[phenixbb] geometry_minimization makes molprobity score worse

Nigel Moriarty nwmoriarty at lbl.gov
Thu Jul 8 11:16:52 PDT 2021


This is very interesting from a different perspective but I should point
out a few things.

1. The CDL, which is the default, changes the backbone bonds and angles
based on phi/psi. Maybe in geometry minimisation this is causing the
"whirlygig." Can you check with cdl=False?

2. In a recent pub

   1. Sobolev OV, Afonine PV, Moriarty NW, Hekkelman ML, Joosten RP,
   Perrakis A, Adams PD: *A Global Ramachandran Score Identifies Protein
   Structures with Unlikely Stereochemistry.* *Structure* 2020, *28*
   :1249-1258.e2. <http://dx.doi.org/10.1016/j.str.2020.08.005> [PMID:
   32857966] [PMCID: PMC7642142]

we argue that percent favoured is not an accurate measure of Rama health.
Could also provide these numbers?



Nigel W. Moriarty
Building 33R0349, Molecular Biophysics and Integrated Bioimaging
Lawrence Berkeley National Laboratory
Berkeley, CA 94720-8235
Phone : 510-486-5709     Email : NWMoriarty at LBL.gov
Fax   : 510-486-5909      Web  : CCI.LBL.gov
ORCID : orcid.org/0000-0001-8857-9464

On Thu, Jul 8, 2021 at 10:28 AM James Holton <jmholton at lbl.gov> wrote:

> Thank you Pavel for your prompt response!
> I agree with everything you wrote below, and that is a good point about
> 2nd derivatives.
> However, what I'm seeing is the opposite of what you might predict. See
> below.
> On 7/7/2021 11:27 PM, Pavel Afonine wrote:
> Hi James,
> thanks for email and sharing your observations!
> 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.
> All this with the x-ray term turned off!
> 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 ?
> 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.
> 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?
> 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
> 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?
> Shall we vote?
> -James
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