The Role of Nucleobase Interactions in RNA Structure and Dynamics

TL;DR

This paper introduces a minimalist nucleobase-centric model for RNA that effectively captures key structural interactions and improves structural prediction and comparison methods.

Contribution

A simple, oriented bead model for RNA accurately identifies base interactions and enhances structure prediction and comparison techniques.

Findings

The model successfully identifies canonical and non-canonical base interactions.

It outperforms state-of-the-art atomistic methods in RNA structure prediction.

The interaction-based metric effectively discriminates between different RNA conformations.

Abstract

The intricate network of interactions observed in RNA three-dimensional structures is often described in terms of a multitude of geometrical properties, including helical parameters, base pairing/stacking, hydrogen bonding and backbone conformation. We show that a simple molecular representation consisting in one oriented bead per nucleotide can account for the fundamental structural properties of RNA. In this framework, canonical Watson-Crick, non-Watson-Crick base-pairing and base-stacking interactions can be unambiguously identified within a well-defined interaction shell. We validate this representation by performing two independent, complementary tests. First, we use it to construct a sequence-independent, knowledge-based scoring function for RNA structural prediction, which compares favorably to fully atomistic, state-of-the-art techniques. Second, we define a metric to measure deviation between RNA structures that directly reports on the differences in the base-base interaction network. The effectiveness of this metric is tested with respect to the ability to discriminate between structurally and kinetically distant RNA conformations, performing better compared to standard techniques. Taken together, our results suggest that this minimalist, nucleobase-centric representation captures the main interactions that are relevant for describing RNA structure and dynamics.