Chapter 6: Molecular Dynamics Simulations of RNA Molecules
Published:01 May 2012
J. Šponer, M. Otyepka, P. Banáš, K. Réblová, and N. G. Walter, in Innovations in Biomolecular Modeling and Simulations, ed. T. Schlick and T. Schlick, The Royal Society of Chemistry, 2012, vol. 2, ch. 6, pp. 129-155.
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The experimental techniques available to study structural dynamics and function of RNA are efficiently complemented by advanced computational methods. Molecular dynamics (MD) simulation is particularly useful as an auxiliary technique to provide deeper insights into known structures derived primarily from atomic-resolution crystal structures of RNAs in functionally relevant states. Careful analysis of MD simulations can identify problematic aspects of an experimental RNA structure, unveil structural characteristics masked by experimental constraints, reveal functionally significant stochastic fluctuations, evaluate the structural impact of base substitutions, modifications and ionization, and predict structurally and potentially functionally important details of the solvent behavior, including the presence of tightly bound water molecules. In contrast, reliable predictions of structure from sequence information remain beyond the applicability of MD tools. MD relies on simple atomistic force fields while high-quality starting structures are required. We comment here on the two latest refinements of the AMBER force field, i.e., parmbsc0 and parmOL. Parmbsc0 is an essential reparametrization of the α/γ torsional profiles. ParmOL is reparametrization of the χ region that suppresses high-anti χ states while also modifying the anti versus syn balance and the shape of the syn region. Still, even with these improvements, MD simulations are far from perfect. Hybrid QM/MM approaches help in the assessment of the plausibility of chemical mechanisms in RNA enzymes. The ultimate utility of computational studies in understanding RNA function requires that the results are neither blindly accepted nor flatly rejected, but rather considered in the context of all available experimental data.