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Linking Crystallographic Model and Data Quality

Published on May 25, 2012in Science41.063
· DOI :10.1126/science.1218231
P. Andrew Karplus49
Estimated H-index: 49
(OSU: Oregon State University),
Kay Diederichs55
Estimated H-index: 55
(University of Konstanz)
Sources
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.
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