def rdkit_to_ccdc(mol):
"""
Convert RDKit mol to CCDC mol
:param mol:
:return:
"""
ccdc_mol = Molecule.from_string(Chem.MolToMolBlock(mol), format='mol')
ccdc_mol.identifier = mol.GetProp("_Name")
return ccdc_mol
def dock(self, number_poses=100):
"""
:return:
"""
# Set up protein and ligand, in case they need to be
if self.prepare_protein:
self.prepare_protein_for_dock()
if self.prepare_ligand:
self.prepare_ligand_for_dock()
reference_ligand = MoleculeReader(self.reference_ligand_path)[0]
prepared_protein = Protein.from_file(self.prepared_protein_path)
prepared_ligand = MoleculeReader(self.prepared_ligand_path)[0]
if self.substructure:
substr_string = make_substructure_molecule(
template_mol_path=self.reference_ligand_path,
query_mol_path=self.prepared_ligand_path)
substructure = Molecule.from_string(substr_string, format='sdf')
with MoleculeWriter(
str(
Path(self.gold_results_directory,
f"{self.lig_name}_substructure.sdf"))) as sdfwr:
sdfwr.write(substructure)
# Set up the docking run
docker = Docker()
docker._conf_file_name = self.conf_file_location
docker_settings = docker.settings
# Prevent it from generating a ton of output ligand files - the ranked docks are in 'concat_ranked_docked_ligands.mol2'
docker_settings._settings.set_delete_rank_files(True)
docker_settings._settings.set_delete_empty_directories(True)
docker_settings._settings.set_delete_all_initialised_ligands(True)
docker_settings._settings.set_delete_all_solutions(True)
docker_settings._settings.set_delete_redundant_log_files(True)
docker_settings._settings.set_save_lone_pairs(False)
# Set up the binding site. Since the sites are based on ragment hits, generate a binding site around the starting hit.
docker_settings.reference_ligand_file = self.reference_ligand_path
docker_settings.binding_site = docker_settings.BindingSiteFromLigand(
prepared_protein, reference_ligand, 6.0)
# Default distance around ligand is 6 A. Should be ok for small fragments.
docker_settings.add_protein_file(self.prepared_protein_path)
docker_settings.diverse_solutions = self.diverse_solutions
# Try a template similarity restraint:
#
if self.substructure:
try:
docker_settings.add_constraint(
docker_settings.ScaffoldMatchConstraint(substructure))
except Exception as e:
docker_settings.add_constraint(
docker_settings.TemplateSimilarityConstraint(
'all', reference_ligand, weight=75.0))
txtstr = 'Substructure search failed. Using template similarity'
log_file = Path(self.results_directory, 'pipeline_error.log')
log_file.write_text(txtstr)
else:
docker_settings.add_constraint(
docker_settings.TemplateSimilarityConstraint('all',
reference_ligand,
weight=150.0))
# Choose the fitness function: options: ['goldscore', 'chemscore', 'asp', 'plp']. plp is the default.
docker_settings.fitness_function = 'plp'
docker_settings.autoscale = self.autoscale
docker_settings.early_termination = False
docker_settings.output_directory = self.gold_results_directory
docker_settings.output_file = str(
Path(self.results_directory, 'concat_ranked_docked_ligands.mol2'))
# Add the ligand
docker_settings.add_ligand_file(
self.prepared_ligand_path, number_poses
) # Second argument determines how many poses are saved
# Perform the docking:
gold_result = docker.dock(file_name=self.conf_file_location)
# pickle.dump(obj=gold_result,file=Path(self.results_directory, 'gold_result').open())
self.docking_result = gold_result
return gold_result
