0          1   11  \n",
       "2           1          1          1          1          1          1    8  \n",
       "3           1          1          0          1          1          1   10  \n",
       "4           1          1          0          1          1          1   12  \n",
       "5           1          1          1          1          0          1   13  \n",
       "6           1          1          1          1          1          1    6  \n",
       "7           1          1          1          1          1          1   12  \n",
       "8           1          1          1          1          0          1    9  \n",
       "9           1          1          1          1          0          1    9  \n",
       "10          1          1          0          1          1          1    8  \n",
       "11          1          1          1          1          0          1   11  \n",
       "12          1          1          1          1          0          1   11  \n",
       "13          1          1          0          1          1          1    9  \n",
       "14          0          0          0          0          1          1   11  \n",
       "15          1          1          1          1          1          1   10  \n",
       "16          1          1          0          1          1          1   10  \n",
       "17          1          1          1          1          1          1    9  \n",
       "18          1          1          0          1          1          1    9  \n",
       "19          0          0          0          0          0          1    7  \n",
       "\n",
       "[20 rows x 54 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prop_complete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Visualization of the IFP table "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is one more way to vizualize generated PL IFPs and water-mediated PL bonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 2100x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "Plot_IF_trajectory(df_prop_complete,ifp_type = np.asarray(['AR','HA','HD','IP','IN',\"IO\",\"WB\"]),head_tail=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2.4 Visualization of protein residues involved in PL interactions\n",
    "For the last point of this part we are gonna visualize the residues that participate in the IFP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing Residues:  42 122 123 146 \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3cb5f0e397f7431a93f091083ed2c1a2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget(max_frame=19)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# obtain string that contains unique residues interacting with water\n",
    "watr = ''.join([resid+' ' for resid in np.unique(df_WB.sele2_resid.values.astype(str))])\n",
    "\n",
    "# obtain residues that participate in aromatic interaction\n",
    "residues = ''.join([str(ifp[6:]+' ') for ifp in df_prop_complete.columns.tolist() \n",
    "                    if (ifp[:2] == 'AR')])\n",
    "print(\"Showing Residues: \", residues)\n",
    "\n",
    "# Visualize the Complex\n",
    "\n",
    "view = nv.NGLWidget()\n",
    "protein = u_mem.select_atoms('protein or (resname INH WAT)')\n",
    "w=nv.show_mdanalysis(protein)\n",
    "w.add_licorice(watr+residues)\n",
    "w"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Generation of IFPs for a PDB file               (HSP90, PDB ID: 2YKI)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lastlz, We will use the trajectory.py script in order to get the properties of our ligand and another IFP analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 IFP calculations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Donor': ['O4', 'C6', 'N8', 'N10', 'C12'], 'Acceptor': ['C2', 'C6', 'N10', 'C19'], 'PosIonizable': ['C9', 'C6', 'C7', 'O4', 'C11'], 'Aromatic': ['O4', 'C7', 'C6', 'C9', 'C11', 'C12', 'H7', 'C31', 'H12', 'H9', 'C1', 'C2', 'N3', 'C11', 'C9', 'C5', 'N10', 'C21', 'C20', 'C18', 'C31', 'H7', 'C13', 'C16', 'C15', 'N14', 'C23', 'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'N30'], 'Hydrophobe': ['C5', 'C13', 'N14', 'C15', 'C16', 'C18', 'C20', 'C23', 'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'N30', 'C31', 'H12'], 'LumpedHydrophobe': ['C13', 'C16', 'C15', 'N14', 'C23', 'C24', 'C25', 'C26', 'C27', 'C28', 'C29', 'N30']}\n",
      "Start HB analysis 09:03:39.984259\n",
      "Start WB analysis 09:03:40.275094\n",
      "Start collecting IFPs:  09:03:40.931716\n",
      "Start building IFP table:  09:03:41.687284\n",
      "IFP database is ready  09:03:41.689284\n",
      "\n",
      " -------------------- Contacts found: --------------------\n",
      "\n",
      "Hydrophobic contacts:  ['HY_ASN51', 'HY_ALA55', 'HY_MET98', 'HY_LEU107', 'HY_ALA111', 'HY_PHE138', 'HY_THR184']\n",
      "\n",
      "Aromatic contacts:  ['AR_PHE138', 'AR_TRP162', 'AR_PHE170']\n",
      "\n",
      "Water Bridges:  ['WB_GLN23']\n",
      "\n",
      "Hydrogen Acceptors:  []\n",
      "\n",
      "Hydrogen Donors:  []\n"
     ]
    }
   ],
   "source": [
    "# Define path to input data\n",
    "ref_pdb = \"Data/2YKI/2yki_MOE.pdb\"\n",
    "ligand_pdb_ = \"Data/2YKI/ligand_2yki_MOE.pdb\"\n",
    "ligand_mol2 = \"Data/2YKI/ligand_2yki_MOE.mol2\"\n",
    "\n",
    "# Load the pdb file into MDAnalysis universe\n",
    "u = mda.Universe(ref_pdb)\n",
    "\n",
    "# Define ligand residue name\n",
    "sel_ligands = 'YKI'\n",
    "\n",
    "# Get the chemical properties of the ligand with the ligand_properties function from the trajectories script\n",
    "properties, mol = ligand_properties(ligand_pdb, ligand_mol2)\n",
    "print(properties)\n",
    "\n",
    "# IFP analysis, for reference look at 1. or 2.\n",
    "df_prop,df_HB,df_WB = IFP(u,sel_ligands,properties,WB_analysis=True,RE=False)\n",
    "df_prop_complete = table_combine(df_HB,df_WB,df_prop,sel_ligands)\n",
    "\n",
    "# Summary for all interactions\n",
    "ifp_type = ['HY','AR','WB','HA','HD']\n",
    "at_type = [[],[],[],[],[]]\n",
    "for v in df_prop_complete.columns.tolist():\n",
    "    for i,t in enumerate(ifp_type):\n",
    "        if v.find(t)>=0:\n",
    "            at_type[i].append(v)\n",
    "            \n",
    "print('\\n',10*'--','Contacts found:',10*'--')\n",
    "print(\"\\nHydrophobic contacts: \",at_type[0])\n",
    "print(\"\\nAromatic contacts: \",at_type[1])\n",
    "print(\"\\nWater Bridges: \",at_type[2])\n",
    "print(\"\\nHydrogen Acceptors: \",at_type[3])\n",
    "print(\"\\nHydrogen Donors: \",at_type[4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>WB_GLN23</th>\n",
       "      <th>HY_ASN51</th>\n",
       "      <th>HY_ALA55</th>\n",
       "      <th>HY_MET98</th>\n",
       "      <th>HY_LEU107</th>\n",
       "      <th>HY_ALA111</th>\n",
       "      <th>AR_PHE138</th>\n",
       "      <th>HY_PHE138</th>\n",
       "      <th>AR_TRP162</th>\n",
       "      <th>AR_PHE170</th>\n",
       "      <th>HY_THR184</th>\n",
       "      <th>WAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   time  WB_GLN23  HY_ASN51  HY_ALA55  HY_MET98  HY_LEU107  HY_ALA111  \\\n",
       "0     0         1         1         1         1          1          1   \n",
       "\n",
       "   AR_PHE138  HY_PHE138  AR_TRP162  AR_PHE170  HY_THR184  WAT  \n",
       "0          1          1          1          1          1    6  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_prop_complete"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Visualization of PL interactions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Showing residues:   23 51 55 98 107 111 138 138 162 170 184  \n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7c7a5452feab42d7b19746f244f76bbe",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "NGLWidget()"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "view = nv.NGLWidget()\n",
    "protein = u.select_atoms('all')\n",
    "w=nv.show_mdanalysis(protein)\n",
    "residues = \"\".join([str(ifp[6:] + \" \") for ifp in df_prop_complete.columns.tolist()])\n",
    "print(\"Showing residues: \", residues)\n",
    "w.add_licorice(residues)\n",
    "w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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