File size: 15,434 Bytes
59aa3b9 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 | """Lightweight PDB/mmCIF readers for Evo-IF inference.
Only the information consumed by ``PDBDataset.load_chains`` and
``PDBDataset.load_assembly`` is retained: polymer type, atom coordinates and
occupancies, and biological-assembly transforms.
"""
from __future__ import annotations
import gzip
import itertools
import re
from collections import OrderedDict, namedtuple
from contextlib import contextmanager
from pathlib import Path
from typing import Iterator, Mapping, Sequence, TextIO
import numpy as np
from Bio.PDB import PDBParser as BioPDBParser
from Bio.PDB.MMCIF2Dict import MMCIF2Dict
Atom = namedtuple("Atom", ["name", "xyz", "occ", "bfac"])
Chain = namedtuple("Chain", ["id", "type", "sequence", "atoms"])
PROTEIN_RESTYPES = {
"ALA",
"ARG",
"ASN",
"ASP",
"CYS",
"GLN",
"GLU",
"GLY",
"HIS",
"ILE",
"LEU",
"LYS",
"MET",
"PHE",
"PRO",
"SER",
"THR",
"TRP",
"TYR",
"VAL",
"UNK",
}
DNA_RESTYPES = {"DA", "DC", "DG", "DT", "DX"}
RNA_RESTYPES = {"A", "C", "G", "U", "RX"}
POLYMER_TYPES = {
"polypeptide(L)",
"polydeoxyribonucleotide",
"polyribonucleotide",
"polydeoxyribonucleotide/polyribonucleotide hybrid",
}
@contextmanager
def _open_text(path: str | Path) -> Iterator[TextIO]:
value = str(Path(path).expanduser())
if value.lower().endswith(".gz"):
with gzip.open(value, "rt") as handle:
yield handle
else:
with open(value, "rt") as handle:
yield handle
def _as_list(value) -> list[str]:
if value is None:
return []
if isinstance(value, list):
return value
if isinstance(value, tuple):
return list(value)
return [value]
def _column(
data: Mapping[str, object],
name: str,
*,
length: int | None = None,
default: str | None = None,
) -> list[str]:
values = _as_list(data.get(name))
if values:
return values
if length is not None and default is not None:
return [default] * length
raise ValueError(f"mmCIF is missing required column {name}")
def _safe_float(value: str, default: float = 0.0) -> float:
if value in {"", ".", "?"}:
return default
return float(value)
def _parse_operation_group(expression: str) -> list[str]:
operations: list[str] = []
for item in expression.strip("() ").split(","):
item = item.strip()
if not item:
continue
match = re.fullmatch(r"(-?\d+)-(-?\d+)", item)
if match is None:
operations.append(item)
continue
start, stop = (int(value) for value in match.groups())
step = 1 if stop >= start else -1
operations.extend(str(value) for value in range(start, stop + step, step))
return operations
def _operation_groups(expression: str) -> list[list[str]]:
parenthesized = re.findall(r"\(([^()]*)\)", expression)
raw_groups = parenthesized or [expression]
groups = [_parse_operation_group(group) for group in raw_groups]
if not groups or any(not group for group in groups):
raise ValueError(f"Invalid mmCIF operation expression: {expression!r}")
return groups
def _read_operation_matrices(data: Mapping[str, object]) -> dict[str, np.ndarray]:
operation_ids = _as_list(data.get("_pdbx_struct_oper_list.id"))
if not operation_ids:
return {}
columns: dict[str, list[str]] = {}
for row in range(3):
columns[f"v{row}"] = _column(
data,
f"_pdbx_struct_oper_list.vector[{row + 1}]",
)
for col in range(3):
columns[f"m{row}{col}"] = _column(
data,
f"_pdbx_struct_oper_list.matrix[{row + 1}][{col + 1}]",
)
matrices: dict[str, np.ndarray] = {}
for index, operation_id in enumerate(operation_ids):
matrix = np.eye(4, dtype=np.float64)
for row in range(3):
matrix[row, 3] = float(columns[f"v{row}"][index])
for col in range(3):
matrix[row, col] = float(columns[f"m{row}{col}"][index])
matrices[operation_id] = matrix
return matrices
def _compose_operations(
expression: str,
matrices: Mapping[str, np.ndarray],
) -> list[np.ndarray]:
groups = _operation_groups(expression)
transforms: list[np.ndarray] = []
for operation_tuple in itertools.product(*groups):
transform = np.eye(4, dtype=np.float64)
for operation_id in operation_tuple:
if operation_id not in matrices:
raise ValueError(
f"mmCIF assembly references missing operation {operation_id!r}"
)
transform = transform @ matrices[operation_id]
transforms.append(transform)
return transforms
def _identity_assembly(chains: Mapping[str, Chain]) -> dict[str, list[tuple[str, np.ndarray]]]:
return {"1": [(chain_id, np.eye(4, dtype=np.float64)) for chain_id in chains]}
def _read_mmcif_assemblies(
data: Mapping[str, object],
chains: Mapping[str, Chain],
) -> dict[str, list[tuple[str, np.ndarray]]]:
assembly_ids = _as_list(data.get("_pdbx_struct_assembly_gen.assembly_id"))
expressions = _as_list(data.get("_pdbx_struct_assembly_gen.oper_expression"))
chain_lists = _as_list(data.get("_pdbx_struct_assembly_gen.asym_id_list"))
if not assembly_ids or not expressions or not chain_lists:
return _identity_assembly(chains)
if not (len(assembly_ids) == len(expressions) == len(chain_lists)):
raise ValueError("Inconsistent mmCIF biological-assembly columns")
matrices = _read_operation_matrices(data)
assemblies: dict[str, list[tuple[str, np.ndarray]]] = {}
for assembly_id, expression, chain_list in zip(
assembly_ids,
expressions,
chain_lists,
):
transforms = _compose_operations(expression, matrices)
selected_chains = [
chain_id.strip()
for chain_id in chain_list.split(",")
if chain_id.strip() in chains
]
entries = assemblies.setdefault(assembly_id, [])
entries.extend(
(chain_id, transform)
for chain_id in selected_chains
for transform in transforms
)
nonempty = {key: value for key, value in assemblies.items() if value}
if not nonempty:
raise ValueError(
"mmCIF declares biological assemblies, but none reference a "
"supported parsed polymer chain"
)
return nonempty
def _mmcif_metadata(data: Mapping[str, object], model_id: str) -> dict[str, object]:
def first(name: str, default=None):
values = _as_list(data.get(name))
return values[0] if values else default
resolution = first("_refine.ls_d_res_high")
if resolution in {None, ".", "?"}:
resolution = first("_em_3d_reconstruction.resolution")
try:
resolution = float(resolution) if resolution not in {None, ".", "?"} else None
except (TypeError, ValueError):
resolution = None
return {
"method": first("_exptl.method"),
"date": first("_pdbx_database_status.recvd_initial_deposition_date"),
"resolution": resolution,
"model_id": model_id,
"assembly_source": "mmcif",
}
class CIFParser:
"""Read polymer coordinates and declared biological assemblies from mmCIF."""
def __init__(
self,
skip_res: Sequence[str] | None = None,
randomize_nmr_model: int = 0,
) -> None:
self.skip_res = set(skip_res or ())
self.randomize_nmr_model = int(randomize_nmr_model)
def parse(self, filename: str | Path):
with _open_text(filename) as handle:
data = MMCIF2Dict(handle)
entity_ids = _column(data, "_entity_poly.entity_id")
entity_types = _column(data, "_entity_poly.type")
entity_type = {
entity_id: polymer_type
for entity_id, polymer_type in zip(entity_ids, entity_types)
if polymer_type in POLYMER_TYPES
}
chain_ids = _column(data, "_atom_site.label_asym_id")
row_count = len(chain_ids)
columns = {
"chain": chain_ids,
"entity": _column(data, "_atom_site.label_entity_id"),
"residue_id": _column(data, "_atom_site.label_seq_id"),
"residue_name": _column(data, "_atom_site.label_comp_id"),
"atom_name": _column(data, "_atom_site.label_atom_id"),
"x": _column(data, "_atom_site.Cartn_x"),
"y": _column(data, "_atom_site.Cartn_y"),
"z": _column(data, "_atom_site.Cartn_z"),
"occupancy": _column(
data,
"_atom_site.occupancy",
length=row_count,
default="1.0",
),
"bfactor": _column(
data,
"_atom_site.B_iso_or_equiv",
length=row_count,
default="0.0",
),
"model": _column(
data,
"_atom_site.pdbx_PDB_model_num",
length=row_count,
default="1",
),
}
if any(len(values) != row_count for values in columns.values()):
raise ValueError("Inconsistent mmCIF atom_site column lengths")
model_ids = list(dict.fromkeys(columns["model"]))
selected_model = model_ids[0]
if self.randomize_nmr_model and len(model_ids) > 1:
selected_model = str(np.random.choice(model_ids))
chain_data: OrderedDict[str, dict[str, object]] = OrderedDict()
for row in zip(*(columns[name] for name in columns)):
(
chain_id,
entity_id,
residue_id,
residue_name,
atom_name,
x,
y,
z,
occupancy,
bfactor,
model_id,
) = row
polymer_type = entity_type.get(entity_id)
if polymer_type is None or model_id != selected_model:
continue
if residue_id in {"", ".", "?"} or residue_name in self.skip_res:
continue
item = chain_data.setdefault(
chain_id,
{"type": polymer_type, "atoms": OrderedDict()},
)
atom_key = (chain_id, residue_id, residue_name, atom_name)
atom = Atom(
name=atom_key,
xyz=[float(x), float(y), float(z)],
occ=_safe_float(occupancy, 1.0),
bfac=_safe_float(bfactor, 0.0),
)
atoms = item["atoms"]
previous = atoms.get(atom_key)
if previous is None or atom.occ > previous.occ:
atoms[atom_key] = atom
chains = {
chain_id: Chain(
id=chain_id,
type=item["type"],
sequence=None,
atoms=item["atoms"],
)
for chain_id, item in chain_data.items()
if item["atoms"]
}
if not chains:
raise ValueError(f"No supported polymer chains found in {filename}")
assemblies = _read_mmcif_assemblies(data, chains)
metadata = _mmcif_metadata(data, selected_model)
return chains, assemblies, [], metadata
def _classify_pdb_chain(residue_names: set[str]) -> str | None:
has_protein = bool(residue_names & PROTEIN_RESTYPES)
has_dna = bool(residue_names & DNA_RESTYPES)
has_rna = bool(residue_names & RNA_RESTYPES)
if has_protein and not has_dna and not has_rna:
return "polypeptide(L)"
if has_dna and not has_protein and not has_rna:
return "polydeoxyribonucleotide"
if has_rna and not has_protein and not has_dna:
return "polyribonucleotide"
if has_dna and has_rna and not has_protein:
return "polydeoxyribonucleotide/polyribonucleotide hybrid"
if has_protein or has_dna or has_rna:
raise ValueError("A PDB chain mixes protein and nucleic-acid residues")
return None
class PDBParser:
"""Read the coordinates present in a PDB file as one identity assembly."""
def __init__(self) -> None:
self._parser = BioPDBParser(QUIET=True)
def parse(self, filename: str | Path):
with _open_text(filename) as handle:
structure = self._parser.get_structure(Path(filename).stem, handle)
try:
model = next(structure.get_models())
except StopIteration as exc:
raise ValueError(f"No coordinate model found in {filename}") from exc
chains: dict[str, Chain] = {}
for bio_chain in model:
residues = list(bio_chain.get_residues())
residue_names = {residue.get_resname().strip() for residue in residues}
polymer_type = _classify_pdb_chain(residue_names)
if polymer_type is None:
continue
allowed = PROTEIN_RESTYPES | DNA_RESTYPES | RNA_RESTYPES
atoms: OrderedDict[tuple[str, str, str, str], Atom] = OrderedDict()
for residue in residues:
residue_name = residue.get_resname().strip()
if residue_name not in allowed:
continue
residue_id = str(int(residue.id[1]))
for bio_atom in residue.get_atoms():
atom_name = bio_atom.get_name().strip()
atom_key = (bio_chain.id, residue_id, residue_name, atom_name)
occupancy = bio_atom.get_occupancy()
bfactor = bio_atom.get_bfactor()
atom = Atom(
name=atom_key,
xyz=bio_atom.get_coord().astype(float).tolist(),
occ=float(occupancy) if occupancy is not None else 0.0,
bfac=float(bfactor) if bfactor is not None else 0.0,
)
previous = atoms.get(atom_key)
if previous is None or atom.occ > previous.occ:
atoms[atom_key] = atom
if atoms:
chains[bio_chain.id] = Chain(
id=bio_chain.id,
type=polymer_type,
sequence=None,
atoms=atoms,
)
if not chains:
raise ValueError(f"No supported polymer chains found in {filename}")
metadata = {
"model_id": str(model.id),
"assembly_source": "coordinates_as_provided",
}
return chains, _identity_assembly(chains), [], metadata
def parse_structure(
filename: str | Path,
*,
skip_res: Sequence[str] | None = None,
randomize_nmr_model: int = 0,
):
"""Dispatch to the lightweight PDB or mmCIF reader by filename suffix."""
value = str(filename).lower()
if value.endswith((".pdb", ".pdb.gz")):
return PDBParser().parse(filename)
if value.endswith((".cif", ".cif.gz", ".mmcif", ".mmcif.gz")):
return CIFParser(
skip_res=skip_res,
randomize_nmr_model=randomize_nmr_model,
).parse(filename)
raise ValueError(f"Unsupported structure format: {filename}")
__all__ = ["Atom", "Chain", "CIFParser", "PDBParser", "parse_structure"]
|