{
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"id": "c47a32d8-c857-41de-a70a-cec48046df12",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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', 5: 'affinity_quantity',\n",
" 7: 'affinity_val', 8: 'affinity_unit', 9:'smiles'})\n",
"#df = df[df['ligand_valid']!='invalid'].copy()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "a4a7384f-8068-43e8-add7-c498aafe61c9",
"metadata": {},
"outputs": [
{
"data": {
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" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>pdb</th>\n",
" <th>ligand_name</th>\n",
" <th>ligand_valid</th>\n",
" <th>affinity_quantity</th>\n",
" <th>6</th>\n",
" <th>affinity_val</th>\n",
" <th>affinity_unit</th>\n",
" <th>smiles</th>\n",
" <th>10</th>\n",
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" <tr>\n",
" <th>0</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>HAE:C:989</td>\n",
" <td>valid</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CC(=O)NO</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NI:C:574</td>\n",
" <td>Part of Protein</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[Ni+2]</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NI:C:575</td>\n",
" <td>Part of Protein</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[Ni+2]</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>NaN</td>\n",
" <td>Family. Representative Entry is</td>\n",
" <td>6H8J</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>SO4:C:611</td>\n",
" <td>invalid</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[O-]S(=O)(=O)[O-]</td>\n",
" <td>NaN</td>\n",
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" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
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" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306520</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>CA:D:1950</td>\n",
" <td>Part of Protein</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>[Ca+2]</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306521</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NGA NAG:F:1</td>\n",
" <td>valid</td>\n",
" <td>Ka</td>\n",
" <td>=</td>\n",
" <td>5910.0</td>\n",
" <td>M^-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306522</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NGA NAG:E:1</td>\n",
" <td>valid</td>\n",
" <td>Ka</td>\n",
" <td>=</td>\n",
" <td>5910.0</td>\n",
" <td>M^-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306523</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NGA NAG:H:1</td>\n",
" <td>valid</td>\n",
" <td>Ka</td>\n",
" <td>=</td>\n",
" <td>5910.0</td>\n",
" <td>M^-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>306524</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>A2G NAG:G:1</td>\n",
" <td>invalid</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>306525 rows × 11 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 pdb ligand_name \\\n",
"0 NaN NaN NaN HAE:C:989 \n",
"1 NaN NaN NaN NI:C:574 \n",
"2 NaN NaN NaN NI:C:575 \n",
"3 NaN Family. Representative Entry is 6H8J NaN \n",
"4 NaN NaN NaN SO4:C:611 \n",
"... ... ... ... ... \n",
"306520 NaN NaN NaN CA:D:1950 \n",
"306521 NaN NaN NaN NGA NAG:F:1 \n",
"306522 NaN NaN NaN NGA NAG:E:1 \n",
"306523 NaN NaN NaN NGA NAG:H:1 \n",
"306524 NaN NaN NaN A2G NAG:G:1 \n",
"\n",
" ligand_valid affinity_quantity 6 affinity_val affinity_unit \\\n",
"0 valid NaN NaN NaN NaN \n",
"1 Part of Protein NaN NaN NaN NaN \n",
"2 Part of Protein NaN NaN NaN NaN \n",
"3 NaN NaN NaN NaN NaN \n",
"4 invalid NaN NaN NaN NaN \n",
"... ... ... ... ... ... \n",
"306520 Part of Protein NaN NaN NaN NaN \n",
"306521 valid Ka = 5910.0 M^-1 \n",
"306522 valid Ka = 5910.0 M^-1 \n",
"306523 valid Ka = 5910.0 M^-1 \n",
"306524 invalid NaN NaN NaN NaN \n",
"\n",
" smiles 10 \n",
"0 CC(=O)NO NaN \n",
"1 [Ni+2] NaN \n",
"2 [Ni+2] NaN \n",
"3 NaN NaN \n",
"4 [O-]S(=O)(=O)[O-] NaN \n",
"... ... .. \n",
"306520 [Ca+2] NaN \n",
"306521 NaN NaN \n",
"306522 NaN NaN \n",
"306523 NaN NaN \n",
"306524 NaN NaN \n",
"\n",
"[306525 rows x 11 columns]"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "e40b1ddc-9a98-4a3b-b8a6-45e3940a3ea2",
"metadata": {},
"outputs": [],
"source": [
"df['is_sep'] = df[1] == 'Family. Representative Entry is '"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "4f00a0d1-78db-4f32-9d12-5e035b70ef98",
"metadata": {},
"outputs": [],
"source": [
"df['cum_sum'] = df['is_sep'].cumsum()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "ebaeb749-656e-4b92-8dbd-2e29eefdcad5",
"metadata": {},
"outputs": [],
"source": [
"quantities = ['ki','kd','ka','k1/2','kb','ic50','ec50']"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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": 9,
"id": "e5b4dd41-1389-408d-bee6-6dbeefc1d5c7",
"metadata": {},
"outputs": [],
"source": [
"groupby = df.groupby('cum_sum')"
]
},
{
"cell_type": "code",
"execution_count": 10,
"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": 11,
"id": "342c8ef3-6808-471b-baa4-f9bdc7f6e8d6",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"88806"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df_expand)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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": 13,
"id": "b5c0fa42-b595-4b96-b2d5-57f0031427dc",
"metadata": {},
"outputs": [],
"source": [
"df_filter = df_expand[df_expand['affinity_quantity'].str.lower().isin(quantities)]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "27c4865b-5337-48e0-9be7-a913b31cfae1",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"88806"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df_expand)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "f3daf4ad-0205-48c0-8c38-36aa4eb561e0",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"25490"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(df_filter)"
]
},
{
"cell_type": "code",
"execution_count": 23,
"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": 15,
"id": "719d147f-75eb-4ead-a54f-4b448a62a9a0",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 24,
"id": "6d158a41-64c6-4fa2-92d5-562aa11e8924",
"metadata": {},
"outputs": [],
"source": [
"df_all = df_filter.merge(df_complex,left_on='pdb_ref',right_on='name')"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "901fe6c6-dc8c-4ce4-82c6-1fb0b718287a",
"metadata": {},
"outputs": [],
"source": [
"df_all = df_all[~df_all['affinity_val'].isnull()]"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "383f9a1c-ffc6-43da-ac5a-5bcb815be28b",
"metadata": {},
"outputs": [
{
"data": {
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" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>2</td>\n",
" <td>4S3F</td>\n",
" <td>0.770000</td>\n",
" <td>4S3F</td>\n",
" <td>MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>ADN:A:901</td>\n",
" <td>valid</td>\n",
" <td>Kd</td>\n",
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" <td>5</td>\n",
" <td>2GL0</td>\n",
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" <td>Ka</td>\n",
" <td>=</td>\n",
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" <td>2WDB</td>\n",
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" <td>2WDB</td>\n",
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
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" <td>2WDB</td>\n",
" <td>169.204738</td>\n",
" <td>2WDB</td>\n",
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25423</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NGA NAG:E:1</td>\n",
" <td>valid</td>\n",
" <td>Ka</td>\n",
" <td>=</td>\n",
" <td>5910.00</td>\n",
" <td>M^-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>10499</td>\n",
" <td>2WDB</td>\n",
" <td>169.204738</td>\n",
" <td>2WDB</td>\n",
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25424</th>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NGA NAG:H:1</td>\n",
" <td>valid</td>\n",
" <td>Ka</td>\n",
" <td>=</td>\n",
" <td>5910.00</td>\n",
" <td>M^-1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>False</td>\n",
" <td>10499</td>\n",
" <td>2WDB</td>\n",
" <td>169.204738</td>\n",
" <td>2WDB</td>\n",
" <td>MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG...</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>25425 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 pdb ligand_name ligand_valid affinity_quantity 6 \\\n",
"0 NaN NaN NaN 2PA:C:613 valid Ki = \n",
"1 NaN NaN NaN HAE:C:800 valid Ki = \n",
"2 NaN NaN NaN 43W:A:902 valid ic50 = \n",
"3 NaN NaN NaN 0CG:A:902 valid ic50 = \n",
"4 NaN NaN NaN ADN:A:901 valid Kd = \n",
"... ... ... ... ... ... ... .. \n",
"25420 NaN NaN NaN MAN NAG:G:1 valid Ka = \n",
"25421 NaN NaN NaN MAN NAG:F:1 valid Ka = \n",
"25422 NaN NaN NaN NGA NAG:F:1 valid Ka = \n",
"25423 NaN NaN NaN NGA NAG:E:1 valid Ka = \n",
"25424 NaN NaN NaN NGA NAG:H:1 valid Ka = \n",
"\n",
" affinity_val affinity_unit \\\n",
"0 0.62 nM \n",
"1 2.60 uM \n",
"2 580.00 nM \n",
"3 770.00 nM \n",
"4 15.00 uM \n",
"... ... ... \n",
"25420 7860.00 M^-1 \n",
"25421 7860.00 M^-1 \n",
"25422 5910.00 M^-1 \n",
"25423 5910.00 M^-1 \n",
"25424 5910.00 M^-1 \n",
"\n",
" smiles 10 is_sep cum_sum \\\n",
"0 NP(=O)(N)O NaN False 1 \n",
"1 CC(=O)NO NaN False 1 \n",
"2 C#CCCOP(=O)(O)OP(=O)(O)O NaN False 2 \n",
"3 C#CCOP(=O)(O)OP(=O)(O)O NaN False 2 \n",
"4 c1nc(c2c(n1)n(cn2)[C@H]3[C@@H]([C@@H]([C@H](O3... NaN False 5 \n",
"... ... .. ... ... \n",
"25420 NaN NaN False 10499 \n",
"25421 NaN NaN False 10499 \n",
"25422 NaN NaN False 10499 \n",
"25423 NaN NaN False 10499 \n",
"25424 NaN NaN False 10499 \n",
"\n",
" pdb_ref affinity_uM name \\\n",
"0 6H8J 0.000620 6H8J \n",
"1 6H8J 2.600000 6H8J \n",
"2 4S3F 0.580000 4S3F \n",
"3 4S3F 0.770000 4S3F \n",
"4 2GL0 15.000000 2GL0 \n",
"... ... ... ... \n",
"25420 2WDB 127.226463 2WDB \n",
"25421 2WDB 127.226463 2WDB \n",
"25422 2WDB 169.204738 2WDB \n",
"25423 2WDB 169.204738 2WDB \n",
"25424 2WDB 169.204738 2WDB \n",
"\n",
" seq \n",
"0 NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE... \n",
"1 NYIVPGEYRVAEGEIEINAGREKTTIRVSNTGDRPIQVGSHIHFVE... \n",
"2 MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE... \n",
"3 MEGMRRPTPTVYVGRVPIGGAHPIAVQSMTNTPTRDVEATTAQVLE... \n",
"4 MTDMSIKFELIDVPIPQGTNVIIGQAHFIKTVEDLYEALVTSVPGV... \n",
"... ... \n",
"25420 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
"25421 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
"25422 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
"25423 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
"25424 MGSSHHHHHHSSGLVPRGSHMASNPSLIRSESWQVYEGNEANLLDG... \n",
"\n",
"[25425 rows x 17 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_all"
]
},
{
"cell_type": "code",
"execution_count": 27,
"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": 5,
"id": "49a160d6-4599-488a-ba02-a65a79535f38",
"metadata": {},
"outputs": [],
"source": [
"df_all = pd.read_parquet('data/moad.parquet')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bebcbe84-fa6d-4e39-bd26-40ed173e0e67",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
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"name": "python3"
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