diff --git a/convert_to_modelcif.py b/convert_to_modelcif.py
index fc30f1cd719ceb2f55ce0404867df9f025f1472e..a20bea145d186b642f5099f60c1a2c6f4695e57c 100755
--- a/convert_to_modelcif.py
+++ b/convert_to_modelcif.py
@@ -29,6 +29,15 @@ import modelcif.model
# ToDo: Get options properly, best get the same names as used in existing
# scripts, e.g. could '--monomer_objects_dir' be used as feature
# directory/ directory with the feature JSON files?
+# ToDo: Monomers work separately - features may come from different set of
+# software, databases... so target sequences may be connected to different
+# versions of the same sequence database, may use different versions of
+# software... can this still go into single protocol steps or would this
+# prevent identifying which MSAs were produced with which software? E.g.
+# can there still be a single "sequence search" step? (This is
+# definitively for later, not the first working version of the converter
+# script)
+# ToDo: sort non-ModelCIF items in the main JSON object into '__meta__'
flags.DEFINE_string(
"ap_output", None, "AlphaPulldown pipeline output directory."
)
@@ -245,19 +254,16 @@ def _store_as_modelcif(
system,
)
- # ToDo: get modelling-experiment auhtors
+ # ToDo: get modelling-experiment authors
# audit_authors
# system.authors.extend(data_json["audit_authors"])
# set up the model to produce coordinates
- # ToDo: model file names are different than expected, got ranked_<N>.pdb
- # and unrelaxed_model_<N>_multimer_v3_pred_0.pdb, expected something
- # like model_<N>_rank_<M>.pdb, used for 'model list name'
model = _Biopython2ModelCIF(
assembly=modelcif.Assembly(asym_units.values()),
asym=asym_units,
bio_pdb_structure=structure,
- name="ToDo: Model <N> (ranked #<M>)",
+ name=data_json["_ma_model_list.model_name"],
)
# create software list from feature metadata
@@ -327,6 +333,7 @@ def _compress_cif_file(cif_file):
def _get_model_details(cmplx_name: str, data_json: dict) -> str:
"""Get the model description."""
ap_versions = []
+ af2_version = None
for mnmr in data_json["__meta__"]: # mnmr = monomer
if (
data_json["__meta__"][mnmr]["software"]["alphapulldown"]["version"]
@@ -337,11 +344,27 @@ def _get_model_details(cmplx_name: str, data_json: dict) -> str:
"version"
]
)
+ # AlphaFold-Multimer builds the model we are looking at, can only be a
+ # single version.
+ if af2_version is None:
+ af2_version = data_json["__meta__"][mnmr]["software"]["alphafold"][
+ "version"
+ ]
+ else:
+ if (
+ data_json["__meta__"][mnmr]["software"]["alphafold"]["version"]
+ != af2_version
+ ):
+ # pylint: disable=line-too-long
+ raise RuntimeError(
+ "Different versions of AlphaFold-Multimer found: "
+ + f"'{data_json['__meta__'][mnmr]['software']['alphafold']['version']}'"
+ + f" vs. '{af2_version}'"
+ )
- # ToDo: fetch AF2 version/ have it in metadata JSON
return (
f"Model generated for {' and '.join(cmplx_name)}, produced "
- + "using AlphaFold-Multimer (<AF2 VERSION>) as implemented by "
+ + f"using AlphaFold-Multimer ({af2_version}) as implemented by "
+ f"AlphaPulldown ({', '.join(ap_versions)})."
)
@@ -377,8 +400,11 @@ def _get_feature_metadata(
return cmplx_name
-def _get_data_block_id_and_struct_and_entry_categories(
- cif_json: dict, cmplx_name: str
+def _get_model_info(
+ cif_json: dict,
+ cmplx_name: str,
+ mdl_id: str,
+ mdl_rank: int,
) -> None:
"""Get 'data_' block ID and data for categories '_struct' and '_entry'."""
cif_json["data_"] = "_".join(cmplx_name)
@@ -386,6 +412,9 @@ def _get_data_block_id_and_struct_and_entry_categories(
cif_json["_struct.pdbx_model_details"] = _get_model_details(
cmplx_name, cif_json
)
+ cif_json[
+ "_ma_model_list.model_name"
+ ] = f"Model {mdl_id} (ranked #{mdl_rank})"
def _get_entities(
@@ -525,8 +554,7 @@ def _get_software_data(meta_json: dict) -> list:
def alphapulldown_model_to_modelcif(
cmplx_name: str,
- mdl_file: str,
- scr_file: str,
+ mdl: tuple,
out_dir: str,
prj_dir: str,
compress: bool = False,
@@ -536,31 +564,31 @@ def alphapulldown_model_to_modelcif(
Metadata for the ModelCIF categories will be fetched from AlphaPulldown
output as far as possible. This expects modelling projects to exists in
AlphaPulldown's output directory structure."""
- # ToDo: ENABLE logging.info(f"Processing '{mdl_file}'...")
+ # ToDo: ENABLE logging.info(f"Processing '{mdl[0]}'...")
modelcif_json = {}
# fetch metadata
cmplx_name = _get_feature_metadata(modelcif_json, cmplx_name, prj_dir)
# fetch/ assemble more data about the modelling experiment
- _get_data_block_id_and_struct_and_entry_categories(
- modelcif_json, cmplx_name
+ _get_model_info(
+ modelcif_json,
+ cmplx_name,
+ mdl[2],
+ mdl[3],
)
# gather target entities (sequences that have been modeled) info
- structure = _get_entities(modelcif_json, mdl_file, cmplx_name, prj_dir)
+ structure = _get_entities(modelcif_json, mdl[0], cmplx_name, prj_dir)
# read quality scores from pickle file
- _get_scores(modelcif_json, scr_file)
+ _get_scores(modelcif_json, mdl[1])
- _store_as_modelcif(modelcif_json, structure, mdl_file, out_dir, compress)
- # ToDo: ENABLE logging.info(f"... done with '{mdl_file}'")
+ _store_as_modelcif(modelcif_json, structure, mdl[0], out_dir, compress)
+ # ToDo: ENABLE logging.info(f"... done with '{mdl[0]}'")
def _get_model_list(ap_dir: str, model_selected: str) -> Tuple[str, str, list]:
"""Get the list of models to be converted.
If `model_selected` is none, all models will be marked for conversion."""
- # ToDo: Question - use 'ranked_*.pdb' or
- # 'unrelaxed_model_*_multimer_v3_pred_0.pdb' models?
-
mdl_path = os.path.join(ap_dir, "models")
cmplx = os.listdir(mdl_path)
# For now, exactly 1 complex is expected in the 'models' subdirectory. If
@@ -576,7 +604,7 @@ def _get_model_list(ap_dir: str, model_selected: str) -> Tuple[str, str, list]:
ranking_dbg = os.path.join(mdl_path, "ranking_debug.json")
if not os.path.isfile(ranking_dbg):
logging.info(
- f"Ranking file '{ranking_dbg} doe snot exist or is no regular "
+ f"Ranking file '{ranking_dbg} does not exist or is no regular "
+ "file."
)
sys.exit()
@@ -584,13 +612,24 @@ def _get_model_list(ap_dir: str, model_selected: str) -> Tuple[str, str, list]:
ranking_dbg = json.load(jfh)
score_files = {}
for i, fle in enumerate(ranking_dbg["order"]):
- score_files[i] = os.path.join(mdl_path, f"result_{fle}.pkl")
+ if not fle.startswith("model_"):
+ raise RuntimeError(
+ "Filename does not start with 'model_', can "
+ + f"not determine model ID: '{fle}'"
+ )
+ score_files[i] = (
+ os.path.join(mdl_path, f"result_{fle}.pkl"),
+ fle.split("_")[1],
+ i,
+ )
# match PDB files with pickle files
if model_selected is not None:
models.append(
(
os.path.join(mdl_path, f"ranked_{model_selected}.pdb"),
- score_files[model_selected],
+ score_files[model_selected][0],
+ score_files[model_selected][1], # model ID
+ score_files[model_selected][2], # model rank
)
)
else:
@@ -598,10 +637,15 @@ def _get_model_list(ap_dir: str, model_selected: str) -> Tuple[str, str, list]:
rank = re.match(r"ranked_(\d+)\.pdb", mdl)
if rank is not None:
rank = int(rank.group(1))
- models.append((os.path.join(mdl_path, mdl), score_files[rank]))
+ models.append(
+ (
+ os.path.join(mdl_path, mdl),
+ score_files[rank][0].score_files[rank][1],
+ )
+ )
# check that files actually exist
- for mdl, scrs in models:
+ for mdl, scrs, *_ in models:
if not os.path.isfile(mdl):
logging.info(
f"Model file '{mdl}' does not exist or is not a regular file."
@@ -642,11 +686,10 @@ def main(argv):
complex_name, model_dir, model_list = _get_model_list(
FLAGS.ap_output, FLAGS.model_selected
)
- for mdl, scrs in model_list:
+ for mdl in model_list:
alphapulldown_model_to_modelcif(
complex_name,
mdl,
- scrs,
model_dir,
FLAGS.ap_output,
FLAGS.compress,
@@ -668,4 +711,4 @@ if __name__ == "__main__":
# ToDo: make sure all functions come with types
# LocalWords: ToDo AlphaPulldown PAEs dir struct coevolution MSA py modeling
-# LocalWords: multimer sif Jupyter aa
+# LocalWords: multimer sif Jupyter aa MSAs