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Better models by discarding data?

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  • 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, CC[subscript 1/2], that can be used for this purpose were characterized and it was shown that CC[subscript 1/2] has superior properties compared with 'merging' R values. A derived quantity, CC*, links data and model quality. Using experimental data sets, the behaviour of CC[subscript 1/2] 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 CC[subscript 1/2] 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.
  • This is the publisher’s final pdf. The published article is copyrighted by the International Union of Crystallography and Wiley-Blackwell and can be found at: http://www.iucr.org/ and http://www.wiley.com/WileyCDA/Brand/id-35.html.
  • Keywords: Refinement, Crystallography, Cryseine dioxygenase, Data Quality
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  • Diederichs, K., & Karplus, P. (2013). Better models by discarding data? Acta Crystallographica Section d-Biological Crystallography, 69, 1215-1222. doi:10.1107/S0907444913001121
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  • 69
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