RNA Dynamics and Interactions Revealed through Atomistic Simulations

TL;DR

This review surveys recent advances in the use of atomistic molecular dynamics simulations to characterize RNA dynamics in diverse contexts, including isolated molecules and complexes with ions, small molecules, or proteins.

Abstract

RNA function is deeply intertwined with its conformational dynamics. In this review, we survey recent advances in the use of atomistic molecular dynamics simulations to characterize RNA dynamics in diverse contexts, including isolated molecules and complexes with ions, small molecules, or proteins. We highlight how enhanced sampling techniques and integrative approaches can improve both the precision and accuracy of the resulting structural ensembles. Finally, we examine the emerging role of artificial intelligence in accelerating progress in RNA modeling and simulation.

Figures (6)

• Figure 1: Typical time scales of RNA dynamic processes and corresponding simulation methods. Molecular dynamics (MD) simulations based on quantum mechanics (QM), hybrid quantum mechanics/molecular mechanics (QM/MM), classical atomistic force fields, and coarse-grained (CG) models can be used to study RNA dynamics across a broad range of time scales and system sizes. Enhanced sampling techniques can extend the effective time scales accessible to molecular simulations. In parallel, RNA secondary-structure prediction methods can be used to model equilibrium ensembles or to mimic the long-time-scale behavior of RNA dynamics.
• Figure 2: Molecular simulations can suffer from both limited accuracy and limited precision (a). Precision can be improved using enhanced sampling methods, which reduce free-energy barriers and enable better exploration of conformational space (b). Accuracy can be improved by integrating experimental data, either by refining force-field parameters or reweighting simulated ensembles to better match observations (c).
• Figure 3: Examples of RNA systems investigated with MD simulations. (a) Canonical A-form duplex simulated in various salt conditions, adapted from He et al. he_structural_2021. (b) RNA hexamer (UCAAUC), and its highly diverse conformational ensemble, trajectory from Fröhlking et al. frohlking_simultaneous_2023. (c) Hairpin with a UUCG tetraloop showing two states with distinct plasticity, adapted from Bottaro et al. bottaro_integrating_2020. (d) Top and side views of an RNA G-quadruplex stabilized with K+, coordinates from Pokorná et al. pokorna_rna_2025-1.
• Figure 4: Conformational sampling and integrative approaches for RNA dynamics. (a) Molecular dynamics simulations, possibly combined with enhanced sampling, are used to explore the free-energy landscape of an RNA system. This yields a conformational ensemble with associated populations. (b) Experimental observables are computed for each structure using forward models. (c) Averages over the ensemble are compared with experimental data, which can then be used to reweight the population estimates (d). Adapted from Bernetti et al. bernetti_reweighting_2021.
• Figure 5: RNA molecules can interact with multiple chemical entities that modulate their function and dynamics. For example, a group II intron prior to reverse splicing (PDB: 6ME0, Reference haack_cryo-em_2019) forms a complex with both its target DNA (blue) and a maturase protein (red). Simulations should account for the importance of ions (pink) and, depending on the context, small organic molecules (gray).