MERGE-RNA: a physics-based model to predict RNA secondary structure ensembles with chemical probing
RNA function is deeply tied to secondary structure, with most molecules 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 that represent the full structural ensemble, which however remain of difficult structural interpretation. To address this, we present MERGE-RNA, a framework that explicitly describes and outputs RNA as a structural ensemble. By modeling the experimental pipeline through a statistical physics framework, MERGERNA learns a small set of transferable and interpretable parameters, enabling the seamless 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, introducing only the minimal sequence-specific adjustments necessary to align the ensemble with experimental data. We validate MERGE-RNA on diverse RNAs, showing that it achieves strong structural accuracy and the ability to fill knowledge gaps in single-conformation reference structures. Furthermore, 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 that remain invisible to traditional analysis methods.
RNA function is deeply tied to secondary structure, with most molecules 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 that represent the full structural ensemble, which however remain of difficult structural interpretation. To address this, we present MERGE-RNA, a framework that explicitly describes and outputs RNA as a structural ensemble. By modeling the experimental pipeline through a statistical physics framework, MERGERNA learns a small set of transferable and interpretable parameters, enabling the seamless 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, introducing only the minimal sequence-specific adjustments necessary to align the ensemble with experimental data. We validate MERGE-RNA on diverse RNAs, showing that it achieves strong structural accuracy and the ability to fill knowledge gaps in single-conformation reference structures. Furthermore, 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 that remain invisible to traditional analysis methods.
Institut de Biologie Moleculaire et Cellulaire