On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets

Jimin Wang, Gary W Brudvig, Victor S. Batista, Peter B. Moore

Research output: Contribution to journalArticle

Abstract

In 2012, Karplus and Diederichs demonstrated that the Pearson correlation coefficient CC1/2 is a far better indicator of the quality and resolution of crystallographic data sets than more traditional measures like merging R-factor or signal-to-noise ratio. More specifically, they proposed that CC1/2 be computed for data sets in thin shells of increasing resolution so that the resolution dependence of that quantity can be examined. Recently, however, the CC1/2 values of entire data sets, i.e., cumulative correlation coefficients, have been used as a measure of data quality. Here, we show that the difference in cumulative CC1/2 value between a data set that has been accurately measured and a data set that has not is likely to be small. Furthermore, structures obtained by molecular replacement from poorly measured data sets are likely to suffer from extreme model bias.

Original languageEnglish
Pages (from-to)2410-2416
Number of pages7
JournalProtein Science
Volume26
Issue number12
DOIs
Publication statusPublished - Dec 1 2017

Fingerprint

R388
Merging
Signal to noise ratio
Signal-To-Noise Ratio
Molecular Structure
Datasets

Keywords

  • CC1/2
  • cumulative correlation coefficients
  • femtosecond serial crystallography
  • model bias
  • photosystem II
  • PSII
  • X-ray free-electron laser

ASJC Scopus subject areas

  • Biochemistry
  • Molecular Biology

Cite this

On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets. / Wang, Jimin; Brudvig, Gary W; Batista, Victor S.; Moore, Peter B.

In: Protein Science, Vol. 26, No. 12, 01.12.2017, p. 2410-2416.

Research output: Contribution to journalArticle

Wang, Jimin ; Brudvig, Gary W ; Batista, Victor S. ; Moore, Peter B. / On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets. In: Protein Science. 2017 ; Vol. 26, No. 12. pp. 2410-2416.
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