diff --git a/actions/ost-compare-structures b/actions/ost-compare-structures
index cfaabbd373dd53b9b53c6282ae21148a59b46c25..9606e32f4b6fd1745befc027b936058482d4355d 100644
--- a/actions/ost-compare-structures
+++ b/actions/ost-compare-structures
@@ -57,6 +57,8 @@ results:
* "cad_exec"
* "usalign_exec"
* "lddt_no_stereochecks"
+ * "min_pep_length"
+ * "min_nuc_length"
The pairwise sequence alignments are computed with Needleman-Wunsch using
BLOSUM62 (NUC44 for nucleotides). Many benchmarking scenarios preprocess the
@@ -492,6 +494,36 @@ def _ParseArgs():
action="store_true",
help=("Dump additional info on model and reference residues that occur "
"in pepnuc alignments."))
+
+ parser.add_argument(
+ "--min-pep-length",
+ dest="min_pep_length",
+ default = 10,
+ type=int,
+ help=("Relevant parameter if short peptides are involved in scoring."
+ "Minimum peptide length for a chain in the target structure to "
+ "be considered in chain mapping. The chain mapping algorithm "
+ "first performs an all vs. all pairwise sequence alignment to "
+ "identify \"equal\" chains within the target structure. We go "
+ "for simple sequence identity there. Short sequences can be "
+ "problematic as they may produce high sequence identity "
+ "alignments by pure chance.")
+ )
+
+ parser.add_argument(
+ "--min-nuc-length",
+ dest="min_nuc_length",
+ default = 4,
+ type=int,
+ help=("Relevant parameter if short nucleotides are involved in scoring."
+ "Minimum nucleotide length for a chain in the target structure to "
+ "be considered in chain mapping. The chain mapping algorithm "
+ "first performs an all vs. all pairwise sequence alignment to "
+ "identify \"equal\" chains within the target structure. We go "
+ "for simple sequence identity there. Short sequences can be "
+ "problematic as they may produce high sequence identity "
+ "alignments by pure chance.")
+ )
return parser.parse_args()
@@ -695,7 +727,9 @@ def _Process(model, reference, args):
usalign_exec = args.usalign_exec,
lddt_no_stereochecks = args.lddt_no_stereochecks,
n_max_naive = args.n_max_naive,
- oum = args.oum)
+ oum = args.oum,
+ min_pep_length = args.min_pep_length,
+ min_nuc_length = args.min_nuc_length)
ir = _GetInconsistentResidues(scorer.aln)
if len(ir) > 0 and args.enforce_consistency:
@@ -876,6 +910,8 @@ def _Main():
out["cad_exec"] = args.cad_exec
out["usalign_exec"] = args.usalign_exec
out["lddt_no_stereochecks"] = args.lddt_no_stereochecks
+ out["min_pep_length"] = args.min_pep_length
+ out["min_nuc_length"] = args.min_nuc_length
out["status"] = "SUCCESS"
with open(args.output, 'w') as fh:
json.dump(out, fh, indent=4, sort_keys=False)
diff --git a/modules/doc/actions.rst b/modules/doc/actions.rst
index eee1181a1275a8615c6893dded821d314114bc68..523926fc33fea5d92baaa86587b03766dc527656 100644
--- a/modules/doc/actions.rst
+++ b/modules/doc/actions.rst
@@ -40,11 +40,16 @@ Details on the usage (output of ``ost compare-structures --help``):
[--local-lddt] [--bb-lddt] [--bb-local-lddt]
[--cad-score] [--local-cad-score]
[--cad-exec CAD_EXEC]
- [--usalign-exec USALIGN_EXEC] [--qs-score]
- [--dockq] [--contact-scores] [--rigid-scores]
+ [--usalign-exec USALIGN_EXEC]
+ [--override-usalign-mapping] [--qs-score]
+ [--dockq] [--ics] [--ips] [--rigid-scores]
[--patch-scores] [--tm-score]
[--lddt-no-stereochecks]
[--n-max-naive N_MAX_NAIVE]
+ [--dump-aligned-residues] [--dump-pepnuc-alns]
+ [--dump-pepnuc-aligned-residues]
+ [--min-pep-length MIN_PEP_LENGTH]
+ [--min-nuc-length MIN_NUC_LENGTH]
Evaluate model against reference
@@ -104,6 +109,8 @@ Details on the usage (output of ``ost compare-structures --help``):
* "cad_exec"
* "usalign_exec"
* "lddt_no_stereochecks"
+ * "min_pep_length"
+ * "min_nuc_length"
The pairwise sequence alignments are computed with Needleman-Wunsch using
BLOSUM62 (NUC44 for nucleotides). Many benchmarking scenarios preprocess the
@@ -351,6 +358,26 @@ Details on the usage (output of ``ost compare-structures --help``):
--dump-pepnuc-aligned-residues
Dump additional info on model and reference residues
that occur in pepnuc alignments.
+ --min-pep-length MIN_PEP_LENGTH
+ Relevant parameter if short peptides are involved in
+ scoring.Minimum peptide length for a chain in the
+ target structure to be considered in chain mapping.
+ The chain mapping algorithm first performs an all vs.
+ all pairwise sequence alignment to identify "equal"
+ chains within the target structure. We go for simple
+ sequence identity there. Short sequences can be
+ problematic as they may produce high sequence identity
+ alignments by pure chance.
+ --min-nuc-length MIN_NUC_LENGTH
+ Relevant parameter if short nucleotides are involved
+ in scoring.Minimum nucleotide length for a chain in
+ the target structure to be considered in chain
+ mapping. The chain mapping algorithm first performs an
+ all vs. all pairwise sequence alignment to identify
+ "equal" chains within the target structure. We go for
+ simple sequence identity there. Short sequences can be
+ problematic as they may produce high sequence identity
+ alignments by pure chance.
.. _ost compare ligand structures:
diff --git a/modules/mol/alg/pymod/scoring.py b/modules/mol/alg/pymod/scoring.py
index 6aaa2276e00e2d7233c2e0e870b3f56ccba1def5..e568fdf3afab4577e258ca8bd021e678e9937b33 100644
--- a/modules/mol/alg/pymod/scoring.py
+++ b/modules/mol/alg/pymod/scoring.py
@@ -141,12 +141,33 @@ class Scorer:
object into USalign to compute TM-score. Experimental feature
with limitations.
:type oum: :class:`bool`
+ :param min_pep_length: Relevant parameter if short peptides are involved in
+ scoring. Minimum peptide length for a chain in the
+ target structure to be considered in chain mapping.
+ The chain mapping algorithm first performs an all vs.
+ all pairwise sequence alignment to identify \"equal\"
+ chains within the target structure. We go for simple
+ sequence identity there. Short sequences can be
+ problematic as they may produce high sequence identity
+ alignments by pure chance.
+ :type min_pep_length: :class:`int`
+ :param min_nuc_length: Relevant parameter if short nucleotides are involved
+ in scoring. Minimum nucleotide length for a chain in
+ the target structure to be considered in chain
+ mapping. The chain mapping algorithm first performs
+ an all vs. all pairwise sequence alignment to
+ identify \"equal\" chains within the target
+ structure. We go for simple sequence identity there.
+ Short sequences can be problematic as they may
+ produce high sequence identity alignments by pure
+ chance.
+ :type min_nuc_length: :class:`int`
"""
def __init__(self, model, target, resnum_alignments=False,
molck_settings = None, cad_score_exec = None,
custom_mapping=None, usalign_exec = None,
lddt_no_stereochecks=False, n_max_naive=40320,
- oum=False):
+ oum=False, min_pep_length = 10, min_nuc_length = 4):
self._target_orig = target
self._model_orig = model
@@ -231,6 +252,8 @@ class Scorer:
self.lddt_no_stereochecks = lddt_no_stereochecks
self.n_max_naive = n_max_naive
self.oum = oum
+ self.min_pep_length = min_pep_length
+ self.min_nuc_length = min_nuc_length
# lazily evaluated attributes
self._stereochecked_model = None
@@ -491,7 +514,9 @@ class Scorer:
if self._chain_mapper is None:
self._chain_mapper = chain_mapping.ChainMapper(self.target,
n_max_naive=1e9,
- resnum_alignments=self.resnum_alignments)
+ resnum_alignments=self.resnum_alignments,
+ min_pep_length=self.min_pep_length,
+ min_nuc_length=self.min_nuc_length)
return self._chain_mapper
@property