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Linking crystallographic model and data quality.

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.

SUBMITTER: Karplus PA

PROVIDER: S-EPMC3457925 | biostudies-literature | 2012 May

REPOSITORIES: biostudies-literature

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Linking crystallographic model and data quality.

Karplus P Andrew PA Diederichs Kay K

Science (New York, N.Y.) 20120501 6084

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 correla ...[more]

PMID: 22628654

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Linking crystallographic model and data quality.

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Linking crystallographic model and data quality.

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