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Hi Sam,<br>
<br>
<blockquote
cite="mid:s2x453186701004011348p84e4edfep9394a0445c7137ac@mail.gmail.com"
type="cite">
<div class="gmail_quote">
<div>When the spread between R-work and R-free widens, this means
that you've optimized R-work at the expense of R-free, i.e. you're
"overfitting". If they're too close together this can indicate that
you've somehow biased your refinement (by improper assignment of R-free
flags in the presence of NCS, accidentally switching R-free sets in the
middle of refinement, or using a very similar MR search model refined
against a different set are the usual reasons). </div>
</div>
</blockquote>
<br>
I would also add to the above list: "improper assignment of R-free
flags in the presence of twinning".<br>
<br>
<blockquote
cite="mid:s2x453186701004011348p84e4edfep9394a0445c7137ac@mail.gmail.com"
type="cite">
<div class="gmail_quote">
<div> Therefore, if a refinement raises R-work and lowers R-free,
this is almost always a good thing.</div>
</div>
</blockquote>
<br>
I think it is important "by how much" it lowers or/and rises. Say you
have a 2.5A resolution data: I would probably still prefer Rwork/Rfree
~ 20/26% over 23/25%, since the 20/26% result would more likely to give
a better map at the cost of "insignificant" Rfree fluctuation. <br>
<br>
<blockquote
cite="mid:s2x453186701004011348p84e4edfep9394a0445c7137ac@mail.gmail.com"
type="cite">
<div class="gmail_quote">
<div> Anything that raises R-free relative to the starting value is
bad.</div>
</div>
</blockquote>
<br>
A possible exception is when you run refinement for the first time (not
in your life, but given a model and data). Then Rwork ~ Rfree at the
start, and they diverge as refinement progresses (may both go down, one
faster than the other, or Rwork may drop and Rfree may increase).
Again, what's important here is "by how much" they drop or rise.<br>
<br>
<blockquote
cite="mid:s2x453186701004011348p84e4edfep9394a0445c7137ac@mail.gmail.com"
type="cite">
<div class="gmail_quote">
<blockquote class="gmail_quote"
style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
<p>2. How can I judge the output from my
refinement? I have looked at Rwork, Rfree, and the molprobity
clashscore and overall score values. I included them below, at the
end of this email. How do I tell which the best refinement is? Which
one would you suggest? I thought the best was wxc = 0.1 since
the R-work and Rfree aren't changed much from the start values but
the geometry is far better.</p>
</blockquote>
<div>The first one in the list, with wxc=0.01, R=0.2104,
R-free=0.2378 is definitely the best, because all of the statistics
that matter are much better than in any other refinement.</div>
<div><br>
</div>
<div>PS. Use POLYGON in the GUI to get a better idea of how good
these statistics are relative to other structures.</div>
</div>
</blockquote>
<br>
Additionally, look at the local model-to-density fit quality: map
correlation reported for per residue (for lower resolutions) or per
atom (at higher resolution). It is available in PHENIX GUI and from the
command line.<br>
<br>
Also, the values 0.2377, 0.2388, 0.2399 ... look all the same to me. If
you run 100 identical refinement runs where in each refinement the only
difference is the random seed, you will get an ensemble of refined
structures and the Rfree/Rwork spread can be as large as 1-2% or so (it
depends on the resolution). This is because the random seed is involved
in target weights calculation and therefore a small change in the
random seed may slightly change the weights and this may be enough for
the refinement to take another route to another "local minimum".<br>
<br>
Pavel.<br>
<br>
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