RNA Folding Pathways in Stop Motion

TL;DR

This paper presents a novel, computationally inexpensive method for predicting RNA folding pathways using fragment ensembles validated against NMR data, providing detailed insights into folding mechanisms of RNA tetraloops.

Contribution

The paper introduces a new approach combining fragment ensembles, diffusion maps, and Markov models to predict RNA folding pathways, validated against experimental data.

Findings

Ensembles accurately describe metastable and intermediate states.

Method outperforms all-atom molecular dynamics in agreement with NMR.

Provides detailed, experimentally consistent folding pathways.

Abstract

We introduce a method for predicting RNA folding pathways, with an application to the most important RNA tetraloops. The method is based on the idea that ensembles of three-dimensional fragments extracted from high-resolution crystal structures are heterogeneous enough to describe metastable as well as intermediate states. These ensembles are first validated by performing a quantitative comparison against available solution NMR data of a set of RNA tetranucleotides. Notably, the agreement is better with respect to the one obtained by comparing NMR with extensive all-atom molecular dynamics simulations. We then propose a procedure based on diffusion maps and Markov models that makes it possible to obtain reaction pathways and their relative probabilities from fragment ensembles. This approach is applied to study the helix-to-loop folding pathway of all the tetraloops from the GNRA and UNCG families. The results give detailed insights into the folding mechanism that are compatible with available experimental data and clarify the role of intermediate states observed in previous simulation studies. The method is computationally inexpensive and can be used to study arbitrary conformational transitions.