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 "cells": [
  {
   "cell_type": "markdown",
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   "source": [
    "# Example of using PySpark to find polymer interaction fingerprint\n",
    "\n",
    "Demo how to calculate polymer interaction data and maps it to polymer chains."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from pyspark import SparkConf, SparkContext                    \n",
    "from mmtfPyspark.io import mmtfReader\n",
    "from mmtfPyspark.interactions import InteractionFilter, InteractionFingerprinter\n",
    "                                                               \n",
    "# Create variables                                             \n",
    "APP_NAME = \"MMTF_Spark\"                                        \n",
    "\n",
    "# Configure Spark                                              \n",
    "conf = SparkConf().setAppName(APP_NAME).setMaster(\"local[*]\")  \n",
    "sc = SparkContext(conf=conf)                                   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download 1OHR structure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "pdb = mmtfReader.download_mmtf_files(['1OHR'], sc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Find ASP-ARG salt bridges"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>structureChainId</th>\n",
       "      <th>queryChainId</th>\n",
       "      <th>targetChainId</th>\n",
       "      <th>groupNumbers</th>\n",
       "      <th>sequenceIndices</th>\n",
       "      <th>sequence</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1OHR.A</td>\n",
       "      <td>B</td>\n",
       "      <td>A</td>\n",
       "      <td>[8]</td>\n",
       "      <td>[7]</td>\n",
       "      <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1OHR.B</td>\n",
       "      <td>A</td>\n",
       "      <td>B</td>\n",
       "      <td>[8]</td>\n",
       "      <td>[7]</td>\n",
       "      <td>PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  structureChainId queryChainId targetChainId groupNumbers sequenceIndices  \\\n",
       "0           1OHR.A            B             A          [8]             [7]   \n",
       "1           1OHR.B            A             B          [8]             [7]   \n",
       "\n",
       "                                            sequence  \n",
       "0  PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...  \n",
       "1  PQITLWQRPLVTIKIGGQLKEALLDTGADDTVLEEMSLPGRWKPKM...  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "interactionFilter = InteractionFilter(distanceCutoff=3.5, minInteractions=1)\n",
    "interactionFilter.set_query_groups(True, \"ASP\")\n",
    "interactionFilter.set_query_atom_names(True, ['OD1','OD2'])\n",
    "interactionFilter.set_target_groups(True, \"ARG\")\n",
    "interactionFilter.set_target_atom_names(True, ['NH1','NH2'])\n",
    "\n",
    "interactions = InteractionFingerprinter.get_polymer_interactions(pdb, interactionFilter)\n",
    "interactions.toPandas().head(10)    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Terminate Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sc.stop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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