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

Abstract: The function of RNA molecules is deeply related to their secondary structure, which determines which nucleobases are accessible for pairing. Most RNA molecules however function through dynamic and heterogeneous structural ensembles. Chemical probing methods (e.g., DMS probing) rely on selective chemical modification of accessible RNA nucleotides to infer base-pairing status, yet the resulting nucleotide-resolution data represent ensemble averages over dynamic RNA conformations. We present MERGE-RNA, a unified, physics-based framework that explicitly models the full experimental pipeline, from the thermodynamics of probe binding to the mutational profiling readout. By integrating measurements across probe concentrations and replicates, our model learns a small set of transferable and interpretable parameters together with minimal sequence-specific soft constraints. This enables the prediction of secondary structure ensembles that best explain the data and the detection of suboptmal structures involved in dynamic processes. We validate MERGE-RNA on diverse RNAs, showing that it achieves strong structural accuracy while preserving essential conformational heterogeneity. In a designed RNA for which we report new DMS data, MERGE-RNA detects transient intermediate states associated with strand displacement, dynamics that remain invisible to traditional methods.