diff --git a/modules/mol/alg/pymod/chain_mapping.py b/modules/mol/alg/pymod/chain_mapping.py
index 29acb66541deedd75b02fa3d4bb07bb65555dda4..a738493f45175560dc10eb11a3ed2ba5d9090808 100644
--- a/modules/mol/alg/pymod/chain_mapping.py
+++ b/modules/mol/alg/pymod/chain_mapping.py
@@ -26,12 +26,14 @@ class MappingResult:
Constructor is directly called within the functions, no need to construct
such objects yourself.
"""
- def __init__(self, target, model, chem_groups, mapping, alns):
+ def __init__(self, target, model, chem_groups, mapping, alns,
+ opt_score=None):
self._target = target
self._model = model
self._chem_groups = chem_groups
self._mapping = mapping
self._alns = alns
+ self._opt_score = opt_score
@property
def target(self):
@@ -89,6 +91,18 @@ class MappingResult:
"""
return self._alns
+ @property
+ def opt_score(self):
+ """ Placeholder property without any guarantee of being set
+
+ Different scores get optimized in the various chain mapping algorithms.
+ Some of them may set their final optimal score in that property.
+ Consult the documentation of the respective chain mapping algorithm
+ for more information. Won't be in the return dict of
+ :func:`JSONSummary`.
+ """
+ return self._opt_score
+
def GetFlatMapping(self, mdl_as_key=False):
""" Returns flat mapping as :class:`dict` for all mapable chains
@@ -742,6 +756,9 @@ class ChainMapper:
scoring ones are extend by *block_seed_size* chains and the best
scoring one is exhaustively extended.
+ Sets :attr:`MappingResult.opt_score` in case of no trivial one-to-one
+ mapping.
+
:param model: Model to map
:type model: :class:`ost.mol.EntityView`/:class:`ost.mol.EntityHandle`
:param inclusion_radius: Inclusion radius for lDDT
@@ -809,11 +826,13 @@ class ChainMapper:
alns)
mapping = None
+ opt_lddt = None
if strategy == "naive":
- mapping = _lDDTNaive(self.target, mdl, inclusion_radius, thresholds,
- self.chem_groups, chem_mapping, ref_mdl_alns,
- self.n_max_naive)
+ mapping, opt_lddt = _lDDTNaive(self.target, mdl, inclusion_radius,
+ thresholds, self.chem_groups,
+ chem_mapping, ref_mdl_alns,
+ self.n_max_naive)
else:
# its one of the greedy strategies - setup greedy searcher
the_greed = _lDDTGreedySearcher(self.target, mdl, self.chem_groups,
@@ -828,6 +847,8 @@ class ChainMapper:
elif strategy == "greedy_block":
mapping = _lDDTGreedyBlock(the_greed, block_seed_size,
block_blocks_per_chem_group)
+ # cached => lDDT computation is fast here
+ opt_lddt = the_greed.lDDT(self.chem_groups, mapping)
alns = dict()
for ref_group, mdl_group in zip(self.chem_groups, mapping):
@@ -839,7 +860,7 @@ class ChainMapper:
alns[(ref_ch, mdl_ch)] = aln
return MappingResult(self.target, mdl, self.chem_groups, mapping,
- alns)
+ alns, opt_score = opt_lddt)
def GetQSScoreMapping(self, model, contact_d = 12.0, strategy = "naive",
@@ -876,6 +897,9 @@ class ChainMapper:
scoring ones are extend by *block_seed_size* chains and the block with
with best QS score is exhaustively extended.
+ Sets :attr:`MappingResult.opt_score` in case of no trivial one-to-one
+ mapping.
+
:param model: Model to map
:type model: :class:`ost.mol.EntityView`/:class:`ost.mol.EntityHandle`
:param contact_d: Max distance between two residues to be considered as
@@ -917,18 +941,21 @@ class ChainMapper:
alns[(ref_ch, mdl_ch)] = aln
return MappingResult(self.target, mdl, self.chem_groups, one_to_one,
alns)
+ mapping = None
+ opt_qsscore = None
if strategy == "naive":
- mapping = _QSScoreNaive(self.target, mdl, self.chem_groups,
- chem_mapping, ref_mdl_alns, contact_d,
- self.n_max_naive)
+ mapping, opt_qsscore = _QSScoreNaive(self.target, mdl,
+ self.chem_groups,
+ chem_mapping, ref_mdl_alns,
+ contact_d, self.n_max_naive)
else:
# its one of the greedy strategies - setup greedy searcher
-
- the_greed = _QSScoreGreedySearcher(self.target, mdl, self.chem_groups,
- chem_mapping, ref_mdl_alns,
- contact_d = contact_d,
- steep_opt_rate=steep_opt_rate)
+ the_greed = _QSScoreGreedySearcher(self.target, mdl,
+ self.chem_groups,
+ chem_mapping, ref_mdl_alns,
+ contact_d = contact_d,
+ steep_opt_rate=steep_opt_rate)
if strategy == "greedy_fast":
mapping = _QSScoreGreedyFast(the_greed)
elif strategy == "greedy_full":
@@ -936,6 +963,9 @@ class ChainMapper:
elif strategy == "greedy_block":
mapping = _QSScoreGreedyBlock(the_greed, block_seed_size,
block_blocks_per_chem_group)
+ # cached => QSScore computation is fast here
+ opt_qsscore = the_greed.Score(mapping, check=False)
+
alns = dict()
for ref_group, mdl_group in zip(self.chem_groups, mapping):
@@ -947,7 +977,7 @@ class ChainMapper:
alns[(ref_ch, mdl_ch)] = aln
return MappingResult(self.target, mdl, self.chem_groups, mapping,
- alns)
+ alns, opt_score = opt_qsscore)
def GetRigidMapping(self, model, strategy = "greedy_single_gdtts",
single_chain_gdtts_thresh=0.4, subsampling=None,
@@ -2207,7 +2237,7 @@ def _lDDTNaive(trg, mdl, inclusion_radius, thresholds, chem_groups,
best_mapping = mapping
best_lddt = lDDT
- return best_mapping
+ return (best_mapping, best_lddt)
def _lDDTGreedyFast(the_greed):
@@ -2630,7 +2660,7 @@ def _QSScoreNaive(trg, mdl, chem_groups, chem_mapping, ref_mdl_alns, contact_d,
if score_result.QS_global > best_score:
best_mapping = mapping
best_score = score_result.QS_global
- return best_mapping
+ return (best_mapping, best_score)
def _QSScoreGreedyFast(the_greed):