[phenixbb] Geometry Restraints - Anisotropic truncation

Frank von Delft frank.vondelft at sgc.ox.ac.uk
Thu May 3 00:15:40 PDT 2012

Is the explanation not simpler?  The volumes of reciprocal space that 
were left out did not in fact contain signal, and it's by removing those 
noise non-reflections that the actual R is revealed.  As James Holton 
posted a while ago, Rfactors calculated for noise give randomly large 

So it seems less less misleading to refer to it as "anisotropy-TRUNCATED".


On 03/05/2012 07:40, Pavel Afonine wrote:
> Hi Kendall,
> I just did this quick test: calculated R-factors using original and 
> anisotropy-corrected Mike Sawaya's data (*)
> Original:
>     r_work : 0.3026
>     r_free : 0.3591
>     number of reflections: 26944
> Truncated:
>     r_work : 0.2640
>     r_free : 0.3178
>     number of reflections: 18176
> The difference in R-factors is not too surprising given how many 
> reflections was removed (about 33%).
> Pavel
> (*) Note, the data available in PDB is anisotropy corrected. The 
> original data set was kindly provided to me by the author.
> On 5/2/12 5:25 AM, Kendall Nettles wrote:
>>   I didnt think the structure was publishable with Rfree of 33%  
>> because I was expecting the reviewers to complain.
>> We have tested a number of data sets on the UCLA server and it 
>> usually doesn't make much difference. I wouldn't expect truncation 
>> alone to change Rfree by 5%, and it usually doesn't. The two times I 
>> have seen dramatic impacts on the maps ( and Rfree ), the highly 
>> anisotrophic sets showed strong waves of difference density as well, 
>> which was fixed by throwing out the noise. We have moved to using 
>> loose data cutoffs for most structures, but I do think anisotropic 
>> truncation can be helpful in rare cases.
>> Kendall
>> On May 1, 2012, at 3:07 PM, "Dale 
>> Tronrud"<det102 at uoxray.uoregon.edu>  wrote:
>>>     While philosophically I see no difference between a spherical 
>>> resolution
>>> cutoff and an elliptical one, a drop in the free R can't be the 
>>> justification
>>> for the switch.  A model cannot be made more "publishable" simply by 
>>> discarding
>>> data.
>>>     We have a whole bunch of empirical guides for judging the 
>>> quality of this
>>> and that in our field.  We determine the resolution limit of a data 
>>> set (and
>>> imposing a "limit" is another empirical choice made) based on Rmrg, 
>>> or Rmes,
>>> or Rpim getting too big or I/sigI getting too small and there is no 
>>> agreement
>>> on how "too big/small" is too "too big/small".
>>>     We then have other empirical guides for judging the quality of 
>>> the models
>>> we produce (e.g. Rwork, Rfree, rmsds of various sorts).  Most people 
>>> seem to
>>> recognize that the these criteria need to be applied differently for 
>>> different
>>> resolutions. A lower resolution model is allowed a higher Rfree, for 
>>> example.
>>>     Isn't is also true that a model refined to data with a cutoff of 
>>> I/sigI of
>>> 1 would be expected to have a free R higher than a model refined to 
>>> data with
>>> a cutoff of 2?  Surely we cannot say that the decrease in free R 
>>> that results
>>> from changing the cutoff criteria from 1 to 2 reflects an improved 
>>> model.  It
>>> is the same model after all.
>>>     Sometimes this shifting application of empirical criteria 
>>> enhances the
>>> adoption of new technology.  Certainly the TLS parametrization of 
>>> atomic
>>> motion has been widely accepted because it results in lower working 
>>> and free
>>> Rs.  I've seen it knock 3 to 5 percent off, and while that certainly 
>>> means
>>> that the model fits the data better, I'm not sure that the quality 
>>> of the
>>> hydrogen bond distances, van der Waals distances, or maps are any 
>>> better.
>>> The latter details are what I really look for in a model.
>>>     On the other hand, there has been good evidence through the 
>>> years that
>>> there is useful information in the data beyond an I/sigI of 2 or an
>>> Rmeas>  100% but getting people to use this data has been a hard 
>>> slog.  The
>>> reason for this reluctance is that the R values of the resulting models
>>> are higher.  Of course they are higher!  That does not mean the models
>>> are of poorer quality, only that data with lower signal/noise has been
>>> used that was discarded in the models you used to develop your "gut 
>>> feeling"
>>> for the meaning of R.
>>>     When you change your criteria for selecting data you have to 
>>> discard
>>> your old notions about the acceptable values of empirical quality 
>>> measures.
>>> You either have to normalize your measure, as Phil Jeffrey 
>>> recommends, by
>>> ensuring that you calculate your R's with the same reflections, or by
>>> making objective measures of map quality.
>>> Dale Tronrud
>>> P.S. It is entirely possible that refining a model to a very optimistic
>>> resolution cutoff and calculating the map to a lower resolution 
>>> might be
>>> better than throwing out the data altogether.
>>> On 5/1/2012 10:34 AM, Kendall Nettles wrote:
>>>> I have seen dramatic improvements in maps and behavior during 
>>>> refinement following use of the UCLA anisotropy server in two 
>>>> different cases. For one of them the Rfree went from 33% to 28%. I 
>>>> don't think it would have been publishable otherwise.
>>>> Kendall
>>>> On May 1, 2012, at 11:10 AM, Bryan Lepore wrote:
>>>>> On Mon, Apr 30, 2012 at 4:22 AM, Phil 
>>>>> Evans<pre at mrc-lmb.cam.ac.uk>   wrote:
>>>>>> Are anisotropic cutoff desirable?
>>>>> is there a peer-reviewed publication - perhaps from Acta
>>>>> Crystallographica - which describes precisely why scaling or
>>>>> refinement programs are inadequate to ameliorate the problem of
>>>>> anisotropy, and argues why the method applied in Strong, et. al. 2006
>>>>> satisfies this need?
>>>>> -Bryan
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