def parse_ensemble(name, ensemble):
if ensemble is None:
df = dt.bp_to_df(dt.read_any(name))
else:
df = []
for subunit, f in ensemble.items():
if isinstance(f, pd.DataFrame):
curr = f
else:
curr = dt.bp_to_df(dt.read_any(f))
curr['subunit'] = subunit
df.append(curr)
df = pd.concat(df)
df['ensemble'] = name
return df
def process_files(input_dir):
"""
Process all protein (pdb) and ligand (sdf) files in input directory.
:param input dir: directory containing PDBBind data
:type input_dir: str
:return structure_dict: dictionary containing each structure, keyed by PDB code. Each PDB is a dict containing protein as Biopython object and ligand as RDKit Mol object
:rtype structure_dict: dict
"""
structure_dict = {}
pdb_files = fi.find_files(input_dir, 'pdb')
for f in tqdm(pdb_files, desc='pdb files'):
pdb_id = fi.get_pdb_code(f)
if pdb_id not in structure_dict:
structure_dict[pdb_id] = {}
if '_protein' in f:
prot = ft.read_any(f)
structure_dict[pdb_id]['protein'] = prot
lig_files = fi.find_files(input_dir, 'sdf')
for f in tqdm(lig_files, desc='ligand files'):
pdb_id = fi.get_pdb_code(f)
structure_dict[pdb_id]['ligand'] = get_ligand(f)
return structure_dict
def __call__(self, x):
name = os.path.splitext(x['id'])[0]
x['id'] = name
orig_file = self.mut_orig_mapping[name]['original']
x['original_atoms'] = fo.bp_to_df(
fo.read_any(os.path.join(self.orig_file_dir, orig_file)))
x['mutated_atoms'] = x.pop('atoms')
x['label'] = str(self.labels.loc[name])
return x
def __getitem__(self, index: int):
if not 0 <= index < self._num_examples:
raise IndexError(index)
file_path = self._file_list[index]
item = {
'atoms': fo.bp_to_df(fo.read_any(file_path)),
'id': file_path.name,
'file_path': str(file_path),
}
if self._transform:
item = self._transform(item)
return item
def convert_to_hdf5(input_dir, label_file, hdf_file):
cif_files = fi.find_files(input_dir, 'cif')
proteins = []
pockets = []
pdb_codes = []
for f in tqdm(cif_files, desc='reading structures'):
pdb_code = fi.get_pdb_code(f)
if '_protein' in f:
pdb_codes.append(pdb_code)
df = dt.bp_to_df(dt.read_any(f))
proteins.append(df)
elif '_pocket' in f:
df = dt.bp_to_df(dt.read_any(f))
pockets.append(df)
print('converting proteins...')
protein_df = pd.concat(proteins)
pocket_df = pd.concat(pockets)
pdb_codes = pd.DataFrame({'pdb': pdb_codes})
protein_df.to_hdf(hdf_file, 'proteins')
pocket_df.to_hdf(hdf_file, 'pockets')
pdb_codes.to_hdf(hdf_file, 'pdb_codes')
print('converting ligands...')
sdf_files = fi.find_files(input_dir, 'sdf')
big_sdf = os.path.join(input_dir, 'all_ligands.sdf')
dt.combine_sdfs(sdf_files, big_sdf)
lig_df = PandasTools.LoadSDF(big_sdf, molColName='Mol')
lig_df.index = pdb_codes
lig_df.to_hdf(hdf_file, 'ligands')
print('converting labels...')
label_df = pd.read_csv(label_file)
label_df = label_df.set_index('pdb').reindex(pdb_codes)
label_df.to_hdf(hdf_file, 'labels')
def process(self):
label_file = os.path.join(self.root, 'pdbbind_refined_set_labels.csv')
label_df = pd.read_csv(label_file)
i = 0
for raw_path in self.raw_paths:
pdb_code = fi.get_pdb_code(raw_path)
y = torch.FloatTensor([get_label(pdb_code, label_df)])
if '_ligand' in raw_path:
mol_graph = graph.mol_to_graph(
dt.read_sdf_to_mol(raw_path, add_hs=True)[0])
elif '_pocket' in raw_path:
prot_graph = graph.prot_df_to_graph(
dt.bp_to_df(dt.read_any(raw_path, name=pdb_code)))
node_feats, edge_index, edge_feats, pos = graph.combine_graphs(
prot_graph, mol_graph, edges_between=True)
data = Data(node_feats, edge_index, edge_feats, y=y, pos=pos)
data.pdb = pdb_code
torch.save(
data,
os.path.join(self.processed_dir, 'data_{}.pt'.format(i)))
i += 1
else:
continue
def _lookup(self, file_path):
return seq.get_chain_sequences(fo.bp_to_df(fo.read_any(file_path)))