Maximizing accuracy of RNA structure in refinement against residual dipolar couplings.

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TitleMaximizing accuracy of RNA structure in refinement against residual dipolar couplings.
Publication TypeJournal Article
Year of Publication2019
AuthorsBergonzo, C, Grishaev, A
JournalJ Biomol NMR
Date Published2019 May 02

Structural information about ribonucleic acid (RNA) is lagging behind that of proteins, in part due to its high charge and conformational variability. Molecular dynamics (MD) has played an important role in describing RNA structure, complementing information from both nuclear magnetic resonance (NMR), or X-ray crystallography. We examine the impact of the choice of the empirical force field for RNA structure refinement using cross-validation against residual dipolar couplings (RDCs) as structural accuracy reporter. Four force fields, representing both the state-of-the art in RNA simulation and the most popular selections in NMR structure determination, are compared for a prototypical A-RNA helix. RNA structural accuracy is also evaluated as a function of both density and nature of input NMR data including RDCs, anisotropic chemical shifts, and distance restraints. Our results show a complex interplay between the experimental restraints and the force fields indicating two best-performing choices: high-fidelity refinement in explicit solvent, and the conformational database-derived potentials. Accuracy of RNA models closely tracks the density of 1-bond C-H RDCs, with other data types having beneficial, but smaller effects. At lower RDC density, or when refining against NOEs only, the two selected force fields are capable of accurately describing RNA helices with little or no experimental RDC data, making them available for the higher order structure assembly or better quantification of the intramolecular dynamics. Unrestrained simulations of simple RNA motifs with state-of-the art MD force fields appear to capture the flexibility inherent in nucleic acids while also maintaining a good agreement with the experimental observables.

Alternate JournalJ. Biomol. NMR
PubMed ID31049778
Grant Listintramural research funding / / National Institute of Standards and Technology /