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Hi Stephen,<br>
<br>
the answer - I don't know. Optimizing the weight you can use different
things, like Rfree, divinations from ideal stereochemistry, LLG, or
combination Rfree and divinations from ideal stereochemistry, etc...
Rfree seemed to me the most obvious and easy, but again, I have no
strong feelings or experience what is better (and what "better"
actually means in this context).<br>
<br>
What do you mean by "less stable/reliable" ?<br>
<br>
The procedure that phenix.refine uses is very simple... The overall
target is (for xyz refinement; similar for B-factors):<br>
<br>
Etotal = wxc * wxc_scale * Exray + wc * Egeom<br>
<br>
wxc is determined as in CNS (ratio of gradient norms), wc = 1.0,
wxc_scale is adjustable parameter which is by default set to 0.5 or so.
In most of cases at "normal" resolutions the automatic weight is good.
If not, then you need to either play with wxc_scale manually or have
phenix.refine do it for you automatically by using optimize_wxc. And
what "optimize_wxc=true" does is just a grid search: it tries different
wxc_scale values and chooses the one that produces the lowest Rfree. I
don't see why it could be unstable or not reliable. Obviously, one can
use any other criterion instead of Rfree. <br>
<br>
Cheers,<br>
Pavel.<br>
<br>
<br>
On 2/13/2008 3:25 AM, Stephen Graham wrote:
<blockquote
cite="mid:cdd145f50802130325y6f04d6edo36b1302abd6d0992@mail.gmail.com"
type="cite">
<pre wrap="">Hi Pavel,
</pre>
<blockquote type="cite">
<pre wrap="">4) Also, clashes related concerns may be a result of inoptimal weight
between Xray target and restrains. Although one can play with the weight
adjusting scales manually by trying different values of "wxc_scale"
parameter ("wxu_scale" is analog for b-factors refinement) the new
version of phenix.refine has an automatic procedure for finding the best
weight (here "the best" means the weight that leads to the lowest Rfree)
(Carsten's suggestion). To use this option:
% phenix.refine model.pdb data.mtz your_parameters.par optimize_wxc=true
</pre>
</blockquote>
<pre wrap=""><!---->
Wouldn't it be better to try and get the highest Free Log Likelihood
Gain (LLG) rather than the lowest Rfree? I would worry that trying to
optimise Rfree rather than LLG or even a free Rxpct (a-la BUSTER)
would be less stable/reliable.
Cheers,
Stephen
</pre>
</blockquote>
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