diff --git a/projects/novelfams/translate2modelcif.py b/projects/novelfams/translate2modelcif.py
new file mode 100644
index 0000000000000000000000000000000000000000..9c3f5f80a3e64e3d8057f42311c7cd3c15bb8b8f
--- /dev/null
+++ b/projects/novelfams/translate2modelcif.py
@@ -0,0 +1,1254 @@
+#! /usr/local/bin/ost
+# -*- coding: utf-8 -*-
+"""Translate models for novelfams from PDB + extra data into ModelCIF."""
+# re-enable Pylint for final version
+# pylint: disable=too-many-lines
+
+from timeit import default_timer as timer
+import argparse
+import filecmp
+import gzip
+import os
+import shutil
+import sys
+import zipfile
+
+import pandas as pd
+import numpy as np
+
+import ihm
+import ihm.citations
+import modelcif
+import modelcif.associated
+import modelcif.dumper
+import modelcif.model
+import modelcif.protocol
+import modelcif.reference
+
+# pylint: disable=unused-import,wrong-import-order
+from ost import io, geom, mol
+
+# pylint: enable=unused-import,wrong-import-order
+
+
+# EXAMPLE for running:
+# ost translate2modelcif.py ...
+
+
+################################################################################
+# HELPERS (mostly copied from from ost/modules/io/tests/test_io_omf.py)
+# to compare PDBs
+def _compare_atoms(
+ a1, a2, occupancy_thresh=0.01, bfactor_thresh=0.01, dist_thresh=0.001
+):
+ if abs(a1.occupancy - a2.occupancy) > occupancy_thresh:
+ return False
+ if abs(a1.b_factor - a2.b_factor) > bfactor_thresh:
+ return False
+ # modification: look at x,y,z spearately
+ if abs(a1.pos.x - a2.pos.x) > dist_thresh:
+ return False
+ if abs(a1.pos.y - a2.pos.y) > dist_thresh:
+ return False
+ if abs(a1.pos.z - a2.pos.z) > dist_thresh:
+ return False
+ if a1.is_hetatom != a2.is_hetatom:
+ return False
+ if a1.element != a2.element:
+ return False
+ return True
+
+
+def _compare_residues(
+ r1,
+ r2,
+ at_occupancy_thresh=0.01,
+ at_bfactor_thresh=0.01,
+ at_dist_thresh=0.001,
+ skip_ss=False,
+ skip_rnums=False,
+):
+ if r1.GetName() != r2.GetName():
+ return False
+ if skip_rnums is False:
+ if r1.GetNumber() != r2.GetNumber():
+ return False
+ if skip_ss is False:
+ if str(r1.GetSecStructure()) != str(r2.GetSecStructure()):
+ return False
+ if r1.one_letter_code != r2.one_letter_code:
+ return False
+ if r1.chem_type != r2.chem_type:
+ return False
+ if r1.chem_class != r2.chem_class:
+ return False
+ anames1 = [a.GetName() for a in r1.atoms]
+ anames2 = [a.GetName() for a in r2.atoms]
+ if sorted(anames1) != sorted(anames2):
+ return False
+ anames = anames1
+ for aname in anames:
+ a1 = r1.FindAtom(aname)
+ a2 = r2.FindAtom(aname)
+ if not _compare_atoms(
+ a1,
+ a2,
+ occupancy_thresh=at_occupancy_thresh,
+ bfactor_thresh=at_bfactor_thresh,
+ dist_thresh=at_dist_thresh,
+ ):
+ return False
+ return True
+
+
+def _compare_chains(
+ ch1,
+ ch2,
+ at_occupancy_thresh=0.01,
+ at_bfactor_thresh=0.01,
+ at_dist_thresh=0.001,
+ skip_ss=False,
+ skip_rnums=False,
+):
+ if len(ch1.residues) != len(ch2.residues):
+ return False
+ for r1, r2 in zip(ch1.residues, ch2.residues):
+ if not _compare_residues(
+ r1,
+ r2,
+ at_occupancy_thresh=at_occupancy_thresh,
+ at_bfactor_thresh=at_bfactor_thresh,
+ at_dist_thresh=at_dist_thresh,
+ skip_ss=skip_ss,
+ skip_rnums=skip_rnums,
+ ):
+ return False
+ return True
+
+
+def _compare_bonds(ent1, ent2):
+ bonds1 = list()
+ for b in ent1.bonds:
+ bond_partners = [str(b.first), str(b.second)]
+ bonds1.append([min(bond_partners), max(bond_partners), b.bond_order])
+ bonds2 = list()
+ for b in ent2.bonds:
+ bond_partners = [str(b.first), str(b.second)]
+ bonds2.append([min(bond_partners), max(bond_partners), b.bond_order])
+ return sorted(bonds1) == sorted(bonds2)
+
+
+def _compare_ent(
+ ent1,
+ ent2,
+ at_occupancy_thresh=0.01,
+ at_bfactor_thresh=0.01,
+ at_dist_thresh=0.001,
+ skip_ss=False,
+ skip_cnames=False,
+ skip_bonds=False,
+ skip_rnums=False,
+ bu_idx=None,
+):
+ if bu_idx is not None:
+ if ent1.GetName() + " " + str(bu_idx) != ent2.GetName():
+ return False
+ else:
+ if ent1.GetName() != ent2.GetName():
+ return False
+ chain_names_one = [ch.GetName() for ch in ent1.chains]
+ chain_names_two = [ch.GetName() for ch in ent2.chains]
+ if skip_cnames:
+ # only check whether we have the same number of chains
+ if len(chain_names_one) != len(chain_names_two):
+ return False
+ else:
+ if chain_names_one != chain_names_two:
+ return False
+ for ch1, ch2 in zip(ent1.chains, ent2.chains):
+ if not _compare_chains(
+ ch1,
+ ch2,
+ at_occupancy_thresh=at_occupancy_thresh,
+ at_bfactor_thresh=at_bfactor_thresh,
+ at_dist_thresh=at_dist_thresh,
+ skip_ss=skip_ss,
+ skip_rnums=skip_rnums,
+ ):
+ return False
+ if not skip_bonds:
+ if not _compare_bonds(ent1, ent2):
+ return False
+ return True
+
+
+def _compare_pdbs(f_name, f1, f2):
+ """Use atom-by-atom comparison on PDB files allowing num. errors."""
+ # first do simple file diff.
+ if filecmp.cmp(f1, f2):
+ return True
+ else:
+ ent1 = io.LoadPDB(f1)
+ ent2 = io.LoadPDB(f2)
+ # allow a bit more errors as input files can have rounding errors
+ if _compare_ent(ent1, ent2, 0.011, 0.011, 0.0011, True, False, True):
+ return True
+ else:
+ # check manually and give warning...
+ atom_names_1 = [a.qualified_name for a in ent1.atoms]
+ atom_names_2 = [a.qualified_name for a in ent2.atoms]
+ assert atom_names_1 == atom_names_2
+ b_diffs = [
+ abs(a1.b_factor - a2.b_factor)
+ for a1, a2 in zip(ent1.atoms, ent2.atoms)
+ ]
+ max_b_diff = max(b_diffs)
+ rmsd = mol.alg.CalculateRMSD(ent1.Select(""), ent2.Select(""))
+ _warn_msg(
+ f"PDB file mismatch web vs top-ranked for {f_name}: "
+ f"RMSD {rmsd:.3f}, max. b_factor diff {max_b_diff:.3f}"
+ )
+ return False
+
+
+################################################################################
+
+
+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_dir",
+ type=str,
+ metavar="<MODEL DIR>",
+ help='Directory with model PDBs named "{ID}_{mdl}.pdb" (with ID and '
+ + "mdl matching info in metadata).",
+ )
+ parser.add_argument(
+ "out_dir",
+ type=str,
+ metavar="<OUTPUT DIR>",
+ help="Path to directory to store results.",
+ )
+ parser.add_argument(
+ "--compress",
+ default=False,
+ action="store_true",
+ help="Compress ModelCIF file with gzip.",
+ )
+ opts = parser.parse_args()
+
+ # check input
+ opts.model_dir = _check_opts_folder(opts.model_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.")
+
+ 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 _LocalPairwisePAE(
+ modelcif.qa_metric.LocalPairwise, modelcif.qa_metric.PAE
+):
+ """Predicted aligned error (in Angstroms)"""
+
+ name = "PAE"
+ software = None
+
+
+class _NmpfamsdbTrgRef(modelcif.reference.TargetReference):
+ """NMPFamsDB as target reference."""
+
+ name = "Other"
+ other_details = "NMPFamsDB"
+
+
+class _LPeptideAlphabetWithX(ihm.LPeptideAlphabet):
+ """Have the default amino acid alphabet plus 'X' for unknown residues."""
+
+ def __init__(self):
+ """Create the alphabet."""
+ super().__init__()
+ self._comps["X"] = self._comps["UNK"]
+
+
+# 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"))
+ 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")
+ 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
+ # what accuracy to use for PAE? (writer uses 3 anyway)
+ self.pae_digits = 3
+
+ # 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
+ lpae = []
+ 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_i = _get_res_num(res_i, self.use_auth)
+ self.qa_metrics.append(
+ _LocalPLDDT(
+ self.asym[ch_name].residue(res_num_i),
+ self.plddts[i],
+ )
+ )
+ i += 1
+
+ # PAE needs to go by residue index as it also stores ones
+ # for missing residues (i.e. X)
+ if "pae" in self.scores_json:
+ pae_i = self.scores_json["pae"][res_num_i - 1]
+ for chn_j in self.ost_entity.chains:
+ for res_j in chn_j.residues:
+ res_num_j = _get_res_num(res_j, self.use_auth)
+ pae_ij = pae_i[res_num_j - 1]
+ lpae.append(
+ _LocalPairwisePAE(
+ self.asym[chn_i.name].residue(res_num_i),
+ self.asym[chn_j.name].residue(res_num_j),
+ round(pae_ij, self.pae_digits),
+ )
+ )
+
+ self.qa_metrics.extend(lpae)
+
+
+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_dir):
+ """Collect PDB files from model_dir.
+
+ Returns a list of paths to PDB files.
+ """
+ pdb_files = [f for f in os.listdir(model_dir) if not f.startswith(".")]
+ pdb_paths = []
+ for f in pdb_files:
+ f_path = os.path.join(model_dir, f)
+ pdb_paths.append(f_path)
+
+ return pdb_paths
+
+
+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 "
+ "fine-tuned 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, max_pLDDT, max_pTM):
+ """Get the model description."""
+ db_url = f"https://bib.fleming.gr/NMPFamsDB/family?id={fam_name}"
+ return (
+ f'Model generated using AlphaFold (v2.0.0) for the "Representative '
+ f'Sequence" of NMPFamsDB Metagenome / Metatranscriptome Family '
+ f"{fam_name}.\n\nThe 5 produced models reached a max. global pLDDT of "
+ f"{round(max_pLDDT, 3)} and max. pTM of {round(max_pTM, 3)}.\n\n"
+ f"See {db_url} for 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")
+
+ cif_ent = {
+ "seqres": ref_seq.string,
+ "pdb_sequence": sqe_gaps,
+ "pdb_chain_id": [_get_ch_name(chn, False)],
+ "fam_name": fam_name,
+ "description": "Representative Sequence of NMPFamsDB Family "
+ + f"{fam_name}",
+ }
+
+ return [cif_ent], ost_ent
+
+
+def _get_modelcif_entities(target_ents, asym_units, system):
+ """Create ModelCIF entities and asymmetric units."""
+ alphabet = _LPeptideAlphabetWithX()
+ for cif_ent in target_ents:
+ mdlcif_ent = modelcif.Entity(
+ # NOTE: sequence here defines residues in model!
+ cif_ent["seqres"],
+ alphabet=alphabet,
+ 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_pae_file(entry_id, mdl_name):
+ """Generate a associated file object to extract PAE to extra file."""
+ return modelcif.associated.LocalPairwiseQAScoresFile(
+ f"{mdl_name}_local_pairwise_qa.cif",
+ categories=["_ma_qa_metric_local_pairwise"],
+ copy_categories=["_ma_qa_metric"],
+ entry_id=entry_id,
+ entry_details="This file is an associated file consisting "
+ + "of local pairwise QA metrics. This is a partial mmCIF "
+ + "file and can be validated by merging with the main "
+ + "mmCIF file containing the model coordinates and other "
+ + "associated data.",
+ details="Predicted aligned error",
+ )
+
+
+def _get_aln_data():
+ """Generate Data object for ALN."""
+ aln_data = modelcif.data.Data("Custom MSA for modelling")
+ aln_data.data_content_type = "coevolution MSA"
+ return aln_data
+
+
+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=_get_aln_data(),
+ )
+ 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 _get_assoc_mdl_file(fle_path, data_json):
+ """Generate a modelcif.associated.File object that looks like a CIF file.
+ The dedicated CIFFile functionality in modelcif would also try to write it.
+ """
+ cfile = modelcif.associated.File(
+ fle_path,
+ details=f"#{data_json['mdl_rank']} ranked model; "
+ + f"pTM {round(data_json['ptm'], 3)}, "
+ + f"pLDDT {round(data_json['plddt_global'], 3)}",
+ )
+ cfile.file_format = "cif"
+ return cfile
+
+
+def _get_assoc_zip_file(fle_path, data_json):
+ """Create a modelcif.associated.File object that looks like a ZIP file.
+ This is NOT the archive ZIP file for the PAEs but to store that in the
+ ZIP archive of the selected model."""
+ zfile = modelcif.associated.File(
+ fle_path,
+ details="archive with multiple files for "
+ + f"#{data_json['mdl_rank']} ranked model",
+ )
+ zfile.file_format = "other"
+ return zfile
+
+
+def _store_as_modelcif(
+ data_json,
+ ost_ent,
+ out_dir,
+ mdl_name,
+ compress,
+ add_pae,
+ add_aln,
+ add_files,
+):
+ """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"],
+ )
+
+ # add primary citation (not using from_pubmed_id to ensure that author names
+ # have no special chars)
+ system.citations.append(
+ ihm.Citation(
+ pmid="37821698",
+ title="Unraveling the functional dark matter through global "
+ + "metagenomics.",
+ journal="Nature",
+ volume=622,
+ page_range=(594, 602),
+ year=2023,
+ authors=[
+ "Pavlopoulos, G.A.",
+ "Baltoumas, F.A.",
+ "Liu, S.",
+ "Selvitopi, O.",
+ "Camargo, A.P.",
+ "Nayfach, S.",
+ "Azad, A.",
+ "Roux, S.",
+ "Call, L.",
+ "Ivanova, N.N.",
+ "Chen, I.M.",
+ "Paez-Espino, D.",
+ "Karatzas, E.",
+ "Iliopoulos, I.",
+ "Konstantinidis, K.",
+ "Tiedje, J.M.",
+ "Pett-Ridge, J.",
+ "Baker, D.",
+ "Visel, A.",
+ "Ouzounis, C.A.",
+ "Ovchinnikov, S.",
+ "Buluc, A.",
+ "Kyrpides, N.C.",
+ ],
+ doi="10.1038/s41586-023-06583-7",
+ is_primary=True,
+ )
+ )
+
+ # 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
+ arc_files = []
+ if add_pae:
+ arc_files.append(_get_assoc_pae_file(system.id, mdl_name))
+ if add_aln:
+ aln_file = _get_assoc_aln_file(data_json["aln_file_name"])
+ arc_files.append(aln_file)
+ aln_data = aln_file.data
+ else:
+ aln_data = _get_aln_data()
+ aln_data.data_other_details = "MSA stored with parent entry"
+ arc_files.extend(add_files)
+ if arc_files:
+ system.repositories.append(_get_associated_files(mdl_name, arc_files))
+
+ # get data and steps
+ protocol = _get_modelcif_protocol(
+ data_json["protocol"],
+ system.target_entities,
+ aln_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
+ if add_aln:
+ 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])
+ if arc_files:
+ _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)")
+ assoc_files = [_get_assoc_mdl_file(mdl_fle, data_json)]
+ if arc_files:
+ assoc_files.append(
+ _get_assoc_zip_file(
+ system.repositories[0].files[0].path, data_json
+ )
+ )
+ return assoc_files
+
+
+def _translate2modelcif_single(
+ f_name,
+ opts,
+ metadata,
+ pdb_files,
+ mdl_rank,
+ aln_file,
+ aln_path,
+ ref_seq,
+ mdl_details,
+ add_files=[],
+):
+ """Convert a single model with its accompanying data to ModelCIF."""
+ mdl_id = f_name
+ if mdl_rank > 1:
+ mdl_id += f"_rank_{mdl_rank}_{metadata.mdl}"
+
+ print(f" translating {mdl_id}...")
+ pdb_start = timer()
+
+ # 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 # used for entry ID
+ mdlcf_json["mdl_rank"] = mdl_rank
+ mdlcf_json["aln_file_name"] = aln_file
+ mdlcf_json["aln_file_path"] = aln_path
+
+ # find model to process
+ pdb_list_sel = [f for f in pdb_files if metadata.mdl in f]
+ if len(pdb_list_sel) != 1:
+ # this should never happen
+ raise RuntimeError(
+ f"Multiple file matches found for {metadata.mdl} in {f_name}"
+ )
+ if mdl_rank == 1:
+ mdlcf_json["mdl_name"] = f"Top ranked model ({metadata.mdl})"
+ else:
+ mdlcf_json["mdl_name"] = f"#{mdl_rank} ranked model ({metadata.mdl})"
+
+ # 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 mdl_rank == 1 and opts.pdb_web_path is not None:
+ pdb_file_web = os.path.join(opts.pdb_web_path, f"{f_name}.pdb")
+ # warning handled in compare function...
+ _compare_pdbs(f_name, pdb_file, pdb_file_web)
+
+ # get scores for this entry
+ mdlcf_json["plddt_global"] = metadata.pLDDT
+ mdlcf_json["ptm"] = metadata.pTM
+ add_pae = mdl_rank == 1 or opts.all_pae
+ if add_pae:
+ pdb_basename = os.path.basename(pdb_file)
+ pae_basename = os.path.splitext(pdb_basename)[0] + ".txt.gz"
+ pae_file = os.path.join(opts.pae_dir, pae_basename)
+ _check_file(pae_file)
+ mdlcf_json["pae"] = np.loadtxt(pae_file)
+ exp_num_res = len(ref_seq.string)
+ if mdlcf_json["pae"].shape != (exp_num_res, exp_num_res):
+ raise RuntimeError(f"Unexpected PAE shape in {pae_file}")
+
+ # fill annotations
+ mdlcf_json["title"] = _get_title(f_name)
+ if mdl_rank != 1:
+ mdlcf_json["title"] += f" (#{mdl_rank} ranked model)"
+ mdlcf_json["model_details"] = mdl_details
+ mdlcf_json["model_group_name"] = _get_model_group_name()
+ print(f" ({timer()-pstart:.2f}s)")
+
+ # save ModelCIF
+ assoc_files = _store_as_modelcif(
+ mdlcf_json,
+ ost_ent,
+ opts.out_dir,
+ mdl_id,
+ opts.compress and mdl_rank == 1,
+ add_pae,
+ opts.all_msa or mdl_rank == 1,
+ add_files,
+ )
+
+ # check if result can be read and has expected seq.
+ mdl_path = os.path.join(opts.out_dir, assoc_files[0].path)
+ 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).")
+
+ return assoc_files
+
+
+def _translate2modelcif(f_name, opts, metadata_fam, pdb_files, ref_seq_check):
+ """Convert a family of models with their accompanying data to ModelCIF."""
+ # re-enable Pylint for final version
+ # pylint: disable=too-many-locals
+ # expected to have exactly 5 models per family
+ if len(metadata_fam) != 5:
+ raise RuntimeError(
+ f"Unexpected number of {len(metadata_fam)} models in "
+ f"metadata for family {f_name}."
+ )
+
+ # skip if done already
+ if opts.compress:
+ cifext = "cif.gz"
+ else:
+ cifext = "cif"
+ mdl_path = os.path.join(opts.out_dir, f"{f_name}.{cifext}")
+ if os.path.exists(mdl_path):
+ print(f" {f_name} already done...")
+ return
+
+ # 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)
+ # expected 11 extra families compared to web data but those don't have MSAs
+ # -> skipped for consistency and to keep code simple here
+ if not os.path.exists(aln_path):
+ _warn_msg(f"Missing MSA for {f_name}. 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}")
+
+ # get global model details
+ mdl_details = _get_model_details(
+ f_name, metadata_fam.pLDDT.max(), metadata_fam.pTM.max()
+ )
+ # rank available models
+ metadata_sorted = metadata_fam.sort_values("pLDDT", ascending=False)
+ add_files = []
+ if opts.all_models:
+ for idx in range(1, 5):
+ assoc_files = _translate2modelcif_single(
+ f_name,
+ opts,
+ metadata_sorted.iloc[idx],
+ pdb_files,
+ idx + 1,
+ aln_file,
+ aln_path,
+ ref_seq,
+ mdl_details,
+ )
+ add_files.extend(assoc_files)
+ # process top ranked one
+ _translate2modelcif_single(
+ f_name,
+ opts,
+ metadata_sorted.iloc[0],
+ pdb_files,
+ 1,
+ aln_file,
+ aln_path,
+ ref_seq,
+ mdl_details,
+ add_files,
+ )
+
+
+def _main():
+ """Run as script."""
+ s_tmstmp = timer()
+ opts = _parse_args()
+
+ # get a list of PDB files with the path to load them.
+ pdb_files = _get_pdb_files(opts.model_dir)
+ n_mdls = len(pdb_files)
+
+ # iterate over models
+ print(f"Processing {n_mdls} models.")
+ tmstmp = s_tmstmp
+ for f_name in sorted(pdb_files):
+ n_mdls -= 1
+ """
+ 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,
+ )
+ """
+ # report progress after a bit of time
+ if timer() - tmstmp > 60:
+ print(
+ f"... {n_mdls} models left after "
+ + f"{(timer() - s_tmstmp)/60:.2f}min, last seen: "
+ + f"{os.path.splitext(os.path.basename(f_name))[0]}"
+ )
+ tmstmp = timer()
+
+ print(
+ f"... done, {n_mdls} models left after "
+ + f"{(timer() - s_tmstmp)/60:.2f}min."
+ )
+
+
+if __name__ == "__main__":
+ _main()