{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Author Search Demo\n",
    "\n",
    "![pdbj](https://pdbj.org/content/default.svg)\n",
    "\n",
    "Example query for human protein-serine/threonine kinases using SIFTS data retrieved with PDBj Mine 2 webservices.\n",
    "\n",
    "\n",
    "## References\n",
    "\n",
    "The \"Structure Integration with Function, Taxonomy and Sequence\" is the authoritative source of up-to-date residue-level annotation of structures in the PDB with data available in UniProt, IntEnz, CATH, SCOP, GO, InterPro,Pfam and PubMed.\n",
    "[SIFTS](https://www.ebi.ac.uk/pdbe/docs/sifts/overview.html) \n",
    "\n",
    "Data are provided through: \n",
    "[Mine 2 SQL](https://pdbj.org/help/mine2-sql)\n",
    "\n",
    "Queries can be designed with the interactive\n",
    "[PDBj Mine 2 query service](https://pdbj.org/mine/sql)\n",
    "\n",
    "\n",
    "\n",
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyspark import SparkConf, SparkContext\n",
    "from mmtfPyspark.webfilters import PdbjMineSearch\n",
    "from mmtfPyspark.mappers import StructureToPolymerChains\n",
    "from mmtfPyspark.io import mmtfReader"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configure Spark Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = SparkConf().setMaster(\"local[*]\") \\\n",
    "                  .setAppName(\"KinaseDemo\")\n",
    "sc = SparkContext(conf = conf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Query for human protein-serine/threonine kinases using SIFTS data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sql = \"SELECT t.pdbid, t.chain FROM sifts.pdb_chain_taxonomy AS t  \"\\\n",
    "            + \"JOIN sifts.pdb_chain_enzyme AS e ON (t.pdbid = e.pdbid AND t.chain = e.chain) \"\\\n",
    "            + \"WHERE t.scientific_name = 'Homo sapiens' AND e.ec_number = '2.7.11.1'\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Read PDB and filter by author"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of entries matching query: 2722\n"
     ]
    }
   ],
   "source": [
    "path = \"../../resources/mmtf_reduced_sample/\"\n",
    "\n",
    "pdb = mmtfReader.read_sequence_file(path, sc) \\\n",
    "                .flatMap(StructureToPolymerChains()) \\\n",
    "                .filter(PdbjMineSearch(sql))\n",
    "\n",
    "print(f\"Number of entries matching query: {pdb.count()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terminate Spark Context"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}