Exploring RNA conformational ensembles in silico: progress and challenges

RNA function is intrinsically linked to its structural polymorphism, with molecules exploring the heterogeneous conformational ensembles resulting from complex energy landscapes. These landscapes arise from competing interactions, small energetic separations between microstates, and strong coupling to the environment, posing significant challenges for both experimental characterization and molecular simulation. In this chapter, we review current computational strategies that aim to explore RNA conformational ensembles in silico, with a specific focus on energy landscape-based approaches and atomistic simulations. We discuss key limitations related to sampling efficiency, force-field accuracy, and ensemble analysis, and illustrate their impact through case studies on a self-cleaving ribozyme and an H-type pseudoknot. Finally, we highlight emerging directions, including closer integration with experimental data and the growing role of machine learning, which will probably reinforce the predictive power of in silico RNA energy landscape exploration.