On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets
Abstract
Abstract In 2012, Karplus and Diederichs demonstrated that the Pearson correlation coefficient CC 1/2 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 CC 1/2 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 CC 1/2 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 CC 1/2 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.
- Authors:
-
- Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520‐8114
- Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520‐8114, Department of Chemistry Yale University New Haven Connecticut 06520‐8107
- Department of Chemistry Yale University New Haven Connecticut 06520‐8107
- Publication Date:
- Sponsoring Org.:
- USDOE
- OSTI Identifier:
- 1405191
- Grant/Contract Number:
- DESC0001423; DE‐FG0205ER15646
- Resource Type:
- Publisher's Accepted Manuscript
- Journal Name:
- Protein Science
- Additional Journal Information:
- Journal Name: Protein Science Journal Volume: 26 Journal Issue: 12; Journal ID: ISSN 0961-8368
- Publisher:
- Wiley Blackwell (John Wiley & Sons)
- Country of Publication:
- United Kingdom
- Language:
- English
Citation Formats
Wang, Jimin, Brudvig, Gary W., Batista, Victor S., and Moore, Peter B. On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets. United Kingdom: N. p., 2017.
Web. doi:10.1002/pro.3314.
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. United Kingdom. https://doi.org/10.1002/pro.3314
Wang, Jimin, Brudvig, Gary W., Batista, Victor S., and Moore, Peter B. Fri .
"On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets". United Kingdom. https://doi.org/10.1002/pro.3314.
@article{osti_1405191,
title = {On the relationship between cumulative correlation coefficients and the quality of crystallographic data sets},
author = {Wang, Jimin and Brudvig, Gary W. and Batista, Victor S. and Moore, Peter B.},
abstractNote = {Abstract In 2012, Karplus and Diederichs demonstrated that the Pearson correlation coefficient CC 1/2 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 CC 1/2 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 CC 1/2 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 CC 1/2 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.},
doi = {10.1002/pro.3314},
journal = {Protein Science},
number = 12,
volume = 26,
place = {United Kingdom},
year = {Fri Oct 27 00:00:00 EDT 2017},
month = {Fri Oct 27 00:00:00 EDT 2017}
}
https://doi.org/10.1002/pro.3314
Web of Science
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