Decoding RNA Structural Ensembles: Energy Landscape Exploration of the TAR Stemloop.

RNAs are key to understanding cellular mechanisms and a prime target for novel therapeutic interventions. However, RNA structural ensembles are notoriously difficult to study due to structural polymorphism and their highly dynamic nature, i.e., the fact that RNAs can adopt multiple structures and the transitions between them are fast. As a result, computational and experimental methods to study the RNA structure are limited in their usefulness, and often, a combination of multiple methods is required. Furthermore, RNA force fields are not yet developed to the same standard as those for proteins. Here, we demonstrate that energy landscape explorations via discrete path sampling enable a full mapping of structural ensembles and can capture mutational changes well. In this contribution, we show that the ensembles derived for the TAR stemloop and a derived stemloop (ES2) are sufficient to reproduce experimental observations without the need to introduce experimental data into the modeling beyond the force field parameters. Our modeling reveals significant complexity in both the structural ensemble and the activation pathway. Furthermore, we identify a transient binding pocket that emerges on the activation pathway.