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<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D">Dear Crystallographers,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D"><o:p>&nbsp;</o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D">In following the thread on Rfree reliability, and in the context of a structure I am currently polishing, I have started to wonder what is the ideal way to
 weight geometry versus R values. I had always thought the rationalization for using geometric restraints was actually to help R values to decrease eventually, since the truth about one’s protein structure is much more likely to agree with the thousands of
 molecules already solved than with the particularities of the model one has built or the reflection data under consideration. What happens, though, when one’s R values are, say, 2% or even 5% lower with less weight on geometry? It seems to me that this is
 a gray area, and it would be great to have some sort of general rule.<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D"><o:p>&nbsp;</o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D">I know that Phenix has a weight-optimization protocol which uses parallel runs with different weights, but I am curious what method is used to decide which
 one was best?<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D"><o:p>&nbsp;</o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D">There may not, however, be such a general rule, since I suppose this question is one we always encounter in science. For example, in fitting a few data points,
 when does one abandon the use of a linear model in favor of something more sophisticated? Or maybe, indeed, someone *<b>has</b>* discovered some reasonable way of approaching this?<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D"><o:p>&nbsp;</o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D">All the best,<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D"><o:p>&nbsp;</o:p></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:&quot;Calibri&quot;,&quot;sans-serif&quot;;color:#1F497D">Jacob Keller<o:p></o:p></span></p>
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<p class="MsoNormal"><o:p>&nbsp;</o:p></p>
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