MERGE-RNA: a physics-based model to predict RNA secondary structure ensembles with chemical probing

TL;DR

MERGE-RNA is a physics-based computational framework that predicts RNA secondary structure ensembles by integrating chemical probing data, surpassing traditional methods in accuracy and interpretability.

Contribution

It introduces a novel ensemble modeling approach that incorporates experimental data and physics principles, providing more accurate and interpretable RNA structural predictions.

Findings

Outperforms standard pseudo-free-energy methods in accuracy

Recovers NMR-resolved conformations and ligand-induced changes

Deconvolves transient intermediate populations in designed RNAs

Abstract

RNA function is tied to secondary structure, operating through dynamic and heterogeneous structural ensembles. While current analysis tools typically output single static structures or averaged contact maps, chemical probing methods like DMS capture nucleotide-resolution signals representing the full structural ensemble, which remain difficult to interpret structurally. To address this, we present MERGE-RNA, a framework that describes and outputs RNA as a structural ensemble. By modeling the physics of the experimental pipeline, MERGE-RNA learns a small set of transferable and interpretable parameters, enabling the integration of measurements across different molecules, probe concentrations, and replicates in a single optimization to improve robustness. Our model employs a maximum-entropy principle to predict thermodynamic populations, with the minimal adjustments necessary to align the ensemble with experimental data. We validate MERGE-RNA on diverse RNAs, showing that it achieves structural accuracy surpassing standard pseudo-free-energy methods and yields ensembles better recapitulating measured DMS reactivity. Applied to the V. vulnificus adenine riboswitch, MERGE-RNA recovers the NMR-resolved conformations and their ligand-induced rearrangement, with population shifts matching the NMR-derived K_d. In a designed RNA construct for which we report new DMS data, MERGE-RNA deconvolves mixed states to reveal transient intermediate populations involved in strand displacement, dynamics invisible to traditional analysis methods.