diff --git a/actions/ost-compare-ligand-structures b/actions/ost-compare-ligand-structures
index a8491fea3dc7c6d780333f36731c4fbdc60e61e4..b991037828f9b39a2e15924cd6cbefc2b5a669ca 100644
--- a/actions/ost-compare-ligand-structures
+++ b/actions/ost-compare-ligand-structures
@@ -47,23 +47,29 @@ options, this is a dictionary with three keys:
content of the JSON output will be \"status\" set to FAILURE and an
additional key: "traceback".
-Each score is opt-in and the respective results are available in two keys:
-
- * "model_ligands": Model ligand centric scoring based on model/reference
- ligand assignment. A score including meta data is reported for each assigned
- model ligand given the assigned target ligand. Unassigned model ligands are
- reported with a null score and a reason why no assignment has been performed.
-
- * "full": The full all vs. all scoring results. Yet another dictionary with
- keys:
-
- * "assignment": List of pairs in form (ref_lig_idx, mdl_lig_idx) specifying
- the ligands in "reference_ligands"/"model_ligands".
- * "scores": A dictionary with key in form (ref_lig_idx, mdl_lig_idx) and
- value yet another dict with score information for each possible
- reference/model ligand pair. The respective score is None if no score could
- be computed. This can simply be a mismatch between the two ligands. This or
- other reasons are reported.
+Each score is opt-in and the respective results are available in three keys:
+
+ * "assigned_scores": A list with data for each pair of assigned ligands.
+ Data is yet another dict containing score specific information for that
+ ligand pair. The following keys are there in any case:
+
+ * "model_ligand": The model ligand
+ * "reference_ligand": The target ligand to which model ligand is assigned to
+ * "score": The score
+ * "coverage": Fraction of model ligand atoms which are covered by target
+ ligand. Will only deviate from 1.0 if --substructure-match is enabled.
+
+ * "model_ligand_unassigned_reason": Dictionary with unassigned model ligands
+ as key and an educated guess why this happened.
+
+ * "reference_ligand_unassigned_reason": Dictionary with unassigned target ligands
+ as key and an educated guess why this happened.
+
+If --full-results is enabled, another element with key "full_results" is added.
+This is a list of data items for each pair of model/reference ligands. The data
+items follow the same structure as in "assigned_scores". If no score for a
+specific pair of ligands could be computed, "score" and "coverage" are set to
+null and a key "reason" is added giving an educated guess why this happened.
"""
import argparse
@@ -212,6 +218,14 @@ def _ParseArgs():
default=3,
help="Set verbosity level. Defaults to 3 (INFO).")
+ parser.add_argument(
+ "--full-results",
+ dest="full_results",
+ default=False,
+ action="store_true",
+ help=("Outputs scoring results for all model/reference ligand pairs "
+ "and store as key \"full_results\""))
+
# arguments relevant for lddt-pli
parser.add_argument(
@@ -219,7 +233,7 @@ def _ParseArgs():
dest="lddt_pli",
default=False,
action="store_true",
- help=("Compute lDDT-PLI score and store as key \"lddt-pli\"."))
+ help=("Compute lDDT-PLI scores and store as key \"lddt_pli\"."))
parser.add_argument(
"--lddt-pli-radius",
@@ -241,7 +255,7 @@ def _ParseArgs():
dest="rmsd",
default=False,
action="store_true",
- help=("Compute RMSD score and store as key \"rmsd\"."))
+ help=("Compute RMSD scores and store as key \"rmsd\"."))
parser.add_argument(
"--radius",
@@ -514,48 +528,69 @@ def _Process(model, model_ligands, reference, reference_ligands, args):
if args.lddt_pli:
out["lddt_pli"] = dict()
- out["lddt_pli"]["model_ligands"] = dict()
- out["lddt_pli"]["full"] = dict()
+ out["lddt_pli"]["assigned_scores"] = list()
for lig_pair in lddtpli_scorer.assignment:
score = float(lddtpli_scorer.score_matrix[lig_pair[0], lig_pair[1]])
coverage = float(lddtpli_scorer.coverage_matrix[lig_pair[0], lig_pair[1]])
aux_data = lddtpli_scorer.aux_matrix[lig_pair[0], lig_pair[1]]
target_key = out["reference_ligands"][lig_pair[0]]
model_key = out["model_ligands"][lig_pair[1]]
- out["lddt_pli"]["model_ligands"][model_key] = {"lddt_pli": score,
- "coverage": coverage,
- "lddt_pli_n_contacts": aux_data["lddt_pli_n_contacts"],
- "model_ligand": model_key,
- "reference_ligand": target_key,
- "bs_ref_res": [_QualifiedResidueNotation(r) for r in
- aux_data["bs_ref_res"]],
- "bs_mdl_res": [_QualifiedResidueNotation(r) for r in
- aux_data["bs_mdl_res"]]}
-
+ out["lddt_pli"]["assigned_scores"].append({"score": score,
+ "coverage": coverage,
+ "lddt_pli_n_contacts": aux_data["lddt_pli_n_contacts"],
+ "model_ligand": model_key,
+ "reference_ligand": target_key,
+ "bs_ref_res": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_ref_res"]],
+ "bs_mdl_res": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_mdl_res"]]})
+
+ out["lddt_pli"]["model_ligand_unassigned_reason"] = dict()
for i in lddtpli_scorer.unassigned_model_ligands:
- model_key = out["model_ligands"][i]
+ key = out["model_ligands"][i]
reason = lddtpli_scorer.guess_model_ligand_unassigned_reason(i)
- out["lddt_pli"]["model_ligands"][model_key] = {"lddt_pli": None,
- "unassigned_reason": reason}
-
- out["lddt_pli"]["full"]["assignment"] = lddtpli_scorer.assignment
- out["lddt_pli"]["full"]["scores"] = dict()
-
- shape = lddtpli_scorer.score_matrix.shape
- for ref_lig_idx in range(shape[0]):
- for mdl_lig_idx in range(shape[1]):
- score = float(lddtpli_scorer.score_matrix[(ref_lig_idx, mdl_lig_idx)])
- state = int(lddtpli_scorer.state_matrix[(ref_lig_idx, mdl_lig_idx)])
- desc = lddtpli_scorer.state_decoding[state]
- pair_key = [ref_lig_idx, mdl_lig_idx]
- out["lddt_pli"]["full"]["scores"][pair_key] = {"score": score,
- "state": desc}
+ out["lddt_pli"]["model_ligand_unassigned_reason"][key] = reason
+
+ out["lddt_pli"]["reference_ligand_unassigned_reason"] = dict()
+ for i in lddtpli_scorer.unassigned_target_ligands:
+ key = out["reference_ligands"][i]
+ reason = lddtpli_scorer.guess_target_ligand_unassigned_reason(i)
+ out["lddt_pli"]["reference_ligand_unassigned_reason"][key] = reason
+
+ if args.full_results:
+ out["lddt_pli"]["full_results"] = list()
+ shape = lddtpli_scorer.score_matrix.shape
+ for ref_lig_idx in range(shape[0]):
+ for mdl_lig_idx in range(shape[1]):
+ state = int(lddtpli_scorer.state_matrix[(ref_lig_idx, mdl_lig_idx)])
+ target_key = out["reference_ligands"][lig_pair[0]]
+ model_key = out["model_ligands"][lig_pair[1]]
+ if state == 0:
+ score = float(lddtpli_scorer.score_matrix[(ref_lig_idx, mdl_lig_idx)])
+ coverage = float(lddtpli_scorer.coverage_matrix[(ref_lig_idx, mdl_lig_idx)])
+ aux_data = lddtpli_scorer.aux_matrix[(ref_lig_idx, mdl_lig_idx)]
+ out["lddt_pli"]["full_results"].append({"score": score,
+ "coverage": coverage,
+ "lddt_pli_n_contacts": aux_data["lddt_pli_n_contacts"],
+ "model_ligand": model_key,
+ "reference_ligand": target_key,
+ "bs_ref_res": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_ref_res"]],
+ "bs_mdl_res": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_mdl_res"]]})
+
+ else:
+ reason = lddtpli_scorer.state_decoding[state]
+ out["lddt_pli"]["full_results"].append({"score": None,
+ "coverage": None,
+ "model_ligand": model_key,
+ "reference_ligand": target_key,
+ "reason": reason})
if args.rmsd:
out["rmsd"] = dict()
- out["rmsd"]["model_ligands"] = dict()
- out["rmsd"]["full"] = dict()
+ out["rmsd"]["assigned_scores"] = list()
for lig_pair in scrmsd_scorer.assignment:
score = float(scrmsd_scorer.score_matrix[lig_pair[0], lig_pair[1]])
coverage = float(scrmsd_scorer.coverage_matrix[lig_pair[0], lig_pair[1]])
@@ -563,42 +598,74 @@ def _Process(model, model_ligands, reference, reference_ligands, args):
target_key = out["reference_ligands"][lig_pair[0]]
model_key = out["model_ligands"][lig_pair[1]]
transform_data = aux_data["transform"].data
- out["rmsd"]["model_ligands"][model_key] = {"rmsd": score,
- "coverage": coverage,
- "lddt_lp": aux_data["lddt_lp"],
- "bb_rmsd": aux_data["bb_rmsd"],
- "model_ligand": model_key,
- "reference_ligand": target_key,
- "chain_mapping": aux_data["chain_mapping"],
- "bs_ref_res": [_QualifiedResidueNotation(r) for r in
- aux_data["bs_ref_res"]],
- "bs_ref_res_mapped": [_QualifiedResidueNotation(r) for r in
- aux_data["bs_ref_res_mapped"]],
- "bs_mdl_res_mapped": [_QualifiedResidueNotation(r) for r in
- aux_data["bs_mdl_res_mapped"]],
- "inconsistent_residues": [_QualifiedResidueNotation(r) for r in
- aux_data["inconsistent_residues"]],
- "transform": [transform_data[i:i + 4]
- for i in range(0, len(transform_data), 4)]}
+ out["rmsd"]["assigned_scores"].append({"score": score,
+ "coverage": coverage,
+ "lddt_lp": aux_data["lddt_lp"],
+ "bb_rmsd": aux_data["bb_rmsd"],
+ "model_ligand": model_key,
+ "reference_ligand": target_key,
+ "chain_mapping": aux_data["chain_mapping"],
+ "bs_ref_res": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_ref_res"]],
+ "bs_ref_res_mapped": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_ref_res_mapped"]],
+ "bs_mdl_res_mapped": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_mdl_res_mapped"]],
+ "inconsistent_residues": [_QualifiedResidueNotation(r) for r in
+ aux_data["inconsistent_residues"]],
+ "transform": [transform_data[i:i + 4]
+ for i in range(0, len(transform_data), 4)]})
+
+ out["rmsd"]["model_ligand_unassigned_reason"] = dict()
for i in scrmsd_scorer.unassigned_model_ligands:
- model_key = out["model_ligands"][i]
+ key = out["model_ligands"][i]
reason = scrmsd_scorer.guess_model_ligand_unassigned_reason(i)
- out["rmsd"]["model_ligands"][model_key] = {"rmsd": None,
- "unassigned_reason": reason}
-
- out["rmsd"]["full"]["assignment"] = scrmsd_scorer.assignment
- out["rmsd"]["full"]["scores"] = dict()
-
- shape = scrmsd_scorer.score_matrix.shape
- for ref_lig_idx in range(shape[0]):
- for mdl_lig_idx in range(shape[1]):
- score = float(scrmsd_scorer.score_matrix[(ref_lig_idx, mdl_lig_idx)])
- state = int(scrmsd_scorer.state_matrix[(ref_lig_idx, mdl_lig_idx)])
- desc = scrmsd_scorer.state_decoding[state]
- pair_key = [ref_lig_idx, mdl_lig_idx]
- out["rmsd"]["full"]["scores"][pair_key] = {"score": score,
- "state": desc}
-
+ out["rmsd"]["model_ligand_unassigned_reason"][key] = reason
+
+ out["rmsd"]["reference_ligand_unassigned_reason"] = dict()
+ for i in scrmsd_scorer.unassigned_target_ligands:
+ key = out["reference_ligands"][i]
+ reason = scrmsd_scorer.guess_target_ligand_unassigned_reason(i)
+ out["rmsd"]["reference_ligand_unassigned_reason"][key] = reason
+
+ if args.full_results:
+ out["rmsd"]["full_results"] = list()
+ shape = scrmsd_scorer.score_matrix.shape
+ for ref_lig_idx in range(shape[0]):
+ for mdl_lig_idx in range(shape[1]):
+ state = int(scrmsd_scorer.state_matrix[(ref_lig_idx, mdl_lig_idx)])
+ target_key = out["reference_ligands"][lig_pair[0]]
+ model_key = out["model_ligands"][lig_pair[1]]
+ if state == 0:
+ score = float(scrmsd_scorer.score_matrix[(ref_lig_idx, mdl_lig_idx)])
+ coverage = float(scrmsd_scorer.coverage_matrix[(ref_lig_idx, mdl_lig_idx)])
+ aux_data = scrmsd_scorer.aux_matrix[(ref_lig_idx, mdl_lig_idx)]
+ transform_data = aux_data["transform"].data
+ out["rmsd"]["full_results"].append({"score": score,
+ "coverage": coverage,
+ "lddt_lp": aux_data["lddt_lp"],
+ "bb_rmsd": aux_data["bb_rmsd"],
+ "model_ligand": model_key,
+ "reference_ligand": target_key,
+ "chain_mapping": aux_data["chain_mapping"],
+ "bs_ref_res": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_ref_res"]],
+ "bs_ref_res_mapped": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_ref_res_mapped"]],
+ "bs_mdl_res_mapped": [_QualifiedResidueNotation(r) for r in
+ aux_data["bs_mdl_res_mapped"]],
+ "inconsistent_residues": [_QualifiedResidueNotation(r) for r in
+ aux_data["inconsistent_residues"]],
+ "transform": [transform_data[i:i + 4]
+ for i in range(0, len(transform_data), 4)]})
+
+ else:
+ reason = scrmsd_scorer.state_decoding[state]
+ out["rmsd"]["full_results"].append({"score": None,
+ "coverage": None,
+ "model_ligand": model_key,
+ "reference_ligand": target_key,
+ "reason": reason})
return out