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Thank you Pavel for your prompt response!<br>
<br>
I agree with everything you wrote below, and that is a good point
about 2nd derivatives. <br>
<br>
However, what I'm seeing is the opposite of what you might predict.
See below. <br>
<br>
<div class="moz-cite-prefix">On 7/7/2021 11:27 PM, Pavel Afonine
wrote:<br>
</div>
<blockquote type="cite"
cite="mid:c408af9f-5623-e5d1-c25b-231ef5f724b5@lbl.gov">Hi James,
<br>
<br>
thanks for email and sharing your observations!
<br>
<br>
<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>
<br>
All this with the x-ray term turned off!<br>
<br>
<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">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>
<br>
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>
<br>
<blockquote type="cite"
cite="mid:c408af9f-5623-e5d1-c25b-231ef5f724b5@lbl.gov">
<br>
<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>
<br>
<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">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>
<br>
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>
<br>
Shall we vote?<br>
<br>
-James<br>
<br>
<br>
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