Predicting the kinetics of RNA oligonucleotides using Markov state models

TL;DR

This study employs molecular dynamics and Markov state models to investigate the rapid folding and stacking kinetics of short RNA oligonucleotides, providing insights consistent with experimental relaxation times.

Contribution

It introduces a computational approach combining atomistic simulations and Markov models to analyze RNA kinetics, addressing limitations of experimental resolution.

Findings

Main relaxation modes involve transitions between folded states.

Predicted kinetics align with experimental relaxation times.

Artificial stabilization effects are identified and corrected.

Abstract

Nowadays different experimental techniques, such as single molecule or relaxation experiments, can provide dynamic properties of biomolecular systems, but the amount of detail obtainable with these methods is often limited in terms of time or spatial resolution. Here we use state-of-the-art computational techniques, namely atomistic molecular dynamics and Markov state models, to provide insight into the rapid dynamics of short RNA oligonucleotides, in order to elucidate the kinetics of stacking interactions. Analysis of multiple microsecond-long simulations indicates that the main relaxation modes of such molecules can consist of transitions between alternative folded states, rather than between random coils and native structures. After properly removing structures that are artificially stabilized by known inaccuracies of the current RNA AMBER force field, the kinetic properties predicted are consistent with the timescales of previously reported relaxation experiments.