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Commit 14a60b46 authored by Gerardo Tauriello's avatar Gerardo Tauriello
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Updated translate2modelcif.py based on Niko-model-set

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translate2modelcif.py
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#! /usr/local/bin/ost
"""Translate models from Tara/ Xabi from PDB + extra data into ModelCIF."""
# ToDo [internal]: get DB versions in - https://colabfold.mmseqs.com, scroll
# down to "Database Information"
# EXAMPLES for running:
"""
ost scripts/translate2modelcif.py "A0A1B0GTU1-O75152" \
--top_ranked_only --out_dir="./modelcif"
"""
import argparse
import datetime
import os
import sys
import gzip, shutil, zipfile
from timeit import default_timer as timer
import numpy as np
......@@ -14,6 +19,7 @@ import requests
import ujson as json
import ihm
import ihm.citations
import modelcif
import modelcif.associated
import modelcif.dumper
......@@ -38,6 +44,27 @@ def _parse_args():
help="Directory with model(s) to be translated. Must be of form "
+ "'<UniProtKB AC>-<UniProtKB AC>'",
)
parser.add_argument(
"--top_ranked_only",
default=False,
action="store_true",
help="Only process top ranked model."
)
parser.add_argument(
"--out_dir",
type=str,
metavar="<OUTPUT DIR>",
default="",
help="Path to separate path to store results " \
"(model_dir used, if none given).",
)
parser.add_argument(
"--compress",
default=False,
action="store_true",
help="Compress ModelCIF file with gzip " \
"(note that QA file is zipped either way).",
)
opts = parser.parse_args()
......@@ -48,42 +75,49 @@ def _parse_args():
_abort_msg(f"Model directory '{opts.model_dir}' does not exist.")
if not os.path.isdir(opts.model_dir):
_abort_msg(f"Path '{opts.model_dir}' does not point to a directory.")
# check out_dir
if not opts.out_dir:
opts.out_dir = opts.model_dir
else:
if not os.path.exists(opts.out_dir):
_abort_msg(f"Output directory '{opts.out_dir}' does not exist.")
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]."""
"""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]."""
"""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]."""
"""Predicted accuracy according to the CA-only lDDT in [0,100]"""
name = "pLDDT"
software = None
class _PAE(modelcif.qa_metric.MetricType):
"""Predicted aligned error (in Angstroms).
See :class:`MetricType` for more information."""
"""Predicted aligned error (in Angstroms)"""
type = "PAE"
other_details = None
class _LocalPairwisePAE(modelcif.qa_metric.LocalPairwise, _PAE):
"""predicted aligned error (in Angstroms)."""
"""Predicted aligned error (in Angstroms)"""
name = "PAE"
software = None
......@@ -118,7 +152,7 @@ class _OST2ModelCIF(modelcif.model.AbInitioModel):
occupancy=atm.occupancy,
)
def add_scores(self, scores_json, entry_id, ac_file_prfx):
def add_scores(self, scores_json, entry_id, mdl_name):
"""Add QA metrics from AF2 scores."""
# global scores
self.qa_metrics.extend(
......@@ -162,25 +196,28 @@ class _OST2ModelCIF(modelcif.model.AbInitioModel):
self.qa_metrics.extend(lpae)
ac_file = f"{os.path.basename(ac_file_prfx)}_local_pairwise_qa.cif"
ac_file = f"{mdl_name}_local_pairwise_qa.cif"
qa_file = modelcif.associated.LocalPairwiseQAScoresFile(
ac_file,
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",
)
return modelcif.associated.Repository(
"",
[
modelcif.associated.LocalPairwiseQAScoresFile(
ac_file,
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.",
)
modelcif.associated.ZipFile(f"{mdl_name}.zip",
files=[qa_file])
],
)
# NOTE: by convention MA expects zip file with same name as model-cif
def _abort_msg(msg, exit_code=1):
......@@ -220,29 +257,131 @@ def _get_audit_authors():
"""Return the list of authors that produced this model."""
# ToDo: tell Xabi that his name can't have a á in mmCIF
return (
"Bartolec T",
"Vazquez-Campos X",
"Johnson M",
"Norman A",
"Payne R",
"Wilkins M",
"Mackay J",
"Low J",
"Bartolec, T.",
"Vazquez-Campos, X.",
"Johnson, M.",
"Norman, A.",
"Payne, R.",
"Wilkins, M.",
"Mackay, J.",
"Low, J.",
)
def _get_protocol_steps_and_software(cnfg_file):
def _parse_colabfold_config(cnfg_file):
"""Read config.json and fetch relevant data from it."""
# NOTE: following code from https://github.com/sokrypton/ColabFold/blob/main/colabfold/batch.py to understand config
# fetch and drop fields which are not relevant for model building
with open(cnfg_file, encoding="utf8") as jfh:
cf_config = json.load(jfh)
if "num_queries" in cf_config:
del cf_config["num_queries"]
# fetch relevant data
# -> MSA mode
if cf_config["msa_mode"] == "MMseqs2 (UniRef+Environmental)":
seq_dbs = ["UniRef", "Environmental"]
use_mmseqs = True
use_msa = True
elif cf_config["msa_mode"] == "MMseqs2 (UniRef only)":
seq_dbs = ["UniRef"]
use_mmseqs = True
use_msa = True
elif cf_config["msa_mode"] == "single_sequence":
seq_dbs = []
use_mmseqs = False
use_msa = False
elif cf_config["msa_mode"] == "custom":
print("WARNING: Custom MSA mode used. Not clear from config what to do here!")
seq_dbs = []
use_mmseqs = False
use_msa = True
else:
raise ValueError(f"Unknown msa_mode {cf_config['msa_mode']}")
# -> model type
if cf_config["model_type"] == "AlphaFold2-multimer-v1":
# AF-Multimer as introduced in AlphaFold v2.1.0
use_multimer = True
multimer_version = 1
elif cf_config["model_type"] == "AlphaFold2-multimer-v2":
# AF-Multimer as introduced in AlphaFold v2.2.0
use_multimer = True
multimer_version = 2
elif cf_config["model_type"] == "AlphaFold2-ptm":
use_multimer = False
multimer_version = None
else:
raise ValueError(f"Unknown model_type {cf_config['model_type']}")
# write description
description = f"Model generated using ColabFold v{cf_config['version']}"
if use_multimer:
description += f" with AlphaFold-Multimer (v{multimer_version})"
else:
description += f" with AlphaFold"
description += f" producing {cf_config['num_models']} models" \
f" with {cf_config['num_recycles']} recycles each"
if cf_config["use_amber"]:
description += ", with AMBER relaxation"
else:
description += ", without model relaxation"
if cf_config["use_templates"]:
print("WARNING: ColabFold may use PDB70 or custom templates. " \
"Not clear from config!")
description += ", using templates"
else:
description += ", without templates"
if cf_config["rank_by"] == "plddt":
description += ", ranked by pLDDT"
elif cf_config["rank_by"] == "ptmscore":
description += ", ranked by pTM"
elif cf_config["rank_by"] == "multimer":
description += ", ranked by ipTM*0.8+pTM*0.2"
else:
raise ValueError(f"Unknown rank_by {cf_config['rank_by']}")
if use_msa:
description += ", starting from"
if use_mmseqs:
msa_type = "MSA"
else:
msa_type = "custom MSA"
if use_multimer:
if cf_config["pair_mode"] == "unpaired+paired":
description += f" paired and unpaired {msa_type}s"
elif cf_config["pair_mode"] == "paired":
description += f" paired {msa_type}s"
elif cf_config["pair_mode"] == "unpaired":
description += f" unpaired {msa_type}s"
else:
raise ValueError(f"Unknown pair_mode {cf_config['pair_mode']}")
else:
description += f" an {msa_type}"
if use_mmseqs:
description += f" from MMseqs2 ({'+'.join(seq_dbs)})"
else:
description += " without an MSA"
description += "."
return {
"config": cf_config,
"seq_dbs": seq_dbs,
"use_mmseqs": use_mmseqs,
"use_msa": use_msa,
"use_multimer": use_multimer,
"multimer_version": multimer_version,
"description": description
}
def _get_protocol_steps_and_software(config_data):
"""Create the list of protocol steps with software and parameters used."""
protocol = []
# modelling step
step = {
"method_type": "modeling",
"name": "ma_protocol_step.step_name",
"details": "Model using AlphaFold-Multimer (AlphaFold v2.2.0), "
+ "without amber relaxation and producing 5 models with up to 3 "
+ "recycles each, starting from paired and unparied MSAs for the "
+ "dimers using MMseqs2.",
"name": None,
"details": config_data["description"],
}
# get input data
# Must refer to data already in the JSON, so we try keywords
......@@ -255,116 +394,116 @@ def _get_protocol_steps_and_software(cnfg_file):
{
"name": "ColabFold",
"classification": "model building",
# ToDo: Get description for ColabFold
"description": "software.description",
"citation": {
"pmid": None,
"title": "ColabFold - Making protein folding accessible to all",
"journal": "bioRxiv",
"volume": None,
"page_range": None,
"year": 2022,
"authors": [
"Mirdita M",
"Schuetze K",
"Moriwaki Y",
"Heo L",
"Ovchinnikov S",
"Steinegger M",
],
"doi": "10.1101/2021.08.15.456425",
},
"description": "Structure prediction",
"citation": ihm.citations.colabfold,
"location": "https://github.com/sokrypton/ColabFold",
"type": "package",
"version": "1.2.0",
},
{
}]
if config_data["use_mmseqs"]:
step["software"].append({
"name": "MMseqs2",
"classification": "data collection",
"description": "Many-against-Many sequence searching",
"citation": {
"pmid": "30615063",
"title": "MMseqs2 desktop and local web server app for fast, "
+ "interactive sequence searches",
"journal": "Bioinformatics",
"volume": 35,
"page_range": (2856, 2858),
"year": 2019,
"authors": [
"Mirdita M",
"Steinegger M",
"Soeding J",
"citation": ihm.Citation(
pmid="30615063",
title="MMseqs2 desktop and local web server app for fast, "
+ "interactive sequence searches.",
journal="Bioinformatics",
volume=35,
page_range=(2856, 2858),
year=2019,
authors=[
"Mirdita, M.",
"Steinegger, M.",
"Soeding, J.",
],
"doi": "10.1093/bioinformatics/bty1057",
},
doi="10.1093/bioinformatics/bty1057",
),
"location": "https://github.com/soedinglab/mmseqs2",
"type": "package",
"version": None,
},
{
})
if config_data["use_multimer"]:
step["software"].append({
"name": "AlphaFold-Multimer",
"classification": "model building",
"description": "Structure prediction",
"citation": {
"pmid": None,
"title": "Protein complex prediction with "
"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",
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",
},
doi="10.1101/2021.10.04.463034",
),
"location": "https://github.com/deepmind/alphafold",
"type": "package",
"version": "2.1.1",
},
]
# get parameters
with open(cnfg_file, encoding="utf8") as jfh:
step["software_parameters"] = json.load(jfh)
"version": None,
})
else:
step["software"].append({
"name": "AlphaFold",
"classification": "model building",
"description": "Structure prediction",
"citation": ihm.citations.alphafold2,
"location": "https://github.com/deepmind/alphafold",
"type": "package",
"version": None,
})
step["software_parameters"] = config_data["config"]
protocol.append(step)
# model selection step
# ToDo [input/ internal]: model selection step on a single model is a bit
# silly, how do we get a list of models?
step = {
"method_type": "model selection",
"name": "ma_protocol_step.step_name",
"details": "Select best model, which is either the top-ranked model "
+ "as determined by the ColabFold pipeline "
+ "(iptmscore*0.8+ptmscore*0.2), or else the model with best "
+ "congruence with crosslinks reported in the related study.",
}
step["input"] = "model"
step["output"] = "model"
step["software"] = []
step["software_parameters"] = {}
protocol.append(step)
# GT-NOTES:
# - input/output should be ok without list of models
# - rank of model is already stored in _ma_model_list.model_name and
# _ma_data.name (in _store_as_modelcif)
# - ColabFold ranking details is already in details of step above.
# - Suggestion: add extra step only if AF-ranking was overruled and
# include it in step above.
# step = {
# "method_type": "model selection",
# "name": "ma_protocol_step.step_name",
# "details": "Select best model, which is either the top-ranked model "
# + "as determined by the ColabFold pipeline "
# + "(iptmscore*0.8+ptmscore*0.2), or else the model with best "
# + "congruence with crosslinks reported in the related study.",
# }
# step["input"] = "model"
# step["output"] = "model"
# step["software"] = []
# step["software_parameters"] = {}
# protocol.append(step)
return protocol
......@@ -387,7 +526,7 @@ def _get_model_details(gene_names):
def _get_model_group_name():
"""Get a name for a model group."""
return "Crosslinked Heterodimer ALphaFold-Multimer v2 Models"
return "Crosslinked Heterodimer AlphaFold-Multimer v2 Models"
def _get_sequence(chn):
......@@ -529,7 +668,8 @@ def _get_entities(pdb_file, up_acs):
upkb = _get_upkb_for_sequence(sqe, up_acs[i])
cif_ent["pdb_sequence"] = sqe
cif_ent["pdb_chain_id"] = chn.name
cif_ent["description"] = f"Model of {upkb['up_gn']} ({upkb['up_ac']})"
cif_ent["description"] = f"{upkb['up_organism']} {upkb['up_gn']} " \
f"({upkb['up_ac']})"
cif_ent.update(upkb)
entities.append(cif_ent)
......@@ -542,8 +682,8 @@ def _get_scores(data, prfx):
with open(scrs_fle, encoding="utf8") as jfh:
scrs_json = json.load(jfh)
# ToDo: is dict.update still the way to go when iterating multiple model
# directories? Aka, does dict.update overwrite old scores?
# NOTE for reuse of data when iterating multiple models: this will overwrite
# scores in data but will not delete any scores if prev. models had more...
data.update(scrs_json)
......@@ -570,10 +710,8 @@ def _get_modelcif_entities(target_ents, source, asym_units, system):
)
],
)
# ToDo [input]: Add details
asym_units[cif_ent["pdb_chain_id"]] = modelcif.AsymUnit(
mdlcif_ent,
details="struct_asym.details",
mdlcif_ent
)
system.target_entities.append(mdlcif_ent)
......@@ -587,25 +725,34 @@ def _assemble_modelcif_software(soft_dict):
soft_dict["location"],
soft_dict["type"],
soft_dict["version"],
citation=ihm.Citation(
pmid=soft_dict["citation"]["pmid"],
title=soft_dict["citation"]["title"],
journal=soft_dict["citation"]["journal"],
volume=soft_dict["citation"]["volume"],
page_range=soft_dict["citation"]["page_range"],
year=soft_dict["citation"]["year"],
authors=soft_dict["citation"]["authors"],
doi=soft_dict["citation"]["doi"],
),
citation=soft_dict["citation"]
)
def _get_modelcif_protocol(protocol_steps, target_entities, model):
def _get_sequence_dbs(seq_dbs):
"""Get ColabFold seq. DBs."""
# NOTE: hard coded for ColabFold versions before 2022/07/13
# -> afterwards UniRef30 updated to 2022_02 (and maybe more changes)
db_dict = {
"UniRef": modelcif.ReferenceDatabase(
"UniRef30",
"http://wwwuser.gwdg.de/~compbiol/colabfold/uniref30_2103.tar.gz",
version="2021_03"
),
"Environmental": modelcif.ReferenceDatabase(
"ColabFold DB",
"http://wwwuser.gwdg.de/~compbiol/colabfold/colabfold_envdb_202108.tar.gz",
version="2021_08"
)
}
return [db_dict[seq_db] for seq_db in seq_dbs]
def _get_modelcif_protocol(protocol_steps, target_entities, model, ref_dbs):
"""Create the protocol for the ModelCIF file."""
protocol = modelcif.protocol.Protocol()
for js_step in protocol_steps:
sftwre = None
# ToDo [input]: Turn into software group if parameters are available
if js_step["software"]:
if len(js_step["software"]) == 1:
sftwre = _assemble_modelcif_software(js_step["software"][0])
......@@ -616,7 +763,6 @@ def _get_modelcif_protocol(protocol_steps, target_entities, model):
sftwre = modelcif.SoftwareGroup(elements=sftwre)
if js_step["software_parameters"]:
params = []
# ToDo [internal]: handle lists!
for k, v in js_step["software_parameters"].items():
params.append(
modelcif.SoftwareParameter(k, v)
......@@ -630,7 +776,7 @@ def _get_modelcif_protocol(protocol_steps, target_entities, model):
if js_step["input"] == "target_sequences":
input_data = modelcif.data.DataGroup(target_entities)
# ToDo: Add databases + versions
input_data.extend(ref_dbs)
elif js_step["input"] == "model":
input_data = model
else:
......@@ -655,7 +801,28 @@ def _get_modelcif_protocol(protocol_steps, target_entities, model):
return protocol
def _store_as_modelcif(interaction_name, data_json, ost_ent, file_prfx):
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(mdl_name):
"""Compress associated files into single zip file and delete original."""
# file names must match ones from add_scores
zip_path = f"{mdl_name}.zip"
files = [f"{mdl_name}_local_pairwise_qa.cif"]
# zip settings tested for good speed vs compression
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_BZIP2) as myzip:
for file in files:
myzip.write(file)
os.remove(file)
def _store_as_modelcif(interaction_name, data_json, ost_ent, out_dir, file_prfx,
compress):
"""Mix all the data into a ModelCIF file."""
print(" generating ModelCIF objects...", end="")
pstart = timer()
......@@ -678,27 +845,31 @@ def _store_as_modelcif(interaction_name, data_json, ost_ent, file_prfx):
data_json["target_entities"], source, asym_units, system
)
# ToDo [input]: Get Assembly name
assembly = modelcif.Assembly(
asym_units.values(), name="ma_struct_assembly_details.assembly_name"
asym_units.values()
)
# audit_authors
system.authors.extend(data_json["audit_authors"])
# set up the model to produce coordinates
# ToDo [input]: Get ma_model_list.model_name
if data_json["rank_num"] == 1:
mdl_list_name = f"Model {data_json['mdl_num']} (top ranked model)"
else:
mdl_list_name = f"Model {data_json['mdl_num']} " \
f"(#{data_json['rank_num']} ranked model)"
model = _OST2ModelCIF(
assembly=assembly,
asym=asym_units,
ost_entity=ost_ent,
name="ma_model_list.model_name",
name=mdl_list_name,
)
print(f" ({timer()-pstart:.2f}s)")
print(" processing QA scores...", end="", flush=True)
pstart = timer()
mdl_name = os.path.basename(file_prfx)
system.repositories.append(
model.add_scores(data_json, system.id, file_prfx)
model.add_scores(data_json, system.id, mdl_name)
)
print(f" ({timer()-pstart:.2f}s)")
......@@ -707,26 +878,39 @@ def _store_as_modelcif(interaction_name, data_json, ost_ent, file_prfx):
)
system.model_groups.append(model_group)
ref_dbs = _get_sequence_dbs(data_json["config_data"]["seq_dbs"])
protocol = _get_modelcif_protocol(
data_json["protocol"], system.target_entities, model
data_json["protocol"], system.target_entities, model, ref_dbs
)
system.protocols.append(protocol)
# write modelcif System to file
print(" write to disk...", end="", flush=True)
pstart = timer()
with open(f"{file_prfx}.cif", "w", encoding="ascii") as mmcif_fh:
modelcif.dumper.write(mmcif_fh, [system])
# NOTE: this will dump PAE on path provided in add_scores
# -> hence we cheat by changing path and back while being exception-safe...
oldpwd = os.getcwd()
os.chdir(out_dir)
try:
with open(f"{mdl_name}.cif", "w", encoding="ascii") as mmcif_fh:
modelcif.dumper.write(mmcif_fh, [system])
_package_associated_files(mdl_name)
if compress:
_compress_cif_file(f"{mdl_name}.cif")
finally:
os.chdir(oldpwd)
print(f" ({timer()-pstart:.2f}s)")
def _create_interaction_json(cnfg_file):
def _create_interaction_json(config_data):
"""Create a dictionary (mimicking JSON) that contains data which is the same
for all models."""
data = {}
data["audit_authors"] = _get_audit_authors()
data["protocol"] = _get_protocol_steps_and_software(cnfg_file)
data["protocol"] = _get_protocol_steps_and_software(config_data)
data["config_data"] = config_data
return data
......@@ -756,14 +940,15 @@ def _main():
up_acs = interaction.split("-")
cnfg = _check_interaction_extra_files_present(opts.model_dir)
mdlcf_json = _create_interaction_json(cnfg)
config_data = _parse_colabfold_config(cnfg)
# iterate model directory
for fle in os.listdir(opts.model_dir):
for fle in sorted(os.listdir(opts.model_dir)):
# iterate PDB files
if not fle.endswith(".pdb"):
continue
if opts.top_ranked_only and "rank_1" not in fle:
continue
print(f" translating {fle}...")
pdb_start = timer()
file_prfx, uid = _check_model_extra_files_present(opts.model_dir, fle)
......@@ -772,14 +957,26 @@ def _main():
# gather data into JSON-like structure
print(" preparing data...", end="")
pstart = timer()
# NOTE: could also be prepared globally if all carefully overwritten
# but not worth the trouble...
mdlcf_json = _create_interaction_json(config_data)
# uid = ..._rank_X_model_Y.pdb
mdl_name_parts = uid.split('_')
assert mdl_name_parts[-4] == "rank"
assert mdl_name_parts[-2] == "model"
mdlcf_json["rank_num"] = int(mdl_name_parts[-3])
mdlcf_json["mdl_num"] = int(mdl_name_parts[-1])
ost_ent = _create_model_json(mdlcf_json, fle, up_acs)
# read quality scores from JSON file
_get_scores(mdlcf_json, file_prfx)
print(f" ({timer()-pstart:.2f}s)")
_store_as_modelcif(uid, mdlcf_json, ost_ent, file_prfx)
# ToDo [internal]: wipe data or is it overwritten in mdlcf_json?
_store_as_modelcif(uid, mdlcf_json, ost_ent, opts.out_dir, file_prfx,
opts.compress)
print(f" ... done with {fle} ({timer()-pdb_start:.2f}s).")
print(f"... done with {opts.model_dir}.")
......
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