From static to dynamic: the need for structural ensembles and a predictive model of RNA folding and function

•Predictive understanding of RNA kinetics and thermodynamics requires energy landscape determination.•Junctions determine relative orientations of helices and embedded tertiary motifs.•New experimental methods determine energy landscapes of helix–junction–helices.•Energy landscapes of components may be used to predict landscapes of complex RNAs.•Quantitative molecular descriptions will provide deep understanding of RNA in biology. To understand RNA, it is necessary to move beyond a descriptive categorization towards quantitative predictions of its molecular conformations and functional behavior. An incisive approach to understanding the function and folding of biological RNA systems involves characterizing small, simple components that are largely responsible for the behavior of complex systems including helix–junction–helix elements and tertiary motifs. State-of-the-art methods have permitted unprecedented insight into the conformational ensembles of these elements revealing, for example, that conformations of helix–junction–helix elements are confined to a small region of the ensemble, that this region is highly dependent on the junction's topology, and that the correct alignment of tertiary motifs may be a rare conformation on the overall folding landscape. Further characterization of RNA components and continued development of experimental and computational methods with the goal of quantitatively predicting RNA folding and functional behavior will be critical to understanding biological RNA systems.