Small-angle X-ray and small-angle neutron scattering (SAXS/SANS) provide unique structural information on biomolecules and their complexes in solution. SANS may provide multiple independent data sets by means of contrast variation experiments, that is, by measuring at different D 2 O concentrations and different perdeuteration conditions of the biomolecular complex. However, even the combined data from multiple SAXS/SANS sets is by far insufficient to define all degrees of freedom of a complex, leading to a significant risk of overfitting when refining biomolecular structures against SAXS/SANS data. Hence, to control against overfitting, the low-information SAXS/ SANS data must be complemented by accurate physical models, and, if possible, refined models should be cross-validated against independent data not used during the refinement. We present a method for refining atomic biomolecular structures against multiple sets of SAXS and SANS data using all-atom molecular dynamics simulations. Using the protein citrate synthase and the protein/RNA complex Sxl−Unr−msl2 mRNA as test cases, we demonstrate how multiple SAXS and SANS sets may be used for refinement and cross-validation, thereby excluding overfitting during refinement. For the Sxl−Unr−msl2 complex, we find that perdeuteration of the Unr domain leads to a unique, slightly compacted conformation, whereas other perdeuteration conditions lead to similar solution conformations compared to the nondeuterated state. In line with our previous method for predicting SAXS curves, SANS curves were predicted with explicit-solvent calculations, taking atomic models for both the hydration layer and the excluded solvent into account, thereby avoiding the use of solvent-related fitting parameters and solventreduced neutron scattering lengths. We expect the method to be useful for deriving and validating solution structures of biomolecules and soft-matter complexes, and for critically assessing whether multiple SAXS and SANS sets are mutually compatible.

A generative adversarial network improves both distributions and spatial structure of the precipitation output of a numerical Earth system model.• Constraining its architecture enables the network to generalize to transient future climates not seen during training.• A gradient-based interpretability method shows that the network has learned to identify geographical regions with strong model biases.

In-solution small-angle X-ray and neutron scattering (SAXS/SANS) have become popular methods to characterize the structure of membrane proteins, solubilized by either detergents or nanodiscs. SANS studies of protein-detergent complexes usually require deuterium-labeled proteins or detergents, which in turn often lead to problems in their expression or purification. Here, we report an approach whose novelty is the combined analysis of SAXS and SANS data from an unlabeled membrane protein complex in solution in two complementary ways. First, an explicit atomic analysis, including both protein and detergent molecules, using the program WAXSiS, which has been adapted to predict SANS data. Second, the use of MONSA which allows one to discriminate between detergent head- and tail-groups in an ab initio approach. Our approach is readily applicable to any detergent-solubilized protein and provides more detailed structural information on protein-detergent complexes from unlabeled samples than SAXS or SANS alone.