diff --git a/modules/mol/alg/pymod/chain_mapping.py b/modules/mol/alg/pymod/chain_mapping.py
index aa314e7df7e8bbbaa2becf3e4bd438aa828e2bbc..2318f6a2a979714e00742ab644b60fd3691c05e9 100644
--- a/modules/mol/alg/pymod/chain_mapping.py
+++ b/modules/mol/alg/pymod/chain_mapping.py
@@ -1014,7 +1014,7 @@ class ChainMapper:
are estimated using all possible combinations of target and model chains
within the same chem groups and build the basis for further extension.
- There are three extension strategies:
+ There are four extension strategies:
* **greedy_single_gdtts**: Iteratively add the model/target chain pair
that adds the most conserved contacts based on the GDT-TS metric
@@ -1022,12 +1022,19 @@ class ChainMapper:
with highest GDT-TS score is returned. However, that mapping is not
guaranteed to be complete (see *single_chain_gdtts_thresh*).
- * **greedy_iterative_gdtts**: Same as single except that the
- transformation gets updated with each added chain pair.
+ * **greedy_iterative_gdtts**: Same as greedy_single_gdtts except that
+ the transformation gets updated with each added chain pair.
- * **greedy_iterative_rmsd**: Same as iterative, i.e. the transformation
- gets updated with each added chain pair. However,
- **single_chain_gdtts_thresh** is only applied to derive the initial
+ * **greedy_single_rmsd**: Conceptually similar to greedy_single_gdtts
+ but the added chain pairs are the ones with lowest RMSD.
+ The mapping with lowest overall RMSD gets returned.
+ *single_chain_gdtts_thresh* is only applied to derive the initial
+ transformations. After that, the minimal RMSD chain pair gets
+ iteratively added without applying any threshold.
+
+ * **greedy_iterative_rmsd**: Same as greedy_single_rmsd exept that
+ the transformation gets updated with each added chain pair.
+ *single_chain_gdtts_thresh* is only applied to derive the initial
transformations. After that, the minimal RMSD chain pair gets
iteratively added without applying any threshold.
@@ -1067,7 +1074,7 @@ class ChainMapper:
"""
strategies = ["greedy_single_gdtts", "greedy_iterative_gdtts",
- "greedy_iterative_rmsd"]
+ "greedy_single_rmsd", "greedy_iterative_rmsd"]
if strategy not in strategies:
raise RuntimeError(f"strategy must be {strategies}")
@@ -1138,6 +1145,13 @@ class ChainMapper:
len(self.target.chains),
len(mdl.chains))
+ elif strategy == "greedy_single_rmsd":
+ mapping = _SingleRigidRMSD(initial_transforms, initial_mappings,
+ self.chem_groups, chem_mapping,
+ trg_group_pos, mdl_group_pos,
+ iterative_superposition)
+
+
elif strategy == "greedy_iterative_rmsd":
mapping = _IterativeRigidRMSD(initial_transforms, initial_mappings,
self.chem_groups, chem_mapping,
@@ -2973,7 +2987,7 @@ def _IterativeRigidGDTTS(initial_transforms, initial_mappings, chem_groups,
for mdl_pos, mdl_chains in zip(mdl_group_pos, chem_mapping):
for m_pos, m in zip(mdl_pos, mdl_chains):
mdl_pos_dict[m] = m_pos
-
+
best_mapping = dict()
best_gdt = 0
for initial_transform, initial_mapping in zip(initial_transforms,
@@ -3045,6 +3059,52 @@ def _IterativeRigidGDTTS(initial_transforms, initial_mappings, chem_groups,
return best_mapping
+def _SingleRigidRMSD(initial_transforms, initial_mappings, chem_groups,
+ chem_mapping, trg_group_pos, mdl_group_pos,
+ iterative_superposition):
+ """
+ Takes initial transforms and sequentially adds chain pairs with lowest RMSD.
+ The mapping from the transform that leads to lowest overall RMSD is
+ returned.
+ """
+ best_mapping = dict()
+ best_ssd = float("inf") # we're actually going for summed squared distances
+ # Since all positions have same lengths and we do a
+ # full mapping, lowest SSD has a guarantee of also
+ # being lowest RMSD
+ for transform in initial_transforms:
+ mapping = dict()
+ mapped_mdl_chains = set()
+ ssd = 0.0
+ for trg_chains, mdl_chains, trg_pos, mdl_pos, in zip(chem_groups,
+ chem_mapping,
+ trg_group_pos,
+ mdl_group_pos):
+ if len(trg_pos) == 0 or len(mdl_pos) == 0:
+ continue # cannot compute valid rmsd
+ ssds = list()
+ t_mdl_pos = list()
+ for m_pos in mdl_pos:
+ t_m_pos = geom.Vec3List(m_pos)
+ t_m_pos.ApplyTransform(transform)
+ t_mdl_pos.append(t_m_pos)
+ for t_pos, t in zip(trg_pos, trg_chains):
+ for t_m_pos, m in zip(t_mdl_pos, mdl_chains):
+ ssd = t_pos.GetSummedSquaredDistances(t_m_pos)
+ ssds.append((ssd, (t,m)))
+ ssds.sort()
+ for item in ssds:
+ p = item[1]
+ if p[0] not in mapping and p[1] not in mapped_mdl_chains:
+ mapping[p[0]] = p[1]
+ mapped_mdl_chains.add(p[1])
+ ssd += item[0]
+
+ if ssd < best_ssd:
+ best_ssd = ssd
+ best_mapping = mapping
+
+ return best_mapping
def _IterativeRigidRMSD(initial_transforms, initial_mappings, chem_groups,
chem_mapping, trg_group_pos, mdl_group_pos,
diff --git a/modules/mol/alg/tests/test_chain_mapping.py b/modules/mol/alg/tests/test_chain_mapping.py
index d012f1e65fa872ac2a6ab1dd203026e3bd72f873..36a633b09479e89aa81297cfb45326b4262fea69 100644
--- a/modules/mol/alg/tests/test_chain_mapping.py
+++ b/modules/mol/alg/tests/test_chain_mapping.py
@@ -283,6 +283,9 @@ class TestChainMapper(unittest.TestCase):
greedy_rigid_res = mapper.GetRigidMapping(mdl, strategy="greedy_iterative_gdtts")
self.assertEqual(greedy_rigid_res.mapping, [['X', 'Y'],[None],['Z']])
+ greedy_rigid_res = mapper.GetRigidMapping(mdl, strategy="greedy_single_rmsd")
+ self.assertEqual(greedy_rigid_res.mapping, [['X', 'Y'],[None],['Z']])
+
greedy_rigid_res = mapper.GetRigidMapping(mdl, strategy="greedy_iterative_rmsd")
self.assertEqual(greedy_rigid_res.mapping, [['X', 'Y'],[None],['Z']])