Assessing and Maximizing Data Quality in Macromolecular Crystallography

Citeable URL: https://ir.library.oregonstate.edu/concern/articles/x920fz65m

Descriptions

Attribute Name	Values
Creator	Karplus, P. Andrew Diederichs, Kay
Abstract	The quality of macromolecular crystal structures depends, in part, on the quality and quantity of the data used to produce them. Here, we review recent shifts in our understanding of how to use data quality indicators to select a high resolution cutoff that leads to the best model, and of the potential to greatly increase data quality through the merging of multiple measurements from multiple passes of single crystals or from multiple crystals. Key factors supporting this shift are the introduction of more robust correlation coefficient based indicators of the precision of merged data sets as well as the recognition of the substantial useful information present in extensive amounts of data once considered too weak to be of value.
Resource Type	Article
DOI	https://doi.org/10.1016/j.sbi.2015.07.003
Date Available	2016-08-02T19:13:00+00:00
Date Issued	2015-10
Citation	Karplus, P. A., & Diederichs, K. (2015). Assessing and maximizing data quality in macromolecular crystallography. Current Opinion in Structural Biology, 34, 60-68. doi:10.1016/j.sbi.2015.07.003
Journal Title	Current Opinion in Structural Biology
Journal Volume	34
Déclaration de droits	Copyright Not Evaluated
Funding Statement (additional comments about funding)	This work was supported in part by NIH grant GM083136 (to PAK).
Publisher	Elsevier
Peer Reviewed	Yes
Language	English [eng]
Replaces	http://hdl.handle.net/1957/59622