Diamonds in the rough: a strong case for the inclusion of weak-intensity X-ray diffraction data

Abstract

Overwhelming evidence exists to show that the inclusion of weak-intensity, high-resolution X-ray diffraction data helps improve the refinement of atomic models by imposing strong constraints on individual and overall temperature B factors and thus the quality of crystal structures. Some researchers consider these data to be of little value and opt to discard them during data processing, particularly at medium and low resolution, at which individual B factors of atomic models cannot be refined. Here, new evidence is provided to show that the inclusion of these data helps to improve the quality of experimental phases by imposing proper constraints on electron-density models during noncrystallographic symmetry (NCS) averaging. Using electron-density correlation coefficients as criteria, the resolution of data has successfully been extended from 3.1 to 2.5 Å resolution with redundancy-independent merging R factors from below 100% to about 310%. It is further demonstrated that phase information can be fully extracted from observed amplitudes through de novo NCS averaging. Averaging starts with uniform density inside double-shelled spherical masks and NCS matrices that are derived from bound heavy-atom clusters at the vertices of cuboctahedrally symmetric protein particles.

Determination of chemical identity and occupancy from experimental density maps

“…The catalase structure just discussed is only one of a few examples in the PDB with very low R ‐factors that do not suffer from severe model bias. There are other ways that one can obtain model‐independent experimental ED maps, one of which is de novo non‐crystallographic symmetry (NCS) averaging used in the E. coli YfbU particle structure determination (which contains 16‐fold NCS) . When the same NCS averaging procedure was applied to another data set of the crystal for this particle at ∼ 1.95‐Å resolution using normalized or sharpened structure factors, the resulting NCS‐averaged maps can readily distinguish among O, N, and C atoms in many places in that structure as well as the added oxygen atoms during data collection, details of which will be described elsewhere.…”

Assessing and maximizing data quality in macromolecular crystallography

“…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••] (http://dx.doi.org/10.1107/S1399004714005318). …”

“…Weak data have further been shown to improve the phasing of a crystal with 16-fold non-crystallographic symmetry. Phase extension and automated modeling using data truncated per conventional criteria at 3.1 Å resolution stalled at R free ~ 35%, whereas using an extended 2.5 Å resolution cutoff produced an excellent model with R free ~ 24.5% and improved electron density maps (Figure 2b; [40••]). Although the signal per reflection is rather weak for the extended data, the tangible impact on phase extension, refinement, and map quality can be rationalized in that the numbers of added reflections are very large, in some cases doubling the data available and they help to minimize series termination error.…”

A guide to the crystallographic analysis of icosahedral viruses

“…[139] This is a more objective statistic since its calculation does not involve the uncertainties in the intensities that are more subjective since the estimation of σ I varies among the various data processing programs. [136,139,145] Several studies [139,147,148] suggest that data sets with highest resolution shells having CC 1/2 in the range 0.1-0.2 produce better atomic models than data sets that have been truncated to a lower resolution limit. Furthermore, the work of Karplus and Diederichs [139,147] demonstrates how the CC 1/2 statistic can be a predictor of model quality through the derived statistic…”

“…In summary, the data set that will yield the best model will be highly redundant, extend to a high-resolution limit characterized by a CC 1/2 statistic in the 0.1-0.2 range, and will be nearly complete in all resolution ranges with only randomly missing reflections. In other words, (1) merging R factors are of little value, especially in determining the high-resolution limit of a data set, [136,139,147,149] (2) strict signal-to-noise criteria discard useful data, degrading data quality and, consequently, model quality, [136,139,143,144,147,148] (3) highly redundant data sets are better than low redundancy sets, [147,149] (4) the CC 1/2 statistic is a better high-resolution limit indicator than previously used statistics, that is, merging R factors and I/σ I , [136,139,145,147] and (5) completeness in all resolution ranges is important, especially for structure solution, although incompleteness in the highest resolution shells only reduces the effective resolution of the data. [138,145,147] In the words of Evans and Murshudov, [136] 'There is no reason to suppose that cutting back the resolution of the data will improve the model.…”