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

TL;DR

This study evaluates elastic network models (ENMs) for RNA by comparing their predictions with molecular dynamics simulations and SHAPE experimental data, finding that simplified models effectively capture RNA dynamics.

Contribution

It extends ENM approaches to RNA molecules and identifies the most accurate and computationally efficient representations for modeling RNA internal motions.

Findings

All-atom and three-beads-per-nucleotide models best match experimental data.

Simplified models balance accuracy and computational efficiency.

ENMs can reliably characterize RNA dynamics.

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.