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from functools import partial
import json
import logging
import os
import threading
from multiprocessing import cpu_count
from shutil import copyfile
import tempfile
import onescience.datapipes.openfold.mmcif_parsing as mmcif_parsing
from onescience.datapipes.openfold.data_pipeline import AlignmentRunner
from onescience.datapipes.openfold.parsers import parse_fasta
from onescience.datapipes.openfold.tools import hhsearch, hmmsearch
from onescience.utils.openfold.np import protein, residue_constants
from utils import add_data_args
logging.basicConfig(level=logging.WARNING)
def run_seq_group_alignments(seq_groups, alignment_runner, args):
dirs = set(os.listdir(args.output_dir))
for seq, names in seq_groups:
first_name = names[0]
alignment_dir = os.path.join(args.output_dir, first_name)
try:
os.makedirs(alignment_dir)
except Exception as e:
logging.warning(f"Failed to create directory for {first_name} with exception {e}...")
continue
fd, fasta_path = tempfile.mkstemp(suffix=".fasta")
with os.fdopen(fd, 'w') as fp:
fp.write(f'>query\n{seq}')
try:
alignment_runner.run(
fasta_path, alignment_dir
)
except Exception as e:
logging.warning(e)
logging.warning(f"Failed to run alignments for {first_name}. Skipping...")
os.remove(fasta_path)
os.rmdir(alignment_dir)
continue
os.remove(fasta_path)
for name in names[1:]:
if(name in dirs):
logging.warning(
f'{name} has already been processed. Skipping...'
)
continue
cp_dir = os.path.join(args.output_dir, name)
os.makedirs(cp_dir, exist_ok=True)
for f in os.listdir(alignment_dir):
copyfile(os.path.join(alignment_dir, f), os.path.join(cp_dir, f))
def parse_and_align(files, alignment_runner, args):
for f in files:
path = os.path.join(args.input_dir, f)
file_id = os.path.splitext(f)[0]
seq_group_dict = {}
if(f.endswith('.cif')):
with open(path, 'r') as fp:
mmcif_str = fp.read()
mmcif = mmcif_parsing.parse(
file_id=file_id, mmcif_string=mmcif_str
)
if(mmcif.mmcif_object is None):
logging.warning(f'Failed to parse {f}...')
if(args.raise_errors):
raise list(mmcif.errors.values())[0]
else:
continue
mmcif = mmcif.mmcif_object
for chain_letter, seq in mmcif.chain_to_seqres.items():
chain_id = '_'.join([file_id, chain_letter])
l = seq_group_dict.setdefault(seq, [])
l.append(chain_id)
elif(f.endswith('.fasta') or f.endswith('.fa')):
with open(path, 'r') as fp:
fasta_str = fp.read()
input_seqs, _ = parse_fasta(fasta_str)
if len(input_seqs) != 1:
msg = f'More than one input_sequence found in {f}'
if(args.raise_errors):
raise ValueError(msg)
else:
logging.warning(msg)
input_sequence = input_seqs[0]
seq_group_dict[input_sequence] = [file_id]
elif(f.endswith('.core')):
with open(path, 'r') as fp:
core_str = fp.read()
core_prot = protein.from_proteinnet_string(core_str)
aatype = core_prot.aatype
seq = ''.join([
residue_constants.restypes_with_x[aatype[i]]
for i in range(len(aatype))
])
seq_group_dict[seq] = [file_id]
else:
continue
seq_group_tuples = [(k,v) for k,v in seq_group_dict.items()]
run_seq_group_alignments(seq_group_tuples, alignment_runner, args)
def main(args):
# Build the alignment tool runner
if args.hmmsearch_binary_path is not None and args.pdb_seqres_database_path is not None:
template_searcher = hmmsearch.Hmmsearch(
binary_path=args.hmmsearch_binary_path,
hmmbuild_binary_path=args.hmmbuild_binary_path,
database_path=args.pdb_seqres_database_path,
)
elif args.hhsearch_binary_path is not None and args.pdb70_database_path is not None:
template_searcher = hhsearch.HHSearch(
binary_path=args.hhsearch_binary_path,
databases=[args.pdb70_database_path],
)
else:
template_searcher = None
alignment_runner = AlignmentRunner(
jackhmmer_binary_path=args.jackhmmer_binary_path,
hhblits_binary_path=args.hhblits_binary_path,
uniref90_database_path=args.uniref90_database_path,
mgnify_database_path=args.mgnify_database_path,
bfd_database_path=args.bfd_database_path,
uniref30_database_path=args.uniref30_database_path,
uniclust30_database_path=args.uniclust30_database_path,
uniprot_database_path=args.uniprot_database_path,
template_searcher=template_searcher,
use_small_bfd=args.bfd_database_path is None,
no_cpus=args.cpus_per_task,
)
files = list(os.listdir(args.input_dir))
# Do some filtering
if(args.mmcif_cache is not None):
with open(args.mmcif_cache, "r") as fp:
cache = json.load(fp)
else:
cache = None
dirs = []
if(cache is not None and args.filter):
dirs = set(os.listdir(args.output_dir))
def prot_is_done(f):
prot_id = os.path.splitext(f)[0]
if(prot_id in cache):
chain_ids = cache[prot_id]["chain_ids"]
for c in chain_ids:
full_name = prot_id + "_" + c
if(not full_name in dirs):
return False
else:
return False
return True
files = [f for f in files if not prot_is_done(f)]
def split_up_arglist(arglist):
# Split up the survivors
if(os.environ.get("SLURM_JOB_NUM_NODES", 0)):
num_nodes = int(os.environ["SLURM_JOB_NUM_NODES"])
if(num_nodes > 1):
node_id = int(os.environ["SLURM_NODEID"])
logging.warning(f"Num nodes: {num_nodes}")
logging.warning(f"Node ID: {node_id}")
arglist = arglist[node_id::num_nodes]
t_arglist = []
for i in range(args.no_tasks):
t_arglist.append(arglist[i::args.no_tasks])
return t_arglist
if(cache is not None and "seqs" in next(iter(cache.values()))):
seq_group_dict = {}
for f in files:
prot_id = os.path.splitext(f)[0]
if(prot_id in cache):
prot_cache = cache[prot_id]
chains_seqs = zip(
prot_cache["chain_ids"], prot_cache["seqs"]
)
for chain, seq in chains_seqs:
chain_name = prot_id + "_" + chain
if(chain_name not in dirs):
l = seq_group_dict.setdefault(seq, [])
l.append(chain_name)
func = partial(run_seq_group_alignments,
alignment_runner=alignment_runner,
args=args
)
seq_groups = [(k,v) for k,v in seq_group_dict.items()]
# Sort them by group length so the tasks are approximately balanced
seq_groups = sorted(seq_groups, key=lambda x: len(x[1]))
task_arglist = [[a] for a in split_up_arglist(seq_groups)]
else:
func = partial(parse_and_align,
alignment_runner=alignment_runner,
args=args,
)
task_arglist = [[a] for a in split_up_arglist(files)]
threads = []
for i, task_args in enumerate(task_arglist):
print(f"Started thread {i}...")
t = threading.Thread(target=func, args=task_args)
threads.append(t)
t.start()
for t in threads:
t.join()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument(
"input_dir", type=str,
help="""Path to directory containing mmCIF, FASTA and/or ProteinNet
.core files"""
)
parser.add_argument(
"output_dir", type=str,
help="Directory in which to output alignments"
)
add_data_args(parser)
parser.add_argument(
"--raise_errors", action="store_true", default=False,
help="Whether to crash on parsing errors"
)
parser.add_argument(
"--cpus_per_task", type=int, default=cpu_count(),
help="Number of CPUs to use"
)
parser.add_argument(
"--mmcif_cache", type=str, default=None,
help="Path to mmCIF cache. Used to filter files to be parsed"
)
parser.add_argument(
"--no_tasks", type=int, default=1,
)
parser.add_argument(
"--filter", type=bool, default=True,
)
args = parser.parse_args()
main(args)
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