Hi Kendall,

removing same amount of data randomly gives Rwork/Rfree ~ 30/35%.

Pavel

On 5/3/12 4:13 AM, Kendall Nettles wrote:

Hi Pavel,

What happens if you throw out that many reflections that have signal? Can you take out a random set of the same size?

Best,

Kendall

On May 3, 2012, at 2:41 AM, "Pavel Afonine"<[email protected]>  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"<[email protected]>   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<[email protected]>    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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