[phenixbb] phenix.refine questions: weights, docs, etc.
dale at uoxray.uoregon.edu
Tue May 29 11:57:18 PDT 2007
Pavel Afonine wrote:
> thanks for your questions!
>> 1) What's the reason for refining bss, xyz and adp in separate cycles? CNS
>> does that too while refmac (I believe) refines them simultaneously. I
>> suspect I missed something, but I thought the power of ML refinement is that
>> no refinable parameters are held constant if they're not supposed to be.
>> (At least, wasn't that the big deal in Sharp when it came out?)
> It is common practice to refine bulk solvent and scale, coordinates,
> ADPs, an other parameters separately. There are numerical issues behind
> this (see for example: Acta Cryst. (1978). A34, 791-809; /Acta Cryst./
> (2005). D*61*, 850-855; ...). CNS does exactly the same: it refines this
> parameters separately. To my knowledge, same for REFMAC. This is not
> target specific. The power of ML refinement is that ML target
> statistically models missing scatterers in a model and and errors (/Acta
> Cryst./ (2002). A*58*, 270-282).
The sets of parameters that can be refined simultaneously is a result of the
choice of optimization method, not the choice of the function being optimized.
The crucial difference is how the second derivatives are handled. In the
methods used in Phenix and CNS the seconds derivatives of all parameters are
assumed to be equal and uncorrelated. To ensure that this assumption is true
only parameters of the same category can be varied in a single cycle. This
means that the coordinates can be varied, but the B factors and scale factors
have to be held fixed. When the B factors are varied the coordinates and
scale factors must be constant. When the scale factors are varied everything
else must be fixed.
Other refinement programs use second derivatives in a more explicit way
than Phenix and do vary more types of parameters in a single cycle. Shelxd is
the most powerful and, by default, varies all parameters of all classes each
cycle. Refmac, to the best of my knowledge, refines both coordinates and ADPs
together, but does refine TLS parameters in a separate step.
Since all the parameters of our models are correlated with one another, it
is better to refine as many of them at once as possible. Implementing
the join refinement of all these kinds of parameters is difficult, so to save
programmers' time approximations are sometimes made.
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