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Commit f5fcccb5 authored by Studer Gabriel's avatar Studer Gabriel
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compare-ligand-structures refactoring

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......@@ -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
......
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