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Acta Crystallogr D Biol Crystallogr. 2013 Jul;69(Pt 7):1215-22. doi: 10.1107/S0907444913001121. Epub 2013 Jun 15.

Better models by discarding data?

Author information

1
Faculty of Biology, University of Konstanz, M647, 78457 Konstanz, Germany. kay.diederichs@uni-konstanz.de

Abstract

In macromolecular X-ray crystallography, typical data sets have substantial multiplicity. This can be used to calculate the consistency of repeated measurements and thereby assess data quality. Recently, the properties of a correlation coefficient, CC1/2, that can be used for this purpose were characterized and it was shown that CC1/2 has superior properties compared with `merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC1/2 and the more conventional indicators were compared in two situations of practical importance: merging data sets from different crystals and selectively rejecting weak observations or (merged) unique reflections from a data set. In these situations controlled `paired-refinement' tests show that even though discarding the weaker data leads to improvements in the merging R values, the refined models based on these data are of lower quality. These results show the folly of such data-filtering practices aimed at improving the merging R values. Interestingly, in all of these tests CC1/2 is the one data-quality indicator for which the behaviour accurately reflects which of the alternative data-handling strategies results in the best-quality refined model. Its properties in the presence of systematic error are documented and discussed.

KEYWORDS:

R value; correlation coefficient; data quality; model quality; outlier rejection

PMID:
23793147
PMCID:
PMC3689524
DOI:
10.1107/S0907444913001121
[Indexed for MEDLINE]
Free PMC Article

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