Ripping RNA by Force using Gaussian Network Models

TL;DR

This paper adapts Gaussian network models to study RNA unfolding under force, revealing how topology influences unfolding pathways and providing a computational approach to interpret single-molecule pulling experiments.

Contribution

It introduces a GNM-based method to predict RNA responses to force, capturing qualitative unfolding pathways and allosteric communication during rupture events.

Findings

GNM captures qualitative unfolding pathways of RNA under force

The model reveals allosteric communication during RNA rupture

Simple GNM cannot fully capture bifurcations or ion effects

Abstract

Using force as a probe to map the folding landscapes of RNA molecules has become a reality thanks to major advances in single molecule pulling experiments. Although the unfolding pathways under tension are complicated to predict studies in the context of proteins have shown that topology plays is the major determinant of the unfolding landscapes. By building on this finding we study the responses of RNA molecules to force by adapting Gaussian network model (GNM) that represents RNAs using a bead-spring network with isotropic interactions. Cross-correlation matrices of residue fluctuations, which are analytically calculated using GNM even upon application of mechanical force, show distinct allosteric communication as RNAs rupture. The model is used to calculate the force-extension curves at full thermodynamic equilibrium, and the corresponding unfolding pathways of four RNA molecules subject to a quasi-statically increased force.Our study finds that the analysis using GNM captures qualitatively the unfolding pathway of \emph{T}. ribozyme elucidated by the optical tweezers measurement. However, the simple model is not sufficient to capture subtle features, such as bifurcation in the unfolding pathways or the ion effects, in the forced-unfolding of RNAs.