Re: [ccp4bb]: A simple question of resolution

[Bernard Santarsiero <bds@uic.edu>]

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First of all, don't ever look at the R's to decide where to cut off the
data.  Some kind of goodness-of-fit is a better measure to test agreement
within a data set.  R's are more a function of the magnitude of the F's
at high resolution.  If you have large F's, then the R's will be small.  

I process the data to at least I/sig of 1.0, and then look at the
completeness at the highest resolution shell.  If you use some kind of
strategy program, you should be able to get good completeness.  The main
refinement programs, like CNS, use a 1 or 2 sigma cutoff for refinement.
So if I process the data to 1sig, then I refine to 1sig. 

I also don't monkey around with the weights when processing the data.  
Trying to make the chi**2's all 1 just assumes that random errors are
the dominating errors.  I think statistical errors are at least as large
as the random errors, and have an unknown distribution.  It's probably not
Gaussian, so you can't make the chi**2's 1.  We've collected small
molecule data on in-house detectors and at the beamline, and have some
idea of what the true weights (and weight contributors) are.

The preferred method would be to refine with F**2 and use all of the data.  
Then you don't need a sigma cutoff, and you could use real weights.
That's done with SHELX and works well with high-resolution, 1.2A or
better, data.  

So my simple answer is collect and process a complete data set to 1sigma
and use that.  If you can't get it very complete, then maybe something
like 50% complete in the highest resolution shell is reasonable.  Don't
use the R's to decide where to cut off the data, or where to cut off the
resolution.  


Bernie Santarsiero
 
Research Associate Professor 
The Center for Pharmaceutical Biotechnology and the
Department of Medicinal Chemistry and Pharmacognosy
University of Illinois at Chicago
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