Interpretation of Solution X-Ray Scattering by Explicit-Solvent Molecular Dynamics

Abstract

Small- and wide-angle x-ray scattering (SWAXS) and molecular dynamics (MD) simulations are complementary approaches that probe conformational transitions of biomolecules in solution, even in a time-resolved manner. However, the structural interpretation of the scattering signals is challenging, while MD simulations frequently suffer from incomplete sampling or from a force-field bias. To combine the advantages of both techniques, we present a method that incorporates solution scattering data as a differentiable energetic restraint into explicit-solvent MD simulations, termed SWAXS-driven MD, with the aim to direct the simulation into conformations satisfying the experimental data. Because the calculations fully rely on explicit solvent, no fitting parameters associated with the solvation layer or excluded solvent are required, and the calculations remain valid at wide angles. The complementarity of SWAXS and MD is illustrated using three biological examples, namely a periplasmic binding protein, aspartate carbamoyltransferase, and a nuclear exportin. The examples suggest that SWAXS-driven MD is capable of refining structures against SWAXS data without foreknowledge of possible reaction paths. In turn, the SWAXS data accelerates conformational transitions in MD simulations and reduces the force-field bias.

SAXS/SANS on Supercharged Proteins Reveals Residue-Specific Modifications of the Hydration Shell

“…Together, our data indicate that a more detailed and heterogeneous description of the HS may be needed to accurately describe high-quality SAS data, in agreement with recent theoretical considerations (74). Interestingly, the importance of ions, revealed by our comparative SAXS/SANS analysis, suggests a strategy to improve existing programs invoking explicit solvent (40)(41)(42)(43)(44)(45)75,76) by incorporating specific interactions of ions with charged surface residues. Further developments of SAXS/SANS programs are particularly crucial for biological macromolecular systems with complex and/or polyelectrolyte surfaces such as RNA/DNA molecules (27,(77)(78)(79), intrinsically disordered proteins (59,(80)(81)(82), and membrane protein/detergent complexes (83)(84)(85)) that all rely strongly on an accurate description of the HS.…”

“…SAXS and SANS have the advantage that the respective contribution of the hydration layer (or shell) to the overall scattering from the macromolecules varies both in magnitude and sign (34). The HS contribution has been implemented in a number of programs to back-calculate SAXS (and, more rarely, SANS) curves from atomic structures, either as a homogeneous shell of a specific thickness (30)(31)(32)35), as grid elements (36,37), as dummy atoms (38,39), as explicit water molecules (40)(41)(42)(43)(44)(45), as a density map (46,47), or by voxelization (48). Accurate description of the HS is essential for the growing field of quasiatomic protein-structure modeling from solution data (34).…”

Anisotropic time-resolved solution X-ray scattering patterns from explicit-solvent molecular dynamics

“…They yield a physically accurate description of solvation, thus avoiding any fitting parameters associated with the solvent, and they naturally account for protein and solvent fluctuations. [33][34][35][36][37] In this article, we present a method to compute anisotropic TR-WAXS patterns from explicit-solvent MD simulations. The calculations build upon the work by Park et al, who introduced an algorithm to average over explicit solvent configurations taken at frozen solute coordinates.…”

Determining Atomistic SAXS Models of Tri-Ubiquitin Chains from Bayesian Analysis of Accelerated Molecular Dynamics Simulations

“…However, a wide variety of other possible enhancement methods could be employed to the same end. 11,12,17,18,72 Nonetheless, many of these methods may require producing multiple copies of the same system that must all be simulated simultaneously or performing simulations over a period of time to compute a history-dependent bias, potentially creating large computational overhead. In contrast, aMD requires no additional system replicas and is straightforward to apply with minimal computational cost.…”