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Assessing and Maximizing Data Quality in Macromolecular Crystallography


Assessing and Maximizing Data Quality in Macromolecular Crystallography

P Andrew Karplus et al. Curr Opin Struct Biol.


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.


Figure 1
Figure 1
Averaging multiple measurements can substantially enhance data quality. (a) CCanom is plotted as a function of resolution for a data set of 1080 18 images in a sulfur-SAD phasing case study [23]. Statistics for data merged from 30 (blue), 120 (cyan), 360 (green), 720 (orange), and 1080 (red) images are shown. Based on 30 images (3.5 fold multiplicity), there is no apparent anomalous signal beyond 4 Å, but with 720 images (75-fold multiplicity) the apparent signal extends beyond 3 Å resolution. Inset shows the quality of the anomalous difference map (maximal rrms) increases substantially and then, as radiation damage systematically alters the structure, decreases even while CCanom stays high. (b–d) Behavior of CC1/2, Rmerge, and 〈I/σmrgd as a function of resolution for individual crystals (breadth of values indicated by cyan swaths) and for a set of data merged from 18 crystals (red traces) and successfully used for sulfur-SAD phasing and refinement at 2.9 Å resolution [11••]. Insets show close-ups of the low or high resolution regions. According to the authors, the best individual crystal would only have been useful to ca. 3.2 Å resolution, and by the panel C inset, the averaged data would have been truncated at near 3.8 Å based on an Rmerge ~ 60% cutoff criterion.
Figure 2
Figure 2
Examples of tangible electron density map improvement enabled by extending resolution cutoffs. (a) Comparison of the 2Fo–Fc electron density (contoured at 1 ρrms) for a region of the prokaryotic sodium channel pore using an 〈I/σmrgd ~2 cutoff (Rpim = 47%, 〈I/σmrgd = 1.9, CC1/2 = 0.78) 4.0 Å resolution (upper panel) versus a more generous CC1/2 ~ 0.1 based cutoff (Rpim = 213%, 〈I/σmrgd = 0.3, CC1/2 = 0.14) 3.46 Å resolution (lower panel). The 4 Å resolution cutoff was already somewhat generous as the Rpim of 47% with a multiplicity of 12 would be expected to correspond to an Rmeas value of above 150% (47%*√12). Used with permission from Figure S1 of [48•]. (b). Comparison of the 2Fo–Fc electron density (contoured at 1 ρrms) for a region of the E. coli YfbU protein using for the phase extension a fairly conventional cutoff (Rmeas = 77%, 〈I/σmrgd = 3.5, CC1/2 = 0.85) of 3.1Å resolution (upper panel) versus a more generous 〈I/σmrgd ~ 0.5 or CC1/2 ~ 0.1 cutoff Rmeas = 302%, 〈I/σmrgd = 0.5, CC1/2 = 0.14) of 2.5 Å resolution (lower panel). The additional weak data did not just extend the resolution of the map, but improved the quality of the phases obtained at 3.1 Å resolution. Images used with permission from the International Union of Crystallography from Figure 3 of [40••] (

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