add more cutoff distances
Browse files- Untitled.ipynb +76 -0
- data/pdbbind_with_contacts.parquet +2 -2
- pdbbind.ipynb +177 -317
- pdbbind.py +3 -2
- protein_ligand_contacts.py +3 -1
Untitled.ipynb
ADDED
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"metadata": {},
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"outputs": [],
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"source": [
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"from datasets import load_dataset"
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]
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Using custom data configuration default\n",
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"Reusing dataset protein_ligand_contacts (/ccs/home/glaser/.cache/huggingface/datasets/jglaser___protein_ligand_contacts/default/1.4.1/2aba91a819153bdd9a95ce28edf727166722133978758c388eb80b7d587ecce7)\n"
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"data": {
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" 0%| | 0/1 [00:00<?, ?it/s]"
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"metadata": {},
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"source": [
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"ds = load_dataset('jglaser/protein_ligand_contacts')"
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data/pdbbind_with_contacts.parquet
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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size 157147287
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pdbbind.ipynb
CHANGED
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{
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"source": [
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"import dask.dataframe as dd\n",
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"from dask.bag import from_delayed\n",
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"from dask import delayed\n",
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"source": [
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"contacts_dask = da.from_npy_stack('data/pdbbind_contacts')\n",
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"contacts_dask = contacts_dask.reshape(-1,contacts_dask.shape[-2]*contacts_dask.shape[-1])"
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"id": "42e95d84-ef27-4417-9479-8b356462b8c3",
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" <tr style=\"text-align: right;\">\n",
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" <td>
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" <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
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" <td>[1043, 2569, 2570, 2573, 2575, 6121, 6122, 612...</td>\n",
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" </tr>\n",
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" <th>1</th>\n",
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" <td>
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" <td>
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" <td>O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1</td>\n",
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" <td>[513, 517, 519, 520, 521, 522, 524, 525, 545, ...</td>\n",
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" </tr>\n",
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" <th>2</th>\n",
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" <td>
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" <td>
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" <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
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" <td>[520, 522, 525, 541, 543, 545, 546, 547, 1038,...</td>\n",
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" <th>3</th>\n",
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" <td>
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" <td>COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...</td>\n",
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" <td>[35195, 35197, 35199, 35201, 35205, 35210, 352...</td>\n",
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" <th>4</th>\n",
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" <td>CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1</td>\n",
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" <td>[36231, 36232, 36234, 36235, 36236, 36237, 362...</td>\n",
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"0 CCCCCCCCCCCCCCCCCCCC(=O)O \n",
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"2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... \n",
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"3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... \n",
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"4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 \n",
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"0 [1043, 2569, 2570, 2573, 2575, 6121, 6122, 612... \n",
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"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)"
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"cell_type": "code",
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"execution_count": 127,
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"id": "ec1afbc1-8072-4818-a925-0a9a6ad04759",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_all = df_complex.merge(df_filter,on='name').drop('affinity',axis=1)"
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"cell_type": "code",
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"execution_count": 128,
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"id": "744f1e6e-a097-4b8f-8d4b-1a54f564dab8",
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"metadata": {},
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"outputs": [
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"data": {
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" .dataframe tbody tr th:only-of-type {\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>name</th>\n",
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" <th>seq</th>\n",
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" <th>smiles</th>\n",
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" <th>affinity_uM</th>\n",
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" <th>affinity_quantity</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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565 |
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" <th>0</th>\n",
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566 |
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" <td>2lbv</td>\n",
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567 |
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" <td>MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE...</td>\n",
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568 |
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" <td>CCCCCCCCCCCCCCCCCCCC(=O)O</td>\n",
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" <td>0.026</td>\n",
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" <td>Kd</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1lt6</td>\n",
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" <td>APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE...</td>\n",
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" <td>O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1</td>\n",
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" <td>500.000</td>\n",
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" <td>IC50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>4lwi</td>\n",
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" <td>VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE...</td>\n",
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" <td>COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2...</td>\n",
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" <td>0.023</td>\n",
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" <td>IC50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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590 |
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" <td>6oyz</td>\n",
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" <td>VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF...</td>\n",
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" <td>COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC...</td>\n",
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" <td>0.185</td>\n",
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" <td>IC50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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598 |
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" <td>4i11</td>\n",
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599 |
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" <td>GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA...</td>\n",
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" <td>CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1</td>\n",
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" <td>27.200</td>\n",
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" <td>IC50</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>...</th>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" <td>...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20822</th>\n",
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614 |
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" <td>2bok</td>\n",
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615 |
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" <td>IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ...</td>\n",
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616 |
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" <td>C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c...</td>\n",
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" <td>0.280</td>\n",
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618 |
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" <td>Ki</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20823</th>\n",
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" <td>4j46</td>\n",
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" <td>GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...</td>\n",
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" <td>CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...</td>\n",
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" <td>5.240</td>\n",
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" <td>Ki</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20824</th>\n",
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" <td>4j46</td>\n",
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" <td>GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD...</td>\n",
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" <td>CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C...</td>\n",
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" <td>5.240</td>\n",
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" <td>Ki</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20825</th>\n",
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" <td>2c80</td>\n",
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" <td>DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...</td>\n",
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640 |
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" <td>CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O</td>\n",
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" <td>4.700</td>\n",
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642 |
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" <td>Kd</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>20826</th>\n",
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" <td>2c80</td>\n",
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" <td>DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP...</td>\n",
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648 |
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" <td>CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O</td>\n",
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649 |
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" <td>4.700</td>\n",
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" <td>Kd</td>\n",
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651 |
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"<p>20827 rows × 5 columns</p>\n",
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"</div>"
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],
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"text/plain": [
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" name seq \\\n",
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659 |
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"0 2lbv MTVPDRSEIAGKWYVVALASNTEFFLREKDKMKMAMARISFLGEDE... \n",
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660 |
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"1 1lt6 APQTITELCSEYRNTQIYTINDKILSYTESMAGKREMVIITFKSGE... \n",
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661 |
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"2 4lwi VETFAFQAEIAQLMSLIINTFYSNKEIFLRELISNSSDALDKIRYE... \n",
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662 |
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"3 6oyz VQLQESGGGLVQTGGSLTLSCATSGRSFSLYAMAWFRQAPGKEREF... \n",
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663 |
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"4 4i11 GSFVEMVDNLRGKSGQGYYVEMTVGSPPQTLNILVDTGSSNFAVGA... \n",
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"... ... ... \n",
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665 |
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"20822 2bok IVGGQECKDGECPWQALLINEENEGFCGGTILSEFYILTAAHCLYQ... \n",
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666 |
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"20823 4j46 GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD... \n",
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667 |
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"20824 4j46 GTIYPRNPAMYSEEARLKSFQNWPDYAHLTPRELASAGLYYTGIGD... \n",
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"20825 2c80 DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP... \n",
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"20826 2c80 DHIKVIYFNGRGRAESIRMTLVAAGVNYEDERISFQDWPKIKPTIP... \n",
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"\n",
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" smiles affinity_uM \\\n",
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"0 CCCCCCCCCCCCCCCCCCCC(=O)O 0.026 \n",
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"1 O=[N+]([O-])c1cccc(OC2OC(CO)C(O)C(O)C2O)c1 500.000 \n",
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674 |
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"2 COc1ccc(-c2c(-c3cc(C(C)C)c(O)cc3O)noc2NC(=O)C2... 0.023 \n",
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675 |
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"3 COC1C(O)C(n2ccc(=O)[nH]c2=O)OC1C(OC1OC(C(=O)NC... 0.185 \n",
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676 |
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"4 CC1(C)Cc2ccccc2C(NC(Cc2ccccc2)C(=O)O)=N1 27.200 \n",
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677 |
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"... ... ... \n",
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678 |
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"20822 C[N+](C)(C)CCCN1C(=O)C2C(C1=O)C(c1ccc(C(=N)N)c... 0.280 \n",
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679 |
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"20823 CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C... 5.240 \n",
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"20824 CCC(C)C(NC(=O)C1CCCN1C(=O)C(NC(=O)C(C)[NH3+])C... 5.240 \n",
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"20825 CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O 4.700 \n",
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"20826 CCCCCCSCC(NC(=O)CCC([NH3+])C(=O)O)C(=O)NCC(=O)O 4.700 \n",
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"\n",
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" affinity_quantity \n",
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"0 Kd \n",
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"1 IC50 \n",
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"2 IC50 \n",
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"3 IC50 \n",
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"4 IC50 \n",
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"... ... \n",
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"20822 Ki \n",
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"20823 Ki \n",
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"20824 Ki \n",
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"20825 Kd \n",
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"20826 Kd \n",
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"\n",
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"[20827 rows x 5 columns]"
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]
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},
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"execution_count": 128,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"df_all"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"id": "8f75499c-8895-4395-867e-7d9a9d394910",
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"metadata": {},
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"outputs": [],
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"source": [
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"df_all.to_parquet('data/pdbbind.parquet')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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{
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"cell_type": "code",
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"execution_count": 8,
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"id": "802cb9bc-2563-4d7f-9a76-3be2d9263a36",
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"metadata": {},
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"outputs": [],
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"source": [
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"cutoffs = [5,8,11]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"id": "d6dda488-f709-4fe7-b372-080042cf7c66",
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"metadata": {},
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"outputs": [],
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"source": [
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"dfs_complex = {c: pd.read_parquet('data/pdbbind_complex_{}.parquet'.format(c)) for c in cutoffs}"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"id": "aebc6791-5bb0-4828-9697-79bc243f8992",
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"metadata": {},
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"outputs": [
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{
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>10gs</td>\n",
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" <td>PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC...</td>\n",
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" <td>[NH3+]C(CCC(=O)NC(CSCc1ccccc1)C(=O)NC(C(=O)O)c...</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>184l</td>\n",
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" <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
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" <td>CC(C)Cc1ccccc1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>186l</td>\n",
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" <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
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" <td>CCCCc1ccccc1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>187l</td>\n",
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" <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
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" <td>Cc1ccc(C)cc1</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>188l</td>\n",
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" <td>MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL...</td>\n",
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" <td>Cc1ccccc1C</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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],
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"text/plain": [
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" name seq \\\n",
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"0 10gs PYTVVYFPVRGRCAALRMLLADQGQSWKEEVVTVETWQEGSLKASC... \n",
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213 |
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"1 184l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
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214 |
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"2 186l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
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215 |
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"3 187l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
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216 |
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"4 188l MNIFEMLRIDEGLRLKIYKDTEGYYTIGIGHLLTKSPSLNAAKSEL... \n",
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"\n",
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218 |
" smiles \n",
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219 |
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"0 [NH3+]C(CCC(=O)NC(CSCc1ccccc1)C(=O)NC(C(=O)O)c... \n",
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220 |
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"1 CC(C)Cc1ccccc1 \n",
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221 |
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"2 CCCCc1ccccc1 \n",
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222 |
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"3 Cc1ccc(C)cc1 \n",
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"4 Cc1ccccc1C "
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]
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},
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"execution_count": 10,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"list(dfs_complex.values())[0].head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"id": "ed3fe035-6035-4d39-b072-d12dc0a95857",
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"metadata": {},
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"outputs": [],
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"source": [
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"import dask.array as da\n",
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"import dask.dataframe as dd\n",
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"from dask.bag import from_delayed\n",
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"from dask import delayed\n",
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"import pyarrow as pa\n",
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"import pyarrow.parquet as pq"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"id": "cd26125b-e68b-4fa3-846e-2b6e7f635fe0",
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"metadata": {},
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255 |
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"outputs": [],
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"source": [
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"contacts_dask = [da.from_npy_stack('data/pdbbind_contacts_{}'.format(c)) for c in cutoffs]\n",
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"contacts_dask = [c.reshape(-1,c.shape[-2]*c.shape[-1]) for c in contacts_dask]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"id": "9c7c9849-2345-4baf-89e7-d412f52353b6",
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"metadata": {},
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"outputs": [
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"dask.array<blocks, shape=(438, 1043460), dtype=float32, chunksize=(438, 1043460), chunktype=numpy.ndarray>"
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]
|
310 |
},
|
311 |
+
"execution_count": 13,
|
312 |
"metadata": {},
|
313 |
"output_type": "execute_result"
|
314 |
}
|
315 |
],
|
316 |
"source": [
|
317 |
+
"contacts_dask[0].blocks[1]"
|
318 |
]
|
319 |
},
|
320 |
{
|
321 |
"cell_type": "code",
|
322 |
+
"execution_count": 14,
|
323 |
+
"id": "0bd8e9b9-9713-4572-bd7f-dc47da9fce91",
|
324 |
+
"metadata": {},
|
325 |
+
"outputs": [
|
326 |
+
{
|
327 |
+
"data": {
|
328 |
+
"text/plain": [
|
329 |
+
"[16206, 16181, 16172]"
|
330 |
+
]
|
331 |
+
},
|
332 |
+
"execution_count": 14,
|
333 |
+
"metadata": {},
|
334 |
+
"output_type": "execute_result"
|
335 |
+
}
|
336 |
+
],
|
337 |
+
"source": [
|
338 |
+
"[len(c) for c in contacts_dask]"
|
339 |
+
]
|
340 |
+
},
|
341 |
+
{
|
342 |
+
"cell_type": "code",
|
343 |
+
"execution_count": 15,
|
344 |
+
"id": "87493934-3839-476a-a975-7da057c320da",
|
345 |
+
"metadata": {},
|
346 |
+
"outputs": [
|
347 |
+
{
|
348 |
+
"data": {
|
349 |
+
"text/plain": [
|
350 |
+
"16206"
|
351 |
+
]
|
352 |
+
},
|
353 |
+
"execution_count": 15,
|
354 |
+
"metadata": {},
|
355 |
+
"output_type": "execute_result"
|
356 |
+
}
|
357 |
+
],
|
358 |
+
"source": [
|
359 |
+
"contacts_dask[0].shape[0]"
|
360 |
+
]
|
361 |
+
},
|
362 |
+
{
|
363 |
+
"cell_type": "code",
|
364 |
+
"execution_count": 16,
|
365 |
+
"id": "1f7815ec-cddf-4bae-b72c-89cdf56cc1f9",
|
366 |
+
"metadata": {},
|
367 |
+
"outputs": [
|
368 |
+
{
|
369 |
+
"data": {
|
370 |
+
"text/plain": [
|
371 |
+
"name 9lpr\n",
|
372 |
+
"seq ANIVGGIEYSINNASLCSVGFSVTRGATKGFVTAGHCGTVNATARI...\n",
|
373 |
+
"smiles CC(C)CC(NC(=O)C1CCCN1C(=O)C(C)NC(=O)C(C)[NH3+]...\n",
|
374 |
+
"Name: 16205, dtype: object"
|
375 |
+
]
|
376 |
+
},
|
377 |
+
"execution_count": 16,
|
378 |
+
"metadata": {},
|
379 |
+
"output_type": "execute_result"
|
380 |
+
}
|
381 |
+
],
|
382 |
+
"source": [
|
383 |
+
"dfs_complex[5].iloc[16205]"
|
384 |
+
]
|
385 |
+
},
|
386 |
+
{
|
387 |
+
"cell_type": "code",
|
388 |
+
"execution_count": 17,
|
389 |
"id": "42e95d84-ef27-4417-9479-8b356462b8c3",
|
390 |
"metadata": {},
|
391 |
"outputs": [],
|
392 |
"source": [
|
393 |
"import numpy as np\n",
|
394 |
+
"all_partitions = []\n",
|
395 |
+
"for c, cutoff in zip(contacts_dask,cutoffs):\n",
|
396 |
+
" def chunk_to_sparse(rcut, chunk, idx_chunk):\n",
|
397 |
+
" res = dfs_complex[rcut].iloc[idx_chunk][['name']].copy()\n",
|
398 |
+
" # pad to account for [CLS] and [SEP]\n",
|
399 |
+
" res['contacts_{}A'.format(rcut)] = [np.where(np.pad(a,pad_width=(1,1)))[0] for a in chunk]\n",
|
400 |
+
" return res\n",
|
401 |
"\n",
|
402 |
+
" partitions = [delayed(chunk_to_sparse)(cutoff,b,k)\n",
|
403 |
+
" for b,k in zip(c.blocks, da.arange(c.shape[0],chunks=c.chunks[0:1]).blocks)\n",
|
404 |
+
" ]\n",
|
405 |
+
" all_partitions.append(partitions)"
|
406 |
]
|
407 |
},
|
408 |
{
|
409 |
"cell_type": "code",
|
410 |
+
"execution_count": 18,
|
411 |
"id": "5520a925-693f-43f0-9e76-df2e128f272e",
|
412 |
"metadata": {},
|
413 |
"outputs": [
|
|
|
433 |
" <tr style=\"text-align: right;\">\n",
|
434 |
" <th></th>\n",
|
435 |
" <th>name</th>\n",
|
436 |
+
" <th>contacts_5A</th>\n",
|
|
|
|
|
437 |
" </tr>\n",
|
438 |
" </thead>\n",
|
439 |
" <tbody>\n",
|
440 |
" <tr>\n",
|
441 |
" <th>0</th>\n",
|
442 |
+
" <td>10gs</td>\n",
|
443 |
+
" <td>[1021, 1022, 1070, 1073, 1075, 3071, 3072, 307...</td>\n",
|
|
|
|
|
444 |
" </tr>\n",
|
445 |
" <tr>\n",
|
446 |
" <th>1</th>\n",
|
447 |
+
" <td>184l</td>\n",
|
448 |
+
" <td>[39279, 39280, 39281, 39282, 42332, 42334, 423...</td>\n",
|
|
|
|
|
449 |
" </tr>\n",
|
450 |
" <tr>\n",
|
451 |
" <th>2</th>\n",
|
452 |
+
" <td>186l</td>\n",
|
453 |
+
" <td>[39277, 39278, 39279, 42332, 42333, 42334, 423...</td>\n",
|
|
|
|
|
454 |
" </tr>\n",
|
455 |
" <tr>\n",
|
456 |
" <th>3</th>\n",
|
457 |
+
" <td>187l</td>\n",
|
458 |
+
" <td>[39271, 39272, 39281, 42331, 42332, 42334, 423...</td>\n",
|
|
|
|
|
459 |
" </tr>\n",
|
460 |
" <tr>\n",
|
461 |
" <th>4</th>\n",
|
462 |
+
" <td>188l</td>\n",
|
463 |
+
" <td>[39271, 39272, 39274, 39275, 42331, 42332, 423...</td>\n",
|
|
|
|
|
464 |
" </tr>\n",
|
465 |
" </tbody>\n",
|
466 |
"</table>\n",
|
467 |
"</div>"
|
468 |
],
|
469 |
"text/plain": [
|
470 |
+
" name contacts_5A\n",
|
471 |
+
"0 10gs [1021, 1022, 1070, 1073, 1075, 3071, 3072, 307...\n",
|
472 |
+
"1 184l [39279, 39280, 39281, 39282, 42332, 42334, 423...\n",
|
473 |
+
"2 186l [39277, 39278, 39279, 42332, 42333, 42334, 423...\n",
|
474 |
+
"3 187l [39271, 39272, 39281, 42331, 42332, 42334, 423...\n",
|
475 |
+
"4 188l [39271, 39272, 39274, 39275, 42331, 42332, 423..."
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
476 |
]
|
477 |
},
|
478 |
+
"execution_count": 18,
|
479 |
"metadata": {},
|
480 |
"output_type": "execute_result"
|
481 |
}
|
482 |
],
|
483 |
"source": [
|
484 |
+
"all_partitions[0][0].compute().head()"
|
485 |
]
|
486 |
},
|
487 |
{
|
488 |
"cell_type": "code",
|
489 |
+
"execution_count": 19,
|
490 |
"id": "4982c3b1-5ce9-4f17-9834-a02c4e136bc2",
|
491 |
"metadata": {},
|
492 |
"outputs": [],
|
493 |
"source": [
|
494 |
+
"ddfs = [dd.from_delayed(p) for p in all_partitions]"
|
495 |
]
|
496 |
},
|
497 |
{
|
498 |
"cell_type": "code",
|
499 |
+
"execution_count": 20,
|
500 |
"id": "f6cdee43-33c6-445c-8619-ace20f90638c",
|
501 |
"metadata": {},
|
502 |
"outputs": [],
|
503 |
"source": [
|
504 |
+
"ddf_all = None\n",
|
505 |
+
"for d in ddfs:\n",
|
506 |
+
" if ddf_all is not None:\n",
|
507 |
+
" ddf_all = ddf_all.merge(d, on='name')\n",
|
508 |
+
" else:\n",
|
509 |
+
" ddf_all = d\n",
|
510 |
+
"ddf_all = ddf_all.merge(df_filter,on='name')\n",
|
511 |
+
"ddf_all = ddf_all.merge(list(dfs_complex.values())[0],on='name')"
|
512 |
]
|
513 |
},
|
514 |
{
|
515 |
"cell_type": "code",
|
516 |
+
"execution_count": 21,
|
517 |
"id": "8f49f871-76f6-4fb2-b2db-c0794d4c07bf",
|
518 |
"metadata": {},
|
519 |
"outputs": [
|
|
|
521 |
"name": "stdout",
|
522 |
"output_type": "stream",
|
523 |
"text": [
|
524 |
+
"CPU times: user 6min 9s, sys: 12min 7s, total: 18min 17s\n",
|
525 |
+
"Wall time: 7min 36s\n"
|
526 |
]
|
527 |
}
|
528 |
],
|
|
|
533 |
},
|
534 |
{
|
535 |
"cell_type": "code",
|
536 |
+
"execution_count": 22,
|
537 |
"id": "45e4b4fa-6338-4abe-bd6e-8aea46e2a09c",
|
538 |
"metadata": {},
|
539 |
"outputs": [],
|
|
|
543 |
},
|
544 |
{
|
545 |
"cell_type": "code",
|
546 |
+
"execution_count": 23,
|
547 |
"id": "7c3db301-6565-4053-bbd4-139bb41dd1c4",
|
548 |
"metadata": {},
|
549 |
"outputs": [
|
550 |
{
|
551 |
"data": {
|
552 |
"text/plain": [
|
553 |
+
"(array([6.35008182]), array([3.56554195]))"
|
554 |
]
|
555 |
},
|
556 |
+
"execution_count": 23,
|
557 |
"metadata": {},
|
558 |
"output_type": "execute_result"
|
559 |
}
|
|
|
567 |
},
|
568 |
{
|
569 |
"cell_type": "code",
|
570 |
+
"execution_count": 24,
|
571 |
"id": "c9d674bb-d6a2-4810-aa2b-e3bc3b4bbc98",
|
572 |
"metadata": {},
|
573 |
"outputs": [],
|
|
|
576 |
"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)"
|
577 |
]
|
578 |
},
|
|
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|
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|
579 |
{
|
580 |
"cell_type": "code",
|
581 |
"execution_count": null,
|
pdbbind.py
CHANGED
@@ -90,6 +90,7 @@ if __name__ == '__main__':
|
|
90 |
|
91 |
filenames = glob.glob('data/pdbbind/v2020-other-PL/*')
|
92 |
filenames.extend(glob.glob('data/pdbbind/refined-set/*'))
|
|
|
93 |
comm = MPI.COMM_WORLD
|
94 |
with MPICommExecutor(comm, root=0) as executor:
|
95 |
if executor is not None:
|
@@ -103,5 +104,5 @@ if __name__ == '__main__':
|
|
103 |
import pandas as pd
|
104 |
df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles})
|
105 |
all_contacts = da.from_array(all_contacts, chunks=chunk_size)
|
106 |
-
da.to_npy_stack('data/
|
107 |
-
df.to_parquet('data/
|
|
|
90 |
|
91 |
filenames = glob.glob('data/pdbbind/v2020-other-PL/*')
|
92 |
filenames.extend(glob.glob('data/pdbbind/refined-set/*'))
|
93 |
+
filenames = sorted(filenames)
|
94 |
comm = MPI.COMM_WORLD
|
95 |
with MPICommExecutor(comm, root=0) as executor:
|
96 |
if executor is not None:
|
|
|
104 |
import pandas as pd
|
105 |
df = pd.DataFrame({'name': names, 'seq': seqs, 'smiles': all_smiles})
|
106 |
all_contacts = da.from_array(all_contacts, chunks=chunk_size)
|
107 |
+
da.to_npy_stack('data/pdbbind_contacts_{}/'.format(cutoff), all_contacts)
|
108 |
+
df.to_parquet('data/pdbbind_complex_{}.parquet'.format(cutoff))
|
protein_ligand_contacts.py
CHANGED
@@ -78,7 +78,9 @@ class ProteinLigandContacts(datasets.ArrowBasedBuilder):
|
|
78 |
"affinity_uM": datasets.Value("float"),
|
79 |
"neg_log10_affinity_M": datasets.Value("float"),
|
80 |
"affinity": datasets.Value("float"),
|
81 |
-
"
|
|
|
|
|
82 |
# These are the features of your dataset like images, labels ...
|
83 |
}
|
84 |
)
|
|
|
78 |
"affinity_uM": datasets.Value("float"),
|
79 |
"neg_log10_affinity_M": datasets.Value("float"),
|
80 |
"affinity": datasets.Value("float"),
|
81 |
+
"contacts_5A": datasets.Sequence(datasets.Value('int64')),
|
82 |
+
"contacts_8A": datasets.Sequence(datasets.Value('int64')),
|
83 |
+
"contacts_11A": datasets.Sequence(datasets.Value('int64')),
|
84 |
# These are the features of your dataset like images, labels ...
|
85 |
}
|
86 |
)
|