assets/2023/scripts/202305-MCBDD-ModuleII-offline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MCBDD 2023 Module II offline activity \n",
    "\n",
    "Jitao David Zhang, May 2023\n",
    "\n",
    "All the analysis below was done with python 3.8.5 using conda version 4.10.1.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 1\n",
    "\n",
    "Retrieve all approved drugs from the ChEMBL database, sort them by approval year and name. \n",
    "\n",
    "The following steps examine the first approval year and names of drugs, and fill a DataFrame with these information. While the first approval year is almost uniquely defined, a drug can have many names. For consistency, we use the INN (international nonproprietary names). See [chembl_webresource_client](https://github.com/chembl/chembl_webresource_client) for more examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from chembl_webresource_client.new_client import new_client\n",
    "import requests\n",
    "import json\n",
    "import pandas as pd\n",
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14816"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs = new_client.drug\n",
    "len(drugs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'applicants': ['Dava Pharmaceuticals Inc',\n",
       "  'American Therapeutics Inc',\n",
       "  'Idt Australia Ltd',\n",
       "  'Watson Laboratories Inc',\n",
       "  'Pfizer Laboratories Div Pfizer Inc',\n",
       "  'Ani Pharmaceuticals Inc',\n",
       "  'Granules Pharmaceuticals Inc',\n",
       "  'Mylan Pharmaceuticals Inc',\n",
       "  'Teva Pharmaceuticals Usa',\n",
       "  'Purepac Pharmaceutical Co'],\n",
       " 'atc_classification': [{'code': 'C02CA01',\n",
       "   'description': 'CARDIOVASCULAR SYSTEM: ANTIHYPERTENSIVES: ANTIADRENERGIC AGENTS, PERIPHERALLY ACTING: Alpha-adrenoreceptor antagonists'}],\n",
       " 'availability_type': 1,\n",
       " 'biotherapeutic': None,\n",
       " 'black_box': 0,\n",
       " 'black_box_warning': '0',\n",
       " 'chirality': 2,\n",
       " 'development_phase': '4.0',\n",
       " 'drug_type': 1,\n",
       " 'first_approval': 1976,\n",
       " 'first_in_class': 0,\n",
       " 'helm_notation': None,\n",
       " 'indication_class': 'Antihypertensive',\n",
       " 'molecule_chembl_id': 'CHEMBL2',\n",
       " 'molecule_properties': {'alogp': '1.78',\n",
       "  'aromatic_rings': 3,\n",
       "  'cx_logd': '1.43',\n",
       "  'cx_logp': '1.65',\n",
       "  'cx_most_apka': None,\n",
       "  'cx_most_bpka': '7.24',\n",
       "  'full_molformula': 'C19H21N5O4',\n",
       "  'full_mwt': '383.41',\n",
       "  'hba': 8,\n",
       "  'hba_lipinski': 9,\n",
       "  'hbd': 1,\n",
       "  'hbd_lipinski': 2,\n",
       "  'heavy_atoms': 28,\n",
       "  'molecular_species': 'NEUTRAL',\n",
       "  'mw_freebase': '383.41',\n",
       "  'mw_monoisotopic': '383.1594',\n",
       "  'np_likeness_score': '-1.29',\n",
       "  'num_lipinski_ro5_violations': 0,\n",
       "  'num_ro5_violations': 0,\n",
       "  'psa': '106.95',\n",
       "  'qed_weighted': '0.73',\n",
       "  'ro3_pass': 'N',\n",
       "  'rtb': 4},\n",
       " 'molecule_structures': {'canonical_smiles': 'COc1cc2nc(N3CCN(C(=O)c4ccco4)CC3)nc(N)c2cc1OC',\n",
       "  'molfile': '\\n     RDKit          2D\\n\\n 28 31  0  0  0  0  0  0  0  0999 V2000\\n    0.9375   -1.9792    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.9292   -2.6917    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.3208   -2.6750    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2875   -3.0417    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.3042   -1.6167    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.3208   -1.9625    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.3917   -0.5917    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.5542   -1.6417    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.7792   -0.9542    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.9500   -3.0167    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.0125   -0.9292    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -0.9250   -1.5917    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5833   -2.6542    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -1.5708   -1.9417    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.2417   -1.5917    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    1.5417   -0.9500    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.1667   -2.0000    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.1667   -0.5792    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    2.7792   -1.6375    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    3.4042    0.0875    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.5792   -0.5125    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    4.9417   -1.5917    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    5.1542   -0.9292    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n    0.2667   -3.7542    0.0000 N   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.2000   -2.9917    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.1833   -1.5792    0.0000 O   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.7958   -1.9125    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n   -2.7958   -2.6542    0.0000 C   0  0  0  0  0  0  0  0  0  0  0  0\\n  2  1  2  0\\n  3  6  1  0\\n  4  2  1  0\\n  5  1  1  0\\n  6  5  2  0\\n  7  9  1  0\\n  8  1  1  0\\n  9 19  1  0\\n 10  3  1  0\\n 11  7  1  0\\n 12  6  1  0\\n 13 14  1  0\\n 14 12  2  0\\n 15 11  1  0\\n 16  8  1  0\\n 17  8  1  0\\n 18 16  1  0\\n 19 17  1  0\\n 20  7  2  0\\n 21 11  2  0\\n 22 15  1  0\\n 23 21  1  0\\n 24  4  1  0\\n 25 13  1  0\\n 26 14  1  0\\n 27 26  1  0\\n 28 25  1  0\\n  3  4  2  0\\n  9 18  1  0\\n 10 13  2  0\\n 22 23  2  0\\nM  END\\n',\n",
       "  'standard_inchi': 'InChI=1S/C19H21N5O4/c1-26-15-10-12-13(11-16(15)27-2)21-19(22-17(12)20)24-7-5-23(6-8-24)18(25)14-4-3-9-28-14/h3-4,9-11H,5-8H2,1-2H3,(H2,20,21,22)',\n",
       "  'standard_inchi_key': 'IENZQIKPVFGBNW-UHFFFAOYSA-N'},\n",
       " 'molecule_synonyms': [{'molecule_synonym': 'CP-12299',\n",
       "   'syn_type': 'RESEARCH_CODE',\n",
       "   'synonyms': 'CP-12299'},\n",
       "  {'molecule_synonym': 'CP-122991',\n",
       "   'syn_type': 'RESEARCH_CODE',\n",
       "   'synonyms': 'CP-122991'},\n",
       "  {'molecule_synonym': 'Prazosin', 'syn_type': 'FDA', 'synonyms': 'Prazosin'},\n",
       "  {'molecule_synonym': 'Prazosin', 'syn_type': 'ATC', 'synonyms': 'PRAZOSIN'},\n",
       "  {'molecule_synonym': 'Prazosin', 'syn_type': 'BAN', 'synonyms': 'PRAZOSIN'},\n",
       "  {'molecule_synonym': 'Prazosin', 'syn_type': 'INN', 'synonyms': 'PRAZOSIN'},\n",
       "  {'molecule_synonym': 'Prazosin',\n",
       "   'syn_type': 'MERCK_INDEX',\n",
       "   'synonyms': 'PRAZOSIN'},\n",
       "  {'molecule_synonym': 'Prazosin',\n",
       "   'syn_type': 'OTHER',\n",
       "   'synonyms': 'PRAZOSIN'}],\n",
       " 'ob_patent': None,\n",
       " 'oral': 1,\n",
       " 'parenteral': 0,\n",
       " 'prodrug': 0,\n",
       " 'research_codes': ['CP-12,299-1',\n",
       "  'NSC-292810',\n",
       "  'CP-12299',\n",
       "  'CP-122991',\n",
       "  'CP-12299-1'],\n",
       " 'rule_of_five': 1,\n",
       " 'sc_patent': None,\n",
       " 'synonyms': ['Prazosin'],\n",
       " 'topical': 0,\n",
       " 'usan_stem': '-azosin',\n",
       " 'usan_stem_definition': 'antihypertensives (prazosin type)',\n",
       " 'usan_stem_substem': '-azosin(-azosin)',\n",
       " 'usan_year': 1968,\n",
       " 'withdrawn_class': None,\n",
       " 'withdrawn_country': None,\n",
       " 'withdrawn_flag': '0',\n",
       " 'withdrawn_reason': None,\n",
       " 'withdrawn_year': None}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'CHEMBL2'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_chembl(drug: list):\n",
    "    \"\"\" return ChEMBL ID\"\"\"\n",
    "    return drug['molecule_chembl_id']\n",
    "\n",
    "get_chembl(drugs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1976"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_FirstApproval(drug: list):\n",
    "    \"\"\" returns the year of first approval\"\"\"\n",
    "    return drug['first_approval']\n",
    "\n",
    "\n",
    "get_FirstApproval(drugs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'molecule_synonym': 'CP-12299',\n",
       "  'syn_type': 'RESEARCH_CODE',\n",
       "  'synonyms': 'CP-12299'},\n",
       " {'molecule_synonym': 'CP-122991',\n",
       "  'syn_type': 'RESEARCH_CODE',\n",
       "  'synonyms': 'CP-122991'},\n",
       " {'molecule_synonym': 'Prazosin', 'syn_type': 'FDA', 'synonyms': 'Prazosin'},\n",
       " {'molecule_synonym': 'Prazosin', 'syn_type': 'ATC', 'synonyms': 'PRAZOSIN'},\n",
       " {'molecule_synonym': 'Prazosin', 'syn_type': 'BAN', 'synonyms': 'PRAZOSIN'},\n",
       " {'molecule_synonym': 'Prazosin', 'syn_type': 'INN', 'synonyms': 'PRAZOSIN'},\n",
       " {'molecule_synonym': 'Prazosin',\n",
       "  'syn_type': 'MERCK_INDEX',\n",
       "  'synonyms': 'PRAZOSIN'},\n",
       " {'molecule_synonym': 'Prazosin', 'syn_type': 'OTHER', 'synonyms': 'PRAZOSIN'}]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drugs[0]['molecule_synonyms']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Prazosin'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_INN(drug: list):\n",
    "    \"\"\"returns INN name, use ; as concatenator if more than one value is available\"\"\"\n",
    "    inn = [x['molecule_synonym'] for x in drug['molecule_synonyms'] if x['syn_type'] == 'INN']\n",
    "    res = ';'.join(inn)\n",
    "    return(res)\n",
    "\n",
    "\n",
    "get_INN(drugs[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "ids = [get_chembl(x) for x in drugs]\n",
    "years = [get_FirstApproval(x) for x in drugs]\n",
    "names = [get_INN(x) for x in drugs]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  ID               Name  FirstApproval\n",
      "271         CHEMBL90                            1939.0\n",
      "1078       CHEMBL821                            1939.0\n",
      "2922       CHEMBL190                            1940.0\n",
      "4625   CHEMBL1201649                            1942.0\n",
      "4333   CHEMBL1201235                            1943.0\n",
      "...              ...                ...            ...\n",
      "2484    CHEMBL167779        Zuclomifene            NaN\n",
      "1327     CHEMBL53904     Zuclopenthixol            NaN\n",
      "12490  CHEMBL4105630         Zuranolone            NaN\n",
      "12952  CHEMBL4297637  Zuretinol acetate            NaN\n",
      "12258  CHEMBL3989838       Zylofuramine            NaN\n",
      "\n",
      "[14816 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "task1 = pd.DataFrame({'ID':ids, 'Name':names, 'FirstApproval':years})\n",
    "task1.sort_values(by=['Name', 'FirstApproval'], axis=0, inplace=True)\n",
    "print(task1)\n",
    "## notice that not all molecules have INN names or approval information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "task1.to_csv('20230507-ChEMBL-drugs-sorted-by-name-and-approval.tsv.gz', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 2\n",
    "\n",
    "For each approved drug since 2013 that you identified in step (1), retrieve a list of UniProt accession numbers, namely protein targets associated with the drug.\n",
    "\n",
    "We first filter the compounds by the approval year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  ID                Name  FirstApproval\n",
      "402     CHEMBL278020                             2013.0\n",
      "2905    CHEMBL376359          Alogliptin         2013.0\n",
      "1929     CHEMBL99946     Levomilnacipran         2013.0\n",
      "7499   CHEMBL2105689        Luliconazole         2013.0\n",
      "1149     CHEMBL43452        Pomalidomide         2013.0\n",
      "...              ...                 ...            ...\n",
      "13050  CHEMBL4297750           Faricimab         2022.0\n",
      "13789  CHEMBL4594505         Teclistamab         2022.0\n",
      "3257    CHEMBL272427  Ursodoxicoltaurine         2022.0\n",
      "13129  CHEMBL4297832          Sutimlimab         2022.0\n",
      "13279  CHEMBL4297990         Tebentafusp         2022.0\n",
      "\n",
      "[453 rows x 3 columns]\n"
     ]
    }
   ],
   "source": [
    "approved_s2013_df = task1.query('FirstApproval >= 2013')\\\n",
    "                         .sort_values(by='FirstApproval', axis=0)\n",
    "print(approved_s2013_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we query UniProts of targets associated with each drug molecule. The query is done in two steps: first we find ChEMBL identifiers of targets, and then we translate these ChEMBL identifiers to UniProt identifiers.\n",
    "\n",
    "To save time, I used only the first 50 drugs to produce the example below. Even this is quite slow, taking quite a few minutes to run. You are welcome to use all compounds as input if you are interested in the results. \n",
    "\n",
    "Some technical detail: queries are done in so-called chunks, with 25 items in a chunk and therefore we use two chunks in total. The use of chunks help to limite the information transmitted between the server and the client, and thereby reducing both server load and client's waiting time. If you wish to process all compounds, you can use a larger chunk size, for instance 50."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "drugs_approved_s2023 = approved_s2013_df.ID[0:50]\n",
    "\n",
    "comp2target = dict()\n",
    "for d in drugs_approved_s2023:\n",
    "    comp2target[d] = set()\n",
    "\n",
    "# Process compounds in chunks so that we prevent huge queries:\n",
    "chunk_size = 25\n",
    "keys = list(comp2target.keys())\n",
    "\n",
    "for i in range(0, len(keys), chunk_size):\n",
    "    # we jump from compounds to targets through activities:\n",
    "    activities = new_client.activity.filter(molecule_chembl_id__in=keys[i:i + chunk_size]).only(\n",
    "        ['molecule_chembl_id', 'target_chembl_id'])\n",
    "    # extracting target ChEMBL IDs from activities:\n",
    "    for act in activities:\n",
    "        comp2target[act['molecule_chembl_id']].add(act['target_chembl_id'])\n",
    "\n",
    "# Now our dictionary maps from compound ChEMBL IDs into target ChEMBL IDs\n",
    "# We next translate the identifier of targets from ChEMBL IDs to UniProt IDs\n",
    "for key, val in comp2target.items():\n",
    "    # We don't know how many targets are associated with any compound. Therefore it is\n",
    "    # better to process targets in chunks, as suggested by the ChEMBL webclient.\n",
    "    lval = list(val)\n",
    "    uniprots = set()\n",
    "    for i in range(0, len(val), chunk_size):\n",
    "        targets = new_client.target.filter(target_chembl_id__in=lval[i:i + chunk_size]).only(\n",
    "            ['target_components'])\n",
    "        uniprots |= {comp['accession'] for t in targets for comp in t['target_components']}\n",
    "\n",
    "    comp2target[key] = uniprots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                ID UniProt\n",
      "0     CHEMBL278020  Q92887\n",
      "1     CHEMBL278020  P81908\n",
      "2     CHEMBL278020  P33261\n",
      "3     CHEMBL278020  P02545\n",
      "4     CHEMBL278020  O15439\n",
      "...            ...     ...\n",
      "1674  CHEMBL408513  Q9UKV0\n",
      "1675  CHEMBL408513  P0DTD1\n",
      "1676  CHEMBL408513  Q92769\n",
      "1677  CHEMBL408513  P08684\n",
      "1678  CHEMBL408513  Q9UQL6\n",
      "\n",
      "[1679 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "comp2target_df = pd.DataFrame([(i, uniprot) for i, j in comp2target.items() for uniprot in j], \n",
    "                  columns=['ID','UniProt'])\n",
    "print(comp2target_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we report *all* targets, irrespective of assays and affinicities. In reality, you may be more interested in specific targets with high affinities. In such situations, it makes sense to se one or more filters that fit your purpose.\n",
    "\n",
    "Finally we export the results into a tab-delimited file again."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "comp2target_df.to_csv('20230507-ChEMBL-drugs-since-2013-UniProt.tsv.gz', sep='\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Task 3\n",
    "\n",
    "For each protein with a UniProt accession number that you identified in step (2),retrieve UniProt keywords associated with it.\n",
    "\n",
    "Again, for the purpose of demonstration, I show the analysis with the first 100 proteins. I used the REST API of UniProt to show how it works, which accepts maximum 100 proteins in one go, that's why I retrieved the keywords of 100 proteins. Similar as what we did before with the ChEMBL database, you can break up big queries into smaller chunks.\n",
    "\n",
    "You can try to query all other proteins with the same procedure. However, for effective use when you have many identifiers, it may make more sense to bulk-download the keyword data from UniProt and run the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "## read in results of task 2\n",
    "task2_res = pd.read_csv('20230507-ChEMBL-drugs-since-2013-UniProt.tsv.gz', sep='\\t', index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "uniq_uniprot = set(task2_res.UniProt)\n",
    "sel_uniprot = list(uniq_uniprot)[:100]\n",
    "sel_uniprot_queryid = ','.join(sel_uniprot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "## see UniProt API document here: https://www.ebi.ac.uk/proteins/api/doc/#!/proteins/search\n",
    "url = 'https://www.ebi.ac.uk/proteins/api/proteins?offset=0&size=100&accession='+sel_uniprot_queryid\n",
    "r = requests.get(url, headers={'Accept': 'application/json'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Host-virus interaction', 'Membrane', 'Nucleotidyltransferase', 'RNA-directed RNA polymerase', 'Transferase', 'Viral RNA replication']\n"
     ]
    }
   ],
   "source": [
    "## the keywords of the first protein can be found here\n",
    "keywords = [list(k.values())[0] for k in r.json()[0]['keywords']]\n",
    "print(keywords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(r.json())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Counter({'Reference proteome': 98, 'Phosphoprotein': 77, 'Nucleotide-binding': 74, '3D-structure': 71, 'ATP-binding': 71, 'Alternative splicing': 69, 'Transferase': 68, 'Kinase': 64, 'Cytoplasm': 56, 'Membrane': 46, 'Serine/threonine-protein kinase': 42, 'Nucleus': 38, 'Cell membrane': 33, 'Disease variant': 32, 'Metal-binding': 31, 'Ubl conjugation': 29, 'Glycoprotein': 28, 'Acetylation': 27, 'Transmembrane': 26, 'Transmembrane helix': 26, 'Tyrosine-protein kinase': 25, 'Direct protein sequencing': 23, 'Repeat': 23, 'Magnesium': 22, 'Signal': 21, 'Disulfide bond': 19, 'Receptor': 19, 'Host-virus interaction': 17, 'Cytoskeleton': 16, 'Apoptosis': 15, 'Cell cycle': 14, 'Cell projection': 12, 'Hydrolase': 12, 'Isopeptide bond': 10, 'Endoplasmic reticulum': 10, 'Transcription': 9, 'Transcription regulation': 9, 'Developmental protein': 9, 'Transport': 9, 'Mitochondrion': 9, 'Intellectual disability': 8, 'Methylation': 8, 'Coiled coil': 8, 'Synapse': 8, 'Endosome': 8, 'Proto-oncogene': 8, 'Cell junction': 8, 'Cytoplasmic vesicle': 8, 'Immunity': 7, 'Lipoprotein': 7, 'Zinc': 7, 'Chromosomal rearrangement': 7, 'Cell division': 7, 'Angiogenesis': 7, 'Neurogenesis': 6, 'Immunoglobulin domain': 6, 'Secreted': 6, 'Cell adhesion': 6, 'SH2 domain': 6, 'Innate immunity': 5, 'Differentiation': 5, 'Lipid transport': 5, 'DNA damage': 5, 'Mitosis': 5, 'SH3 domain': 5, 'DNA-binding': 4, 'Ribosome biogenesis': 4, 'Translation regulation': 4, 'Stress response': 4, 'Postsynaptic cell membrane': 4, 'Host cell receptor for virus entry': 4, 'Palmitate': 4, 'Activator': 4, 'Iron': 4, 'RNA-binding': 3, 'Biological rhythms': 3, 'Adaptive immunity': 3, 'Golgi apparatus': 3, 'GTP-binding': 3, 'Alternative initiation': 3, 'Myristate': 3, 'Lysosome': 3, 'Lipid metabolism': 3, 'cAMP': 3, 'Calmodulin-binding': 3, 'Epilepsy': 3, 'Ion transport': 3, 'Centromere': 3, 'Chromosome': 3, 'DNA repair': 3, 'Kinetochore': 3, 'Zinc-finger': 3, 'Chromosome partition': 2, 'Helicase': 2, 'Muscle protein': 2, 'Chromatin regulator': 2, 'Translocase': 2, 'Serine esterase': 2, 'Lyase': 2, 'Cardiomyopathy': 2, 'Deafness': 2, 'Prenylation': 2, 'ADP-ribosylation': 2, 'Inflammatory response': 2, 'Glycosyltransferase': 2, 'Protease': 2, 'Zymogen': 2, 'Heme': 2, 'Microsome': 2, 'Monooxygenase': 2, 'Oxidoreductase': 2, 'Mitochondrion inner membrane': 2, '2Fe-2S': 2, 'Iron-sulfur': 2, 'Mitochondrion outer membrane': 2, 'Actin-binding': 2, 'Motor protein': 2, 'Myosin': 2, 'Dwarfism': 2, 'Manganese': 2, 'mRNA processing': 2, 'Tumor suppressor': 2, 'Lipid droplet': 2, 'Wnt signaling pathway': 2, 'Nucleotidyltransferase': 1, 'RNA-directed RNA polymerase': 1, 'Viral RNA replication': 1, 'Inflammasome': 1, 'Congenital myasthenic syndrome': 1, 'Repressor': 1, 'Neurotransmitter degradation': 1, 'Hydroxylation': 1, 'Ectodermal dysplasia': 1, 'Transducer': 1, 'Sialic acid': 1, 'Chaperone': 1, 'Neurodegeneration': 1, 'Purine salvage': 1, 'Carboxypeptidase': 1, 'Cholesterol metabolism': 1, 'Fatty acid metabolism': 1, 'Steroid metabolism': 1, 'Sterol metabolism': 1, 'NADP': 1, 'Heme biosynthesis': 1, 'Porphyrin biosynthesis': 1, 'Transit peptide': 1, 'Synaptosome': 1, 'Cataract': 1, 'Cell shape': 1, 'Diabetes mellitus': 1, 'Proteasome': 1, 'Sodium': 1, 'Sodium transport': 1, 'Sugar transport': 1, 'Symport': 1, 'SCID': 1, 'Virulence': 1, 'mRNA transport': 1, 'Calcium': 1, 'Ion channel': 1, 'Ligand-gated ion channel': 1, 'EGF-like domain': 1, 'Glaucoma': 1, 'Alternative promoter usage': 1, 'Coated pit': 1, 'Endocytosis': 1, 'Meiosis': 1, 'Glycation': 1, 'Carbohydrate metabolism': 1, 'Glycogen metabolism': 1, 'ANK repeat': 1, 'Neurotransmitter transport': 1, 'Mitochondrion nucleoid': 1, 'Hearing': 1, 'Non-syndromic deafness': 1, 'Sensory transduction': 1, 'Vision': 1, 'Allosteric enzyme': 1, 'tRNA processing': 1, 'Protease inhibitor': 1, 'Serine protease inhibitor': 1, 'Microtubule': 1, 'mRNA splicing': 1, 'Schiff base': 1, 'Signal-anchor': 1, 'Aminopeptidase': 1, 'Cleavage on pair of basic residues': 1, 'Serine protease': 1})\n"
     ]
    }
   ],
   "source": [
    "## now let's try to fetch all keywords\n",
    "def get_keywords(protein: list):\n",
    "    res = [list(k.values())[0] for k in protein['keywords']]\n",
    "    if res == None:\n",
    "        res = []\n",
    "    return(res)\n",
    "\n",
    "all_keywords=[]\n",
    "for jlist in r.json():\n",
    "    all_keywords.extend(get_keywords(jlist))\n",
    "    \n",
    "keyword_counter = Counter(all_keywords)\n",
    "print(keyword_counter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We visualize the most common 20 keywords with a histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "most_common_keywords = keyword_counter.most_common(20)\n",
    "y = [count for keyword, count in most_common_keywords]\n",
    "x = [keyword for keyword, count in most_common_keywords]\n",
    "\n",
    "plt.rcParams[\"figure.figsize\"] = (10,7)\n",
    "plt.rcParams.update({'font.size': 16})\n",
    "\n",
    "plt.bar(x, y, color='lightblue')\n",
    "plt.title(\"The most frequent keywords\")\n",
    "plt.ylabel(\"Frequency\")\n",
    "plt.xticks(rotation=90)\n",
    "for i, (tag, count) in enumerate(most_common_keywords):\n",
    "    plt.text(i, count, f' {count} ', rotation=90, size=16,\n",
    "             ha='center', va='top' if i < 10 else 'bottom', color='white' if i < 10 else 'black')\n",
    "plt.xlim(-0.6, len(x)-0.4) # optionally set tighter x lims\n",
    "plt.tight_layout() # change the whitespace such that all labels fit nicely\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that many proteins are annotated with the keyword 'kinase', the relevant keywords 'phosphoprotein' and 'ATP-binding' (kinase phosphorylates other proteins with ATP), as well as the keyword 'membrane proteins' and relevant concepts such as 'transmembrane'. Recall that GPCRs, ion channels, and kinases are among the frequent classes of drug targets. At the same time, nucleotide-binding, i.e. DNA- or RNA-binding proteins are well present."
   ]
  }
 ],
 "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": "3.11.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}