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| """gdock integration sampling module.""" | |
| import time | |
| from pathlib import Path | |
| from haddock import log | |
| from haddock.core.defaults import MODULE_DEFAULT_YAML | |
| from haddock.core.typing import Any, FilePath | |
| from haddock.libs import libpdb | |
| from haddock.libs.libontology import PDBFile | |
| from haddock.modules import BaseHaddockModule | |
| from haddock.modules.sampling.gdock.gdock import ( | |
| GdockWrapper, | |
| gdock_is_available, | |
| parse_restraints, | |
| ) | |
| RECIPE_PATH = Path(__file__).resolve().parent | |
| DEFAULT_CONFIG = Path(RECIPE_PATH, MODULE_DEFAULT_YAML) | |
| class HaddockModule(BaseHaddockModule): | |
| """HADDOCK3 gdock module.""" | |
| name = RECIPE_PATH.name | |
| def __init__( | |
| self, | |
| order: int, | |
| path: Path, | |
| *ignore: Any, | |
| initial_params: FilePath = DEFAULT_CONFIG, | |
| **everything: Any, | |
| ) -> None: | |
| super().__init__(order, path, initial_params) | |
| def confirm_installation(cls) -> None: | |
| """Confirm if the module is ready to use.""" | |
| if not gdock_is_available(): | |
| raise Exception( | |
| "You are trying to use the `gdock` module but it is not" | |
| " available, please check the installation instructions" | |
| ) | |
| def _split_combination( | |
| self, combination: tuple[PDBFile, ...] | |
| ) -> tuple[PDBFile, str, PDBFile, str]: | |
| """Return (receptor, rec_chain, ligand, lig_chain) from a combination. | |
| The molecule order follows the `molecules` list in the run config, so | |
| index 0 is always the receptor and index 1 is always the ligand. | |
| Chain IDs are read directly from the files. | |
| """ | |
| if len(combination) != 2: | |
| self.finish_with_error( | |
| f"gdock requires exactly 2 input molecules per combination, " | |
| f"got {len(combination)}: {[c.file_name for c in combination]}" | |
| ) | |
| receptor, ligand = combination[0], combination[1] | |
| _, rec_chains = libpdb.identify_chainseg( | |
| Path(receptor.path, receptor.file_name) | |
| ) | |
| _, lig_chains = libpdb.identify_chainseg(Path(ligand.path, ligand.file_name)) | |
| if not rec_chains or not lig_chains: | |
| self.finish_with_error( | |
| f"Could not detect chain IDs in receptor '{receptor.file_name}' " | |
| f"or ligand '{ligand.file_name}'" | |
| ) | |
| return receptor, rec_chains[0], ligand, lig_chains[0] # type: ignore | |
| def _run(self) -> None: | |
| """Execute module.""" | |
| # Each combination is a tuple with one PDBFile per input molecule, | |
| # e.g. (receptor, ligand), as produced by `topoaa`. | |
| models_to_dock = self.previous_io.retrieve_models( | |
| crossdock=self.params["crossdock"] | |
| ) | |
| log.info(f"ncores={self.params['ncores']} sampling={self.params['sampling']}") | |
| expected: list[PDBFile] = [] | |
| for idx, combination in enumerate(models_to_dock, start=1): | |
| receptor, rec_chain, ligand, lig_chain = self._split_combination( | |
| combination | |
| ) | |
| # Convert restraints using chain IDs read from the actual files. | |
| restraints = None | |
| ambig_fname = self.params["ambig_fname"] | |
| if ambig_fname: | |
| restraints = parse_restraints(ambig_fname, rec_chain, lig_chain) | |
| gdock_wrapper = GdockWrapper( | |
| receptor_pdb_file=Path(receptor.path, receptor.file_name), | |
| ligand_pdb_file=Path(ligand.path, ligand.file_name), | |
| restraints=restraints, | |
| max_generations=self.params["max_generations"], | |
| number_of_individuals=self.params["number_of_individuals"], | |
| ncores=self.params["ncores"], | |
| seed=self.params["seed"], | |
| sampling=self.params["sampling"], | |
| ) | |
| t0 = time.monotonic() | |
| gdock_wrapper.run() | |
| elapsed = time.monotonic() - t0 | |
| log.info( | |
| f"gdock run {idx}: {receptor.file_name} + {ligand.file_name}" | |
| f" generations={gdock_wrapper.result['generationsRun']}" | |
| f" models={len(gdock_wrapper.result['models'])}" | |
| f" elapsed={elapsed:.1f}s" | |
| ) | |
| models = gdock_wrapper.save_models(".", prefix=f"gdock_{idx}") | |
| for m in models: | |
| expected.append( | |
| PDBFile( | |
| m["file_name"], | |
| topology=[receptor.topology, ligand.topology], | |
| path=self.path, | |
| score=m["fitness"], | |
| unw_energies={ | |
| "vdw": m["vdw"], | |
| "elec": m["elec"], | |
| "desolv": m["desolv"], | |
| "air": m["air"], | |
| }, | |
| ) | |
| ) | |
| self.output_models = expected | |
| self.export_io_models() | |