Better models by discarding data?

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

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.

MeSH terms

  • Algorithms*
  • Crystallography, X-Ray*
  • Cysteine Dioxygenase / chemistry*
  • Data Interpretation, Statistical*
  • Image Interpretation, Computer-Assisted*
  • Models, Molecular
  • Quality Indicators, Health Care*
  • Research Design*
  • Software

Substances

  • Cysteine Dioxygenase