File size: 49,642 Bytes
fb8a87c b44701d fb8a87c | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 | from __future__ import annotations
import io
import itertools
import random
import re
import warnings
from dataclasses import asdict, dataclass, replace
from functools import cached_property
from pathlib import Path
from subprocess import check_output
from tempfile import TemporaryDirectory
from typing import Any, Iterable, Sequence
import biotite.structure as bs
import brotli
import msgpack
import msgpack_numpy
import numpy as np
import torch
from biotite.database import rcsb
from biotite.file import InvalidFileError
from biotite.structure.io.pdb import PDBFile
from biotite.structure.io.pdbx import CIFCategory, CIFColumn, CIFData, CIFFile
from biotite.structure.io.pdbx import set_structure as set_structure_pdbx
from biotite.structure.io.pdbx.convert import _get_transformations, get_structure
from biotite.structure.util import matrix_rotate
from scipy.spatial import KDTree
from . import esmfold2_residue_constants as residue_constants
from .esmfold2_misc import slice_python_object_as_numpy
from .esmfold2_affine3d import Affine3D
from .esmfold2_aligner import Aligner
from .esmfold2_atom_indexer import AtomIndexer
from .esmfold2_metrics import compute_gdt_ts, compute_lddt_ca
from .esmfold2_mmcif_parsing import MmcifWrapper, NoProteinError
from .esmfold2_protein_chain import (
ProteinChain,
_str_key_to_int_key,
chain_to_ndarray,
index_by_atom_name,
infer_CB,
)
from .esmfold2_utils_types import PathOrBuffer
msgpack_numpy.patch()
SINGLE_LETTER_CHAIN_IDS = (
"ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789"
)
def _parse_operation_expression(expression):
"""
Get successive operation steps (IDs) for the given
``oper_expression``.
Form the cartesian product, if necessary.
Copied from biotite and fixed a bug
"""
# Split groups by parentheses:
# use the opening parenthesis as delimiter
# and just remove the closing parenthesis
expressions_per_step = expression.replace(")", "").split("(")
expressions_per_step = [e for e in expressions_per_step if len(e) > 0]
# Important: Operations are applied from right to left
expressions_per_step.reverse()
operations = []
for expr in expressions_per_step:
cur_expr = expr.split(",")
cur_op = []
# Deal with e='1-10,20-30,40-50' type expressions
for e in cur_expr:
if "-" in e:
first, last = e.split("-")
cur_op.extend(str(id) for id in range(int(first), int(last) + 1))
else:
cur_op.append(e)
operations.append(cur_op)
# Cartesian product of operations
return list(itertools.product(*operations))
def _apply_transformations_fast(chains, transformation_dict, operations):
"""
Get subassembly by applying the given operations to the input
structure containing affected asym IDs.
"""
# Additional first dimesion for 'structure.repeat()'
results = []
# Apply corresponding transformation for each copy in the assembly
for c in chains:
for operation in operations:
coord = c.atom37_positions.copy()
# Execute for each transformation step
# in the operation expression
for op_step in operation:
T = transformation_dict[op_step]
# Rotate
coord = matrix_rotate(coord, T.rotation)
# Translate
coord += T.target_translation
new_chain = replace(c, atom37_positions=coord)
results.append(new_chain)
return results
@dataclass
class ProteinComplexMetadata:
entity_lookup: dict[int, int]
chain_lookup: dict[int, str]
mmcif: MmcifWrapper | None = None
# This is a dictionary that maps assembly ids to the list of unique chains
# in that assembly. Allows for usage of `switch_assembly`.
assembly_composition: dict[str, list[str]] | None = None
@dataclass
class DockQSingleScore:
native_chains: tuple[str, str]
DockQ: float
interface_rms: float
ligand_rms: float
fnat: float
fnonnat: float
clashes: float
F1: float
DockQ_F1: float
@dataclass
class DockQResult:
total_dockq: float
native_interfaces: int
chain_mapping: dict[str, str]
interfaces: dict[tuple[str, str], DockQSingleScore]
# zip(aligned.chain_iter(), native.chain_iter()) gives you the pairing
# aligned.rmsd(native) should give you a low rmsd irrespective of shuffling
aligned: ProteinComplex
aligned_rmsd: float
@dataclass(frozen=True)
class ProteinComplex:
"""Dataclass with atom37 representation of an entire protein complex."""
id: str
sequence: str
entity_id: np.ndarray # entities map to unique sequences
chain_id: np.ndarray # multiple chains might share an entity id
sym_id: np.ndarray # complexes might be copies of the same chain
residue_index: np.ndarray
insertion_code: np.ndarray
atom37_positions: np.ndarray
atom37_mask: np.ndarray
confidence: np.ndarray
# This metadata is parsed from the MMCIF file. For synthetic data, we do a best effort.
metadata: ProteinComplexMetadata
atom37_confidence: np.ndarray | None = None # [L, 37] per-atom pLDDT
def __post_init__(self):
l = len(self.sequence)
assert self.atom37_positions.shape[0] == l, (self.atom37_positions.shape, l)
assert self.atom37_mask.shape[0] == l, (self.atom37_mask.shape, l)
assert self.residue_index.shape[0] == l, (self.residue_index.shape, l)
assert self.insertion_code.shape[0] == l, (self.insertion_code.shape, l)
assert self.confidence.shape[0] == l, (self.confidence.shape, l)
assert self.entity_id.shape[0] == l, (self.entity_id.shape, l)
assert self.chain_id.shape[0] == l, (self.chain_id.shape, l)
assert self.sym_id.shape[0] == l, (self.sym_id.shape, l)
if self.atom37_confidence is not None:
assert self.atom37_confidence.shape == self.atom37_mask.shape, (
self.atom37_confidence.shape,
self.atom37_mask.shape,
)
def __getitem__(self, idx: int | list[int] | slice | np.ndarray):
"""This function slices protein complexes without consideration of chain breaks
NOTE: When slicing with a boolean mask, it's possible that the output array won't
be the expected length. This is because we do our best to preserve chainbreak tokens.
"""
if isinstance(idx, int):
idx = [idx]
if isinstance(idx, list):
raise ValueError(
"ProteinComplex doesn't supports indexing with lists of indices"
)
if isinstance(idx, np.ndarray):
is_chainbreak = np.asarray([s == "|" for s in self.sequence])
idx = idx.astype(bool) | is_chainbreak
complex = self._unsafe_slice(idx)
if len(complex) == 0:
return complex
# detect runs of chainbreaks by searching for instances of '||' in complex.sequence
chainbreak_runs = np.asarray(
[
complex.sequence[i : i + 2] == "||"
for i in range(len(complex.sequence) - 1)
]
+ [complex.sequence[-1] == "|"]
)
# We should remove as many chainbreaks as possible from the start of the sequence
for i in range(len(chainbreak_runs)):
if complex.sequence[i] == "|":
chainbreak_runs[i] = True
else:
break
complex = complex._unsafe_slice(~chainbreak_runs)
return complex
def _unsafe_slice(self, idx: int | list[int] | slice | np.ndarray):
sequence = slice_python_object_as_numpy(self.sequence, idx)
return replace(
self,
sequence=sequence,
entity_id=self.entity_id[..., idx],
chain_id=self.chain_id[..., idx],
sym_id=self.sym_id[..., idx],
residue_index=self.residue_index[..., idx],
insertion_code=self.insertion_code[..., idx],
atom37_positions=self.atom37_positions[..., idx, :, :],
atom37_mask=self.atom37_mask[..., idx, :],
confidence=self.confidence[..., idx],
atom37_confidence=self.atom37_confidence[..., idx, :]
if self.atom37_confidence is not None
else None,
)
def __len__(self):
return len(self.sequence)
@property
def num_chains(self):
return len(self.chain_boundaries)
@cached_property
def atoms(self) -> AtomIndexer:
return AtomIndexer(self, property="atom37_positions", dim=-2)
@cached_property
def atom_mask(self) -> AtomIndexer:
return AtomIndexer(self, property="atom37_mask", dim=-1)
@cached_property
def chain_lengths(self) -> np.ndarray:
return np.diff(self.chain_boundaries, axis=1).flatten()
@cached_property
def chain_boundaries(self) -> list[tuple[int, int]]:
cb = [-1]
for i, s in enumerate(self.sequence):
if s == "|":
cb.append(i)
cb.append(len(self))
return [(cb[i] + 1, cb[i + 1]) for i in range(len(cb) - 1)]
def get_chain_by_index(self, index: int) -> ProteinChain:
try:
start, end = self.chain_boundaries[index]
return self[start:end].as_chain()
except IndexError:
raise IndexError(f"Chain index {index} out of bounds")
def get_chain_by_id(
self, chain_id: str, sample_chain_if_duplicate: bool = True
) -> ProteinChain:
valid_indices = [
index
for index, id_of_index in self.metadata.chain_lookup.items()
if id_of_index == chain_id
]
if not valid_indices:
raise KeyError(f"Chain ID {chain_id} not found")
if sample_chain_if_duplicate:
index_to_return = random.choice(valid_indices)
return self.get_chain_by_index(index_to_return)
else:
if len(valid_indices) > 1:
raise ValueError(f"Multiple chains with chain ID {chain_id} found")
return self.get_chain_by_index(valid_indices[0])
def chain_iter(self) -> Iterable[ProteinChain]:
for start, end in self.chain_boundaries:
c = self[start:end]
yield c.as_chain()
def as_chain(self, force_conversion: bool = False) -> ProteinChain:
"""Convert the ProteinComplex to a ProteinChain.
Args:
force_conversion (bool): Forces the conversion into a protein chain even if the complex has multiple chains.
The purpose of this is to use ProteinChain specific functions (like cbeta_contacts).
"""
if not force_conversion:
assert len(np.unique(self.chain_id)) == 1, f"{self.id}"
assert len(np.unique(self.entity_id)) == 1, f"{self.id}"
if self.chain_id[0] not in self.metadata.chain_lookup:
warnings.warn("Chain ID not found in metadata, using 'A' as default")
if self.entity_id[0] not in self.metadata.entity_lookup:
warnings.warn("Entity ID not found in metadata, using None as default")
chain_id = self.metadata.chain_lookup.get(self.chain_id[0], "A")
entity_id = self.metadata.entity_lookup.get(self.entity_id[0], None)
else:
chain_id = "A"
entity_id = None
return ProteinChain(
id=self.id,
sequence=self.sequence,
chain_id=chain_id,
entity_id=entity_id,
atom37_positions=self.atom37_positions,
atom37_mask=self.atom37_mask,
residue_index=self.residue_index,
insertion_code=self.insertion_code,
confidence=self.confidence,
mmcif=self.metadata.mmcif,
atom37_confidence=self.atom37_confidence,
)
@classmethod
def from_pdb(
cls, path: PathOrBuffer, id: str | None = None, is_predicted: bool = False
) -> "ProteinComplex":
atom_array = PDBFile.read(path).get_structure(
model=1, extra_fields=["b_factor"]
)
chains = []
for chain in bs.chain_iter(atom_array):
chain = chain[~chain.hetero]
if len(chain) == 0:
continue
chains.append(ProteinChain.from_atomarray(chain, id, is_predicted))
return ProteinComplex.from_chains(chains)
def to_pdb(self, path: PathOrBuffer, include_insertions: bool = True):
atom_array = None
for chain in self.chain_iter():
carr = (
chain.atom_array
if include_insertions
else chain.atom_array_no_insertions
)
atom_array = carr if atom_array is None else atom_array + carr
f = PDBFile()
f.set_structure(atom_array)
f.write(path)
def to_pdb_string(self, include_insertions: bool = True) -> str:
buf = io.StringIO()
self.to_pdb(buf, include_insertions=include_insertions)
buf.seek(0)
return buf.read()
def normalize_chain_ids_for_pdb(self):
# Since PDB files have 1-letter chain IDs and don't support the idea of a symmetric index,
# we can normalize it instead which might be necessary for DockQ and to_pdb.
ids = SINGLE_LETTER_CHAIN_IDS
chains = []
for i, chain in enumerate(self.chain_iter()):
chain = replace(chain, chain_id=ids[i])
if i > len(ids):
raise RuntimeError("Too many chains to write to PDB file")
chains.append(chain)
return ProteinComplex.from_chains(chains)
def find_assembly_ids_with_chain(self, id: str) -> list[str]:
good_chains = []
if (comp := self.metadata.assembly_composition) is not None:
for assembly_id, chain_ids in comp.items():
if id in chain_ids:
good_chains.append(assembly_id)
else:
raise ValueError(
"Cannot switch assemblies on this ProteinComplex, you must create the assembly from mmcif to support this"
)
return good_chains
def switch_assembly(self, id: str):
assert self.metadata.mmcif is not None
return get_assembly_fast(self.metadata.mmcif, assembly_id=id)
def state_dict(self, backbone_only=False, json_serializable=False):
"""This state dict is optimized for storage, so it turns things to fp16 whenever
possible. Note that we also only support int32 residue indices, I'm hoping we don't
need more than 2**32 residues..."""
dct = {k: v for k, v in vars(self).items()}
if backbone_only:
dct["atom37_mask"][:, 3:] = False
dct["atom37_positions"] = dct["atom37_positions"][dct["atom37_mask"]]
if dct.get("atom37_confidence") is not None:
dct["atom37_confidence"] = dct["atom37_confidence"][dct["atom37_mask"]]
else:
dct.pop("atom37_confidence", None)
for k, v in dct.items():
if isinstance(v, np.ndarray):
match v.dtype:
case np.int64:
dct[k] = v.astype(np.int32)
case np.float64 | np.float32:
dct[k] = v.astype(np.float16)
case _:
pass
if json_serializable:
dct[k] = v.tolist()
elif isinstance(v, ProteinComplexMetadata):
dct[k] = asdict(v)
dct["metadata"]["mmcif"] = None
# These can be populated with non-serializable objects and are not needed for reconstruction
dct.pop("atoms", None)
dct.pop("atom_mask", None)
dct.pop("per_chain_kd_trees", None)
return dct
def to_blob(self, backbone_only=False) -> bytes:
return brotli.compress(msgpack.dumps(self.state_dict(backbone_only)), quality=5)
@classmethod
def from_state_dict(cls, dct):
# Note: assembly_composition is *supposed* to have string keys.
dct = _str_key_to_int_key(dct, ignore_keys=["assembly_composition"])
for k, v in dct.items():
if isinstance(v, list):
dct[k] = np.array(v)
atom37 = np.full((*dct["atom37_mask"].shape, 3), np.nan)
atom37[dct["atom37_mask"]] = dct["atom37_positions"]
dct["atom37_positions"] = atom37
if "atom37_confidence" in dct:
atom37_conf = np.full(dct["atom37_mask"].shape, np.nan, dtype=np.float32)
atom37_conf[dct["atom37_mask"]] = dct["atom37_confidence"]
dct["atom37_confidence"] = atom37_conf
dct = {
k: (
v.astype(np.float32)
if k in ["atom37_positions", "confidence", "atom37_confidence"]
else v
)
for k, v in dct.items()
}
if "chain_boundaries" in dct:
del dct["chain_boundaries"]
if "chain_boundaries" in dct["metadata"]:
del dct["metadata"]["chain_boundaries"]
dct["metadata"] = ProteinComplexMetadata(**dct["metadata"])
return cls(**dct)
@classmethod
def from_blob(cls, input: Path | str | io.BytesIO | bytes):
"""NOTE(@zlin): blob + sparse coding + brotli + fp16 reduces memory
of chains from 52G/1M chains to 20G/1M chains, I think this is a good first
shot at compressing and dumping chains to disk. I'm sure there's better ways."""
match input:
case Path() | str():
bytes = Path(input).read_bytes()
case io.BytesIO():
bytes = input.getvalue()
case _:
bytes = input
return cls.from_state_dict(
msgpack.loads(brotli.decompress(bytes), strict_map_key=False)
)
@classmethod
def from_rcsb(cls, pdb_id: str, keep_source: bool = False) -> ProteinComplex:
f: io.StringIO = rcsb.fetch(pdb_id, "cif") # type: ignore
return cls.from_mmcif(f, id=pdb_id, keep_source=keep_source, is_predicted=False)
@classmethod
def from_mmcif(
cls,
path: PathOrBuffer,
id: str | None = None,
assembly_id: str | None = None,
is_predicted: bool = False,
keep_source: bool = False,
):
"""Return a ProteinComplex object from an mmcif file.
TODO(@zeming): there's actually multiple complexes per file, but for ease of implementation,
we only consider the first defined complex!
Args:
path (str | Path | io.TextIO): Path or buffer to read mmcif file from. Should be uncompressed.
id (str, optional): String identifier to assign to structure. Will attempt to infer otherwise.
is_predicted (bool): If True, reads b factor as the confidence readout. Default: False.
chain_id (str, optional): Select a chain corresponding to (author) chain id.
"""
mmcif = MmcifWrapper.read(path, id)
return get_assembly_fast(mmcif, assembly_id=assembly_id)
@classmethod
def from_chains(
cls,
chains: Sequence[ProteinChain],
mmcif: MmcifWrapper | None = None,
all_assembly_metadata_dictionary: dict[str, list[str]] | None = None,
):
if not chains:
raise ValueError(
"Cannot create a ProteinComplex from an empty list of chains"
)
# TODO(roshan): Make a proper protein complex class
def join_arrays(arrays: Sequence[np.ndarray], sep: np.ndarray):
full_array = []
for array in arrays:
full_array.append(array)
full_array.append(sep)
full_array = full_array[:-1]
return np.concatenate(full_array, 0)
sep_tokens = {
"residue_index": np.array([-1]),
"insertion_code": np.array([""]),
"atom37_positions": np.full([1, 37, 3], np.nan),
"atom37_mask": np.zeros([1, 37], dtype=bool),
"confidence": np.array([0]),
}
any_has_atom37_conf = any(c.atom37_confidence is not None for c in chains)
if any_has_atom37_conf:
sep_tokens["atom37_confidence"] = np.full([1, 37], np.nan, dtype=np.float32)
def _get_chain_attr(chain: ProteinChain, name: str) -> np.ndarray:
val = getattr(chain, name)
if val is None and name == "atom37_confidence":
return np.full([len(chain), 37], np.nan, dtype=np.float32)
return val
array_args: dict[str, np.ndarray] = {
name: join_arrays([_get_chain_attr(chain, name) for chain in chains], sep)
for name, sep in sep_tokens.items()
}
multimer_arrays = []
chain2num_max = -1
chain2num = {}
ent2num_max = -1
ent2num = {}
total_index = 0
for i, c in enumerate(chains):
num_res = c.residue_index.shape[0]
if c.chain_id not in chain2num:
chain2num[c.chain_id] = (chain2num_max := chain2num_max + 1)
chain_id_array = np.full([num_res], chain2num[c.chain_id], dtype=np.int64)
if c.entity_id is None:
entity_num = (ent2num_max := ent2num_max + 1)
else:
if c.entity_id not in ent2num:
ent2num[c.entity_id] = (ent2num_max := ent2num_max + 1)
entity_num = ent2num[c.entity_id]
entity_id_array = np.full([num_res], entity_num, dtype=np.int64)
sym_id_array = np.full([num_res], i, dtype=np.int64)
multimer_arrays.append(
{
"chain_id": chain_id_array,
"entity_id": entity_id_array,
"sym_id": sym_id_array,
}
)
total_index += num_res + 1
sep = np.array([-1])
update = {
name: join_arrays([dct[name] for dct in multimer_arrays], sep=sep)
for name in ["chain_id", "entity_id", "sym_id"]
}
array_args.update(update)
metadata = ProteinComplexMetadata(
mmcif=mmcif,
chain_lookup={v: k for k, v in chain2num.items()},
entity_lookup={v: k for k, v in ent2num.items()},
assembly_composition=all_assembly_metadata_dictionary,
)
return cls(
id=chains[0].id,
sequence=residue_constants.CHAIN_BREAK_TOKEN.join(
chain.sequence for chain in chains
),
metadata=metadata,
**array_args,
)
def infer_oxygen(self) -> ProteinComplex:
"""Oxygen position is fixed given N, CA, C atoms. Infer it if not provided."""
O_missing_indices = np.argwhere(
~np.isfinite(self.atoms["O"]).all(axis=1)
).squeeze()
O_vector = torch.tensor([0.6240, -1.0613, 0.0103], dtype=torch.float32)
N, CA, C = torch.from_numpy(self.atoms[["N", "CA", "C"]]).float().unbind(dim=1)
N = torch.roll(N, -3)
N[..., -1, :] = torch.nan
# Get the frame defined by the CA-C-N atom
frames = Affine3D.from_graham_schmidt(CA, C, N)
O = frames.apply(O_vector)
atom37_positions = self.atom37_positions.copy()
atom37_mask = self.atom37_mask.copy()
atom37_positions[O_missing_indices, residue_constants.atom_order["O"]] = O[
O_missing_indices
].numpy()
atom37_mask[O_missing_indices, residue_constants.atom_order["O"]] = ~np.isnan(
atom37_positions[O_missing_indices, residue_constants.atom_order["O"]]
).any(-1)
new_chain = replace(
self, atom37_positions=atom37_positions, atom37_mask=atom37_mask
)
return new_chain
def infer_cbeta(self, infer_cbeta_for_glycine: bool = False) -> ProteinComplex:
"""Return a new chain with inferred CB atoms at all residues except GLY.
Args:
infer_cbeta_for_glycine (bool): If True, infers a beta carbon for glycine
residues, even though that residue doesn't have one. Default off.
NOTE(rverkuil): The reason for having this switch in the first place
is that sometimes we want a (inferred) CB coordinate for every residue,
for example for making a pairwise distance matrix, or doing an RMSD
calculation between two designs for a given structural template, w/
CB atoms.
"""
atom37_positions = self.atom37_positions.copy()
atom37_mask = self.atom37_mask.copy()
N, CA, C = np.moveaxis(self.atoms[["N", "CA", "C"]], 1, 0)
# See usage in trDesign codebase.
# https://github.com/gjoni/trDesign/blob/f2d5930b472e77bfacc2f437b3966e7a708a8d37/02-GD/utils.py#L140
inferred_cbeta_positions = infer_CB(C, N, CA, 1.522, 1.927, -2.143)
if not infer_cbeta_for_glycine:
inferred_cbeta_positions[np.array(list(self.sequence)) == "G", :] = np.nan
atom37_positions[:, residue_constants.atom_order["CB"]] = (
inferred_cbeta_positions
)
atom37_mask[:, residue_constants.atom_order["CB"]] = ~np.isnan(
atom37_positions[:, residue_constants.atom_order["CB"]]
).any(-1)
new_chain = replace(
self, atom37_positions=atom37_positions, atom37_mask=atom37_mask
)
return new_chain
@classmethod
def from_open_source(cls, pc: ProteinComplex):
# TODO(@zeming): deprecated, should delete
return pc
@classmethod
def concat(cls, objs: list[ProteinComplex]) -> ProteinComplex:
pdb_ids = [obj.id for obj in objs]
if len(set(pdb_ids)) > 1:
raise RuntimeError(
"Concatention of protein complexes across different PDB ids is unsupported"
)
return ProteinComplex.from_chains(
list(itertools.chain.from_iterable(obj.chain_iter() for obj in objs))
)
def _sanity_check_complexes_are_comparable(self, other: ProteinComplex):
assert len(self) == len(other), "Protein complexes must have the same length"
assert len(list(self.chain_iter())) == len(
list(other.chain_iter())
), "Protein complexes must have the same number of chains"
def rmsd(
self,
target: ProteinComplex,
also_check_reflection: bool = False,
mobile_inds: list[int] | np.ndarray | None = None,
target_inds: list[int] | np.ndarray | None = None,
only_compute_backbone_rmsd: bool = False,
compute_chain_assignment: bool = True,
):
"""
Compute the RMSD between this protein chain and another.
Args:
target (ProteinComplex): The target (other) protein complex to compare to.
also_check_reflection (bool, optional): If True, also check if the reflection of the mobile atoms has a lower RMSD.
mobile_inds (list[int], optional): The indices of the mobile atoms to align. These are NOT residue indices
target_inds (list[int], optional): The indices of the target atoms to align. These are NOT residue indices
only_compute_backbone_rmsd (bool, optional): If True, only compute the RMSD of the backbone atoms.
"""
if compute_chain_assignment:
aligned = self.dockq(target).aligned
else:
aligned = self
aligner = Aligner(
aligned if mobile_inds is None else aligned[mobile_inds],
target if target_inds is None else target[target_inds],
only_compute_backbone_rmsd,
)
avg_rmsd = aligner.rmsd
if not also_check_reflection:
return avg_rmsd
aligner = Aligner(
aligned if mobile_inds is None else aligned[mobile_inds],
target if target_inds is None else target[target_inds],
only_compute_backbone_rmsd,
use_reflection=True,
)
avg_rmsd_neg = aligner.rmsd
return min(avg_rmsd, avg_rmsd_neg)
def lddt_ca(
self,
target: ProteinComplex,
mobile_inds: list[int] | np.ndarray | None = None,
target_inds: list[int] | np.ndarray | None = None,
compute_chain_assignment: bool = True,
**kwargs,
) -> float | np.ndarray:
"""Compute the LDDT between this protein complex and another.
Arguments:
target (ProteinComplex): The other protein complex to compare to.
mobile_inds (list[int], np.ndarray, optional): The indices of the mobile atoms to align. These are NOT residue indices
target_inds (list[int], np.ndarray, optional): The indices of the target atoms to align. These are NOT residue indices
Returns:
float | np.ndarray: The LDDT score between the two protein chains, either
a single float or per-residue LDDT scores if `per_residue` is True.
"""
if compute_chain_assignment:
aligned = self.dockq(target).aligned
else:
aligned = self
lddt = compute_lddt_ca(
torch.tensor(aligned.atom37_positions[mobile_inds]).unsqueeze(0),
torch.tensor(target.atom37_positions[target_inds]).unsqueeze(0),
torch.tensor(aligned.atom37_mask[mobile_inds]).unsqueeze(0),
**kwargs,
)
return float(lddt) if lddt.numel() == 1 else lddt.numpy().flatten()
def gdt_ts(
self,
target: ProteinComplex,
mobile_inds: list[int] | np.ndarray | None = None,
target_inds: list[int] | np.ndarray | None = None,
compute_chain_assignment: bool = True,
**kwargs,
) -> float | np.ndarray:
"""Compute the GDT_TS between this protein complex and another.
Arguments:
target (ProteinComplex): The other protein complex to compare to.
mobile_inds (list[int], np.ndarray, optional): The indices of the mobile atoms to align. These are NOT residue indices
target_inds (list[int], np.ndarray, optional): The indices of the target atoms to align. These are NOT residue indices
Returns:
float: The GDT_TS score between the two protein chains.
"""
if compute_chain_assignment:
aligned = self.dockq(target).aligned
else:
aligned = self
gdt_ts = compute_gdt_ts(
mobile=torch.tensor(
index_by_atom_name(aligned.atom37_positions[mobile_inds], "CA"),
dtype=torch.float32,
).unsqueeze(0),
target=torch.tensor(
index_by_atom_name(target.atom37_positions[target_inds], "CA"),
dtype=torch.float32,
).unsqueeze(0),
atom_exists_mask=torch.tensor(
index_by_atom_name(aligned.atom37_mask[mobile_inds], "CA", dim=-1)
& index_by_atom_name(target.atom37_mask[target_inds], "CA", dim=-1)
).unsqueeze(0),
**kwargs,
)
return float(gdt_ts) if gdt_ts.numel() == 1 else gdt_ts.numpy().flatten()
def dockq(self, native: ProteinComplex):
# This function uses dockqv2 to compute the DockQ score. Because it does a mapping
# over all possible chains, it's quite slow. Be careful not to use this in an inference loop
# or something that requires fast scoring. It defaults to 8 CPUs.
#
# TODO(@zeming): Because we haven't properly implemented protein complexes for mmcif,
# if your protein has multi-letter or repeated chain IDs, this will fail. Please call
# pc = pc.normalize_chain_ids_for_pdb() before calling this function in that case (limit is 62 chains)
try:
pass
except BaseException:
raise RuntimeError(
"DockQ is not installed. Please update your environment."
)
self._sanity_check_complexes_are_comparable(native)
def sanity_check_chain_ids(pc: ProteinComplex):
ids = []
for i, chain in enumerate(pc.chain_iter()):
if i > len(SINGLE_LETTER_CHAIN_IDS):
raise ValueError("Too many chains to write to PDB file")
if len(chain.chain_id) > 1:
raise ValueError(
"We only supports single letter chain IDs for DockQ"
)
ids.append(chain.chain_id)
if len(set(ids)) != len(ids):
raise ValueError(f"Duplicate chain IDs in protein complex: {ids}")
return ids
sanity_check_chain_ids(self)
sanity_check_chain_ids(native)
with TemporaryDirectory() as tdir:
dir = Path(tdir)
self.to_pdb(dir / "self.pdb")
native.to_pdb(dir / "native.pdb")
output = check_output(["DockQ", dir / "self.pdb", dir / "native.pdb"])
lines = output.decode().split("\n")
# Remove the header comments
start_index = next(
i for i, line in enumerate(lines) if line.startswith("Model")
)
lines = lines[start_index:]
result = {}
interfaces = []
current_interface: dict = {}
for line in lines:
line = line.strip()
if not line:
continue
if line.startswith("Model :"):
pass # Tmp pdb file location, it's useless...
elif line.startswith("Native :"):
pass # Tmp pdb file location, it's useless...
elif line.startswith("Total DockQ"):
total_dockq_match = re.search(
r"Total DockQ over (\d+) native interfaces: ([\d.]+) with (.*) model:native mapping",
line,
)
if total_dockq_match:
result["value"] = float(total_dockq_match.group(2))
result["native interfaces"] = int(total_dockq_match.group(1))
native_chains, self_chains = total_dockq_match.group(3).split(":")
result["mapping"] = dict(zip(native_chains, self_chains))
else:
raise RuntimeError(
"Failed to parse DockQ output, maybe your DockQ version is wrong?"
)
elif line.startswith("Native chains:"):
if current_interface:
interfaces.append(current_interface)
current_interface = {
"Native chains": line.split(":")[1].strip().split(", ")
}
elif line.startswith("Model chains:"):
current_interface["Model chains"] = (
line.split(":")[1].strip().split(", ")
)
elif ":" in line:
key, value = line.split(":", 1)
current_interface[key.strip()] = float(value.strip())
if current_interface:
interfaces.append(current_interface)
def parse_dict(d: dict[str, Any]) -> DockQSingleScore:
return DockQSingleScore(
native_chains=tuple(d["Native chains"]), # type: ignore
DockQ=float(d["DockQ"]),
interface_rms=float(d["irms"]),
ligand_rms=float(d["Lrms"]), # Note the capitalization difference
fnat=float(d["fnat"]),
fnonnat=float(d["fnonnat"]),
clashes=float(d["clashes"]),
F1=float(d["F1"]),
DockQ_F1=float(d["DockQ_F1"]),
)
inv_mapping = {v: k for k, v in result["mapping"].items()}
self_chain_map = {c.chain_id: c for c in self.chain_iter()}
realigned = []
for chain in native.chain_iter():
realigned.append(self_chain_map[inv_mapping[chain.chain_id]])
realigned = ProteinComplex.from_chains(realigned)
aligner = Aligner(realigned, native)
realigned = aligner.apply(realigned)
result = DockQResult(
total_dockq=result["value"],
native_interfaces=result["native interfaces"],
chain_mapping=result["mapping"],
interfaces={
(i["Model chains"][0], i["Model chains"][1]): parse_dict(i)
for i in interfaces
},
aligned=realigned,
aligned_rmsd=aligner.rmsd,
)
return result
@cached_property
def per_chain_kd_trees(self):
# Iterate over chains, build KDTree for each chain
kdtrees = []
CA = self.atoms["CA"]
for start, end in self.chain_boundaries:
chain_CA = CA[start:end]
chain_CA = chain_CA[np.isfinite(chain_CA).all(axis=-1)]
kdtrees.append(KDTree(chain_CA))
return kdtrees
def chain_adjacency(self, cutoff: float = 8.0) -> np.ndarray:
# Compute adjacency matrix for protein complex
num_chains = self.num_chains
adjacency = np.zeros((num_chains, num_chains), dtype=bool)
for (i, kdtree), (j, kdtree2) in itertools.combinations(
enumerate(self.per_chain_kd_trees), 2
):
adj = kdtree.query_ball_tree(kdtree2, cutoff)
any_is_adjacent = any(len(a) > 0 for a in adj)
adjacency[i, j] = any_is_adjacent
adjacency[j, i] = any_is_adjacent
return adjacency
def chain_adjacency_by_index(self, index: int, cutoff: float = 8.0) -> np.ndarray:
num_chains = len(self.chain_boundaries)
adjacency = np.zeros(num_chains, dtype=bool)
for i, kdtree in enumerate(self.per_chain_kd_trees):
if i == index:
continue
adj = kdtree.query_ball_tree(self.per_chain_kd_trees[index], cutoff)
adjacency[i] = any(len(a) > 0 for a in adj)
return adjacency
def add_prefix_to_chain_ids(self, prefix: str) -> ProteinComplex:
"""Rename all chains in the complex with a given prefix.
Args:
prefix (str): The prefix to use for the new chain IDs. Each chain will be
named as "{prefix}_{chain_id}".
Returns:
ProteinComplex: A new protein complex with renamed chains.
"""
new_chains = []
for chain in self.chain_iter():
# Create new chain with updated chain_id
new_chain = replace(chain, chain_id=f"{prefix}_{chain.chain_id}")
new_chains.append(new_chain)
return ProteinComplex.from_chains(new_chains)
def sasa(self, by_residue: bool = True):
chain = self.as_chain(force_conversion=True)
return chain.sasa(by_residue=by_residue)
def to_mmcif_string(self) -> str:
"""Convert the ProteinComplex to mmCIF format.
Returns:
str: The mmCIF content as a string.
"""
# Convert the ProteinComplex to a biotite AtomArray
# Collect all atoms from all chains
all_atoms = []
for chain in self.chain_iter():
chain_atom_array = chain.atom_array
# Convert AtomArray to list of atoms and add to collection
all_atoms.extend(chain_atom_array)
# Create combined AtomArray from all atoms
if not all_atoms:
raise ValueError("No atoms found in protein complex")
atom_array = bs.array(all_atoms)
# Create CIF file
f = CIFFile()
set_structure_pdbx(f, atom_array, data_block=self.id)
# Add entity information for proper mmCIF structure
self._add_entity_information(f)
# Write to string
output = io.StringIO()
f.write(output)
return output.getvalue()
def _add_entity_information(self, cif_file: CIFFile) -> None:
"""Add entity, entity_poly, and struct_asym sections to CIF file."""
# Group chains by sequence to create unique entities
entity_map = {} # sequence -> entity_id
chain_to_entity = {} # chain_id -> entity_id
entity_sequences = {} # entity_id -> sequence
entity_id_counter = 1
for chain in self.chain_iter():
sequence = chain.sequence
if sequence not in entity_map:
entity_map[sequence] = entity_id_counter
entity_sequences[entity_id_counter] = sequence
entity_id_counter += 1
chain_to_entity[chain.chain_id] = entity_map[sequence]
# Create _entity section
entity_ids = []
entity_types = []
entity_descriptions = []
for entity_id in sorted(entity_sequences.keys()):
entity_ids.append(str(entity_id))
entity_types.append("polymer")
entity_descriptions.append(f"Protein chain (entity {entity_id})")
cif_file.block["entity"] = CIFCategory(
name="entity",
columns={
"id": CIFColumn(
data=CIFData(array=np.array(entity_ids), dtype=np.str_)
),
"type": CIFColumn(
data=CIFData(array=np.array(entity_types), dtype=np.str_)
),
"pdbx_description": CIFColumn(
data=CIFData(array=np.array(entity_descriptions), dtype=np.str_)
),
},
)
# Create _entity_poly section
poly_entity_ids = []
poly_types = []
poly_nstd_linkages = []
poly_sequences = []
for entity_id in sorted(entity_sequences.keys()):
poly_entity_ids.append(str(entity_id))
poly_types.append("polypeptide(L)")
poly_nstd_linkages.append("no")
poly_sequences.append(entity_sequences[entity_id])
cif_file.block["entity_poly"] = CIFCategory(
name="entity_poly",
columns={
"entity_id": CIFColumn(
data=CIFData(array=np.array(poly_entity_ids), dtype=np.str_)
),
"type": CIFColumn(
data=CIFData(array=np.array(poly_types), dtype=np.str_)
),
"nstd_linkage": CIFColumn(
data=CIFData(array=np.array(poly_nstd_linkages), dtype=np.str_)
),
"pdbx_seq_one_letter_code": CIFColumn(
data=CIFData(array=np.array(poly_sequences), dtype=np.str_)
),
},
)
# Create _struct_asym section
asym_ids = []
asym_entity_ids = []
asym_details = []
for chain in self.chain_iter():
asym_ids.append(chain.chain_id)
asym_entity_ids.append(str(chain_to_entity[chain.chain_id]))
asym_details.append("")
cif_file.block["struct_asym"] = CIFCategory(
name="struct_asym",
columns={
"id": CIFColumn(data=CIFData(array=np.array(asym_ids), dtype=np.str_)),
"entity_id": CIFColumn(
data=CIFData(array=np.array(asym_entity_ids), dtype=np.str_)
),
"details": CIFColumn(
data=CIFData(array=np.array(asym_details), dtype=np.str_)
),
},
)
def get_assembly_fast(
mmcif: MmcifWrapper,
assembly_id=None,
model=None,
data_block=None,
altloc="first",
use_author_fields=True,
):
pdbx_file = mmcif.raw
if pdbx_file is None:
raise InvalidFileError("No mmCIF data loaded")
assembly_gen_category = pdbx_file.block["pdbx_struct_assembly_gen"]
if assembly_gen_category is None:
raise InvalidFileError("File has no 'pdbx_struct_assembly_gen' category")
struct_oper_category = pdbx_file.block["pdbx_struct_oper_list"]
if struct_oper_category is None:
raise InvalidFileError("File has no 'pdbx_struct_oper_list' category")
if assembly_id is None:
assembly_id = assembly_gen_category["assembly_id"].data.array[0]
elif assembly_id not in assembly_gen_category["assembly_id"].data.array:
raise KeyError(f"File has no Assembly ID '{assembly_id}'")
### Calculate all possible transformations
transformations = _get_transformations(struct_oper_category)
### Get structure according to additional parameters
structure = get_structure(
pdbx_file, model, data_block, altloc, ["label_asym_id"], use_author_fields
)[0] # type: ignore
# TODO(@zeming) This line will remove all non-protein structural elements,
# we should remove this when we want to parse these too.
structure: bs.AtomArray = structure[
bs.filter_amino_acids(structure) & ~structure.hetero # type: ignore
]
if len(structure) == 0:
raise NoProteinError
unique_asym_ids = np.unique(structure.label_asym_id) # type: ignore
asym2chain = {}
asym2auth = {}
for asym_id in unique_asym_ids:
sub_structure: bs.AtomArray = structure[structure.label_asym_id == asym_id] # type: ignore
chain_id: str = sub_structure[0].chain_id # type: ignore
(
sequence,
atom_positions,
atom_mask,
residue_index,
insertion_code,
confidence,
entity_id,
) = chain_to_ndarray(sub_structure, mmcif, chain_id, False)
asym2chain[asym_id] = ProteinChain(
id=mmcif.id or "unknown",
sequence=sequence,
chain_id=chain_id,
entity_id=entity_id,
atom37_positions=atom_positions,
atom37_mask=atom_mask,
residue_index=residue_index,
insertion_code=insertion_code,
confidence=confidence,
mmcif=None,
)
asym2auth[asym_id] = chain_id
### Get transformations and apply them to the affected asym IDs
assembly = []
assembly_id_dict: dict[str, list[str]] = {}
# Process the target assembly ID
for aid, op_expr, asym_id_expr in zip(
assembly_gen_category["assembly_id"].data.array,
assembly_gen_category["oper_expression"].data.array,
assembly_gen_category["asym_id_list"].data.array,
):
if aid == assembly_id:
# Parse operations and asym IDs for this specific entry
operations = _parse_operation_expression(op_expr)
asym_ids = asym_id_expr.split(",")
# Filter affected asym IDs to only protein chains, preserving order
sub_structures = [
asym2chain[asym_id] for asym_id in asym_ids if asym_id in asym2chain
]
# Apply transformations
sub_assembly = _apply_transformations_fast(
sub_structures, transformations, operations
)
assembly.extend(sub_assembly)
# Build assembly_id_dict for this entry
assembly_id_dict[aid] = assembly_id_dict.get(aid, []) + [
asym2auth[id_] for id_ in asym_ids if id_ in asym2auth
]
if len(assembly) == 0:
raise NoProteinError
return ProteinComplex.from_chains(assembly, mmcif, assembly_id_dict)
def protein_chain_to_protein_complex(chain: ProteinChain) -> ProteinComplex:
if "|" not in chain.sequence:
return ProteinComplex.from_chains([chain])
chain_breaks = np.array(list(chain.sequence)) == "|"
chain_break_inds = np.where(chain_breaks)[0]
chain_break_inds = np.concatenate([[0], chain_break_inds, [len(chain)]])
chain_break_inds = np.array(list(zip(chain_break_inds[:-1], chain_break_inds[1:])))
complex_chains = []
for start, end in chain_break_inds:
if start != 0:
start += 1
complex_chains.append(chain[start:end])
complex_chains = [
ProteinChain.from_atom37(
chain.atom37_positions,
sequence=chain.sequence,
chain_id=SINGLE_LETTER_CHAIN_IDS[i],
entity_id=i,
)
for i, chain in enumerate(complex_chains)
]
return ProteinComplex.from_chains(complex_chains)
|