Just a note: James Holton said "true B-factor" not "true B-factors".
 I believe he was talking about the overall B not the individual B's.

Dale Tronrud

On 3/8/2020 3:25 PM, Rangana Warshamanage wrote:
> Sorry for not being clear enough.
> If B-factors at the end of refinement are the "true B-factors" then they
> represent a true property of data. They should be good enough to assess
> the model quality directly. This is what I meant by B factor validation.
> However, how far are the final B-factors similar to true B-factors is
> another question.
> 
> Rangana
> 
> 
> On Sun, Mar 8, 2020 at 7:06 PM Ethan A Merritt <merr...@uw.edu
> <mailto:merr...@uw.edu>> wrote:
> 
>     On Sunday, 8 March 2020 01:08:32 PDT Rangana Warshamanage wrote:
>     > "The best estimate we have of the "true" B factor is the model B
>     factors
>     > we get at the end of refinement, once everything is converged,
>     after we
>     > have done all the building we can.  It is this "true B factor"
>     that is a
>     > property of the data, not the model, "
>     >
>     > If this is the case, why can't we use model B factors to validate our
>     > structure? I know some people are skeptical about this approach
>     because B
>     > factors are refinable parameters.
>     >
>     > Rangana
> 
>     It is not clear to me exactly what you are asking.
> 
>     B factors _should_ be validated, precisely because they are refined
>     parameters
>     that are part of your model.   Where have you seen skepticism?
> 
>     Maybe you thinking of the frequent question "should the averaged
>     refined B
>     equal the Wilson B reported by data processing?".  That discussion usual
>     wanders off into explanations of why the Wilson B estimate is or is not
>     reliable, what "average B" actually means, and so on.  For me the bottom
>     line is that comparison of Bavg to the estimated Wilson B is an
>     extremely
>     weak validation test.  There are many better tests for model quality.
> 
>             Ethan
> 
> 
> 
> 
> 
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