Filter common ligands and ligands with <3 atoms

also expand multiple copies of the same ligand binding to the same
receptor into separate rows (e.g., one bound ligand per chain)

README.md

@@ -10,7 +10,7 @@ tags:
 ## How to use the data sets
 
 This dataset contains more about 80,000 unique pairs of protein sequences and ligand SMILES, and the coordinates
-of their complexes from the PDB. Only ligands with a molecular weight >= 100 Da are included.
+of their complexes from the PDB. 
 
 SMILES are assumed to be tokenized by the regex from P. Schwaller.
 
@@ -20,6 +20,16 @@ Every receptor coordinate maps onto the Calpha coordinate of that residue.
 
 The dataset can be used to fine-tune a language model, all data comes from PDBind-cn.
 
+## Ligand selection criteria
+
+Only ligands with
+
+- at least 3 atoms,
+- a molecular weight >= 100 Da,
+- that don't occur more than 75 times in different PDB complexes (this includes common additives like PEG, ADP, ..)
+
+are considered.
+
 ### Use the already preprocessed data
 
 Load a test/train split using

data/pdb.parquet

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:c503ec9218e87f192d710838a0123f01ac1e48b75b288f0adc229cb025d1b592
-size 1005021071
+oid sha256:bbe6a448b46a5e6c2dd1b32ca878b5a658505f06334c173aa7565a3a6a848413
+size 988455052

parse_complexes.py

@@ -38,6 +38,28 @@ molecule_regex = r"""(\[[^\]]+]|Br?|Cl?|N|O|S|P|F|I|b|c|n|o|s|p|\(|\)|\.|=|#|-|\
 max_seq = 2046 # = 2048 - 2 (accounting for [CLS] and [SEP])
 max_smiles = 510 # = 512 - 2
 
+# filter out these common additives which occur in more than 75 complexes in the PDB
+ubiquitous_ligands = ['PEG', 'ADP', 'FAD', 'NAD', 'ATP', 'MPD', 'NAP', 'GDP', 'MES',
+       'GTP', 'FMN', 'HEC', 'TRS', 'CIT', 'PGE', 'ANP', 'SAH', 'NDP',
+       'PG4', 'EPE', 'AMP', 'COA', 'MLI', 'FES', 'GNP', 'MRD', 'GSH',
+       'FLC', 'AGS', 'NAI', 'SAM', 'PCW', '1PE', 'TLA', 'BOG', 'CYC',
+       'UDP', 'PX4', 'NAG', 'IMP', 'POP', 'UMP', 'PLM', 'HEZ', 'TPP',
+       'ACP', 'LDA', 'ACO', 'CLR', 'BGC', 'P6G', 'LMT', 'OGA', 'DTT',
+       'POV', 'FBP', 'AKG', 'MLA', 'ADN', 'NHE', '7Q9', 'CMP', 'BTB',
+       'PLP', 'CAC', 'SIN', 'C2E', '2AN', 'OCT', '17F', 'TAR', 'BTN',
+       'XYP', 'MAN', '5GP', 'GAL', 'GLC', 'DTP', 'DGT', 'PEB', 'THP',
+       'BEZ', 'CTP', 'GSP', 'HED', 'ADE', 'TYD', 'TTP', 'BNG', 'IHP',
+       'FDA', 'PEP', 'ALF', 'APR', 'MTX', 'MLT', 'LU8', 'UTP', 'APC',
+       'BLA', 'C8E', 'D10', 'CHT', 'BO2', '3BV', 'ORO', 'MPO', 'Y01',
+       'OLC', 'B3P', 'G6P', 'PMP', 'D12', 'NDG', 'A3P', '78M', 'F6P',
+       'U5P', 'PRP', 'UPG', 'THM', 'SFG', 'MYR', 'FEO', 'PG0', 'CXS',
+       'AR6', 'CHD', 'WO4', 'C5P', 'UFP', 'GCP', 'HDD', 'SRT', 'STU',
+       'CDP', 'TCL', '04C', 'MYA', 'URA', 'PLG', 'MTA', 'BMP', 'SAL',
+       'TA1', 'UD1', 'OLA', 'BCN', 'LMR', 'BMA', 'OAA', 'TAM', 'MBO',
+       'MMA', 'SPD', 'MTE', 'AP5', 'TMP', 'PGA', 'GLA', '3PG', 'FUL',
+       'PQQ', '9TY', 'DUR', 'PPV', 'SPM', 'SIA', 'DUP', 'GTX', '1PG',
+       'GUN', 'ETF', 'FDP', 'MFU', 'G2P', 'PC', 'DST', 'INI']
+
 def get_protein_sequence_and_coords(receptor):
     calpha = receptor.select('calpha')
     xyz = calpha.getCoords()
@@ -120,15 +142,20 @@ def process_ligand(ligand, res_name, expo_dict):
     :param expo_dict: dictionary with LigandExpo
     :return: molecule with bond orders assigned
     """
-    output = StringIO()
-    sub_mol = ligand.select(f"resname {res_name}")
     sub_smiles = expo_dict['SMILES'][res_name]
     template = AllChem.MolFromSmiles(sub_smiles)
-    writePDBStream(output, sub_mol)
-    pdb_string = output.getvalue()
-    rd_mol = AllChem.MolFromPDBBlock(pdb_string)
-    new_mol = AllChem.AssignBondOrdersFromTemplate(template, rd_mol)
-    return new_mol, template
+
+    allres = ligand.select(f"resname {res_name}")
+    res = np.unique(allres.getResindices())
+    mols = []
+    for i in res:
+        sub_mol = ligand.select(f"resname {res_name} and resindex {i}")
+        output = StringIO()
+        writePDBStream(output, sub_mol)
+        pdb_string = output.getvalue()
+        rd_mol = AllChem.MolFromPDBBlock(pdb_string)
+        mols.append(AllChem.AssignBondOrdersFromTemplate(template, rd_mol))
+    return mols, template
 
 def process_entry(df_dict, pdb_fn):
     try:
@@ -141,6 +168,8 @@ def process_entry(df_dict, pdb_fn):
         """
         protein, ligand = get_pdb_components(pdb_fn)
 
+        pdb_name = os.path.basename(pdb_fn).split('.')[-3][3:]
+
         ligand_mols = []
         ligand_names = []
 
@@ -148,19 +177,21 @@ def process_entry(df_dict, pdb_fn):
             # filter ligands by molecular weight
             res_name_list = list(set(ligand.getResnames()))
             for res in res_name_list:
-                mol, template = process_ligand(ligand, res, df_dict)
+                mols, template = process_ligand(ligand, res, df_dict)
 
                 mol_wt = ExactMolWt(template)
                 natoms = template.GetNumAtoms()
 
-                if mol_wt >= mol_wt_cutoff and natoms >= min_atoms:
-                    ligand_mols.append(mol)
-                    ligand_names.append(res)
+                if mol_wt >= mol_wt_cutoff and natoms >= min_atoms and res not in ubiquitous_ligands:
+                    if len(mols) > 1:
+                        print('Found {} copies of {} ligand {}'.format(len(mols),pdb_name,res))
+                    ligand_mols += mols
+                    ligand_names += [res]*len(mols)
+
 
         ligand_smiles = []
         ligand_xyz = []
 
-        pdb_name = os.path.basename(pdb_fn).split('.')[-3][3:]
         for mol, name in zip(ligand_mols, ligand_names):
             print('Processing {} and {}'.format(pdb_name, name))
             smi, xyz = tokenize_ligand(mol)
@@ -184,7 +215,7 @@ if __name__ == '__main__':
             # read ligand table
             df_dict = read_ligand_expo()
 
-            result = executor.map(partial(process_entry, df_dict), filenames, chunksize=512)
+            result = executor.map(partial(process_entry, df_dict), filenames, chunksize=2048)
             result = list(result)
 
             # expand sequences and ligands