A round-robin approach provides a detailed assessment of biomolecular small-angle scattering data reproducibility and yields consensus curves for benchmarking

Abstract

Through an expansive international effort that involved data collection on 12 small-angle X-ray scattering (SAXS) and four small-angle neutron scattering (SANS) instruments, 171 SAXS and 76 SANS measurements for five proteins (ribonuclease A, lysozyme, xylanase, urate oxidase and xylose isomerase) were acquired. From these data, the solvent-subtracted protein scattering profiles were shown to be reproducible, with the caveat that an additive constant adjustment was required to account for small errors in solvent subtraction. Further, the major features of the obtained consensus SAXS data over the q measurement range 0–1 Å−1 are consistent with theoretical prediction. The inherently lower statistical precision for SANS limited the reliably measured q-range to <0.5 Å−1, but within the limits of experimental uncertainties the major features of the consensus SANS data were also consistent with prediction for all five proteins measured in H2O and in D2O. Thus, a foundation set of consensus SAS profiles has been obtained for benchmarking scattering-profile prediction from atomic coordinates. Additionally, two sets of SAXS data measured at different facilities to q > 2.2 Å−1 showed good mutual agreement, affirming that this region has interpretable features for structural modelling. SAS measurements with inline size-exclusion chromatography (SEC) proved to be generally superior for eliminating sample heterogeneity, but with unavoidable sample dilution during column elution, while batch SAS data collected at higher concentrations and for longer times provided superior statistical precision. Careful merging of data measured using inline SEC and batch modes, or low- and high-concentration data from batch measurements, was successful in eliminating small amounts of aggregate or interparticle interference from the scattering while providing improved statistical precision overall for the benchmarking data set.

Experimental Evidence for the Role of Dynamics in pH-Dependent Enzymatic Activity

“…Figure B presents a comparison of the change in the radius of gyration ( R g ) with temperature for xylanase samples at pH 4.5, pH 5.5, and pH 8.0. The radius of gyration R g of the xylanase monomer obtained here ranges from 14.3 to 17.4 Å, which is consistent with the experimental results of SAXS, SEC-SAXS, and SEC-SANS . The R g of pH 5.5 remains stable at approximately 15 Å, with a noticeable change as the temperature increases.…”

Computation of X-ray and Neutron Scattering Patterns to Benchmark Atomistic Simulations against Experiments

“…This example was chosen to demonstrate the performance of the Q-clean method in a real-life example. The calculated results are compared with the experiments [64]. Both r-clean and Q-clean produced a good match with the experimental data and removed the finite size effect seen in the naïvely calculated pattern.…”

“…The quality of match between the experiment and calculated pattern deteriorates slightly in the region where Q is greater than 0.1 Å −1 . At a Q higher than 0.2 Å −1 , the experimental data suffer from statistical error [64] and incoherent scattering, which was not included in the calculations. The difference between the r-clean and Q-clean results at Q values above 0.1 Å −1 will be discussed later.…”

2023 update of template tables for reporting biomolecular structural modelling of small-angle scattering data

“…Input on potential revisions to the guidelines was first requested via email from members of the IUCr CSAS, the SASvtf and the 46 co-authors of the recently published benchmarking study for biomolecular modelling (Trewhella et al, 2022). Subsequent in-person discussions at the open meeting of the IUCr CSAS at the 18th International Small-Angle Scattering Conference (SAS2022; Campinas, Brazil, 11-16 September 2022) led to the formation of a small working group to (i) consider, in light of the input received, whether information could be better organized within the template table to achieve greater clarity and whether some information would be better described elsewhere, for example in the experimental methods text, (ii) generalize the sample descriptions, explicitly including nonprotein components, for example DNA/RNA and glycans/carbohydrates, (iii) develop a SAS-cv template that includes the additional recommendations from the 2017 guidelines for this class of experiment and (iv) test the revised templates with example data sets.…”

“…Answer: From the paper, it appears that the assumption was that these smearing effects were assumed to be minimal, which at the time of these experiments was a common assumption for smoothly changing SAS profiles from proteins in solution. This assumption is generally reasonable based on a recent benchmarking study of five proteins in solution (Trewhella et al, 2022). A reviewer might have asked either for some discussion or evidence supporting this assumption, or whether the authors had considered explicitly accounting for data smearing as SASREF7 includes the option of a resolution file to smear the theoretical curve for comparison with experiment.…”