[phenixbb] model vs Wilson b-factor

[MARTYN SYMMONS]

Perhaps worth pointing out that that Wilson B is the based on the assumption of randomly distributed atoms. This is not at all how proteins are, and in particular secondary structures give a preponderance of spacings in the 4 angstrom-ish region and a peak of mean intensity in these shells. For this reason the apparent fall-off in the Wilson in this resolution range is steeper as you are falling down off of the peak due to this seondary structure giving favoured spacings that produce a deviation from randomness in this resolution range. So it will be dependent on the secondary structure of the an individual protein . So the Wilson gets about right when you deal with spacings that tend to be unbiased by secondary structure - which unfortunately is the bit that is missing in the low resolution crystal data. Wilson fall off in low resolution looks steep because the random assumption is invalid. 

Maybe you can guess the secondary content of your protein from where the bump is in the Wilson plot - beta gives a bulge in the 4 ang region - alpha in the 5 to 9 ang region. 

all the best
               Martyn 

Martyn Symmons
Cambridge




----- Original Message ----
From: Pavel Afonine <PAfonine at lbl.gov>
To: Gino.Cingolani at jefferson.edu; PHENIX user mailing list <phenixbb at phenix-online.org>
Sent: Wednesday, 21 April, 2010 18:56:47
Subject: Re: [phenixbb] model vs Wilson b-factor

Hi Gino,

here are a few points:

- my understanding (please correct me if I'm wrong) is that the accuracy of Wilson B estimate drops with the resolution: lower the resolution, less accurate is the estimate;

- Wilson B is not a given calculated value - it's just an estimate;

- the total atomic B-factor includes the trace of overall anisotropic scale matrix (see Fmodel formula for the total model structure factor: Fmodel = scale_overall * exp(-h*U_overall*ht) * (Fcalc + k_sol * exp(-B_sol*s^2) * Fmask) ). You can try to disable this and see if this was the cause (use "apply_back_trace_of_b_cart=true" keyword for this).

- the things you "tried to resolve this discrepancy" will unlikely to change the average B-factor;

- assuming that you used the proper model parameterization and refinement strategy given your model and data quality,  I would just accept these values as a matter of fact.

Pavel.

> we've solved a large structure (~20,000 residues/asymm unit), with 4-fold ncs and diffraction data to 3.3A.
> 
> The Rfree/Rfac is ~28%-24% with OK geometry with no major outliers in the Ramachandran plot.
> I would think I'm done (.. after 6 years!). 
> However, my refined model b-factor (~130A2) is >> Wilson b-factor (~80A2). Obviously I'm not too happy with it.
> 
> Here is what I tried to resolve this discrepancy: --> play with wxu_scale --> play with B-factor weight in ncs restraint (4-fold ncs)
> --> play with number of macrocycles --> Redefine tls groups
> 
> So far nothing really works, except switching from
> individual_adp to group_adp. However, this increases my Rfree by almost 3%.
> 
> Any ideas?
>  
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