Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data
Christina Bergonzo, Alexander Grishaev
Molecular dynamics (MD) simulations play an important role in interpreting experimental Nuclear Magnetic Resonance (NMR) data to describe Ribonucleic Acid (RNA) structure. In this work, we examine the accuracy of structural representation resulting from application of a number of explicit and implicit solvent models and refinement protocols against experimental data ranging from high density of residual dipolar coupling (RDC) restraints to completely unrestrained simulations. For a prototype A-form RNA helix, our results indicate that AMBER RNA force field with either implicit or explicit solvent can produce a realistic dynamic representation of RNA helical structure, accurately cross-validating with respect to a diverse array of NMR observables. In refinement against NMR distance restraints, modern MD force fields are found to be equally adequate, with high fidelity cross-validation to the residual dipolar couplings (RDCs) and residual chemical shift anisotropies (RCSAs), while slightly over-estimating structural order as monitored via NMR relaxation data. With restraints trimmed to encode only for base pairing information, cross-validation quality significantly deteriorates, now exhibiting a pronounced dependence on the choice of the solvent model. This deterioration is found to be partially reversible by increasing planarity restraints on the nucleobase geometry. For completely unrestrained MD simulations, the choice of water model becomes very important, with the best-performing TIP4P-Ew accurately reproducing both the RDC and RCSA data, while closely matching the NMR-derived order parameters. The information provided here will serve as a foundation for MD-based refinement of solution state NMR structures of RNA.
and Grishaev, A.
Accuracy of MD solvent models in RNA structure refinement assessed via liquid-crystal NMR and spin relaxation data, Journal of Structural Biology, [online], https://doi.org/10.1016/j.jsb.2019.07.001
(Accessed February 27, 2024)