{ "cells": [ { "cell_type": "code", "execution_count": 3, "id": "c47a32d8-c857-41de-a70a-cec48046df12", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 92, "id": "e0c6bd53-3417-44bd-b1b4-81802b37fbfc", "metadata": {}, "outputs": [], "source": [ "df = pd.read_csv('binding_moad/every.csv',header=None,skiprows=2)\n", "df = df.rename(columns={2:'pdb',3: 'ligand_name', 4: 'ligand_valid', 7: 'affinity_val', 8: 'affinity_unit', 9:'smiles'})\n", "#df = df[df['ligand_valid']!='invalid'].copy()" ] }, { "cell_type": "code", "execution_count": 93, "id": "e40b1ddc-9a98-4a3b-b8a6-45e3940a3ea2", "metadata": {}, "outputs": [], "source": [ "df['is_sep'] = df[1] == 'Family. Representative Entry is '" ] }, { "cell_type": "code", "execution_count": 94, "id": "4f00a0d1-78db-4f32-9d12-5e035b70ef98", "metadata": {}, "outputs": [], "source": [ "df['cum_sum'] = df['is_sep'].cumsum()" ] }, { "cell_type": "code", "execution_count": 95, "id": "52c0c66c-1eb0-415b-b019-bc77419ccbd7", "metadata": {}, "outputs": [], "source": [ "from pint import UnitRegistry\n", "ureg = UnitRegistry()\n", "\n", "def to_uM(affinity_unit):\n", " try:\n", " val = ureg(str(affinity_unit[0])+str(affinity_unit[1]))\n", " return val.m_as(ureg.uM)\n", " except Exception:\n", " pass\n", " \n", " try:\n", " val = ureg(str(affinity_unit[0])+str(affinity_unit[1]))\n", " return 1/val.m_as(1/ureg.uM)\n", " except Exception:\n", " pass" ] }, { "cell_type": "code", "execution_count": 96, "id": "e5b4dd41-1389-408d-bee6-6dbeefc1d5c7", "metadata": {}, "outputs": [], "source": [ "groupby = df.groupby('cum_sum')" ] }, { "cell_type": "code", "execution_count": 121, "id": "61b8276c-54fe-4989-af5f-723994e1df7e", "metadata": {}, "outputs": [], "source": [ "def group(df):\n", " pdb = df[df['is_sep']]['pdb'].values\n", " if len(pdb) > 0:\n", " pdb = pdb[0]\n", " df['pdb_ref'] = pdb\n", " return df[df['ligand_valid']=='valid']\n", "df_expand = groupby.apply(group).reset_index(drop=True)" ] }, { "cell_type": "code", "execution_count": 124, "id": "8bb2dfac-5f11-455c-9dee-3607b47b4232", "metadata": {}, "outputs": [], "source": [ "df_expand['affinity_uM'] = df_expand[['affinity_val','affinity_unit']].apply(to_uM,axis=1)" ] }, { "cell_type": "code", "execution_count": 125, "id": "0dc39f62-5b18-4a86-9a44-17d1925da2ad", "metadata": {}, "outputs": [], "source": [ "df_complex = pd.read_parquet('data/moad_complex.parquet')\n", "df_complex['name'] = df_complex['name'].str.upper()" ] }, { "cell_type": "code", "execution_count": 128, "id": "6d158a41-64c6-4fa2-92d5-562aa11e8924", "metadata": {}, "outputs": [], "source": [ "df_all = df_expand.merge(df_complex,left_on='pdb_ref',right_on='name')" ] }, { "cell_type": "code", "execution_count": 129, "id": "901fe6c6-dc8c-4ce4-82c6-1fb0b718287a", "metadata": {}, "outputs": [], "source": [ "df_all = df_all[~df_all['affinity_val'].isnull()]" ] }, { "cell_type": "code", "execution_count": 130, "id": "383f9a1c-ffc6-43da-ac5a-5bcb815be28b", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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01pdbligand_nameligand_valid56affinity_valaffinity_unitsmiles10is_sepcum_sumpdb_refaffinity_uMnameseq
0NaNNaNNaN2PA:C:613validKi=0.62nMNP(=O)(N)ONaNFalse16H8J0.0006206H8JNYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE...
2NaNNaNNaNHAE:C:800validKi=2.60uMCC(=O)NONaNFalse16H8J2.6000006H8JNYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE...
7NaNNaNNaN43W:A:902validic50=580.00nMC#CCCOP(=O)(O)OP(=O)(O)ONaNFalse24S3F0.5800004S3FMEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE...
16NaNNaNNaN0CG:A:902validic50=770.00nMC#CCOP(=O)(O)OP(=O)(O)ONaNFalse24S3F0.7700004S3FMEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE...
17NaNNaNNaNADN:A:901validKd=15.00uMc1nc(c2c(n1)n(cn2)[C@H]3[C@@H]([C@@H]([C@H](O3...NaNFalse52GL015.0000002GL0MTDMSIKFELIDVPIPQGTNVIIGQAHFIKTVEDLYEALVTSVPGV...
......................................................
51900NaNNaNNaNMAN NAG:G:1validKa=7860.00M^-1NaNNaNFalse104992WDB127.2264632WDBMGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...
51901NaNNaNNaNMAN NAG:F:1validKa=7860.00M^-1NaNNaNFalse104992WDB127.2264632WDBMGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...
51902NaNNaNNaNNGA NAG:F:1validKa=5910.00M^-1NaNNaNFalse104992WDB169.2047382WDBMGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...
51903NaNNaNNaNNGA NAG:E:1validKa=5910.00M^-1NaNNaNFalse104992WDB169.2047382WDBMGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...
51904NaNNaNNaNNGA NAG:H:1validKa=5910.00M^-1NaNNaNFalse104992WDB169.2047382WDBMGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...
\n", "

25425 rows × 17 columns

\n", "
" ], "text/plain": [ " 0 1 pdb ligand_name ligand_valid 5 6 affinity_val \\\n", "0 NaN NaN NaN 2PA:C:613 valid Ki = 0.62 \n", "2 NaN NaN NaN HAE:C:800 valid Ki = 2.60 \n", "7 NaN NaN NaN 43W:A:902 valid ic50 = 580.00 \n", "16 NaN NaN NaN 0CG:A:902 valid ic50 = 770.00 \n", "17 NaN NaN NaN ADN:A:901 valid Kd = 15.00 \n", "... ... ... ... ... ... ... .. ... \n", "51900 NaN NaN NaN MAN NAG:G:1 valid Ka = 7860.00 \n", "51901 NaN NaN NaN MAN NAG:F:1 valid Ka = 7860.00 \n", "51902 NaN NaN NaN NGA NAG:F:1 valid Ka = 5910.00 \n", "51903 NaN NaN NaN NGA NAG:E:1 valid Ka = 5910.00 \n", "51904 NaN NaN NaN NGA NAG:H:1 valid Ka = 5910.00 \n", "\n", " affinity_unit smiles 10 \\\n", "0 nM NP(=O)(N)O NaN \n", "2 uM CC(=O)NO NaN \n", "7 nM C#CCCOP(=O)(O)OP(=O)(O)O NaN \n", "16 nM C#CCOP(=O)(O)OP(=O)(O)O NaN \n", "17 uM c1nc(c2c(n1)n(cn2)[C@H]3[C@@H]([C@@H]([C@H](O3... NaN \n", "... ... ... .. \n", "51900 M^-1 NaN NaN \n", "51901 M^-1 NaN NaN \n", "51902 M^-1 NaN NaN \n", "51903 M^-1 NaN NaN \n", "51904 M^-1 NaN NaN \n", "\n", " is_sep cum_sum pdb_ref affinity_uM name \\\n", "0 False 1 6H8J 0.000620 6H8J \n", "2 False 1 6H8J 2.600000 6H8J \n", "7 False 2 4S3F 0.580000 4S3F \n", "16 False 2 4S3F 0.770000 4S3F \n", "17 False 5 2GL0 15.000000 2GL0 \n", "... ... ... ... ... ... \n", "51900 False 10499 2WDB 127.226463 2WDB \n", "51901 False 10499 2WDB 127.226463 2WDB \n", "51902 False 10499 2WDB 169.204738 2WDB \n", "51903 False 10499 2WDB 169.204738 2WDB \n", "51904 False 10499 2WDB 169.204738 2WDB \n", "\n", " seq \n", "0 NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE... \n", "2 NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE... \n", "7 MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE... \n", "16 MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE... \n", "17 MTDMSIKFELIDVPIPQGTNVIIGQAHFIKTVEDLYEALVTSVPGV... \n", "... ... \n", "51900 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n", "51901 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n", "51902 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n", "51903 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n", "51904 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n", "\n", "[25425 rows x 17 columns]" ] }, "execution_count": 130, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all" ] }, { "cell_type": "code", "execution_count": 133, "id": "bebc962b-10f7-478c-8e23-e2d3722e875c", "metadata": {}, "outputs": [], "source": [ "df_all[['pdb','ligand_name','smiles','name','affinity_uM','seq']].to_parquet('data/moad.parquet')" ] }, { "cell_type": "code", "execution_count": null, "id": "6ceb8706-273c-4a83-8cda-c7e33fc87e38", "metadata": {}, "outputs": [], "source": [] } ], "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": "3.8.10" } }, "nbformat": 4, "nbformat_minor": 5 }