title: Better models by discarding data?
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.
authors.affiliation: Faculty of Biology, University of Konstanz, M647, 78457 Konstanz, Germany. kay.diederichs@uni-konstanz.de
authors.forename: K
authors.initials: K
authors.lastname: Diederichs
authors.forename: P A
authors.initials: PA
authors.lastname: Karplus
mesh_terms: D000465:Algorithms; D018360:Crystallography, X-Ray; D050558:Cysteine Dioxygenase / Q000737:chemistry*; D003627:Data Interpretation, Statistical; D007090:Image Interpretation, Computer-Assisted; D008958:Models, Molecular; D019984:Quality Indicators, Health Care; D012107:Research Design; D012984:Software
chemical_list: D050558:Cysteine Dioxygenase
keywords: R value; correlation coefficient; data quality; model quality; outlier rejection