Linking crystallographic model and data quality

Science. 2012 May 25;336(6084):1030-3. doi: 10.1126/science.1218231.

Abstract

In macromolecular x-ray crystallography, refinement R values measure the agreement between observed and calculated data. Analogously, R(merge) values reporting on the agreement between multiple measurements of a given reflection are used to assess data quality. Here, we show that despite their widespread use, R(merge) values are poorly suited for determining the high-resolution limit and that current standard protocols discard much useful data. We introduce a statistic that estimates the correlation of an observed data set with the underlying (not measurable) true signal; this quantity, CC*, provides a single statistically valid guide for deciding which data are useful. CC* also can be used to assess model and data quality on the same scale, and this reveals when data quality is limiting model improvement.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Crystallography, X-Ray*
  • Cysteine Dioxygenase / chemistry*
  • Data Interpretation, Statistical
  • Models, Molecular*
  • Protein Conformation*
  • Proteins / chemistry*
  • Research Design

Substances

  • Proteins
  • Cysteine Dioxygenase