{ "cells": [ { "cell_type": "markdown", "id": "834aeced-c3c5-42a0-bad1-41e009dd86ee", "metadata": {}, "source": [ "### Preprocessing" ] }, { "cell_type": "code", "execution_count": 1, "id": "86476f6e-802a-463b-a1b0-2ae228bb92af", "metadata": {}, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "id": "9b2be11c-f4bb-4107-af49-abd78052afcf", "metadata": {}, "outputs": [], "source": [ "df = pd.read_table('data/pdbbind/index/INDEX_general_PL_data.2020',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n", "df = df.rename(columns={'#': 'name','release': 'affinity'})\n", "df_refined = pd.read_table('data/pdbbind/index/INDEX_refined_data.2020',skiprows=4,sep=r'\\s+',usecols=[0,4]).drop(0)\n", "df_refined = df_refined.rename(columns={'#': 'name','release': 'affinity'})\n", "df = pd.concat([df,df_refined])" ] }, { "cell_type": "code", "execution_count": 3, "id": "68983ab8-bf11-4ed6-ba06-f962dbdc077e", "metadata": {}, "outputs": [], "source": [ "quantities = ['ki','kd','ka','k1/2','kb','ic50','ec50']" ] }, { "cell_type": "code", "execution_count": 4, "id": "3acbca3c-9c0b-43a1-a45e-331bf153bcfa", "metadata": {}, "outputs": [], "source": [ "from pint import UnitRegistry\n", "ureg = UnitRegistry()\n", "\n", "def to_uM(affinity):\n", " val = ureg(affinity)\n", " try:\n", " return val.m_as(ureg.uM)\n", " except Exception:\n", " pass\n", " \n", " try:\n", " return 1/val.m_as(1/ureg.uM)\n", " except Exception:\n", " pass" ] }, { "cell_type": "code", "execution_count": 5, "id": "58e5748b-2cea-43ff-ab51-85a5021bd50b", "metadata": {}, "outputs": [], "source": [ "df['affinity_uM'] = df['affinity'].str.split('[=\\~><]').str[1].apply(to_uM)\n", "df['affinity_quantity'] = df['affinity'].str.split('[=\\~><]').str[0]" ] }, { "cell_type": "code", "execution_count": 6, "id": "d92f0004-68c1-4487-94b9-56b4fd598de4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df['affinity_quantity'].hist()" ] }, { "cell_type": "code", "execution_count": 7, "id": "aa358835-55f3-4551-9217-e76a15de4fe8", "metadata": {}, "outputs": [], "source": [ "df_filter = df[df['affinity_quantity'].str.lower().isin(quantities)]" ] }, { "cell_type": "code", "execution_count": 8, "id": "d6dda488-f709-4fe7-b372-080042cf7c66", "metadata": {}, "outputs": [], "source": [ "df_complex = pd.read_parquet('data/pdbbind_complex.parquet')" ] }, { "cell_type": "code", "execution_count": 19, "id": "ed3fe035-6035-4d39-b072-d12dc0a95857", "metadata": {}, "outputs": [], "source": [ "import dask.array as da\n", "import dask.dataframe as dd\n", "from dask.bag import from_delayed\n", "from dask import delayed\n", "import pyarrow as pa\n", "import pyarrow.parquet as pq" ] }, { "cell_type": "code", "execution_count": 10, "id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0", "metadata": {}, "outputs": [], "source": [ "contacts_dask = da.from_npy_stack('data/pdbbind_contacts')\n", "contacts_dask = contacts_dask.reshape(-1,contacts_dask.shape[-2]*contacts_dask.shape[-1])" ] }, { "cell_type": "code", "execution_count": 11, "id": "0e773f9d-6555-49c0-b0af-202c5e19b2cc", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameseqsmiles
02lbvMTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...CCCCCCCCCCCCCCCCCCCC(=O)O
11lt6APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1
24lwiVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...
36oyzVQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...
44i11GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1
\n", "
" ], "text/plain": [ " name seq \\\n", "0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n", "1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n", "2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n", "3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n", "4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n", "\n", " smiles \n", "0 CCCCCCCCCCCCCCCCCCCC(=O)O \n", "1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 \n", "2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... \n", "3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... \n", "4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_complex.head()" ] }, { "cell_type": "code", "execution_count": 12, "id": "9c7c9849-2345-4baf-89e7-d412f52353b6", "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "
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Array Chunk
Bytes 1.71 GiB 1.71 GiB
Shape (438, 1048576) (438, 1048576)
Count 75 Tasks 1 Chunks
Type float32 numpy.ndarray
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" ], "text/plain": [ "dask.array" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contacts_dask.blocks[1]" ] }, { "cell_type": "code", "execution_count": 76, "id": "d21a5d74-69f1-4a5a-85f8-917d3c1d690a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([ 1046, 2578, 2579, 2582, 2584, 6144, 6145, 6146, 6149,\n", " 6156, 6157, 6158, 6159, 6160, 6161, 6162, 6163, 6166,\n", " 6168, 7173, 7174, 7175, 7176, 18432, 18433, 18434, 18435,\n", " 18436, 18437, 18438, 19456, 19457, 19458, 19459, 19467, 19468,\n", " 19469, 19472, 19473, 19474, 19475, 20498, 20499, 20502, 20504,\n", " 24576, 24577, 24578, 24579, 24580, 24581, 24586, 24587, 24588,\n", " 25090, 25091, 25092, 25093, 25603, 25604, 25605, 25606, 25607,\n", " 25608, 26116, 26117, 33281, 33283, 33291, 33292, 43009, 43015,\n", " 43016, 43017, 43018, 43019, 43020, 43021, 43022, 43023, 44046,\n", " 44047, 44048, 44049, 44050, 44051, 44056, 45582, 45583, 45584,\n", " 45585, 45586, 45587, 45592, 46606, 46607, 46608, 46609, 46610,\n", " 46611, 46614, 46616, 48654, 48655, 49672, 49673, 49674, 49675,\n", " 49676, 49677, 49678, 49679, 49680, 50696, 50697, 57350, 57351,\n", " 57352, 57353, 57354, 58368, 58369, 58370, 58371, 58372, 58373,\n", " 58374, 58375, 58376, 58377, 58378, 58379, 58380, 58381]),)" ] }, "execution_count": 76, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.where(contacts_dask.blocks[0].compute()[0])" ] }, { "cell_type": "code", "execution_count": 121, "id": "42e95d84-ef27-4417-9479-8b356462b8c3", "metadata": {}, "outputs": [], "source": [ "def chunk_to_sparse(chunk, idx_chunk):\n", " res = df_complex.iloc[idx_chunk].copy()\n", " res['contacts'] = [np.where(a) for a in chunk]\n", " return res\n", "\n", "partitions = [delayed(chunk_to_sparse)(b,k)\n", " for b,k in zip(contacts_dask.blocks, da.arange(contacts_dask.shape[0],chunks=contacts_dask.chunks[0:1]).blocks)\n", " ]" ] }, { "cell_type": "code", "execution_count": 123, "id": "5520a925-693f-43f0-9e76-df2e128f272e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameseqsmilescontacts
02lbvMTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...CCCCCCCCCCCCCCCCCCCC(=O)O([1046, 2578, 2579, 2582, 2584, 6144, 6145, 61...
11lt6APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1([514, 518, 520, 521, 522, 523, 525, 526, 546,...
24lwiVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...([521, 523, 526, 542, 544, 546, 547, 548, 1041...
36oyzVQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...([35332, 35334, 35336, 35338, 35342, 35347, 35...
44i11GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1([36372, 36373, 36375, 36376, 36377, 36378, 36...
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" ], "text/plain": [ " name seq \\\n", "0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n", "1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n", "2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n", "3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n", "4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n", "\n", " smiles \\\n", "0 CCCCCCCCCCCCCCCCCCCC(=O)O \n", "1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 \n", "2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... \n", "3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... \n", "4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 \n", "\n", " contacts \n", "0 ([1046, 2578, 2579, 2582, 2584, 6144, 6145, 61... \n", "1 ([514, 518, 520, 521, 522, 523, 525, 526, 546,... \n", "2 ([521, 523, 526, 542, 544, 546, 547, 548, 1041... \n", "3 ([35332, 35334, 35336, 35338, 35342, 35347, 35... \n", "4 ([36372, 36373, 36375, 36376, 36377, 36378, 36... " ] }, "execution_count": 123, "metadata": {}, "output_type": "execute_result" } ], "source": [ "partitions[0].compute().head()" ] }, { "cell_type": "code", "execution_count": 125, "id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2", "metadata": {}, "outputs": [], "source": [ "ddf = dd.from_delayed(partitions)" ] }, { "cell_type": "code", "execution_count": 129, "id": "f6cdee43-33c6-445c-8619-ace20f90638c", "metadata": {}, "outputs": [], "source": [ "ddf_all = ddf.merge(df_filter, on='name').drop(columns=['affinity','affinity_quantity'],axis=1)" ] }, { "cell_type": "code", "execution_count": 132, "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf", "metadata": {}, "outputs": [], "source": [ "df_all_contacts = ddf_all.compute()" ] }, { "cell_type": "code", "execution_count": 134, "id": "45e4b4fa-6338-4abe-bd6e-8aea46e2a09c", "metadata": {}, "outputs": [], "source": [ "df_all_contacts['neg_log10_affinity_M'] = 6-np.log10(df_all_contacts['affinity_uM'])" ] }, { "cell_type": "code", "execution_count": 136, "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([6.3455065]), array([3.57430038]))" ] }, "execution_count": 136, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from sklearn.preprocessing import StandardScaler\n", "scaler = StandardScaler()\n", "df_all_contacts['affinity'] = scaler.fit_transform(df_all_contacts['neg_log10_affinity_M'].values.reshape(-1,1))\n", "scaler.mean_, scaler.var_" ] }, { "cell_type": "code", "execution_count": 138, "id": "c9d674bb-d6a2-4810-aa2b-e3bc3b4bbc98", "metadata": {}, "outputs": [], "source": [ "df_all_contacts.drop(columns=['name'],axis=1).to_parquet('data/pdbbind_with_contacts.parquet')" ] }, { "cell_type": "code", "execution_count": 127, "id": "ec1afbc1-8072-4818-a925-0a9a6ad04759", "metadata": {}, "outputs": [], "source": [ "df_all = df_complex.merge(df_filter,on='name').drop('affinity',axis=1)" ] }, { "cell_type": "code", "execution_count": 128, "id": "744f1e6e-a097-4b8f-8d4b-1a54f564dab8", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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nameseqsmilesaffinity_uMaffinity_quantity
02lbvMTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...CCCCCCCCCCCCCCCCCCCC(=O)O0.026Kd
11lt6APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1500.000IC50
24lwiVETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...0.023IC50
36oyzVQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...0.185IC50
44i11GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N127.200IC50
..................
208222bokIVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ...C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c...0.280Ki
208234j46GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...5.240Ki
208244j46GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...5.240Ki
208252c80DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O4.700Kd
208262c80DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O4.700Kd
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20827 rows × 5 columns

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" ], "text/plain": [ " name seq \\\n", "0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n", "1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n", "2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n", "3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n", "4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n", "... ... ... \n", "20822 2bok IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ... \n", "20823 4j46 GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD... \n", "20824 4j46 GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD... \n", "20825 2c80 DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP... \n", "20826 2c80 DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP... \n", "\n", " smiles affinity_uM \\\n", "0 CCCCCCCCCCCCCCCCCCCC(=O)O 0.026 \n", "1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 500.000 \n", "2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... 0.023 \n", "3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... 0.185 \n", "4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 27.200 \n", "... ... ... \n", "20822 C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c... 0.280 \n", "20823 CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C... 5.240 \n", "20824 CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C... 5.240 \n", "20825 CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O 4.700 \n", "20826 CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O 4.700 \n", "\n", " affinity_quantity \n", "0 Kd \n", "1 IC50 \n", "2 IC50 \n", "3 IC50 \n", "4 IC50 \n", "... ... \n", "20822 Ki \n", "20823 Ki \n", "20824 Ki \n", "20825 Kd \n", "20826 Kd \n", "\n", "[20827 rows x 5 columns]" ] }, "execution_count": 128, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_all" ] }, { "cell_type": "code", "execution_count": 14, "id": "8f75499c-8895-4395-867e-7d9a9d394910", "metadata": {}, "outputs": [], "source": [ "df_all.to_parquet('data/pdbbind.parquet')" ] }, { "cell_type": "code", "execution_count": 15, "id": "0af83f72-5bf6-4643-aa0e-0a94d51e7da7", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "20827" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(df_all)" ] } ], "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": "3.9.6" } }, "nbformat": 4, "nbformat_minor": 5 }