| """LightDock integration sampling module. |
| |
| For more details about this module, please `refer to the haddock3 user manual |
| <https://www.bonvinlab.org/haddock3-user-manual/modules/sampling.html#lightdock-module>`_ |
| """ |
| import shutil |
| import subprocess |
| 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.libio import working_directory |
| from haddock.libs.libontology import Format, PDBFile |
| from haddock.libs.libutil import check_subprocess |
| from haddock.modules import BaseHaddockModule |
|
|
|
|
| RECIPE_PATH = Path(__file__).resolve().parent |
| DEFAULT_CONFIG = Path(RECIPE_PATH, MODULE_DEFAULT_YAML) |
|
|
|
|
| class HaddockModule(BaseHaddockModule): |
| """HADDOCK3 Lightdock 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) |
|
|
| @classmethod |
| def confirm_installation(cls) -> None: |
| """Confirm this module is installed.""" |
| check_subprocess('lightdock3.py -h') |
|
|
| def _run(self) -> None: |
| """Execute module.""" |
| |
| models: list[PDBFile] = [ |
| p |
| for p in self.previous_io.output |
| if p.file_type == Format.PDB |
| ] |
|
|
| |
| if len(models) > 1: |
| _msg = "Only one model allowed in LightDock sampling module" |
| self.finish_with_error(_msg) |
|
|
| model = models[0] |
| |
| _path = Path(model.path, model.file_name) |
| segids, chains = libpdb.identify_chainseg(_path) |
| if set(segids) != set(chains): |
| log.info("No chain IDs found, using segid information") |
| libpdb.swap_segid_chain( |
| Path(model.path, model.file_name), |
| Path(self.path, model.file_name), |
| ) |
| else: |
| |
| shutil.copyfile( |
| Path(model.path, model.file_name), |
| Path(self.path, model.file_name), |
| ) |
|
|
| model_with_chains = self.path / model.file_name |
| |
| new_models = libpdb.split_by_chain(model_with_chains) |
| if model_with_chains in new_models: |
| self.finish_with_error(f"Input {model_with_chains} cannot be" |
| " split by chain") |
|
|
| |
| rec_chain = self.params["receptor_chains"][0] |
| lig_chain = self.params["ligand_chains"][0] |
| receptor_pdb_file = (f"{Path(model.file_name).stem}_" |
| f"{rec_chain}.{Format.PDB}") |
| ligand_pdb_file = (f"{Path(model.file_name).stem}_" |
| f"{lig_chain}.{Format.PDB}") |
|
|
| |
| log.info("Running LightDock setup") |
| with working_directory(self.path): |
| swarms = self.params["swarms"] |
| glowworms = self.params["glowworms"] |
| noxt = self.params["noxt"] |
| noh = self.params["noh"] |
| cmd = (f"lightdock3_setup.py {receptor_pdb_file}" |
| f" {ligand_pdb_file} -s {swarms} -g {glowworms}") |
| if noxt: |
| cmd += " --noxt" |
| if noh: |
| cmd += " --noh" |
| subprocess.call(cmd, shell=True) |
|
|
| |
| log.info("Running LightDock simulation") |
| with working_directory(self.path): |
| steps = self.params["steps"] |
| scoring = self.params["scoring"] |
| cores = self.params['ncores'] or 1 |
| cmd = f"lightdock3.py setup.json {steps} -c {cores} -s {scoring}" |
| subprocess.call(cmd, shell=True) |
|
|
| |
|
|
| |
| log.info("Generating ranking") |
| with working_directory(self.path): |
| steps = self.params["steps"] |
| swarms = self.params["swarms"] |
| cmd = f"lgd_rank.py {swarms} {steps}" |
| subprocess.call(cmd, shell=True) |
|
|
| |
| |
| log.info("Generating top structures") |
| with working_directory(self.path): |
| |
| shutil.copyfile( |
| Path(self.path, receptor_pdb_file), |
| Path(self.path, f"tmp_{receptor_pdb_file}"), |
| ) |
| shutil.copyfile( |
| Path(self.path, ligand_pdb_file), |
| Path(self.path, f"tmp_{ligand_pdb_file}") |
| ) |
| shutil.copy( |
| Path(self.path, receptor_pdb_file), |
| Path(self.path, f"lightdock_{receptor_pdb_file}"), |
| ) |
| shutil.copy( |
| Path(self.path, ligand_pdb_file), |
| Path(self.path, f"lightdock_{ligand_pdb_file}"), |
| ) |
| |
| steps = self.params["steps"] |
| top = self.params["top"] |
| cmd = (f"lgd_top.py {receptor_pdb_file} {ligand_pdb_file}" |
| f" rank_by_scoring.list {top}") |
| subprocess.call(cmd, shell=True) |
|
|
| |
| expected: list[PDBFile] = [] |
| top = self.params["top"] |
| for i in range(top): |
| file_name = f"top_{i+1}.{Format.PDB}" |
| tidy_file_name = f"haddock_top_{i+1}.{Format.PDB}" |
| libpdb.tidy(self.path / file_name, self.path / tidy_file_name) |
| expected.append(PDBFile(tidy_file_name, |
| topology=model.topology, |
| path=self.path)) |
|
|
| self.output_models = models |
| self.export_io_models() |
|
|
