{ "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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" ] }, "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)]\n", "df_filter = df_filter.dropna()" ] }, { "cell_type": "code", "execution_count": 8, "id": "802cb9bc-2563-4d7f-9a76-3be2d9263a36", "metadata": {}, "outputs": [], "source": [ "cutoffs = [5,8,11,15]" ] }, { "cell_type": "code", "execution_count": 9, "id": "d8e71a8c-11a3-41f0-ab61-3ddc57e10961", "metadata": {}, "outputs": [], "source": [ "dfs_complex = {c: pd.read_parquet('data/pdbbind_complex_{}.parquet'.format(c)) for c in cutoffs}" ] }, { "cell_type": "code", "execution_count": 10, "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": 11, "id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(2046, 510)\n" ] } ], "source": [ "contacts_dask = [da.from_npy_stack('data/pdbbind_contacts_{}'.format(c)) for c in cutoffs]\n", "shape = contacts_dask[0][0].shape\n", "print(shape)" ] }, { "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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Bytes 2.72 GiB 2.72 GiB
Shape (700, 2046, 510) (700, 2046, 510)
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Type float32 numpy.ndarray
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" ], "text/plain": [ "dask.array" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contacts_dask[0].blocks[1]" ] }, { "cell_type": "code", "execution_count": 13, "id": "0bd8e9b9-9713-4572-bd7f-dc47da9fce91", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[16232, 16228, 16226, 16223]" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[len(c) for c in contacts_dask]" ] }, { "cell_type": "code", "execution_count": 14, "id": "87493934-3839-476a-a975-7da057c320da", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "16232" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "contacts_dask[0].shape[0]" ] }, { "cell_type": "code", "execution_count": 15, "id": "42e95d84-ef27-4417-9479-8b356462b8c3", "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "all_partitions = []\n", "for c, cutoff in zip(contacts_dask,cutoffs):\n", " def chunk_to_sparse(rcut, chunk, idx_chunk):\n", " res = dfs_complex[rcut].iloc[idx_chunk][['name']].copy()\n", " # pad to account for [CLS] and [SEP]\n", " res['contacts_{}A'.format(rcut)] = [np.where(np.pad(a,pad_width=(1,1)).flatten())[0] for a in chunk]\n", " return res\n", "\n", " partitions = [delayed(chunk_to_sparse)(cutoff,b,k)\n", " for b,k in zip(c.blocks, da.arange(c.shape[0],chunks=c.chunks[0:1]).blocks)\n", " ]\n", " all_partitions.append(partitions)" ] }, { "cell_type": "code", "execution_count": 16, "id": "5520a925-693f-43f0-9e76-df2e128f272e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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namecontacts_5A
010gs[3083, 3084, 3086, 3087, 3088, 3089, 3094, 309...
1184l[39945, 39946, 39947, 39948, 43010, 43012, 430...
2186l[39943, 39944, 39945, 43010, 43011, 43012, 430...
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4188l[39937, 39938, 39940, 39941, 43009, 43010, 430...
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" ], "text/plain": [ " name contacts_5A\n", "0 10gs [3083, 3084, 3086, 3087, 3088, 3089, 3094, 309...\n", "1 184l [39945, 39946, 39947, 39948, 43010, 43012, 430...\n", "2 186l [39943, 39944, 39945, 43010, 43011, 43012, 430...\n", "3 187l [39937, 39938, 39947, 43009, 43010, 43012, 430...\n", "4 188l [39937, 39938, 39940, 39941, 43009, 43010, 430..." ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_partitions[0][0].compute().head()" ] }, { "cell_type": "code", "execution_count": 17, "id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2", "metadata": {}, "outputs": [], "source": [ "ddfs = [dd.from_delayed(p) for p in all_partitions]" ] }, { "cell_type": "code", "execution_count": 18, "id": "f6cdee43-33c6-445c-8619-ace20f90638c", "metadata": {}, "outputs": [], "source": [ "ddf_all = None\n", "for d in ddfs:\n", " if ddf_all is not None:\n", " ddf_all = ddf_all.merge(d, on='name')\n", " else:\n", " ddf_all = d\n", "ddf_all = ddf_all.merge(df_filter,on='name')\n", "ddf_all = ddf_all.merge(list(dfs_complex.values())[0],on='name')" ] }, { "cell_type": "code", "execution_count": 19, "id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 8min 53s, sys: 11min 31s, total: 20min 24s\n", "Wall time: 3min 29s\n" ] } ], "source": [ "%%time\n", "df_all_contacts = ddf_all.compute()" ] }, { "cell_type": "code", "execution_count": 20, "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": 21, "id": "7c3db301-6565-4053-bbd4-139bb41dd1c4", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(array([6.34387834]), array([3.57815698]))" ] }, "execution_count": 21, "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": 22, "id": "c9d674bb-d6a2-4810-aa2b-e3bc3b4bbc98", "metadata": {}, "outputs": [], "source": [ "# save to parquet\n", "df_all_contacts.drop(columns=['name','affinity_quantity']).astype({'affinity': 'float32','neg_log10_affinity_M': 'float32'}).to_parquet('data/pdbbind_with_contacts.parquet',index=False)" ] } ], "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 }