def testTorsionFingerprints(self):
# we use the xray structure from the paper (JCIM, 52, 1499, 2012): 1DWD
refFile = os.path.join(RDConfig.RDCodeDir, 'Chem', 'test_data', '1DWD_ligand.pdb')
ref = Chem.MolFromSmiles(
'NC(=[NH2+])c1ccc(C[C@@H](NC(=O)CNS(=O)(=O)c2ccc3ccccc3c2)C(=O)N2CCCCC2)cc1')
mol = Chem.MolFromPDBFile(refFile)
mol = AllChem.AssignBondOrdersFromTemplate(ref, mol)
# the torsion lists
tors_list, tors_list_rings = TorsionFingerprints.CalculateTorsionLists(mol)
self.assertEqual(len(tors_list), 11)
self.assertEqual(len(tors_list_rings), 4)
self.assertAlmostEqual(tors_list[-1][1], 180.0, 4)
tors_list, tors_list_rings = TorsionFingerprints.CalculateTorsionLists(mol, maxDev='spec')
self.assertAlmostEqual(tors_list[-1][1], 90.0, 4)
self.assertRaises(ValueError, TorsionFingerprints.CalculateTorsionLists, mol, maxDev='test')
tors_list, tors_list_rings = TorsionFingerprints.CalculateTorsionLists(mol, symmRadius=0)
self.assertEqual(len(tors_list[0][0]), 2)
# the weights
weights = TorsionFingerprints.CalculateTorsionWeights(mol)
self.assertAlmostEqual(weights[4], 1.0)
self.assertEqual(len(weights), len(tors_list + tors_list_rings))
weights = TorsionFingerprints.CalculateTorsionWeights(mol, 15, 14)
self.assertAlmostEqual(weights[3], 1.0)
self.assertRaises(ValueError, TorsionFingerprints.CalculateTorsionWeights, mol, 15, 3)
# the torsion angles
tors_list, tors_list_rings = TorsionFingerprints.CalculateTorsionLists(mol)
torsions = TorsionFingerprints.CalculateTorsionAngles(mol, tors_list, tors_list_rings)
self.assertEqual(len(weights), len(torsions))
self.assertAlmostEqual(torsions[2][0][0], 232.5346, 4)
# the torsion fingerprint deviation
tfd = TorsionFingerprints.CalculateTFD(torsions, torsions)
self.assertAlmostEqual(tfd, 0.0)
refFile = os.path.join(RDConfig.RDCodeDir, 'Chem', 'test_data', '1PPC_ligand.pdb')
mol2 = Chem.MolFromPDBFile(refFile)
mol2 = AllChem.AssignBondOrdersFromTemplate(ref, mol2)
torsions2 = TorsionFingerprints.CalculateTorsionAngles(mol2, tors_list, tors_list_rings)
weights = TorsionFingerprints.CalculateTorsionWeights(mol)
tfd = TorsionFingerprints.CalculateTFD(torsions, torsions2, weights=weights)
self.assertAlmostEqual(tfd, 0.0691, 4)
tfd = TorsionFingerprints.CalculateTFD(torsions, torsions2)
self.assertAlmostEqual(tfd, 0.1115, 4)
# the wrapper functions
tfd = TorsionFingerprints.GetTFDBetweenMolecules(mol, mol2)
self.assertAlmostEqual(tfd, 0.0691, 4)
mol.AddConformer(mol2.GetConformer(), assignId=True)
mol.AddConformer(mol2.GetConformer(), assignId=True)
tfd = TorsionFingerprints.GetTFDBetweenConformers(mol, confIds1=[0], confIds2=[1, 2])
self.assertEqual(len(tfd), 2)
self.assertAlmostEqual(tfd[0], 0.0691, 4)
tfdmat = TorsionFingerprints.GetTFDMatrix(mol)
self.assertEqual(len(tfdmat), 3)
def prune_last_conformer(
mol: Chem.Mol, tfd_thresh: float,
energies: List[float]) -> Tuple[Chem.Mol, List[float]]:
"""Prunes the last conformer of the molecule.
If no conformers in `mol` have a TFD (Torsional Fingerprint Deviation) with the last conformer of less than `tfd_thresh`,
the last conformer is kept. Otherwise, the lowest energy conformer with TFD less than `tfd_thresh` is kept and all other conformers
are discarded.
Parameters
----------
mol : RDKit Mol
The molecule to be pruned. The conformers in the molecule should be ordered by ascending energy.
tfd_thresh : float
The minimum threshold for TFD between conformers.
energies : list of float
A list of all the energies of the conformers in `mol`.
Returns
-------
mol : RDKit Mol
The updated molecule after pruning, with conformers sorted by ascending energy.
energies : list of float
A list of all the energies of the conformers in `mol` after pruning and sorting by ascending energy.
"""
if tfd_thresh < 0 or mol.GetNumConformers() <= 1:
return mol, energies
idx = bisect.bisect(energies[:-1], energies[-1])
tfd = TorsionFingerprints.GetTFDBetweenConformers(
mol,
range(0,
mol.GetNumConformers() - 1), [mol.GetNumConformers() - 1],
useWeights=False)
tfd = np.array(tfd)
# if lower energy conformer is within threshold, drop new conf
if not np.all(tfd[:idx] >= tfd_thresh):
energies = energies[:-1]
mol.RemoveConformer(mol.GetNumConformers() - 1)
return mol, energies
else:
keep = list(range(0, idx))
keep.append(mol.GetNumConformers() - 1)
keep += [
x for x in range(idx,
mol.GetNumConformers() - 1)
if tfd[x] >= tfd_thresh
]
new = Chem.Mol(mol)
new.RemoveAllConformers()
for i in keep:
conf = mol.GetConformer(i)
new.AddConformer(conf, assignId=True)
return new, [energies[i] for i in keep]
def get_tfd(mol):
return TorsionFingerprints.GetTFDBetweenConformers(mol, [0], [1])
