[phenixbb] phenix and weak data
pafonine at lbl.gov
Thu Dec 6 18:19:31 PST 2012
that's the point: there are many hand-waving arguments supported with no
or weak inconclusive data, and little rock-sold evidence!
I'm not aware of a paper *clearly* demonstrating what kind of
improvement using weak data in refinement brings? I mean not an R-factor
improvement by a fraction of a percent or "cosmetics" like this, but a
case where it is demonstrated that using it allowed more model to be
built, or showing two maps side-by-side obtained without and with weak
data where the latter would significantly be more useful (not just
appears more pleasant after tweaking contouring thresholds to show the
Regarding refinement itself: consider rigid-body refinement. One may
think that with today's technology you would just dump all the data into
refinement machinery and Maximum-likelihood target would do the trick
(weight high-res data properly). No. For rigid-body refinement to
actually work you still need to cut the high resolution end. See
Automatic multiple-zone rigid-body refinement with a large convergence
radius. P. V. Afonine, R. W. Grosse-Kunstleve, A. Urzhumtsev and P. D.
Adams J. Appl. Cryst. 42, 607-615 (2009)).
Same logic might be applicable with weak data. Its amount and weakness
may be just sufficient to make refinement target profile complex enough
to stuck refinement or impede its convergence. On the other hand it may
be just good enough to make refinement behave better and yield better
model. Using it may harm refinement at the beginning but may help
towards the end (remember arguments behind STIR option in SHELX?!), so
the question may not be just "whether or not?", but also "when?".
Maps that are mostly used (2mFo-DFc and mFo-DFc) are calculated without
using experimental sigmas, unless you modify them using techniques such
as maximization of entropy or so, where sigmas may be used somehow (but
don't have to, though). So even if one weights weak data smartly for
refinement and uses it the right moment, one still need to think about
how to use it in map calculation so it brings good rather than noise
All in all, yes, *conceptually* it is good to use weak data in
refinement and map calculation, but two questions - 1) how and when? and
2) whether it's going to change anything significantly? - are yet to
answer. It's in todo list to answer these questions.
Finally, FYI: refinement targets that phenix.refine uses are described
here (they are coded exactly as discussed in these papers):
Pannu, N.S., Murshudov, G.N., Dodson, E.J. & Read, R.J. (1998). Acta
Cryst. D54, 1285-1294. "Incorporation of Prior Phase Information
Strengthens Maximum-Likelihood Structure Refinement"
V.Y., Lunin, P.V. Afonine & A.G., Urzhumtsev. Acta Cryst. (2002). A58,
270-282. "Likelihood-based refinement. I. Irremovable model errors"
Flavor of LS and accounting for scales in ML and MLHL functions:
P.V. Afonine, R.W. Grosse-Kunstleve & P.D. Adams. Acta Cryst. (2005).
D61, 850-855. "A robust bulk-solvent correction and anisotropic scaling
and they work fine in phenix.refine since 2004.
All the best,
On 12/6/12 2:35 PM, Douglas Theobald wrote:
> Hi all,
> Many have argued that we should include weak data in refinement --- e.g., reflections much weaker than I/sigI=2 --- in order to take advantage of the useful information found in large numbers of uncertain data points (like argued in the recent Karplus and Diederichs Science paper on CC1/2). This makes sense to me as long as the uncertainty attached to each HKL is properly accounted for. However, I was surprised to hear rumors that with phenix "the data are not properly weighted in refinement by incorporating observed sigmas" and such. I was wondering if the phenix developers could comment on the sanity of including weak data in phenix refinement, and on how phenix handles it.
> Douglas L. Theobald
> Assistant Professor
> Department of Biochemistry
> Brandeis University
> Waltham, MA 02454-9110
> dtheobald at brandeis.edu
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