Bio Chemistry

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ASSESSING AND MAXIMIZING DATA QUALITY IN MACROMOLECULAR CRYSTALLOGRAPHY

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.

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PBM_kLfqJ

Reference

In Table 1, we list and comment on the utility of eight common statistical indicators reported by current data reduction software, including the new CC1/2 and CC* ([5]). The equations for each are in the literature and are not given here. These indicators all report on data precision, so if substantial systematic errors are present the indicators need not reflect the data accuracy [4]. We have arranged the data precision indicators into three groups according our view of their utility, and we also specify for each one the crucial distinction of whether it reports on the precision of individual or of merged measurements (Table 1).

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