Elastic network models for RNA: a comparative assessment with molecular dynamics and SHAPE experiments

Abstract

Elastic network models (ENMs) are valuable and efficient tools for characterizing the collective internal dynamics of proteins based on the knowledge of their native structures. The increasing evidence that the biological functionality of RNAs is often linked to their innate internal motions, poses the question of whether ENM approaches can be successfully extended to this class of biomolecules. This issue is tackled here by considering various families of elastic networks of increasing complexity applied to a representative set of RNAs. The fluctuations predicted by the alternative ENMs are stringently validated by comparison against extensive molecular dynamics simulations and SHAPE experiments. We find that simulations and experimental data are systematically best reproduced by either an all-atom or a three-beads-per-nucleotide representation (sugar-base-phosphate), with the latter arguably providing the best balance of accuracy and computational complexity.

This article has been accepted for publication in Nucleic Acids Research Published by Oxford University Press

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• Generalization of the Elastic Network model for the study of large conformational changes in biomolecules
• Effects and limitations of a nucleobase-driven backmapping procedure for nucleic acids using steered Molecular Dynamics
• Simple Elastic Network Models for Exhaustive Analysis of Long Double-Stranded DNA Dynamics with Sequence Geometry Dependence
• Molecular dynamics simulations with grand-canonical reweighting suggest cooperativity effects in RNA structure probing experiments
• Time- and ensemble-average statistical mechanics of the Gaussian Network Model