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Hi Keitaro,<br>
<br>
<blockquote
cite="mid:AANLkTi=oFzYvFZQ7TW8BY_Q0E4z7AhRUyWzyfJG0DYm6@mail.gmail.com"
type="cite">
<pre wrap="">Here is the typical distribution of Rfree, Rwork and Rfree-Rwork for structures in PDB refined at 2.5A resolution:
</pre>
<pre wrap="">
Are their statistics applied to twinning cases?
I think such kind of statistics should be (slightly) different from
normal cases.. not?
</pre>
</blockquote>
<br>
you are right: this analysis does not discriminate structures by
twinning, although I don't see why the R-factor stats should be
(much) different.<br>
<br>
<blockquote
cite="mid:AANLkTi=oFzYvFZQ7TW8BY_Q0E4z7AhRUyWzyfJG0DYm6@mail.gmail.com"
type="cite">
<blockquote type="cite">
<pre wrap="">Did you use PHENIX to select free-R flags? It is important.
</pre>
</blockquote>
<pre wrap="">
Yes, I used phenix to select R-free-flags with use_lattice_symmetry=true.
</pre>
</blockquote>
<br>
Good.<br>
<br>
<blockquote
cite="mid:AANLkTi=oFzYvFZQ7TW8BY_Q0E4z7AhRUyWzyfJG0DYm6@mail.gmail.com"
type="cite">
<pre wrap="">Do you have any way to know the refinement is biased or not because of
wrong R-free-flags selections?
</pre>
</blockquote>
<br>
If you used PHENIX to select free-R flags then it is unlikely to be
wrong. By wrong I mean:<br>
- not taking lattice symmetry into account;<br>
- making distribution of flags not uniform across the resolution
range such that each relatively thin resolution bin receives
"enough" of test reflections, etc...<br>
<br>
However, the refinement outcome may vary depending on the choice of
free-R flags anyway, but for the different reasons. This is because
of refinement artifacts.
For example, if you run a hundred of identical Simulated Annealing
refinement jobs where the only difference between each job is the
random seed, then you will get an ensemble of somewhat (mostly
slightly) different structures, and depending on resolution the
R-factors may range within 0-3% (lower the resolution, higher the
spread).
<br>
We know that the profile of a function that we optimize in
refinement is very complex, and the optimizers we use are very
simple to thoroughly search this profile. So by the end of
refinement we never end up in the global minimum, but ALWAYS get
stuck in a local minimum. Depending on initial condition the
optimization may take a different pathway and end up in a different
local minimum. Even plus/minus one reflection may trigger this
change, or even rounding errors, etc. So, the ensemble of models you
see after multi-start SA refinement does not necessarily reflects
what's in the crystal. Yes, among the models in the whole ensemble,
some some side chains may adopt one or another alternative
conformations and then this variability of refinement results would
be reflecting what's in crystal. This is extensively discussed in
this paper:
<br>
<br>
Interpretation of ensembles created by multiple iterative rebuilding
of macromolecular models. T. C. Terwilliger, R. W. Grosse-Kunstleve,
P. V. Afonine, P. D. Adams, N. W. Moriarty, P. H. Zwart, R. J. Read,
D. Turk and L.-W. Hung Acta Cryst. D63, 597-610 (2007).
<br>
<br>
Some illustrative discussion is here:
<br>
<a class="moz-txt-link-freetext"
href="http://www.phenix-online.org/presentations/latest/pavel_validation.pdf">http://www.phenix-online.org/presentations/latest/pavel_validation.pdf</a>
<br>
<br>
Having said this, it shouldn't be too surprising if you select say
10 different free-r flag sets, then do thorough refinement (to
achieve convergence and remove memory from test reflections), and in
the end you get somewhat different Rwork/Rfree. You can try it to
get some "confidence range" for the spread of Rwork and Rfree. <br>
<br>
You can also do the above experiment with the SA.<br>
<br>
However, apart from academic interest / making yourself confident
about the numbers you get, I don't really see any practical use of
these tests.<br>
<br>
<blockquote
cite="mid:AANLkTi=oFzYvFZQ7TW8BY_Q0E4z7AhRUyWzyfJG0DYm6@mail.gmail.com"
type="cite">
<blockquote type="cite">
<pre wrap="">ML is better than LS because ML better account for model errors and incompleteness taking the latter into account statistically.
</pre>
</blockquote>
<pre wrap="">
Do they come from sigma-A estimation?
</pre>
</blockquote>
<br>
See (and references therein):<br>
<br>
<meta charset="utf-8">
V.Yu., Lunin & T.P., Skovoroda. Acta Cryst. (1995). A51,
880-887. "R-free likelihood-based estimates of errors for phases
calculated from atomic models"<br>
<br>
Pannu, N.S., Murshudov, G.N., Dodson, E.J. & Read, R.J. (1998).
Acta Cryst. D54, 1285-1294. "Incorporation of Prior Phase
Information Strengthens Maximum-Likelihood Structure Refinement"<br>
<br>
V.Y., Lunin, P.V. Afonine & A.G., Urzhumtsev. Acta Cryst.
(2002). A58, 270-282. "Likelihood-based refinement. I. Irremovable
model errors"<br>
<br>
<meta charset="utf-8">
<span class="Apple-style-span" style="border-collapse: separate;
color: rgb(0, 0, 0); font-family: Times; font-style: normal;
font-variant: normal; font-weight: normal; letter-spacing: normal;
line-height: normal; orphans: 2; text-indent: 0px; text-transform:
none; white-space: normal; widows: 2; word-spacing: 0px;
font-size: medium;"><span class="Apple-style-span"
style="font-family: verdana,helvetica,arial,sans-serif;
font-size: 14px;">A.G. Urzhumtsev, T.P. Skovoroda & V.Y.
Lunin. J. Appl. Cryst. (1996). 29, 741-744. "A procedure
compatible with X-PLOR for the calculation of electron-density
maps weighted using an R-free-likelihood approach"</span></span><br>
<br>
R. J. Read. Acta Cryst. (1986). A42, 140-149. "Improved Fourier
coefficients for maps using phases from partial structures with
errors"<br>
<br>
<blockquote
cite="mid:AANLkTi=oFzYvFZQ7TW8BY_Q0E4z7AhRUyWzyfJG0DYm6@mail.gmail.com"
type="cite">
<blockquote type="cite">
<pre wrap="">I do not know what's implemented in Refmac - I'm not aware of a
corresponding publication.
</pre>
</blockquote>
<pre wrap="">
FYI, I think No. 13 of this slide describes the likelihood function in
case of twin..
<a class="moz-txt-link-freetext" href="http://www.ysbl.york.ac.uk/refmac/Presentations/refmac_Osaka.pdf">http://www.ysbl.york.ac.uk/refmac/Presentations/refmac_Osaka.pdf</a>
</pre>
</blockquote>
<br>
I see a lot of handwaiving and jiggling the magic words "maximum"
and "likelihood", but I don't see any details about underlying
statistical model, approximation assumptions (if any), derivation
and mathematical analysis of the new function behavior, etc... I
know all this is beyond the scope of conference slides, so this is
why I said above "I'm not aware of a corresponding publication"
meaning a proper peer-reviewed publication where all these important
details are explained.<br>
<br>
<blockquote
cite="mid:AANLkTi=oFzYvFZQ7TW8BY_Q0E4z7AhRUyWzyfJG0DYm6@mail.gmail.com"
type="cite">
<blockquote type="cite">
<pre wrap="">Typically, when people send us the "reproducer" (all inputs that are enough
to reproduce the problem) then we can work much more efficiently, otherwise
it takes a lot of emails before one can start having a clue about the
problem.
</pre>
</blockquote>
<pre wrap="">
I fully understand it, but I'm sorry I couldn't..
</pre>
</blockquote>
<br>
No problems.<br>
Please let us know if we can be of any help.<br>
<br>
All the best,<br>
Pavel.<br>
<br>
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