[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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