"""
Configuration dataclasses for the database, matrix and io.
"""
import json
from dataclasses import dataclass
from pathlib import Path
from lXtractor.core.exceptions import MissingData
from kinactive.base import DATA_LINKS_PATH
PK_NAME = "PK"
DFG_MAP = {"in": 0, "out": 1, "other": 2}
DFG_MAP_REV = {v: k for k, v in DFG_MAP.items()}
[docs]
@dataclass
class DBConfig:
"""
Database config.
Default parameters were used to create lXt-PK data collection.
To reproduce locally, you may change the paths (``*_dir*``) and adjust
the number of cpus (``*_cpus``).
"""
#: progress bar output.
verbose: bool = True
#: database dump path.
target_dir: Path = Path("db")
#: raw PDB structures.
pdb_dir: Path = Path("pdb") / "structures"
#: info on PDB structures.
pdb_dir_info: Path = Path("pdb") / "info"
#: raw UniProt sequences.
seq_dir: Path = Path("uniprot") / "fasta"
#: max trials for fetching an entry from external resources.
max_fetch_trials: int = 2
#: #cpus for ``ChainIO`` (10-20 usually works fine).
io_cpus: int = 1
#: #cpus for ``ChainInitializer`` (10-20 usually works fine).
init_cpus: int = 1
#: #cpus for pairwise sequence alignments. Increase to max number possible.
init_map_numbering_cpus: int = 1
#: #cpus for adding structures to a chain. Valid if `init_map_numbering_cpus`
#: is > 1.
init_add_structure_cpus: int = 1
#: Tolerate initialization failures. Should be ``True`` unless testing
init_tolerate_failures: bool = True
#: A path to the PK profile (supplied with the package)
profile: Path = Path(__file__).parent / "resources" / "PF00069.hmm"
#: A map between TK and PK profile nodes
tk2pk: Path = Path(__file__).parent / "resources" / "tk2pk.json"
#: the domain name to use for extraction.
pk_map_name: str = PK_NAME
#: a minimum BitScore to qualify for hit.
pk_min_score: float = 50
#: min domain size for canonical sequences.
pk_min_seq_domain_size: int = 150
#: min domain size for structure sequences.
pk_min_str_domain_size: int = 100
#: min coverage of the hmm nodes.
pk_min_cov_hmm: float = 0.5
#: min coverage of the sequence.
pk_min_cov_seq: float = 0.5
#: min matching residues' fraction between structure and canonical sequences.
pk_min_str_seq_match: float = 0.8
#: minimum sequence size to filter raw sequences from UniProt.
min_seq_size: int = 150
#: maximum sequence size to filter raw sequences from UniProt.
max_seq_size: int = 5000
#: PDB files format.
pdb_fmt: str = "mmtf.gz"
#: The number of threads to use when fetching data from the PDB.
pdb_num_fetch_threads: int = 10
#: The minimum structure size (in residues) to filter raw structures.
pdb_str_min_size: int = 100
#: The chunk size to split UniProt ids into when fetching the data from UniProt.
uniprot_chunk_size: int = 100
#: The number of threads to use when fetching the data from UniProt.
uniprot_num_fetch_threads: int = 10
def __post_init__(self):
if not self.profile.exists():
raise MissingData(f"Missing PK profile under {self.profile} path")
[docs]
@dataclass
class MatrixConfig:
"""
The superposition-based matrix configuration. This matrix is used to compute
"""
#: Path to dump the results.
dir: Path = Path("clustering")
#: The number of the most covered HMM nodes to use for superposing.
n_super_pos: int = 30
#: The PK domain name. Should be the same as used in :class:`kinactive.db.DB`.
pk_map_name: str = PK_NAME
#: The number of cpus to use for parallel computation. Adjust carefully.
n_proc: int | None = None
#: The chunk size for distributing data between processes.
chunksize: int = 5000
#: DFG-Asp/Phe positions.
df_pos: tuple[int, int] = (141, 142)
#: Backbone atom names used for superposing.
bb_atoms: tuple[str, ...] = ("CA",)
#: DFG-Phe atom names used for RMSD computation.
phe_atoms: tuple[str, ...] = ("CA", "CB", "CG", "CD1", "CD2", "CE1", "CE2", "CZ")
#: DFG-Asp atom names used for RMSD computation.
asp_atoms: tuple[str, ...] = ("CA", "CB", "CG", "OD1", "OD2")
@dataclass
class _DumpNames:
cls_keyword: str = "classifier"
reg_keyword: str = "regressor"
bin_model: str = "model.bin"
json_model: str = "model.json"
features: str = "features.txt"
targets: str = "targets.txt"
params: str = "params.json"
in_model_dirname: str = "in"
out_model_dirname: str = "out"
other_model_dirname: str = "other"
meta_model_dirname: str = "meta"
positions_ca: str = "positions_CA.txt"
distances: str = "distances.csv"
summary_parent_seq = "initial_seq_summary.csv"
summary_parent_str = "initial_str_summary.csv"
summary_child_seq = "domain_seq_summary.csv"
summary_child_str = "domain_str_summary.csv"
canonical_seq_vs = "defaults_can_seq_vs.csv"
structure_seq_vs = "defaults_str_seq_vs.csv"
ligand_vs = "default_lig_vs.csv"
structure_vs = "default_str_vs.csv"
@property
def summary_file_names(self) -> tuple[str, str, str, str]:
return (
self.summary_parent_seq,
self.summary_parent_str,
self.summary_child_seq,
self.summary_child_str,
)
@dataclass
class _ModelPaths:
base: Path = Path(__file__).parent / "resources" / "models"
kinactive_classifier: Path = base / "kinactive_classifier"
dfg_classifier: Path = base / "DFG_classifier"
@dataclass
class _ColNames:
dfg: str = "DFG"
dfg_manual: str = "DFG_manual"
dfg_pred = "DFG_pred"
dfg_cls: str = "DFG_cls"
dfg_cls_pred: str = "DFG_cls_pred"
is_dfg_in: str = "is_DFG_in"
is_dfg_out: str = "is_DFG_out"
is_dfg_other: str = "is_DFG_other"
dfg_in_proba: str = "in_proba"
dfg_out_proba: str = "out_proba"
dfg_other_proba: str = "other_proba"
dfg_in_meta_prob: str = "in_meta_proba"
dfg_out_meta_prob: str = "out_meta_prob"
dfg_other_meta_prob: str = "other_meta_prob"
ahao_col = "DFGout_holo"
aaio_col = "DFGout_apo"
ahao_prob_col = "DFGout_holo_prob"
aaio_prob_col = "DFGout_apo_prob"
rmsd_ca: str = "RMSD_CA"
rmsd_df: str = "RMSD_DF"
id_fix: str = "ID_fix"
id_mob: str = "ID_mob"
@property
def is_dfg_cols(self) -> tuple[str, str, str]:
return self.is_dfg_in, self.is_dfg_out, self.is_dfg_other
@property
def dfg_proba_cols(self) -> tuple[str, str, str]:
return self.dfg_in_proba, self.dfg_out_proba, self.dfg_other_proba
@property
def dfg_meta_proba_cols(self) -> tuple[str, str, str]:
return self.dfg_in_meta_prob, self.dfg_out_meta_prob, self.dfg_other_meta_prob
@property
def dfg_cols(self) -> list[str]:
return [
self.dfg,
self.dfg_pred,
self.dfg_cls,
self.dfg_cls_pred,
*self.is_dfg_cols,
*self.dfg_proba_cols,
*self.dfg_meta_proba_cols,
]
[docs]
def load_data_links(path: Path = DATA_LINKS_PATH) -> dict[str, str]:
with path.open() as f:
return json.load(f)
DumpNames = _DumpNames()
ColNames = _ColNames()
ModelPaths = _ModelPaths()
DefaultMatrixConfig = MatrixConfig()
if __name__ == "__main__":
raise RuntimeError