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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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | """Calculate CAPRI metrics for the input models.
By default the following metrics are calculated:
- FNAT (fraction of native contacts), namely the fraction of
intermolecular contacts in the docked complex that are also
present in the reference complex.
- IRMSD (interface root mean square deviation), namely the RMSD
of the interface of the docked complex with respect
to the reference complex.
- LRMSD (ligand root mean square deviation), namely the RMSD of the
ligand of the docked complex with respect to the
reference complex upon superposition of the receptor.
- DOCKQ, a measure of the quality of the docked model obtained
by combining FNAT, IRMSD and LRMSD (see
Basu and Wallner 2016, 11 (8), e0161879).
- ILRMSD (interface ligand root mean square deviation), the RMSD of the
ligand of the docked complex with respect to the reference complex
upon superposition of the interface of the receptor.
- GLOBAL_RMSD, the full RMSD between the reference and the model.
The following files are generated:
- **capri_ss.tsv**: a table with the CAPRI metrics for each model.
- **capri_clt.tsv**: a table with the CAPRI metrics for each cluster of models (if clustering information is available).
For more details about this module, please `refer to the haddock3 user manual
<https://www.bonvinlab.org/haddock3-user-manual/modules/analysis.html#caprieval-module>`_
"""
from pathlib import Path
from haddock.core.defaults import MODULE_DEFAULT_YAML
from haddock.core.typing import FilePath, Union
from haddock.libs.libontology import PDBFile
from haddock.libs.libparallel import Scheduler
from haddock.libs.libaa2cg import martinize
from haddock.libs.libstructure import find_ff
from haddock.libs.libpdb import handle_input_reference
from haddock.modules import BaseHaddockModule
from haddock.libs.libcapri import (
CAPRI,
capri_cluster_analysis,
extract_data_from_capri_class,
extract_models_best_references,
)
from haddock.modules.analysis.caprieval.capri import dump_weights
RECIPE_PATH = Path(__file__).resolve().parent
DEFAULT_CONFIG = Path(RECIPE_PATH, MODULE_DEFAULT_YAML)
class HaddockModule(BaseHaddockModule):
"""HADDOCK3 module to calculate the CAPRI metrics."""
name = RECIPE_PATH.name
def __init__(
self,
order: int,
path: Path,
init_params: FilePath = DEFAULT_CONFIG,
) -> None:
super().__init__(order, path, init_params)
@classmethod
def confirm_installation(cls) -> None:
"""Confirm if contact executable is compiled."""
return
@staticmethod
def is_nested(models: list[Union[PDBFile, list[PDBFile]]]) -> bool:
for model in models:
if isinstance(model, list):
return True
return False
def get_reference(self, models: list[PDBFile]) -> list[Path]:
"""Manage to obtain the reference structure to be used downstream.
Parameters
----------
models : list[PDBFile]
List of input model to be evaluated, among which the best
can serve as reference structure if none provided.
Returns
-------
references : list[Path]
List of paths to the reference(s) structure to be used downstream.
"""
if self.params["reference_fname"]:
_reference = Path(self.params["reference_fname"])
references = handle_input_reference(_reference)
else:
self.log(
"No reference structure provided. "
"Using the structure with the lowest score from previous step"
)
# Sort by score to find the "best"
models.sort()
best_model = models[0]
assert isinstance(best_model, PDBFile), "Best model is not a PDBFile"
best_model_fname = best_model.rel_path
references = [best_model_fname]
return references
def _run(self) -> None:
"""Execute module."""
# Get the models generated in previous step
models = self.previous_io.retrieve_models(individualize=True)
if self.is_nested(models):
raise ValueError(
"CAPRI module cannot be executed after "
"modules that produce a nested list of models."
)
# dump previously used weights
dump_weights(self.order)
# Find force-field
ff = find_ff(models)
# Get reference file
if ff == "martini2":
references = [
Path(martinize(ref_aa, self.path.resolve().parent, False))
for ref_aa in self.get_reference(models)
]
else:
references = self.get_reference(models)
# Each model is a job; this is not the most efficient way
# but by assigning each model to an individual job
# we can handle scenarios in which the models are hetergoneous
# for example during CAPRI scoring
jobs: list[CAPRI] = []
# Loop over models
for i, model_to_be_evaluated in enumerate(models, start=1):
# `models_to_be_evaluated` cannot be a list,
# `CAPRI` class is expecting a single model
if isinstance(model_to_be_evaluated, list):
raise ValueError(
"CAPRI module cannot handle a list of `model_to_be_evaluated`"
)
# Loop over references
for ref_id, reference in enumerate(references, start=1):
jobs.append(
CAPRI(
identificator=i,
model=model_to_be_evaluated,
path=Path("."),
reference=reference,
params=self.params,
ref_id=ref_id,
ff=ff,
)
)
engine = Scheduler(
tasks=jobs,
ncores=self.params["ncores"],
max_cpus=self.params["max_cpus"],
)
engine.run()
jobs = engine.results
jobs = sorted(jobs, key=lambda capri: capri.identificator)
# Extract best references per input model
best_ref_jobs = extract_models_best_references(jobs)
# Write standard capri_ss file
extract_data_from_capri_class(
capri_objects=best_ref_jobs,
output_fname=Path(".", "capri_ss.tsv"),
sort_key=self.params["sortby"],
sort_ascending=self.params["sort_ascending"],
add_reference_id=len(references) > 1,
)
# Perform cluster analysis
capri_cluster_analysis(
capri_list=best_ref_jobs,
model_list=models, # type: ignore # ignore this here only if we are checking the return type of `retrieve_models` is not nested!!
output_fname="capri_clt.tsv",
clt_threshold=self.params["clt_threshold"],
# output_count=len(capri_jobs),
sort_key=self.params["sortby"],
sort_ascending=self.params["sort_ascending"],
path=Path("."),
)
# In case multiple references are provided, generate an additional file
# containing the information to traceback metrics related to each ref
if len(references) > 1:
extract_data_from_capri_class(
capri_objects=jobs,
output_fname=Path(".", "capri_ss_multiref.tsv"),
sort_key=self.params["sortby"],
sort_ascending=self.params["sort_ascending"],
add_reference_id=True,
)
# Send models to the next step, no operation is done on them
self.output_models = models # type: ignore # ignore this here only if we are checking the return type of `retrieve_models` is not nested!!
self.export_io_models()
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