Exploring RNA structure and dynamics through enhanced sampling simulations

Abstract

RNA function is intimately related to its structural dynamics. Molecular dynamics simulations are useful for exploring biomolecular flexibility but are severely limited by the accessible timescale. Enhanced sampling methods allow this timescale to be effectively extended in order to probe biologically-relevant conformational changes and chemical reactions. Here, we review the role of enhanced sampling techniques in the study of RNA systems. We discuss the challenges and promises associated with the application of these methods to force-field validation, exploration of conformational landscapes and ion/ligand-RNA interactions, as well as catalytic pathways. Important technical aspects of these methods, such as the choice of the biased collective variables and the analysis of multi-replica simulations, are examined in detail. Finally, a perspective on the role of these methods in the characterization of RNA dynamics is provided.

arXiv commentAccepted for publication on Current Opinion in Structural Biology

References in corpus (10)

• Computer Folding of RNA Tetraloops: Identification of Key Force Field Deficiencies
• Combining simulations and solution experiments as a paradigm for RNA force field refinement
• Enhanced Conformational Sampling using Replica Exchange with Collective-Variable Tempering
• Free Energy Landscape of GAGA and UUCG RNA Tetraloops
• Multivalent ion-mediated nucleic acid helix-helix interactions: RNA versus DNA
• Unravelling Mg${}^{2+}$-RNA binding with atomistic molecular dynamics
• Empirical corrections to the Amber RNA force field with Target Metadynamics
• RNA Folding Pathways in Stop Motion
• Kissing loop interaction in adenine riboswitch: insights from umbrella sampling simulations
• Understanding In-line Probing Experiments by Modeling Cleavage of Non-reactive RNA Nucleotides

Cited by in corpus (6)

• Using the Maximum Entropy Principle to Combine Simulations and Solution Experiments
• Reweighting of molecular simulations with explicit-solvent SAXS restraints elucidates ion-dependent RNA ensembles
• The mechanism of RNA base fraying: molecular dynamics simulations analyzed with core-set Markov state models
• Molecular dynamics simulations of chemically modified ribonucleotides
• Automatic learning of hydrogen-bond fixes in an AMBER RNA force field
• Unraveling the Mechanism of Drug Binding to SARS-CoV-2 RNA Pseudoknot with Thermodynamics-Driven Machine Learning