Hi Sereina,
I beg to differ on the advisability of minimisation, even after using the
parameters you suggest to generate the conformation. I've recently been
using the CCDC's excellent Python API to analyse the results of the
generated conformations. This lets you very quickly assess whether any of
the bond distances or angles in a molecule is "unusual" with reference to
the data in the CSD. I've not done a systematic examination but my
anecdotal result is that you get far fewer such unusual geometries if you
give it a quick minimisation afterwards. Using the default optimiser
settings is quick, painless and, in my limited number of tests, worth the
effort.
I'd be interested to know if anyone else has different experience.
Cheers,
Dave
On Wednesday, 22 June 2016, Sereina <sereina.rini...@gmail.com> wrote:
> Based on the code snippets, Paolo has not used the basic-knowledge terms
> whereas Tim did.
>
> When setting useExpTorsionAnglePrefs=True and useBasicKnowledge=True, a
> minimization is in principle not necessary anymore (unless there are
> aliphatic rings, because we currently don’t have torsion rules for them).
>
> Best,
> Sereina
>
>
> On 22 Jun 2016, at 05:02, Greg Landrum <greg.land...@gmail.com
> <javascript:_e(%7B%7D,'cvml','greg.land...@gmail.com');>> wrote:
>
> I don't have anything to add to the pieces about the alignment (Paolo is
> the expert there!), but one comment on the conformation generation: If you
> used the background knowledge terms in the embedding, I don't think you
> should be getting the really distorted aromatic rings. Since in this case
> that does happen, for at least some conformations, I suspect there may be
> something wrong in the code.
>
> I'll take a look at that (and ask Sereina too).
>
> Best,
> -greg
>
>
> On Tue, Jun 21, 2016 at 10:30 PM, Paolo Tosco <paolo.to...@unito.it
> <javascript:_e(%7B%7D,'cvml','paolo.to...@unito.it');>> wrote:
>
>> Dear Tim,
>>
>> the Align() method returns an RMSD value, which however is computed only
>> on a limited number of atom pairs, namely those that the algorithm was able
>> to match between the two molecules, so a low value is not particularly
>> informative of the overall goodness of the alignment, as it only indicates
>> that the matched atoms were matched nicely, but there might only be a few
>> of those in the core, while side chains are scattered all over.
>> The Score() method instead returns the O3AScore for the alignment, which
>> is a better way to assess the quality of the superimposition.
>>
>> The other problem in your script is that the i index is incremented
>> before recording it in the lowest/highest variables, so the confIds are
>> shifted by 1, as the conformation index in the RDKit is 0-based.
>>
>> I also noticed that without minimizing the conformations the aromatic
>> rings look quite distorted, so I added a MMFF minimization, and I increased
>> the number of generated conformations and the pruneRmsThreshold. Setting to
>> False the experimental torsion angle preferences and basic knowledge about
>> rings seems to yield a larger variety of geometries which helps reproducing
>> this quite peculiar x-ray geometry which is probably not so commonly found.
>> Please find the modified script below.
>>
>> Hope this helps, kind regards
>> Paolo
>>
>>
>> #!/usr/bin/env python
>>
>>
>> from rdkit import Chem, RDConfig
>> from rdkit.Chem import AllChem, rdMolAlign
>>
>> ref = Chem.MolFromSmiles('NC(=[NH2+])c1ccc(C[C@
>> @H](NC(=O)CNS(=O)(=O)c2ccc3ccccc3c2)C(=O)N2CCCCC2)cc1')
>> mol1 =
>> Chem.MolFromPDBFile(RDConfig.RDBaseDir+'/rdkit/Chem/test_data/1DWD_ligand.pdb')
>> mol1 = AllChem.AssignBondOrdersFromTemplate(ref, mol1)
>> mol2 =
>> Chem.MolFromPDBFile(RDConfig.RDBaseDir+'/rdkit/Chem/test_data/1PPC_ligand.pdb')
>> mol2 = AllChem.AssignBondOrdersFromTemplate(ref, mol2)
>>
>> pyO3A = rdMolAlign.GetO3A(mol1, mol2)
>> rmsd = pyO3A.Align()
>> score = pyO3A.Score()
>> print "Orig",score
>> Chem.MolToMolFile(mol1, "orig.mol")
>>
>> cids = AllChem.EmbedMultipleConfs(mol1, numConfs=250, maxAttempts=100,
>> pruneRmsThresh=0.5, useExpTorsionAnglePrefs=False,
>> useBasicKnowledge=False)
>> AllChem.MMFFOptimizeMoleculeConfs(mol1, mmffVariant='MMFF94s')
>> pyO3As = rdMolAlign.GetO3AForProbeConfs(mol1, mol2, numThreads=0)
>> i = 0
>> lowest = 999999999.9
>> highest = 0.0
>> for pyO3A in pyO3As:
>> rmsd = pyO3A.Align()
>> score = pyO3A.Score()
>> if score < lowest:
>> lowest = score
>> lowestConfId = i
>> if score > highest:
>> highest = score
>> highestConfId = i
>> i +=1
>>
>> print "Lowest:", lowest, lowestConfId
>> print "Highest:", highest, highestConfId
>>
>> Chem.MolToMolFile(mol1, "lowest.mol", confId=lowestConfId)
>> Chem.MolToMolFile(mol1, "highest.mol", confId=highestConfId)
>>
>>
>> On 06/21/16 15:41, Tim Dudgeon wrote:
>>
>> Hi All,
>>
>> I'm trying to get to grips with using Open3D Align in RDKit, but hitting
>> problems.
>>
>> My approach is to generate random conformers of the probe molecule and
>> align it to the reference molecule. My example is cobbled together from
>> the examples in the cookbook.
>>
>>
>> from rdkit import Chem, RDConfig
>> from rdkit.Chem import AllChem, rdMolAlign
>>
>> ref = Chem.MolFromSmiles('NC(=[NH2+])c1ccc(C[C@
>> @H](NC(=O)CNS(=O)(=O)c2ccc3ccccc3c2)C(=O)N2CCCCC2)cc1')
>> mol1 =
>> Chem.MolFromPDBFile(RDConfig.RDBaseDir+'/rdkit/Chem/test_data/1DWD_ligand.pdb')
>> mol1 = AllChem.AssignBondOrdersFromTemplate(ref, mol1)
>> mol2 =
>> Chem.MolFromPDBFile(RDConfig.RDBaseDir+'/rdkit/Chem/test_data/1PPC_ligand.pdb')
>> mol2 = AllChem.AssignBondOrdersFromTemplate(ref, mol2)
>>
>> pyO3A = rdMolAlign.GetO3A(mol1, mol2)
>> score = pyO3A.Align()
>> print "Orig",score
>> Chem.MolToMolFile(mol1, "orig.mol")
>>
>> cids = AllChem.EmbedMultipleConfs(mol1, numConfs=100, maxAttempts=100,
>> pruneRmsThresh=0.1, useExpTorsionAnglePrefs=True, useBasicKnowledge=True)
>>
>> pyO3As = rdMolAlign.GetO3AForProbeConfs(mol1, mol2, numThreads=0)
>> i = 0
>> lowest = 999999999.9
>> highest = 0.0
>> for pyO3A in pyO3As:
>> i +=1
>> score = pyO3A.Align()
>> if score < lowest:
>> lowest = score
>> lowestConfId = i
>> if score > highest:
>> highest = score
>> highestConfId = i
>>
>> print "Lowest:", lowest, lowestConfId
>> print "Highest:", highest, highestConfId
>>
>> Chem.MolToMolFile(mol1, "lowest.mol", confId=lowestConfId)
>> Chem.MolToMolFile(mol1, "highest.mol", confId=highestConfId)
>>
>> What I'm finding is that the alignments with the lowest and highest O3A
>> scores are much worse alignments (visually) than the original structure
>> (the structure from 1DWD). Typical scores are:
>>
>> Original 1DWD structure: 0.38
>> Lowest scoring conformer: 0.186
>> Highest scoring conformer: 0.78
>>
>> Now I'm assuming that lower O3A align scores are better (though that's
>> not specifically stated), so as my lowest score is lower than the original
>> alignment I would have expected it to be a better alignment, but its
>> clearly much worse. And if I'm wrong and higher scores are better then the
>> same applies.
>>
>> Clearly I've not understood something correctly!
>>
>> Tim
>>
>>
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