[phenixbb] geometry_minimization makes molprobity score worse
James Holton
jmholton at lbl.gov
Thu Jul 8 16:28:44 PDT 2021
Thank you Pavel for the m.pdb file. I did the dynamics run. What am I
looking for? I still see adjacent CA-CA distances all below 3.8 A. The
other three minimizers give me all CA-CA > 3.85 A.
Maybe I am missing your point?
On 7/8/2021 11:29 AM, Pavel Afonine wrote:
>
> Hi James,
>
>>>> Greetings all, and I hope this little observation helps improve
>>>> things somehow.
>>>>
>>>> I did not expect this result, but there it is. My MolProbity score
>>>> goes from 0.7 to 1.9 after a run of phenix.geometry_minimization
>>>>
>>>> I started with an AMBER-minimized model (based on 1aho), and that
>>>> got me my best MolProbity score so far (0.7). But, even with
>>>> hydrogens and waters removed the geometry_minimization run
>>>> increases the clashscore from 0 to 3.1 and Ramachandran favored
>>>> drops from 98% to 88% with one residue reaching the outlier level.
>>>
>>> It is not a secret that 'standard geometry restraints' used in
>>> Phenix and alike (read Refmac, etc) are very simplistic. They are
>>> not aware of main chain preferential conformations (Ramachandran
>>> plot), favorable side chain rotamer conformations. They don't even
>>> have any electrostatic/attraction terms -- only anti-bumping
>>> repulsion! Standard geometry restraints won't like any NCI
>>> (non-covalent interaction) and likely will make interacting atoms
>>> break apart rather than stay close together interacting.
>>
>> Yes, there's the rub: I'm not seeing "interacting atoms break apart",
>> but rather they are being smashed together. Torsion angles are also
>> being twisted out of allowed regions of the Ramachandran plot.
>
> I think this can go both ways depending on local arrangement. For
> example, if atoms interact via NCI but something else pushes them
> apart, they will split. But if nothing pushes them they may just stay
> together.
>
> Also, if H are not present atoms can come close enough to each-other
> creating a clash from MolProbity viewpoint (because it adds H for
> clash evaluations).
>
>>> With this in mind any high quality (high-resolution) atomic model or
>>> the one optimized using sufficiently high-level QM is going to have
>>> a more realistic geometry than the result of geometry regularization
>>> against very simplistic restraints target. An example:
>>>
>>> https://journals.iucr.org/d/issues/2020/12/00/lp5048/lp5048.pdf
>>>
>>> and previous papers on the topic.
>>
>> I agree, but what doesn't make sense to me is how the "simplistic
>> restraints" of phenix.geometry_minimization would be so inconsistent
>> with the "simplistic restraints" in phenix.molprobity ?
>
> MolProbity way to quantify clashes and repulsion terms in standard
> restraints are different. If everything else is favorable they may
> match, but otherwise they don't have to (by the way they are defined
> and calculated).
>
>> What I am doing here is starting with an energy-minimized model of a
>> 1.0 A structure (1aho). It's not a fancy QM, just the ff14SB
>> potential in AMBER. I get my best molprobity scores this way, but I
>> need an x-ray refinement program like phenix.refine to compare these
>> models with reality. It troubles me that the "geometry" in the x-ray
>> refinement program all by itself messes up my molprobity score.
>
> If in this case AMBER force-field does a better job, then you can run
> X-ray refinement using AMBER based restraints. Nigel can help with
> that in case it does not work right off the box.
>
>>>> Just for comparison, with refmac5 in "refi type ideal" mode I see
>>>> the MolProbity rise to 1.13, but Clashscore remains zero, some
>>>> Ramas go from favored to allowed, but none rise to the level of
>>>> outliers.
>>>
>>> I believe this is because of the nature of minimizer used. Refmac
>>> uses 2nd derivative based one, which in a nutshell means it can move
>>> the model much less (just a bit in vicinity of a local minimum) than
>>> any program that uses gradients only (like Phenix).
>> good point.
>>
>> So, what should I do to stabilize phenix.geometry_minimization? Crank
>> up the non-bonded weight? Restrain to starting coordinates?
>
> Restraining to starting coordinates is a fine option (there is an
> option to do it).
>
> Cranking up nonbonded weight might have side effects:
>
> http://phenix-online.org/presentations/nb_weight.pdf
>
>>>> Files and logs here:
>>>> https://bl831.als.lbl.gov/~jamesh/bugreports/phenixmin_070721.tgz
>>>>
>>>> I suspect this might have something to do with library values for
>>>> main-chain bonds and angles? They do seem to vary between
>>>> programs. Phenix having the shortest CA-CA distance by up to 0.08
>>>> A. After running thorough minimization on a poly-A peptide I get:
>>>> bond amber refmac phenix shelxl Stryer
>>>> C-N 1.330 1.339 1.331 1.325 1.32
>>>> N-CA 1.462 1.482 1.455 1.454 1.47
>>>> CA-C 1.542 1.534 1.521 1.546 1.53
>>>> CA-CA 3.862 3.874 *3.794* 3.854
>>>>
>>>> So, which one is "right" ?
>>>
>>> I'd say they are all the same, within their 'sigmas' which are from
>>> memory about 0.02A:
>> I note that 3.874 - 3.794 = 0.08 > 0.02
>
> Right, but I was talking about covalent bonds as defined in Monomer
> Library or GeoStd, and for those it looks like they stay within their
> 'sigmas'. There is no explicit restraints on CA-CA distances.
>
>> This brings me to my pet theory. I think what is going on is small
>> errors like this build up a considerable amount of tension in the
>> long main chain. For this 64-mer, the contour length of the main
>> chain after idealization is ~5 A shorter after
>> phenix.geometry_minimization than it is after shelxl or amber. That 5
>> A has to come from somewhere. Without stretching bonds or bending
>> angles the only thing left to do is twisting torsions. A kind of
>> "whirlygig" effect.
>>
>> The question is: is the phenix CA-CA distance too short? Or is the
>> amber CA-CA distance too long?
>
> I don't know the answer, but try this (m.pdb is a nearly perfect
> alpha-helix from 1US0):
>
> phenix.dynamics m.pdb number_of_steps=5000
>
> and compare the result (eg., in PyMol) with the staring model.
>
> Pavel
>
>
>
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