code/application.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generating protein structure network\n",
    "\n",
    "The aim of this notebook is to create a network of a protein structure. The nodes of the network are the amino acids and the edges are the defined by the euclidean distance between them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Essentials\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following code to install and import the necessary libraries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %pip install -q biopython networkx matplotlib numpy pandas pyvis seaborn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import os, sys\n",
    "from Bio import PDB\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.colors as mcolors\n",
    "from pyvis.network import Network\n",
    "import seaborn as sns\n",
    "\n",
    "# os.chdir(os.path.expanduser('..'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run these functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_PDB_structure(pdb_id:str)->PDB.Structure.Structure:\n",
    "    '''\n",
    "    Takes a pdb id and returns its sructure\n",
    "\n",
    "    Starts by retriving the pdb file, saving it in data/{pdb_id}/{pdb_id}.pdb\n",
    "    then reads the file and returns the structure\n",
    "\n",
    "    param:\n",
    "    ------\n",
    "    - pdb_id: str, pdb id of the structure to be retrived\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - structure: Bio.PDB.Structure.Structure, structure of the pdb file\n",
    "    '''\n",
    "    pdb_id=pdb_id.lower()\n",
    "    pdb_list = PDB.PDBList()\n",
    "    pdb_list.retrieve_pdb_file(pdb_id, pdir=f'data/{pdb_id}', file_format='pdb')\n",
    "    os.rename(f'data/{pdb_id}/pdb{pdb_id}.ent', f'data/{pdb_id}/{pdb_id}.pdb')\n",
    "        \n",
    "    parser = PDB.PDBParser()\n",
    "    structure = parser.get_structure(pdb_id, f'data/{pdb_id}/{pdb_id}.pdb')\n",
    "    return structure\n",
    "\n",
    "def get_residues_coordinates(structure:PDB.Structure.Structure)->dict:\n",
    "    '''\n",
    "    takes a structure and returns a dictionary of the residues and their coordinates\n",
    "    the coordinates are the coordinates of the alpha carbon\n",
    "\n",
    "    param:\n",
    "    ------\n",
    "    - structure: Bio.PDB.Structure.Structure, structure of the pdb file\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - sequence: dict, dictionary of the residues and their coordinates\n",
    "    '''\n",
    "\n",
    "    sequence={}\n",
    "\n",
    "    for model in structure:\n",
    "        for chain in model:\n",
    "            for residue in chain:  \n",
    "                # print(residue.get_id())  \n",
    "                \n",
    "                if residue.get_id()[0] != ' ':\n",
    "                    continue                      \n",
    "                aa_name=aa[residue.get_resname()]\n",
    "                index=residue.get_id()[1] \n",
    "\n",
    "                res=aa_name+str(index)\n",
    "\n",
    "                for atom in residue:\n",
    "                    if atom.get_name() == 'CA':\n",
    "                        alphaC_coord=list(atom.get_coord())\n",
    "                        # print(atom.get_coord())\n",
    "                        sequence[res]=alphaC_coord\n",
    "    return sequence\n",
    "\n",
    "def euclidian_distance(c1:list, c2:list)->float:\n",
    "    '''\n",
    "    takes 2 vectors in 3D space and returns the euclidian distance between them\n",
    "\n",
    "    param:\n",
    "    ------\n",
    "    - c1: list, list of 3 floats, coordinates of the first vector\n",
    "    - c2: list, list of 3 floats, coordinates of the second vector\n",
    "\n",
    "    $$\\sqrt{{(coordinates_{1_1} - coordinates_{2_1})}^2 + {(coordinates_{1_2} - coordinates_{2_2})}^2 + {(coordinates_{1_3} - coordinates_{2_3})}^2}$$\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - distance: float, euclidian distance between the 2 points\n",
    "    '''\n",
    "    distance = ((c1[0]-c2[0])**2 + (c1[1]-c2[1])**2 + (c1[2]-c2[2])**2)**0.5\n",
    "    return distance\n",
    "\n",
    "def visualize_map(df, id=None):\n",
    "    '''takes a dataframe and viz as heatmap\n",
    "    \n",
    "    param:\n",
    "    ------\n",
    "    - df: pd.DataFrame\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - None\n",
    "    '''\n",
    "    plt.figure(figsize=(8,6))\n",
    "    sns.heatmap(df, cmap='viridis')\n",
    "    plt.xticks(rotation=70)\n",
    "    plt.title(f'Contact Map of the residues in the protein {id}')\n",
    "\n",
    "    if id!=None:\n",
    "        plt.savefig(f'data/{id}/{id}_contact_map.png')\n",
    "\n",
    "    return None\n",
    "    \n",
    "def create_distance_map(sequence:dict)->np.array:\n",
    "    '''\n",
    "    takes a sequence of residues and their coordinates and returns a contact map\n",
    "\n",
    "    param:\n",
    "    ------\n",
    "    - sequence: dict, dictionary of the residues and their coordinates\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - contact_map: pd.DataFrame, contact map of the residues\n",
    "    '''\n",
    "    contact_map = np.zeros((len(sequence), len(sequence)))\n",
    "    for i, (res1, coord1) in enumerate(sequence.items()):\n",
    "        for j, (res2, coord2) in enumerate(sequence.items()):\n",
    "            contact_map[i][j] = euclidian_distance(coord1, coord2)\n",
    "    df=pd.DataFrame(contact_map, index=sequence.keys(), columns=sequence.keys())\n",
    "\n",
    "    # visualize_map(df)\n",
    "    \n",
    "    return df\n",
    "\n",
    "def create_network(distance_df:pd.DataFrame, threshold=9)->nx.Graph:\n",
    "    '''\n",
    "    takes a distance map and establishes an edge whenever teh distance is $<$ threshold\n",
    "\n",
    "    typically distance is measured in Angstroms, so a threshold of 5 would mean that the residues are within 5 Angstroms of each other\n",
    "\n",
    "    param:\n",
    "    -----\n",
    "    - distance_df: pd.DataFrame, dataframe of ditsnaces as entries\n",
    "    - threshold: float, threshold for the distance (default=8)\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - G: nx.Graph, networkx weighted graph (weight=distance)\n",
    "    '''\n",
    "    G = nx.Graph()\n",
    "\n",
    "    for i, row in distance_df.iterrows():\n",
    "        for j, dist in row.items():\n",
    "            if dist < threshold:\n",
    "                G.add_edge(i, j, weight=dist)\n",
    "\n",
    "    G.remove_edges_from(nx.selfloop_edges(G))\n",
    "    return G\n",
    "\n",
    "def get_color_gradient(n, colormap='viridis'):\n",
    "    cmap = plt.get_cmap(colormap)\n",
    "    norm = mcolors.Normalize(vmin=0, vmax=n-1)\n",
    "    scalar_map = plt.cm.ScalarMappable(norm=norm, cmap=cmap)\n",
    "    return [mcolors.to_hex(scalar_map.to_rgba(i)) for i in range(n)]\n",
    "\n",
    "def visualize_network(G:nx.Graph, positions)->Network:\n",
    "    '''\n",
    "    takes a graph and create a viz using pyviz - creates a Network object and returns it\n",
    "\n",
    "    the weights (originally are distances) here are inverse of the distances (the closer teh rgeater the weight)\n",
    "\n",
    "    param:\n",
    "    ------\n",
    "    - G: nx.Graph, graph object\n",
    "\n",
    "    return:\n",
    "    -------\n",
    "    - net: Network, pyviz network object\n",
    "    '''\n",
    "    num_nodes = G.number_of_nodes()\n",
    "    colors = get_color_gradient(num_nodes)\n",
    "\n",
    "    node_color_mapping = {node: colors[i] for i, node in enumerate(G.nodes)}\n",
    "\n",
    "    for node in G.nodes:\n",
    "        G.nodes[node]['pos'] = positions[node]\n",
    "        G.nodes[node]['color'] = node_color_mapping[node]\n",
    "\n",
    "    net = Network(notebook=True)\n",
    "\n",
    "    for node in G.nodes:\n",
    "        x, y = G.nodes[node]['pos']\n",
    "        x=float(x); y=float(y)\n",
    "        color = G.nodes[node]['color']\n",
    "        net.add_node(node, x=x, y=y, color=color)\n",
    "\n",
    "    for edge in G.edges:\n",
    "        inv_dist = 1/G.get_edge_data(edge[0], edge[1])['weight']\n",
    "        net.add_edge(edge[0], edge[1], value=inv_dist)\n",
    "        net.add_edge(edge[0], edge[1])\n",
    "    \n",
    "    return net\n",
    "\n",
    "create_2d_layout = lambda d: {k: v[:2] for k, v in d.items()} #this is used to convert pos from 3d space to 2d for viz\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Workflow"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extracting PDB id\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "aa = {\n",
    "    'ALA': 'A', 'CYS': 'C', 'ASP': 'D', 'GLU': 'E',\n",
    "    'PHE': 'F', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I',\n",
    "    'LYS': 'K', 'LEU': 'L', 'MET': 'M', 'ASN': 'N',\n",
    "    'PRO': 'P', 'GLN': 'Q', 'ARG': 'R', 'SER': 'S',\n",
    "    'THR': 'T', 'VAL': 'V', 'TRP': 'W', 'TYR': 'Y'\n",
    "}\n",
    "\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "#ignore bio.PDB discontinuos chains warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading PDB structure '1iyt'...\n",
      "structure retrieved\n"
     ]
    }
   ],
   "source": [
    "pdb_id = input('Enter the pdb id: ')\n",
    "structure = get_PDB_structure(pdb_id)\n",
    "print(f'{pdb_id} structure retrieved')\n",
    "sequence = get_residues_coordinates(structure)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating contact map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df=create_distance_map(sequence)\n",
    "visualize_map(df, id=pdb_id)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "t=eval(input('set distance threshold in Angstroms to establish edges (default 8):'))\n",
    "G = create_network(df, threshold=t)\n",
    "nx.write_graphml(G, f'data/{pdb_id}/{pdb_id}_{t}_network.graphml')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: When  cdn_resources is 'local' jupyter notebook has issues displaying graphics on chrome/safari. Use cdn_resources='in_line' or cdn_resources='remote' if you have issues viewing graphics in a notebook.\n",
      "data/1iyt/1iyt_8_network_viz.html\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"100%\"\n",
       "            height=\"600px\"\n",
       "            src=\"data/1iyt/1iyt_8_network_viz.html\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.IFrame at 0x7f1589237650>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "positions = create_2d_layout(sequence)\n",
    "\n",
    "net=visualize_network(G, positions)\n",
    "net.show(f'data/{pdb_id}/{pdb_id}_{t}_network_viz.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from IPython.display import display, HTML\n",
    "\n",
    "# with open(f'data/{pdb_id}/{pdb_id}_{t}_network_viz.html', 'r') as f:\n",
    "#     html_string = f.read()\n",
    "\n",
    "# display(HTML(html_string))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<iframe src=\"test/karate_graph.html\" width=\"100%\" height=\"600\"></iframe>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}