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import json
import multiprocessing
import pickle
import traceback
from dataclasses import asdict, dataclass, replace
from functools import partial
from pathlib import Path
from typing import Any, Optional
import numpy as np
import rdkit
from mmcif import parse_mmcif
from p_tqdm import p_umap
from redis import Redis
from tqdm import tqdm
from boltz.data.filter.static.filter import StaticFilter
from boltz.data.filter.static.ligand import ExcludedLigands
from boltz.data.filter.static.polymer import (
ClashingChainsFilter,
ConsecutiveCA,
MinimumLengthFilter,
UnknownFilter,
)
from boltz.data.types import ChainInfo, InterfaceInfo, Record, Target
@dataclass(frozen=True, slots=True)
class PDB:
"""A raw MMCIF PDB file."""
id: str
path: str
class Resource:
"""A shared resource for processing."""
def __init__(self, host: str, port: int) -> None:
"""Initialize the redis database."""
self._redis = Redis(host=host, port=port)
def get(self, key: str) -> Any: # noqa: ANN401
"""Get an item from the Redis database."""
value = self._redis.get(key)
if value is not None:
value = pickle.loads(value) # noqa: S301
return value
def __getitem__(self, key: str) -> Any: # noqa: ANN401
"""Get an item from the resource."""
out = self.get(key)
if out is None:
raise KeyError(key)
return out
def fetch(datadir: Path, max_file_size: Optional[int] = None) -> list[PDB]:
"""Fetch the PDB files."""
data = []
excluded = 0
for file in datadir.rglob("*.cif*"):
# The clustering file is annotated by pdb_entity id
pdb_id = str(file.stem).lower()
# Check file size and skip if too large
if max_file_size is not None and (file.stat().st_size > max_file_size):
excluded += 1
continue
# Create the target
target = PDB(id=pdb_id, path=str(file))
data.append(target)
print(f"Excluded {excluded} files due to size.") # noqa: T201
return data
def finalize(outdir: Path) -> None:
"""Run post-processing in main thread.
Parameters
----------
outdir : Path
The output directory.
"""
# Group records into a manifest
records_dir = outdir / "records"
failed_count = 0
records = []
for record in records_dir.iterdir():
path = record
try:
with path.open("r") as f:
records.append(json.load(f))
except: # noqa: E722
failed_count += 1
print(f"Failed to parse {record}") # noqa: T201
if failed_count > 0:
print(f"Failed to parse {failed_count} entries.") # noqa: T201
else:
print("All entries parsed successfully.")
# Save manifest
outpath = outdir / "manifest.json"
with outpath.open("w") as f:
json.dump(records, f)
def parse(data: PDB, resource: Resource, clusters: dict) -> Target:
"""Process a structure.
Parameters
----------
data : PDB
The raw input data.
resource: Resource
The shared resource.
Returns
-------
Target
The processed data.
"""
# Get the PDB id
pdb_id = data.id.lower()
# Parse structure
parsed = parse_mmcif(data.path, resource)
structure = parsed.data
structure_info = parsed.info
# Create chain metadata
chain_info = []
for i, chain in enumerate(structure.chains):
key = f"{pdb_id}_{chain['entity_id']}"
chain_info.append(
ChainInfo(
chain_id=i,
chain_name=chain["name"],
msa_id="", # FIX
mol_type=int(chain["mol_type"]),
cluster_id=clusters.get(key, -1),
num_residues=int(chain["res_num"]),
)
)
# Get interface metadata
interface_info = []
for interface in structure.interfaces:
chain_1 = int(interface["chain_1"])
chain_2 = int(interface["chain_2"])
interface_info.append(
InterfaceInfo(
chain_1=chain_1,
chain_2=chain_2,
)
)
# Create record
record = Record(
id=data.id,
structure=structure_info,
chains=chain_info,
interfaces=interface_info,
)
return Target(structure=structure, record=record)
def process_structure(
data: PDB,
resource: Resource,
outdir: Path,
filters: list[StaticFilter],
clusters: dict,
) -> None:
"""Process a target.
Parameters
----------
item : PDB
The raw input data.
resource: Resource
The shared resource.
outdir : Path
The output directory.
"""
# Check if we need to process
struct_path = outdir / "structures" / f"{data.id}.npz"
record_path = outdir / "records" / f"{data.id}.json"
if struct_path.exists() and record_path.exists():
return
try:
# Parse the target
target: Target = parse(data, resource, clusters)
structure = target.structure
# Apply the filters
mask = structure.mask
if filters is not None:
for f in filters:
filter_mask = f.filter(structure)
mask = mask & filter_mask
except Exception: # noqa: BLE001
traceback.print_exc()
print(f"Failed to parse {data.id}")
return
# Replace chains and interfaces
chains = []
for i, chain in enumerate(target.record.chains):
chains.append(replace(chain, valid=bool(mask[i])))
interfaces = []
for interface in target.record.interfaces:
chain_1 = bool(mask[interface.chain_1])
chain_2 = bool(mask[interface.chain_2])
interfaces.append(replace(interface, valid=(chain_1 and chain_2)))
# Replace structure and record
structure = replace(structure, mask=mask)
record = replace(target.record, chains=chains, interfaces=interfaces)
target = replace(target, structure=structure, record=record)
# Dump structure
np.savez_compressed(struct_path, **asdict(structure))
# Dump record
with record_path.open("w") as f:
json.dump(asdict(record), f)
def process(args) -> None:
"""Run the data processing task."""
# Create output directory
args.outdir.mkdir(parents=True, exist_ok=True)
# Create output directories
records_dir = args.outdir / "records"
records_dir.mkdir(parents=True, exist_ok=True)
structure_dir = args.outdir / "structures"
structure_dir.mkdir(parents=True, exist_ok=True)
# Load clusters
with Path(args.clusters).open("r") as f:
clusters: dict[str, str] = json.load(f)
clusters = {k.lower(): v.lower() for k, v in clusters.items()}
# Load filters
filters = [
ExcludedLigands(),
MinimumLengthFilter(min_len=4, max_len=5000),
UnknownFilter(),
ConsecutiveCA(max_dist=10.0),
ClashingChainsFilter(freq=0.3, dist=1.7),
]
# Set default pickle properties
pickle_option = rdkit.Chem.PropertyPickleOptions.AllProps
rdkit.Chem.SetDefaultPickleProperties(pickle_option)
# Load shared data from redis
resource = Resource(host=args.redis_host, port=args.redis_port)
# Get data points
print("Fetching data...")
data = fetch(args.datadir)
# Check if we can run in parallel
max_processes = multiprocessing.cpu_count()
num_processes = max(1, min(args.num_processes, max_processes, len(data)))
parallel = num_processes > 1
# Run processing
print("Processing data...")
if parallel:
# Create processing function
fn = partial(
process_structure,
resource=resource,
outdir=args.outdir,
clusters=clusters,
filters=filters,
)
# Run processing in parallel
p_umap(fn, data, num_cpus=num_processes)
else:
for item in tqdm(data):
process_structure(
item,
resource=resource,
outdir=args.outdir,
clusters=clusters,
filters=filters,
)
# Finalize
finalize(args.outdir)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Process MSA data.")
parser.add_argument(
"--datadir",
type=Path,
required=True,
help="The data containing the MMCIF files.",
)
parser.add_argument(
"--clusters",
type=Path,
required=True,
help="Path to the cluster file.",
)
parser.add_argument(
"--outdir",
type=Path,
default="data",
help="The output directory.",
)
parser.add_argument(
"--num-processes",
type=int,
default=multiprocessing.cpu_count(),
help="The number of processes.",
)
parser.add_argument(
"--redis-host",
type=str,
default="localhost",
help="The Redis host.",
)
parser.add_argument(
"--redis-port",
type=int,
default=7777,
help="The Redis port.",
)
parser.add_argument(
"--use-assembly",
action="store_true",
help="Whether to use assembly 1.",
)
parser.add_argument(
"--max-file-size",
type=int,
default=None,
)
args = parser.parse_args()
process(args)
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