diff --git a/projects/dark-matter-metagenomics/translate2modelcif.py b/projects/dark-matter-metagenomics/translate2modelcif.py
new file mode 100644
index 0000000000000000000000000000000000000000..cd3a91dc656e5acbc65b4462b259ccada4a2a426
--- /dev/null
+++ b/projects/dark-matter-metagenomics/translate2modelcif.py
@@ -0,0 +1,877 @@
+#! /usr/local/bin/ost
+# -*- coding: utf-8 -*-
+
+"""Translate models for Sergey from PDB + extra data into ModelCIF."""
+
+# EXAMPLES for running:
+"""
+ost translate2modelcif.py ./raw_data ./raw_data/ptm_plddt.all.txt \
+ ./web_dloads/pivot ./modelcif --prefix=F000347 \
+ --pdb-web-path=./web_dloads/pdb \
+ --refseq-path=./web_dloads/consensus_all.fasta
+"""
+# NOTE: add "--compress" for final runs
+
+from timeit import default_timer as timer
+import argparse
+import filecmp
+import gzip
+import os
+import pandas as pd
+import shutil
+import sys
+import zipfile
+
+import ihm
+import ihm.citations
+import modelcif
+import modelcif.associated
+import modelcif.dumper
+import modelcif.model
+import modelcif.protocol
+import modelcif.reference
+
+from ost import io
+
+
+def _abort_msg(msg, exit_code=1):
+ """Write error message and exit with exit_code."""
+ print(f"{msg}\nAborting.", file=sys.stderr)
+ sys.exit(exit_code)
+
+
+def _warn_msg(msg):
+ """Write a warning message to stdout."""
+ print(f"WARNING: {msg}")
+
+
+def _check_file(file_path):
+ """Make sure a file exists and is actually a file."""
+ if not os.path.exists(file_path):
+ _abort_msg(f"File not found: '{file_path}'.")
+ if not os.path.isfile(file_path):
+ _abort_msg(f"File path does not point to file: '{file_path}'.")
+
+
+def _check_folder(dir_path):
+ """Make sure a file exists and is actually a file."""
+ if not os.path.exists(dir_path):
+ _abort_msg(f"Path not found: '{dir_path}'.")
+ if not os.path.isdir(dir_path):
+ _abort_msg(f"Path does not point to a directory: '{dir_path}'.")
+
+
+def _check_opts_folder(dir_path):
+ """Remove trailing '/' (return fixed one) and check if path valid."""
+ if dir_path.endswith("/"):
+ dir_path = dir_path[:-1]
+ _check_folder(dir_path)
+ return dir_path
+
+
+def _parse_args():
+ """Parse command line arguments."""
+ parser = argparse.ArgumentParser(
+ formatter_class=argparse.RawDescriptionHelpFormatter,
+ description=__doc__,
+ )
+
+ parser.add_argument(
+ "model_base_dir",
+ type=str,
+ metavar="<MODEL BASE DIR>",
+ help="Directory with pub_data* directories with model PDBs.",
+ )
+ parser.add_argument(
+ "metadata_file",
+ type=str,
+ metavar="<METADATA FILE>",
+ help="Path to table with metadata.",
+ )
+ parser.add_argument(
+ "msa_data_dir",
+ type=str,
+ metavar="<MSA DIR>",
+ help="Directory with F*.fasta files for custom MSAs.",
+ )
+ parser.add_argument(
+ "out_dir",
+ type=str,
+ metavar="<OUTPUT DIR>",
+ help="Path to directory to store results.",
+ )
+ parser.add_argument(
+ "--prefix",
+ type=str,
+ metavar="<PREFIX>",
+ default="",
+ help="Only process families starting with given prefix. By default " \
+ + "all families are processed.",
+ )
+ parser.add_argument(
+ "--compress",
+ default=False,
+ action="store_true",
+ help="Compress ModelCIF file with gzip "
+ "(note that acc. data is zipped either way).",
+ )
+ parser.add_argument(
+ "--pdb-web-path",
+ type=str,
+ metavar="<PDB WEB PATH>",
+ default=None,
+ help="Optional path to directory with F*.pdb files as available on " \
+ + "NMPFamsDB web site. Used to check if top ranked model the same.",
+ )
+ parser.add_argument(
+ "--refseq-path",
+ type=str,
+ metavar="<PDB REFSEQ PATH>",
+ default=None,
+ help="Optional path to fasta file with all ref. seq. as available on " \
+ + "NMPFamsDB web site. Used to check if it matches with MSA.",
+ )
+ opts = parser.parse_args()
+
+ # check input
+ opts.model_base_dir = _check_opts_folder(opts.model_base_dir)
+ _check_file(opts.metadata_file)
+ opts.msa_data_dir = _check_opts_folder(opts.msa_data_dir)
+ # check out_dir
+ if opts.out_dir.endswith("/"):
+ opts.out_dir = opts.out_dir[:-1]
+ if not os.path.exists(opts.out_dir):
+ os.makedirs(opts.out_dir)
+ if not os.path.isdir(opts.out_dir):
+ _abort_msg(f"Path '{opts.out_dir}' does not point to a directory.")
+ # check optional paths
+ if opts.pdb_web_path is not None:
+ opts.pdb_web_path = _check_opts_folder(opts.pdb_web_path)
+ if opts.refseq_path is not None:
+ _check_file(opts.refseq_path)
+ return opts
+
+
+# pylint: disable=too-few-public-methods
+class _GlobalPTM(modelcif.qa_metric.Global, modelcif.qa_metric.PTM):
+ """Predicted accuracy according to the TM-score score in [0,1]"""
+ name = "pTM"
+ software = None
+
+
+class _GlobalPLDDT(modelcif.qa_metric.Global, modelcif.qa_metric.PLDDT):
+ """Predicted accuracy according to the CA-only lDDT in [0,100]"""
+ name = "pLDDT"
+ software = None
+
+
+class _LocalPLDDT(modelcif.qa_metric.Local, modelcif.qa_metric.PLDDT):
+ """Predicted accuracy according to the CA-only lDDT in [0,100]"""
+ name = "pLDDT"
+ software = None
+
+
+class _NmpfamsdbTrgRef(modelcif.reference.TargetReference):
+ """PDB as target reference."""
+ name = "Other"
+ other_details = "NMPFamsDB"
+# pylint: enable=too-few-public-methods
+
+
+def _get_res_num(r, use_auth=False):
+ """Get res. num. from auth. IDs if reading from mmCIF files."""
+ if use_auth:
+ return int(r.GetStringProp("pdb_auth_resnum"))
+ else:
+ return r.number.num
+
+
+def _get_ch_name(ch, use_auth=False):
+ """Get chain name from auth. IDs if reading from mmCIF files."""
+ if use_auth:
+ return ch.GetStringProp("pdb_auth_chain_name")
+ else:
+ return ch.name
+
+
+class _OST2ModelCIF(modelcif.model.AbInitioModel):
+ """Map OST entity elements to ihm.model"""
+
+ def __init__(self, *args, **kwargs):
+ """Initialise a model"""
+ for i in ["ost_entity", "asym", "scores_json"]:
+ if i not in kwargs:
+ raise TypeError(f"Required keyword argument '{i}' not found.")
+ self.ost_entity = kwargs.pop("ost_entity")
+ self.asym = kwargs.pop("asym")
+ self.scores_json = kwargs.pop("scores_json")
+
+ # use auth IDs for res. nums and chain names
+ self.use_auth = False
+
+ # fetch plddts per residue
+ self.plddts = []
+ for res in self.ost_entity.residues:
+ b_factors = [a.b_factor for a in res.atoms]
+ assert len(set(b_factors)) == 1 # must all be equal!
+ self.plddts.append(b_factors[0])
+
+ super().__init__(*args, **kwargs)
+
+
+ def get_atoms(self):
+ # ToDo [internal]: Take B-factor out since its not a B-factor?
+ # NOTE: this assumes that _get_res_num maps residue to pos. in seqres
+ # within asym
+ for atm in self.ost_entity.atoms:
+ yield modelcif.model.Atom(
+ asym_unit=self.asym[_get_ch_name(atm.chain, self.use_auth)],
+ seq_id=_get_res_num(atm.residue, self.use_auth),
+ atom_id=atm.name,
+ type_symbol=atm.element,
+ x=atm.pos[0],
+ y=atm.pos[1],
+ z=atm.pos[2],
+ het=atm.is_hetatom,
+ biso=atm.b_factor,
+ occupancy=atm.occupancy,
+ )
+
+ def add_scores(self):
+ """Add QA metrics from AF2 scores."""
+ # global scores
+ self.qa_metrics.extend(
+ (
+ _GlobalPLDDT(self.scores_json["plddt_global"]),
+ _GlobalPTM(self.scores_json["ptm"]),
+ )
+ )
+
+
+ # local scores
+ i = 0
+ for chn_i in self.ost_entity.chains:
+ ch_name = _get_ch_name(chn_i, self.use_auth)
+ for res_i in chn_i.residues:
+ # local pLDDT
+ res_num = _get_res_num(res_i, self.use_auth)
+ self.qa_metrics.append(
+ _LocalPLDDT(
+ self.asym[ch_name].residue(res_num),
+ self.plddts[i],
+ )
+ )
+ i += 1
+
+
+def _get_audit_authors():
+ """Return the list of authors that produced this model."""
+ return (
+ "Pavlopoulos, Georgios A.",
+ "Baltoumas, Fotis A.",
+ "Liu, Sirui",
+ "Selvitopi, Oguz",
+ "Camargo, Antonio Pedro",
+ "Nayfach, Stephen",
+ "Azad, Ariful",
+ "Roux, Simon",
+ "Call, Lee",
+ "Ivanova, Natalia N.",
+ "Chen, I-Min",
+ "Paez-Espino, David",
+ "Karatzas, Evangelos",
+ "Novel Metagenome Protein Families Consortium",
+ "Iliopoulos, Ioannis",
+ "Konstantinidi, Konstantinos",
+ "Tiedje, James M.",
+ "Pett-Ridge, Jennifer",
+ "Baker, David",
+ "Visel, Axel",
+ "Ouzounis, Christos A.",
+ "Ovchinnikov, Sergey",
+ "Buluc, Aydin",
+ "Kyrpides, Nikos C.",
+ )
+
+
+def _get_metadata(metadata_file):
+ """Read csv file with metedata and prepare for next steps."""
+ metadata = pd.read_csv(metadata_file, sep=' ',
+ names=["ID", "mdl", "pTM", "pLDDT"])
+ return metadata
+
+
+def _get_pdb_files(model_base_dir):
+ """Collect PDB files from pub_data_* folders.
+ Returns dict with key = family name and value = list of paths to PDB files.
+ """
+ pdb_files_split = dict() # to return
+ pdb_files_raw = set() # to check for duplicates
+ pub_paths = [f for f in os.listdir(model_base_dir) \
+ if f.startswith("pub_data_")]
+ # NOTE: we sort pub_paths to ensure that pub_data_02 is before _03
+ for pub_path in sorted(pub_paths):
+ sub_path = os.path.join(model_base_dir, pub_path)
+ pdb_files_new = [f for f in os.listdir(sub_path) \
+ if not f.startswith(".")]
+ for f in pdb_files_new:
+ f_path = os.path.join(sub_path, f)
+ f_name = f.split('_')[0]
+ if f_name in pdb_files_split:
+ pdb_files_split[f_name].append(f_path)
+ else:
+ pdb_files_split[f_name] = [f_path]
+ # check global list
+ pdb_files_new_set = set(pdb_files_new)
+ new_duplicates = pdb_files_raw.intersection(pdb_files_new_set)
+ if new_duplicates:
+ _warn_msg(f"{len(new_duplicates)} duplicated files found in " \
+ f"{sub_path}.")
+ pdb_files_raw = pdb_files_raw.union(pdb_files_new_set)
+ return pdb_files_split
+
+
+def _get_config():
+ """Define AF setup."""
+ msa_description = "MSA created by calculating the central or \"pivot\" " \
+ "sequence of each seed MSA, and refining each " \
+ "alignment using that sequence as the guide."
+ mdl_description = "Model generated using AlphaFold (v2.0.0 with models " \
+ "finetuned to return ptm weights) producing 5 models, " \
+ "without model relaxation, without templates, ranked " \
+ "by pLDDT, starting from a custom MSA."
+ af_config = {}
+ return {
+ "af_config": af_config,
+ "af_version": "2.0.0",
+ "mdl_description": mdl_description,
+ "msa_description": msa_description,
+ "use_templates": False,
+ "use_small_bfd": False,
+ "use_multimer": False,
+ }
+
+
+def _get_protocol_steps_and_software(config_data):
+ """Create the list of protocol steps with software and parameters used."""
+ protocol = []
+
+ # MSA step
+ step = {
+ "method_type": "coevolution MSA",
+ "name": None,
+ "details": config_data["msa_description"],
+ }
+ step["input"] = "target_sequences"
+ step["output"] = "MSA"
+ step["software"] = []
+ step["software_parameters"] = {}
+ protocol.append(step)
+
+ # modelling step
+ step = {
+ "method_type": "modeling",
+ "name": None,
+ "details": config_data["mdl_description"],
+ }
+ # get input data
+ # Must refer to data already in the JSON, so we try keywords
+ step["input"] = "target_sequences_and_MSA"
+ # get output data
+ # Must refer to existing data, so we try keywords
+ step["output"] = "model"
+ # get software
+ if config_data["use_multimer"]:
+ step["software"] = [{
+ "name": "AlphaFold-Multimer",
+ "classification": "model building",
+ "description": "Structure prediction",
+ "citation": ihm.Citation(
+ pmid=None,
+ title="Protein complex prediction with "
+ + "AlphaFold-Multimer.",
+ journal="bioRxiv",
+ volume=None,
+ page_range=None,
+ year=2021,
+ authors=[
+ "Evans, R.",
+ "O'Neill, M.",
+ "Pritzel, A.",
+ "Antropova, N.",
+ "Senior, A.",
+ "Green, T.",
+ "Zidek, A.",
+ "Bates, R.",
+ "Blackwell, S.",
+ "Yim, J.",
+ "Ronneberger, O.",
+ "Bodenstein, S.",
+ "Zielinski, M.",
+ "Bridgland, A.",
+ "Potapenko, A.",
+ "Cowie, A.",
+ "Tunyasuvunakool, K.",
+ "Jain, R.",
+ "Clancy, E.",
+ "Kohli, P.",
+ "Jumper, J.",
+ "Hassabis, D.",
+ ],
+ doi="10.1101/2021.10.04.463034",
+ ),
+ "location": "https://github.com/deepmind/alphafold",
+ "type": "package",
+ "version": config_data["af_version"],
+ }]
+ else:
+ step["software"] = [{
+ "name": "AlphaFold",
+ "classification": "model building",
+ "description": "Structure prediction",
+ "citation": ihm.citations.alphafold2,
+ "location": "https://github.com/deepmind/alphafold",
+ "type": "package",
+ "version": config_data["af_version"],
+ }]
+ step["software_parameters"] = config_data["af_config"]
+ protocol.append(step)
+
+ return protocol
+
+
+def _get_title(fam_name):
+ """Get a title for this modelling experiment."""
+ return f"AlphaFold model for NMPFamsDB Family {fam_name}"
+
+
+def _get_model_details(fam_name):
+ """Get the model description."""
+ db_url = f"https://bib.fleming.gr/NMPFamsDB/family?id={fam_name}"
+ # TODO: check if ok to use HTML for the URL
+ db_url = f"<a href=\"{db_url}\" target=\"_blank\">{db_url}</a>"
+ return f"Model generated using AlphaFold (v2.0.0) for the " \
+ f"\"Representative Sequence\" of NMPFamsDB Metagenome / " \
+ f"Metatranscriptome Family {fam_name}.\n\nSee {db_url} for " \
+ f"additional details."
+
+
+def _get_model_group_name():
+ """Get a name for a model group."""
+ return None
+
+
+def _get_sequence(chn, use_auth=False):
+ """Get the sequence out of an OST chain incl. '-' for gaps in resnums."""
+ # initialise (add gaps if first is not at num. 1)
+ lst_rn = _get_res_num(chn.residues[0], use_auth)
+ idx = 1
+ sqe = "-" * (lst_rn - 1) \
+ + chn.residues[0].one_letter_code
+
+ for res in chn.residues[idx:]:
+ lst_rn += 1
+ while lst_rn != _get_res_num(res, use_auth):
+ sqe += "-"
+ lst_rn += 1
+ sqe += res.one_letter_code
+ return sqe
+
+
+def _get_entities(pdb_file, ref_seq, fam_name):
+ """Gather data for the mmCIF (target) entities."""
+ _check_file(pdb_file)
+
+ ost_ent = io.LoadPDB(pdb_file)
+ if ost_ent.chain_count != 1:
+ raise RuntimeError(
+ f"Unexpected oligomer in {pdb_file}"
+ )
+ chn = ost_ent.chains[0]
+ sqe_gaps = _get_sequence(chn)
+
+ # NOTE: can have gaps to accommodate "X" in ref_seq
+ exp_seq = sqe_gaps.replace('-', 'X')
+ len_diff = len(ref_seq.string) - len(exp_seq)
+ if len_diff > 0:
+ exp_seq += 'X' * len_diff
+ if exp_seq != ref_seq.string:
+ raise RuntimeError(f"Sequence in {pdb_file} does not match ref_seq")
+
+ # TODO: waiting for input on whether they change handling of "X" in seq
+ # -> HERE: assuming that "X" were in modelled sequence and PDB can have gaps
+ cif_ent = {
+ "seqres": ref_seq.string, # HACK for testing: .replace('X', 'A')
+ "pdb_sequence": sqe_gaps,
+ "pdb_chain_id": [_get_ch_name(chn, False)],
+ "fam_name": fam_name,
+ "description": f"Representative Sequence of NMPFamsDB Family {fam_name}"
+ }
+
+ return [cif_ent], ost_ent
+
+
+def _get_modelcif_entities(target_ents, asym_units, system):
+ """Create ModelCIF entities and asymmetric units."""
+ for cif_ent in target_ents:
+ mdlcif_ent = modelcif.Entity(
+ # NOTE: sequence here defines residues in model!
+ cif_ent["seqres"],
+ description=cif_ent["description"],
+ source=None,
+ references=[
+ _NmpfamsdbTrgRef(
+ cif_ent["fam_name"],
+ cif_ent["fam_name"],
+ align_begin=1,
+ align_end=len(cif_ent["seqres"])
+ )
+ ],
+ )
+ # NOTE: this assigns (potentially new) alphabetic chain names
+ for pdb_chain_id in cif_ent["pdb_chain_id"]:
+ asym_units[pdb_chain_id] = modelcif.AsymUnit(
+ mdlcif_ent, strand_id=pdb_chain_id,
+ )
+ system.target_entities.append(mdlcif_ent)
+
+
+def _get_assoc_aln_file(fle_path):
+ """Generate a modelcif.associated.File object pointing to FASTA formatted
+ file containing MSA.
+ """
+ cfile = modelcif.associated.File(
+ fle_path,
+ details="Custom MSA for modelling",
+ data=modelcif.data.Data("Custom MSA for modelling")
+ )
+ cfile.file_format = "fasta"
+ cfile.file_content = "multiple sequence alignments"
+ return cfile
+
+
+def _get_associated_files(mdl_name, arc_files):
+ """Create entry for associated files."""
+ # package all into zip file
+ return modelcif.associated.Repository(
+ "",
+ [modelcif.associated.ZipFile(f"{mdl_name}.zip", files=arc_files)],
+ )
+ # NOTE: by convention MA expects zip file with same name as model-cif
+
+
+def _assemble_modelcif_software(soft_dict):
+ """Create a modelcif.Software instance from dictionary."""
+ return modelcif.Software(
+ soft_dict["name"],
+ soft_dict["classification"],
+ soft_dict["description"],
+ soft_dict["location"],
+ soft_dict["type"],
+ soft_dict["version"],
+ citation=soft_dict["citation"],
+ )
+
+
+def _get_modelcif_protocol_software(js_step):
+ """Assemble software entries for a ModelCIF protocol step."""
+ if js_step["software"]:
+ if len(js_step["software"]) == 1:
+ sftwre = _assemble_modelcif_software(js_step["software"][0])
+ else:
+ sftwre = []
+ for sft in js_step["software"]:
+ sftwre.append(_assemble_modelcif_software(sft))
+ sftwre = modelcif.SoftwareGroup(elements=sftwre)
+ if js_step["software_parameters"]:
+ params = []
+ for key, val in js_step["software_parameters"].items():
+ params.append(modelcif.SoftwareParameter(key, val))
+ if isinstance(sftwre, modelcif.SoftwareGroup):
+ sftwre.parameters = params
+ else:
+ sftwre = modelcif.SoftwareGroup(
+ elements=(sftwre,), parameters=params
+ )
+ return sftwre
+ return None
+
+
+def _get_modelcif_protocol_data(data_label, target_entities, aln_data, model):
+ """Assemble data for a ModelCIF protocol step."""
+ if data_label == "target_sequences":
+ data = modelcif.data.DataGroup(target_entities)
+ elif data_label == "MSA":
+ data = aln_data
+ elif data_label == "target_sequences_and_MSA":
+ data = modelcif.data.DataGroup(target_entities)
+ data.append(aln_data)
+ elif data_label == "model":
+ data = model
+ else:
+ raise RuntimeError(f"Unknown protocol data: '{data_label}'")
+ return data
+
+
+def _get_modelcif_protocol(protocol_steps, target_entities, aln_data, model):
+ """Create the protocol for the ModelCIF file."""
+ protocol = modelcif.protocol.Protocol()
+ for js_step in protocol_steps:
+ sftwre = _get_modelcif_protocol_software(js_step)
+ input_data = _get_modelcif_protocol_data(
+ js_step["input"], target_entities, aln_data, model
+ )
+ output_data = _get_modelcif_protocol_data(
+ js_step["output"], target_entities, aln_data, model
+ )
+
+ protocol.steps.append(
+ modelcif.protocol.Step(
+ input_data=input_data,
+ output_data=output_data,
+ name=js_step["name"],
+ details=js_step["details"],
+ software=sftwre,
+ )
+ )
+ protocol.steps[-1].method_type = js_step["method_type"]
+ return protocol
+
+
+def _compress_cif_file(cif_file):
+ """Compress cif file and delete original."""
+ with open(cif_file, 'rb') as f_in:
+ with gzip.open(cif_file + '.gz', 'wb') as f_out:
+ shutil.copyfileobj(f_in, f_out)
+ os.remove(cif_file)
+
+
+def _package_associated_files(repo):
+ """Compress associated files into single zip file and delete original."""
+ # zip settings tested for good speed vs compression
+ for archive in repo.files:
+ with zipfile.ZipFile(archive.path, "w", zipfile.ZIP_BZIP2) as cif_zip:
+ for zfile in archive.files:
+ cif_zip.write(zfile.path, arcname=zfile.path)
+ os.remove(zfile.path)
+
+
+def _store_as_modelcif(data_json, ost_ent, out_dir, mdl_name, compress):
+ """Mix all the data into a ModelCIF file."""
+ print(" generating ModelCIF objects...", end="")
+ pstart = timer()
+ # create system to gather all the data
+ system = modelcif.System(
+ title=data_json["title"],
+ id=data_json["mdl_id"].upper(),
+ model_details=data_json["model_details"],
+ )
+
+ # create an asymmetric unit and an entity per target sequence
+ asym_units = {}
+ _get_modelcif_entities(
+ data_json["target_entities"], asym_units, system
+ )
+
+ # audit_authors
+ system.authors.extend(data_json["audit_authors"])
+
+ # set up the model to produce coordinates
+ model = _OST2ModelCIF(
+ assembly=modelcif.Assembly(asym_units.values()),
+ asym=asym_units,
+ ost_entity=ost_ent,
+ scores_json=data_json,
+ name=data_json["mdl_name"],
+ )
+ print(f" ({timer()-pstart:.2f}s)")
+ print(" processing QA scores...", end="", flush=True)
+ pstart = timer()
+ model.add_scores()
+ print(f" ({timer()-pstart:.2f}s)")
+
+ model_group = modelcif.model.ModelGroup(
+ [model], name=data_json["model_group_name"]
+ )
+ system.model_groups.append(model_group)
+
+ # handle additional files
+ aln_file = _get_assoc_aln_file(data_json["aln_file_name"])
+ system.repositories.append(
+ _get_associated_files(mdl_name, [aln_file])
+ )
+
+ # get data and steps
+ protocol = _get_modelcif_protocol(
+ data_json["protocol"], system.target_entities, aln_file.data, model,
+ )
+ system.protocols.append(protocol)
+
+ # write modelcif System to file (NOTE: no PAE here!)
+ print(" write to disk...", end="", flush=True)
+ pstart = timer()
+ # copy aln file to compress them
+ shutil.copyfile(
+ data_json["aln_file_path"],
+ os.path.join(out_dir, data_json["aln_file_name"])
+ )
+ # NOTE: we change path and back while being exception-safe to handle zipfile
+ oldpwd = os.getcwd()
+ os.chdir(out_dir)
+ mdl_fle = f"{mdl_name}.cif"
+ try:
+ with open(mdl_fle, "w", encoding="ascii") as mmcif_fh:
+ modelcif.dumper.write(mmcif_fh, [system])
+ _package_associated_files(system.repositories[0])
+ if compress:
+ _compress_cif_file(mdl_fle)
+ mdl_fle += ".gz"
+ finally:
+ os.chdir(oldpwd)
+ print(f" ({timer()-pstart:.2f}s)")
+
+
+def _translate2modelcif(f_name, opts, metadata_fam, pdb_files, ref_seq_check):
+ """Convert a model with its accompanying data to ModelCIF."""
+
+ # TODO: unclear what to do with such cases; skipped for now
+ if len(metadata_fam) != 5:
+ _warn_msg(f"Unexpected number of {len(metadata_fam)} models in " \
+ f"metadata for family {f_name}. Skipping...")
+ return
+ #
+
+ mdl_id = f_name
+ # skip if done already
+ if opts.compress:
+ cifext = "cif.gz"
+ else:
+ cifext = "cif"
+ mdl_path = os.path.join(opts.out_dir, f"{mdl_id}.{cifext}")
+ if os.path.exists(mdl_path):
+ print(f" {mdl_id} already done...")
+ return
+
+ # go for it...
+ print(f" translating {mdl_id}...")
+ pdb_start = timer()
+
+ # get aln_data and ref. seq. for this entry
+ aln_file = f"{f_name}.fasta"
+ aln_path = os.path.join(opts.msa_data_dir, aln_file)
+ # TODO: if we need to handle files without ALN, this needs fixing
+ # -> e.g. 11 extra models in pub_data_* cannot be handled right now
+ if not os.path.exists(aln_path):
+ _warn_msg(f"Cannot deal with missing MSA for {f_name} (yet). " \
+ f"Skipping...")
+ return
+ #
+ aln = io.LoadAlignment(aln_path) # note: this checks that it's an actual MSA
+ ref_seq = aln.sequences[0]
+ if ref_seq_check is not None and ref_seq_check.string != ref_seq.string:
+ raise RuntimeError(f"Sequence mismatch for {f_name}")
+ # TODO: allow "X" (or whatever can be used to label unknown AA) if needed
+ if "X" in ref_seq.string:
+ _warn_msg(f"Cannot deal with 'X' in ref_seq for {f_name} (yet). " \
+ f"Skipping...")
+ return
+ #
+
+ # gather data into JSON-like structure
+ print(" preparing data...", end="")
+ pstart = timer()
+
+ config_data = _get_config()
+ mdlcf_json = {}
+ mdlcf_json["audit_authors"] = _get_audit_authors()
+ mdlcf_json["protocol"] = _get_protocol_steps_and_software(config_data)
+ mdlcf_json["config_data"] = config_data
+ mdlcf_json["mdl_id"] = mdl_id
+ mdlcf_json["aln_file_name"] = aln_file
+ mdlcf_json["aln_file_path"] = aln_path
+
+ # find model to process
+ # TODO: here just top pLDDT model processed; extend for more if needed...
+ top_metadata = metadata_fam.loc[metadata_fam.pLDDT.idxmax()]
+ pdb_list_sel = [f for f in pdb_files if top_metadata.mdl in f]
+ if len(pdb_list_sel) != 1:
+ # this should only happen if duplicated file in pub_data_*
+ # TODO: for now no warning shown and we just pick first hit
+ # -> first hit is from lowest "pub_data_*"
+ # -> unclear if we should worry about it...
+ pass
+ mdlcf_json["mdl_name"] = f"Top ranked model ({top_metadata.mdl})"
+
+ # get scores for this entry
+ mdlcf_json["plddt_global"] = top_metadata.pLDDT
+ mdlcf_json["ptm"] = top_metadata.pTM
+
+ # process coordinates
+ pdb_file = pdb_list_sel[0]
+ target_entities, ost_ent = _get_entities(pdb_file, ref_seq, f_name)
+ mdlcf_json["target_entities"] = target_entities
+ # sanity check (only for top ranked model!)
+ if opts.pdb_web_path is not None:
+ pdb_file_web = os.path.join(opts.pdb_web_path, f"{f_name}.pdb")
+ if not filecmp.cmp(pdb_file, pdb_file_web):
+ raise RuntimeError(f"PDB file mismatch web vs top-ranked for " \
+ f"{f_name}")
+
+ # fill annotations
+ mdlcf_json["title"] = _get_title(f_name)
+ mdlcf_json["model_details"] = _get_model_details(f_name)
+ mdlcf_json["model_group_name"] = _get_model_group_name()
+ print(f" ({timer()-pstart:.2f}s)")
+
+ # save ModelCIF
+ _store_as_modelcif(mdlcf_json, ost_ent, opts.out_dir, mdl_id, opts.compress)
+
+ # check if result can be read and has expected seq.
+ ent, ss = io.LoadMMCIF(mdl_path, seqres=True)
+ exp_seqs = [trg_ent["pdb_sequence"] \
+ for trg_ent in mdlcf_json["target_entities"]]
+ assert ent.chain_count == len(exp_seqs), f"Bad chain count {mdl_id}"
+ # here we expect auth = label IDs
+ ent_seq = "".join([_get_sequence(chn, False) for chn in ent.chains])
+ ent_seq_a = "".join([_get_sequence(chn, True) for chn in ent.chains])
+ assert ent_seq == ent_seq_a
+ assert ent_seq == "".join(exp_seqs), f"Bad seq. {mdl_id}"
+ ent_seqres = "".join([ss.FindSequence(chn.name).string \
+ for chn in ent.chains])
+ exp_seqres = "".join([trg_ent["seqres"] \
+ for trg_ent in mdlcf_json["target_entities"]])
+ assert ent_seqres == exp_seqres, f"Bad seqres {mdl_id}"
+
+ print(f" ... done with {mdl_id} ({timer()-pdb_start:.2f}s).")
+
+
+def _main():
+ """Run as script."""
+ opts = _parse_args()
+
+ # parse/fetch global data
+ metadata_full = _get_metadata(opts.metadata_file)
+ pdb_files_split = _get_pdb_files(opts.model_base_dir)
+ if opts.refseq_path is not None:
+ refseqs = io.LoadSequenceList(opts.refseq_path)
+ else:
+ refseqs = None
+
+ # get on with models
+ print(f"Working on {opts.prefix}*...")
+
+ # iterate over models
+ for f_name in sorted(pdb_files_split):
+ if f_name.startswith(opts.prefix):
+ _translate2modelcif(
+ f_name, opts,
+ metadata_full[metadata_full.ID == f_name],
+ pdb_files_split[f_name],
+ refseqs.FindSequence(f_name) if refseqs is not None else None,
+ )
+
+ print(f"... done with {opts.prefix}*.")
+
+
+if __name__ == "__main__":
+ _main()