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5631464 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 | """Energy minimization refinement with CNS.
The ``[emref]`` module refine the input complexes by energy minimization using
conjugate gradient method implemented in CNS.
Coordinates of the energy minimized structures are saved, and each
complex is then evaluated using the HADDOCK scoring function.
For more details about this module, please `refer to the haddock3 user manual
<https://www.bonvinlab.org/haddock3-user-manual/modules/refinement.html#emref-module>`_
"""
from pathlib import Path
from haddock.core.defaults import MODULE_DEFAULT_YAML
from haddock.core.typing import FilePath
from haddock.gear.haddockmodel import HaddockModel
from haddock.libs.libcns import prepare_cns_input, prepare_expected_pdb
from haddock.libs.libontology import PDBFile
from haddock.libs.libsubprocess import CNSJob
from haddock.modules import get_engine
from haddock.modules.base_cns_module import BaseCNSModule
RECIPE_PATH = Path(__file__).resolve().parent
DEFAULT_CONFIG = Path(RECIPE_PATH, MODULE_DEFAULT_YAML)
class HaddockModule(BaseCNSModule):
"""HADDOCK3 module energy minimization refinement."""
name = RECIPE_PATH.name
def __init__(
self, order: int, path: Path, initial_params: FilePath = DEFAULT_CONFIG
) -> None:
"""."""
cns_script = Path(RECIPE_PATH, "cns", "emref.cns")
super().__init__(order, path, initial_params, cns_script=cns_script)
@classmethod
def confirm_installation(cls) -> None:
"""Confirm module is installed."""
return
def _run(self) -> None:
"""Execute module."""
# Pool of jobs to be executed by the CNS engine
jobs: list[CNSJob] = []
# Get the models generated in previous step
try:
models_to_refine = self.previous_io.retrieve_models()
except Exception as e:
self.finish_with_error(e)
self.output_models = []
sampling_factor = self.params["sampling_factor"]
if sampling_factor > 1:
self.log(f"sampling_factor={sampling_factor}")
if sampling_factor == 0:
self.log("[Warning] sampling_factor cannot be 0, setting it to 1")
sampling_factor = 1
if sampling_factor > 100:
self.log("[Warning] sampling_factor is larger than 100")
max_nmodels = self.params["max_nmodels"]
nmodels = len(models_to_refine) * sampling_factor
if nmodels > max_nmodels:
self.finish_with_error(
f"Too many models ({nmodels}) to refine, max_nmodels ="
f" {max_nmodels}. Please reduce the number of models or"
" decrease the sampling_factor."
)
# checking the ambig_fname:
try:
prev_ambig_fnames = [mod.restr_fname for mod in models_to_refine]
except Exception as e: # noqa:F841
# cannot extract restr_fname info from tuples
prev_ambig_fnames = [None for model in models_to_refine]
ambig_fnames = self.get_ambig_fnames(prev_ambig_fnames)
model_idx = 0
idx = 1
for model in models_to_refine:
# assign ambig_fname
if ambig_fnames:
ambig_fname = ambig_fnames[model_idx]
else:
ambig_fname = self.params["ambig_fname"]
model_idx += 1
for s_ind in range(self.params["sampling_factor"]):
# offset the seed per sampling_factor
# so that each refinement with same staring point
# gets different seed and produce different end models
if isinstance(model, PDBFile) and model.seed is not None:
offsetted_seed = model.seed + s_ind
else:
offsetted_seed = None
emref_input = prepare_cns_input(
idx,
model,
self.path,
self.recipe_str,
self.params,
"emref",
ambig_fname=ambig_fname,
native_segid=True,
debug=self.params["debug"],
seed=offsetted_seed,
)
out_file = f"emref_{idx}.out"
err_fname = f"emref_{idx}.cnserr"
# create the expected PDBobject
expected_pdb = prepare_expected_pdb(model, idx, ".", "emref")
expected_pdb.restr_fname = ambig_fname
try:
expected_pdb.ori_name = model.file_name
except AttributeError:
expected_pdb.ori_name = None
self.output_models.append(expected_pdb)
job = CNSJob(emref_input, out_file, err_fname, envvars=self.envvars)
jobs.append(job)
idx += 1
# Run CNS Jobs
self.log(f"Running CNS Jobs n={len(jobs)}")
Engine = get_engine(self.params["mode"], self.params)
engine = Engine(jobs)
engine.run()
self.log("CNS jobs have finished")
# Get the weights needed for the CNS module
_weight_keys = ("w_vdw", "w_elec", "w_desolv", "w_air", "w_bsa")
weights = {e: self.params[e] for e in _weight_keys}
for pdb in self.output_models:
if pdb.is_present():
haddock_model = HaddockModel(pdb.file_name)
pdb.unw_energies = haddock_model.energies
haddock_score = haddock_model.calc_haddock_score(**weights)
pdb.score = haddock_score
self.export_io_models(faulty_tolerance=self.params["tolerance"])
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