[phenixbb] Geometry Restraints - Anisotropic truncation

Dale Tronrud det102 at uoxray.uoregon.edu
Thu May 3 10:05:23 PDT 2012

   The fact that the R value stats get better when you toss out data is
NOT an indication that those data contain no signal.  It simply indicates
that that subset has a lower signal/noise than the remaining data.  If
you decide to throw away all data with less than average signal to noise
you will get better and better R values until you have no data left at

   Tests along the line of what Tom has recommended are in the right
direction, but they have already been done.  I have unpublished work
where I took a project with a 1.25A data set, as judged by I/sigI > 2
and near 100% completeness and tested the addition of higher resolution
data out to 1.1A with very poor stats on both counts.  I found that
the Rfree calculated only to 1.25A improved by adding the noisy data,
and the esd's (I was using shelxl) dropped indicating that the model
was more precise.  I performed the appropriate control to show that
you couldn't just add any numbers out there, you had to use the measured
numbers to get the improvements.

   At the CCP4 meeting in January Kay Diederichs reported on work he has
done with P. A. Karplus which was much more rigorous.  They show that
a lot of data beyond the usual cut-off limits is useful to improving
the final model by several measures and they have developed a tool for
determining, on an objective level, at what resolution there is no longer
signal.  That resolution limit was found to be much higher than what we
used to and our final R values will be higher as a consequence.  But the
models that result are better when assessed by properly controlled tests.
This work will be in print shortly.

   An important point is that the Fc's must never be used to judge the
quality of the Fo's in a production environment.  At the very least you
have to recognize that you don't have reliable Fc's at the start of
refinement and yet you need to decide what data to use.  If all you are
doing is changing your resolution limit after refinement to "clean up
your stats" you are wasting your time.  That sort of thing has nothing
to do with building better models.  The Diederichs and Karplus test
looks directly at the F^2s in the unmerged data to see what signal is

   None of this says anything about the merits of spherical verses
elliptical cutoff surfaces.  These tests only discuss the radius of
whatever surface you choose.  It seems to me if the signal/noise ratio
drops off faster in some directions than others that the point where
there is no signal will differ too.  Whatever those elliptical cutoff
limits are, they should be much more generous than current practice
and not determined by looking at R values.

Dale Tronrud

On 05/03/12 08:24, Terwilliger, Thomas C wrote:
> Hi Kendall,
> Yes, I think you could use this kind of approach to make overall
> decisions of any kind, including those you suggest. I would not use
> Rsleep for anything at all, other than calculating a final number.   I
> would use a fixed Rfree set (which could be a subset of the total free
> set or the whole set) for all such decision making. If a lot of such
> decisions are made with Rfree...then yes it would be good to have an
> Rsleep to make sure that everything is ok.
> All the best,
> Tom T
> ------------------------------------------------------------------------
> *From:* Kendall Nettles [knettles at scripps.edu]
> *Sent:* Thursday, May 03, 2012 9:05 AM
> *To:* Terwilliger, Thomas C; PHENIX user mailing list
> *Subject:* Fwd: [phenixbb] Geometry Restraints - Anisotropic truncation
> Hi Tom, 
> Do you think something like this could be used during refinement to
> identify the "best" resolution limits? If you have an Rsleep set would
> Rfree be sufficient for this? I imagine  collecting data with a ring of
> noise and then let the optimal resolution be determined during
> refinement. My understanding of this is that the modern refinement
> algorithms can handle some noise in the reflections, but maybe this
> could be a way to optimize how much signal is needed to contribute in a
> positive fashion? 
> Kendall
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