Well, in the example I made the individual atomic B factors, the Wilson
B, and "true B-factor" were all the same thing. Keeps it simple.
But to be clear, yes: the B factors at the end of refinement are not the
"true B-factors", they are our best estimation of them. Given that no
protein model has ever explained the data to within experimental error,
there is clearly a difference between these two things.
I suppose suspicions of B factors arise historically because of how much
they have been abused. In all our efforts to fit Gaussian pegs into
weird-shaped holes we have done a lot of crazy things with B factors.
Observations/parameters and all that. You can usually tell something is
amiss when you see physically unreasonable things, like covalently
bonded neighbors having drastically different Bs. And of course, like
all good validation methods these eventually turned into restraints.
Thru-bond and thru-space B factor restraints are the default now.
That said, I do expect that the "average" B factor is accurate for most
structures. This is because even a small error in overall B is easily
corrected in scaling. The distribution of atomic B factors, however, is
harder. The connection to the Wilson B depends on which part of the
Wilson plot you look at. Any atoms with very large B factors (such as
the waters in the bulk solvent) will not contribute significantly to
high-angle structure factors. So, if you are looking at the slope of
the Wilson plot at high (-ish) angle you will not see anything coming
from these atoms. Similar is true for disordered side chains, etc.
A common way a refined B factor can go wrong is if the atom in question
really should be split into two conformers. In those cases the refined
B factor will be too high, and too anisotropic. You can also fill in
little bits of noise or systematic error with low-occupancy water atoms
that have B factors so sharp as to only contribute to a few map grid
points. These are probably not real. On the other hand, anything built
into a wrong place or just general nothingness will often refine to a
very large B factor.
So yes, all these are caveats in the model-vs-data difference. However,
if you have a data set that extends to 1.8 A resolution those outermost
spots will be due to the atoms in the structure with the lowest "true" B
factors. Atoms with high B factors don't really play a role in
determining the outer resolution limit.
-James Holton
MAD Scientist
On 3/9/2020 1:07 AM, Dale Tronrud wrote:
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 <[email protected]
<mailto:[email protected]>> 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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