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

MERGE-RNA accurately predicts RNA secondary structure ensembles, surpassing standard methods with a 20% improvement in recapitulating DMS reactivity data.

• Why it matters: Understanding RNA function depends on detailed knowledge of its dynamic structural ensembles, which are difficult to interpret from current static or averaged models, limiting insights into RNA behavior and interactions.
• What they did: The authors developed MERGE-RNA, a physics-based framework that models experimental processes and learns transferable parameters from chemical probing data, applying a maximum-entropy approach to predict thermodynamic populations across diverse RNAs.
• The result: MERGE-RNA successfully recovers known conformations, captures ligand-induced rearrangements, and reveals transient intermediate states, enabling deeper understanding of RNA dynamics and facilitating more accurate structural predictions.