Rdkit_graph_drawing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3020a46e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import requests\n",
    "import os\n",
    "import urllib\n",
    "from rdkit import Chem\n",
    "from rdkit.Chem.Draw import MolsToGridImage\n",
    "from rdkit.Chem.Draw import IPythonConsole\n",
    "from rdkit.Chem import Draw\n",
    "from rdkit.Chem import AllChem\n",
    "from rdkit.Chem import Graphs\n",
    "from rdkit.Chem import rdmolops\n",
    "from rdkit.Chem import Draw\n",
    "# with CoordGen\n",
    "from rdkit.Chem import rdCoordGen\n",
    "from rdkit.Chem.Draw import MolDrawing, DrawingOptions\n",
    "\n",
    "IPythonConsole.drawOptions.addAtomIndices = True #индексируем атомы"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b791f0d",
   "metadata": {},
   "source": [
    "## Скачиваем данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed261827",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://bitbucket.org/sib-pig/rban/raw/0190ed2f49d6e52d8454f7e99add24a715ece4aa/src/main/resources/molecules/monomer/nrproMonomers.json'\n",
    "response = requests.get(url)\n",
    "\n",
    "monomers = json.loads(response.text)\n",
    "#response.raise_for_status()  # raises exception when not a 2xx response\n",
    "#if response.status_code != 204:\n",
    " #   print(response.text)\n",
    "  #  monomers = json.loads(response)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4efff295",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Можно раскомментировать, чтобы убедиться что это json\n",
    "#monomers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "76fdc658",
   "metadata": {},
   "source": [
    "### Создаем словарь monomer:smiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99cf3d06",
   "metadata": {},
   "outputs": [],
   "source": [
    "monomers_dict = {}\n",
    "for mon in monomers:\n",
    "    monomers_dict[mon['monomer']] = mon['smiles'] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4cb5e536",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Вывод словаря\n",
    "#print(monomers_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "709b5291",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'NC(Cc1ccccc1)C(=O)O'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monomers_dict['Phe'] #проверка,что в словаре есть нужные аминокислоты"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "a67c40e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CCCCCCC=CCC(O)CC(O)=O'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monomers_dict['C12:1(5)-OH(3)'] "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca1e4d9c",
   "metadata": {},
   "source": [
    "### Выделим необходимые для построения регулярные выражения"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "9ea74ecf",
   "metadata": {},
   "outputs": [],
   "source": [
    "part_O_C_OH = Chem.MolFromSmiles('C(=O)O')#O=C-OH "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a8f5ef76",
   "metadata": {},
   "outputs": [],
   "source": [
    "without_radical = Chem.MolFromSmiles('NCC(=O)O')#NH2_C_C_O_OH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "2cc2122e",
   "metadata": {},
   "outputs": [],
   "source": [
    "amino_group = Chem.MolFromSmiles('NC')#NH2_C"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "414ead10",
   "metadata": {},
   "outputs": [],
   "source": [
    "without_OH = Chem.MolFromSmiles('NCC(=O)')#NH2-C-C=O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1141f340",
   "metadata": {},
   "outputs": [],
   "source": [
    "part_OH = Chem.MolFromSmiles('CO')#NH2-C-C=O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "82422648",
   "metadata": {},
   "outputs": [],
   "source": [
    "part_O = Chem.MolFromSmiles('C(=O)')#C=O"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "daff5615",
   "metadata": {},
   "source": [
    "## Создаем граф"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2dd6910a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def make_list_of_moleculas(list_of_acids):\n",
    "    \"\"\"\n",
    "    Makes list of moleculas from string of moleculas\n",
    "    \n",
    "    Returns: list of moleculas\n",
    "    \"\"\"\n",
    "    list_of_mols = []\n",
    "    for acid in list_of_acids:\n",
    "        list_of_mols.append(Chem.MolFromSmiles(monomers_dict[acid]))\n",
    "    return list_of_mols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "acd9f012",
   "metadata": {},
   "outputs": [],
   "source": [
    "class Graph:\n",
    "    \"\"\"\n",
    "    Graph class represents a graph, which we got from input\n",
    "\n",
    "    - list_of_monomers: list of vertex in graph\n",
    "    - list_of_edges: list of edges in graph\n",
    "    - processed: list of flags, that showa wheather we have already visited these vertexes or not\n",
    "\n",
    "    You might want to add other fields/methods for Peptide\n",
    "    \"\"\"\n",
    "    def __init__(self):\n",
    "        \n",
    "        self.list_of_monomers = []\n",
    "        self.list_of_edges = []\n",
    "        #self.types = {}\n",
    "        self.processed = []\n",
    "    \n",
    "\n",
    "    def read_from_string(self, graph_string): \n",
    "        '''\n",
    "        Converting a string like (Phe,Ser,Cl2-Pro,Abu,Ser;0,1;1,2;2,3;3,4;4,0) to a graph\n",
    "        '''\n",
    "        list = graph_string.split(';')\n",
    "        self.list_of_monomers = make_list_of_moleculas(list[0].split(','))\n",
    "        for i in range(len(self.list_of_monomers)):\n",
    "            self.processed.append(False)\n",
    "        for i in range(1, len(list)):\n",
    "            pair = (int(list[i].split(',')[0]), int(list[i].split(',')[1]))\n",
    "            self.list_of_edges.append(pair)\n",
    "     \n",
    "    def was_processed(self, index):\n",
    "        '''\n",
    "        Check if the monomer was add to peptide.\n",
    "        \n",
    "        Returns: True, if the mnomer was processed\n",
    "        '''\n",
    "        return self.processed[index]\n",
    "\n",
    "    def type_of_edge(self, edge):\n",
    "        '''\n",
    "        Check a type of edge\n",
    "        \n",
    "        Returns: type of edge: amino, etc\n",
    "        '''\n",
    "        pass \n",
    "    \n",
    "    def get_successors(self, index):\n",
    "        '''\n",
    "        Get a list of successors of the monomer.\n",
    "        \n",
    "        Returns: list of successors\n",
    "        '''\n",
    "        successors = []\n",
    "        for edges in self.list_of_edges:\n",
    "            if edges[0] == index:\n",
    "                successors.append(edges[1])    \n",
    "        return successors\n",
    "    def get_parents(self, index):\n",
    "        parents = []\n",
    "        for edges in self.list_of_edges:\n",
    "            if edges[1] == index:\n",
    "                parents.append(edges[0])    \n",
    "        return parents\n",
    "\n",
    "    def type_of_graph(self):\n",
    "        '''\n",
    "        Check a type of graph:  simple sequence, simple cycle or complex graph.\n",
    "        \n",
    "        Returns: string type\n",
    "        '''\n",
    "        cnt = 0\n",
    "        out_deg=[]\n",
    "        for i in range(len(self.list_of_monomers)):\n",
    "            out_deg.append(len(self.get_successors(i)))\n",
    "            if out_deg[i] >=2:\n",
    "                return \"Complex\"\n",
    "            if out_deg[i]==1:\n",
    "                cnt+=1\n",
    "        if cnt == len(self.list_of_monomers):\n",
    "            return \"Cycle\"\n",
    "        elif cnt == len(self.list_of_monomers)-1:\n",
    "            return \"SimpleSeq\"\n",
    "        else:\n",
    "            return \"Graph is not connected\"\n",
    "        \n",
    "    def find_starting_monomer(self):\n",
    "        '''\n",
    "        Find an index of monomer from which better to start building peptide. Find a vertex of amino group with min degree \n",
    "        \n",
    "        Returns: An index of monomer\n",
    "        '''\n",
    "        min_degree=5 #кажется больше не нужно с химической точки зрения\n",
    "        for i in range(len(self.list_of_monomers)):\n",
    "            if min_degree > len(self.list_of_monomers[i].GetSubstructMatches(amino_group)):\n",
    "                min_degree = len(self.list_of_monomers[i].GetSubstructMatches(amino_group))\n",
    "                index = i\n",
    "        return index\n",
    "            "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "c238e741",
   "metadata": {},
   "outputs": [],
   "source": [
    "peptide_graph = Graph()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8cc58bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "peptide_graph.read_from_string(\"Cl2-Pro,Abu,Ser,Phe,Ser;0,1;1,2;2,3;3,4;4,0\") #Граф для примера\n",
    "#peptide_graph.read_from_string(\"Phe,Ser,Cl2-Pro,Abu,Ser;0,1;1,2;2,3;3,4;4,0\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bad608a2",
   "metadata": {},
   "source": [
    "## Построение пептида"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d7a47d86",
   "metadata": {},
   "outputs": [],
   "source": [
    "monomers = peptide_graph.list_of_monomers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "a16167f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MolsToGridImage(monomers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ca63cab2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#класс будет усложнятся по мере усложнений\n",
    "class Peptide: \n",
    "    \"\"\"\n",
    "    Peptide class represents a peptide, which we build\n",
    "\n",
    "    - mol: molecula, which we already built\n",
    "    - successors: list of successors of subgraph, that represent the molecula \n",
    "\n",
    "    You might want to add other fields/methods for Peptide\n",
    "    \"\"\"\n",
    "    def __init__(self, mol,successors):\n",
    "        self.mol = mol\n",
    "        self.successors=successors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "9daf003c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def remove_OH(mol):\n",
    "    \"\"\"\n",
    "    Removes -OH from carboxyl group of molecula\n",
    "    \n",
    "    Returns: Molecula without -OH\n",
    "    \"\"\"\n",
    "    mw1 = Chem.RWMol(mol)\n",
    "    mw1.RemoveAtom(mol.GetSubstructMatches(without_radical)[0][4])\n",
    "    return mw1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c07e9341",
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_amino_index(amino_linkages):\n",
    "    \"\"\"\n",
    "    Finds amino group with minimum amount of linkages\n",
    "    \n",
    "    Returns: amino group index\n",
    "    \"\"\"\n",
    "    dict_freq = {}\n",
    "    for link in amino_linkages:\n",
    "        if  link[0] in dict_freq:\n",
    "            dict_freq[link[0]] +=1\n",
    "        else:\n",
    "            dict_freq[link[0]] =1\n",
    "    min_freq_link = (\"\", 4)\n",
    "    for link in dict_freq:\n",
    "        if dict_freq[link] < min_freq_link[1]:\n",
    "            min_freq_link = (link,dict_freq[link]) \n",
    "    return min_freq_link[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3d2663b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def peptide_linkage(mol_without_OH, next_mol):\n",
    "    \"\"\"\n",
    "    Makes peptide linkage between peptide and new monomer\n",
    "    \n",
    "    Returns: new smiles, peptide\n",
    "    \"\"\"\n",
    "    combo = Chem.CombineMols(mol_without_OH, next_mol)\n",
    "    edcombo = Chem.EditableMol(combo)\n",
    "    combo.GetSubstructMatches(without_OH)\n",
    "    edcombo.AddBond(mol_without_OH.GetNumAtoms()-2, combo.GetSubstructMatches(amino_group)[-1][0],\n",
    "                    order=Chem.rdchem.BondType.SINGLE)\n",
    "    back = edcombo.GetMol()\n",
    "    back2 = Chem.MolToSmiles(back)\n",
    "    peptide = Chem.MolFromSmiles(back2)\n",
    "    return back2, peptide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e069ea3",
   "metadata": {},
   "outputs": [],
   "source": [
    "def monomers_to_peptide(graph):\n",
    "    \"\"\"\n",
    "    Link monomers in sequence or cycle\n",
    "    \n",
    "    Returns: smiles, peptide\n",
    "    \"\"\"\n",
    "    start_index = peptide_graph.find_starting_monomer()\n",
    "    peptide = Peptide(graph.list_of_monomers[start_index], graph.get_successors(start_index))\n",
    "    graph.processed[start_index]=True\n",
    "    while len(peptide.successors) != 0 and graph.was_processed(peptide.successors[0]) == False:\n",
    "        graph.processed[peptide.successors[0]]=True #помечаем присоединяемый мономер как посещенный\n",
    "        #если будет больше соседей, придется усложнять функцию\n",
    "        smile, peptide.mol = peptide_linkage(remove_OH(peptide.mol), graph.list_of_monomers[peptide.successors[0]])\n",
    "        peptide.successors = graph.get_successors(peptide.successors[0])\n",
    "        \n",
    "    if graph.was_processed(peptide.successors[0]):\n",
    "        peptide.mol = remove_OH(peptide.mol)\n",
    "        peptw = Chem.RWMol(peptide.mol)\n",
    "        peptw.AddBond(peptide.mol.GetNumAtoms()-2, find_amino_index(peptide.mol.GetSubstructMatches(amino_group)),\n",
    "                    order=Chem.rdchem.BondType.SINGLE)\n",
    "        back = peptw.GetMol()\n",
    "        smile = Chem.MolToSmiles(back)\n",
    "        peptide.mol = Chem.MolFromSmiles(smile)\n",
    "    return smile, peptide.mol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b92ac046",
   "metadata": {},
   "outputs": [],
   "source": [
    "smile, peptide = monomers_to_peptide(peptide_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "778592af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<rdkit.Chem.rdchem.Mol at 0x26cb0a33760>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "peptide"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "102d688f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CCC1NC(=O)C2C(Cl)C(Cl)CN2C(=O)C(CO)NC(=O)C(Cc2ccccc2)NC(=O)C(CO)NC1=O'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "smile"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e28d151",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}