Predicting 3D structure, flexibility and stability of RNA hairpins in monovalent and divalent ion solutions

TL;DR

This study enhances a coarse-grained RNA model to accurately predict 3D structures, flexibility, and stability of RNA hairpins in various ion solutions, aiding understanding of RNA behavior in biological contexts.

Contribution

The paper introduces an improved RNA folding model incorporating sequence-dependent stacking and refined electrostatics for better predictions in ion solutions.

Findings

Accurately predicts RNA hairpin 3D structures from sequence data.

Reliable predictions of RNA hairpin flexibility across ion conditions.

Successful stability predictions for diverse RNA hairpins.

Abstract

A full understanding of RNA-mediated biology would require the knowledge of three-dimensional (3D) structures, structural flexibility and stability of RNAs. To predict RNA 3D structures and stability, we have previously proposed a three-bead coarse-grained predictive model with implicit salt/solvent potentials. In this study, we will further develop the model by improving the implicit-salt electrostatic potential and involving a sequence-dependent coaxial stacking potential to enable the model to simulate RNA 3D structure folding in divalent/monovalent ion solutions. As compared with the experimental data, the present model can predict 3D structures of RNA hairpins with bulge/internal loops (<77nt) from their sequences at the corresponding experimental ion conditions with an overall improved accuracy, and the model also makes reliable predictions for the flexibility of RNA hairpins with bulge loops of different length at extensive divalent/monovalent ion conditions. In addition, the model successfully predicts the stability of RNA hairpins with various loops/stems in divalent/monovalent ion solutions.