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<p>Hi James,<br>
</p>
<blockquote type="cite"
cite="mid:192cf406-b24c-27d6-a315-942087d052ef@lbl.gov">
<blockquote type="cite"
cite="mid:c408af9f-5623-e5d1-c25b-231ef5f724b5@lbl.gov">
<blockquote type="cite">Greetings all, and I hope this little
observation helps improve things somehow. <br>
<br>
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 <br>
<br>
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. <br>
</blockquote>
<br>
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. <br>
</blockquote>
<br>
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. <br>
</blockquote>
<p>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.<br>
</p>
<p>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).<br>
</p>
<blockquote type="cite"
cite="mid:192cf406-b24c-27d6-a315-942087d052ef@lbl.gov">
<blockquote type="cite"
cite="mid:c408af9f-5623-e5d1-c25b-231ef5f724b5@lbl.gov">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: <br>
<br>
<a class="moz-txt-link-freetext"
href="https://journals.iucr.org/d/issues/2020/12/00/lp5048/lp5048.pdf"
moz-do-not-send="true">https://journals.iucr.org/d/issues/2020/12/00/lp5048/lp5048.pdf</a>
<br>
<br>
and previous papers on the topic. <br>
</blockquote>
<br>
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
?<br>
</blockquote>
<br>
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).<br>
<br>
<blockquote type="cite"
cite="mid:192cf406-b24c-27d6-a315-942087d052ef@lbl.gov"> 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.<br>
</blockquote>
<br>
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.<br>
<br>
<blockquote type="cite"
cite="mid:192cf406-b24c-27d6-a315-942087d052ef@lbl.gov">
<blockquote type="cite"
cite="mid:c408af9f-5623-e5d1-c25b-231ef5f724b5@lbl.gov">
<blockquote type="cite">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. <br>
</blockquote>
<br>
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). <br>
</blockquote>
good point.<br>
<br>
So, what should I do to stabilize phenix.geometry_minimization?
Crank up the non-bonded weight? Restrain to starting coordinates?<br>
</blockquote>
<br>
Restraining to starting coordinates is a fine option (there is an
option to do it).
<p>Cranking up nonbonded weight might have side effects:</p>
<p><a class="moz-txt-link-freetext" href="http://phenix-online.org/presentations/nb_weight.pdf">http://phenix-online.org/presentations/nb_weight.pdf</a><br>
</p>
<blockquote type="cite"
cite="mid:192cf406-b24c-27d6-a315-942087d052ef@lbl.gov">
<blockquote type="cite"
cite="mid:c408af9f-5623-e5d1-c25b-231ef5f724b5@lbl.gov">
<blockquote type="cite">Files and logs here: <br>
<a class="moz-txt-link-freetext"
href="https://bl831.als.lbl.gov/~jamesh/bugreports/phenixmin_070721.tgz"
moz-do-not-send="true">https://bl831.als.lbl.gov/~jamesh/bugreports/phenixmin_070721.tgz</a>
<br>
<br>
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: <br>
bond amber refmac phenix shelxl Stryer <br>
C-N 1.330 1.339 1.331 1.325 1.32 <br>
N-CA 1.462 1.482 1.455 1.454 1.47 <br>
CA-C 1.542 1.534 1.521 1.546 1.53 <br>
CA-CA 3.862 3.874 <font color="#ff0000"><b>3.794</b></font>
3.854 <br>
<br>
So, which one is "right" ? <br>
</blockquote>
<br>
I'd say they are all the same, within their 'sigmas' which are
from memory about 0.02A: <br>
</blockquote>
I note that 3.874 - 3.794 = 0.08 > 0.02<br>
</blockquote>
<br>
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.<br>
<br>
<blockquote type="cite"
cite="mid:192cf406-b24c-27d6-a315-942087d052ef@lbl.gov"> 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.<br>
<br>
The question is: is the phenix CA-CA distance too short? Or is
the amber CA-CA distance too long?<br>
</blockquote>
<p>I don't know the answer, but try this (m.pdb is a nearly perfect
alpha-helix from 1US0):</p>
<p>phenix.dynamics m.pdb number_of_steps=5000</p>
<p>and compare the result (eg., in PyMol) with the staring model.</p>
<p>Pavel</p>
<p><br>
</p>
<br>
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