Structured RNA usually folds hierarchically, with its sequence defining stable secondary structure that constrains how the RNA then folds in three dimensions.
RNA secondary structure defines helix-junction-helix motifs that are the essential building blocks of RNA. We recently developed a computational sampling algorithm that allows us to extensively explore the conformational space of RNA by embedding different levels of the degrees of freedom used for sampling. For example, helices can move independently and in correlation with one or more other helices.
Additionally, these helices are not perfectly rigid: bases within helices are free to move. The combination of hierarchical degrees of freedom facilitates the exploration of physically relevant RNA structures sampled using any potential energy function.
We benchmarked and compared our algorithm to others using simple and symmetric RNA systems.
We also modeled the effects of sequence mutations on the structural building blocks of tRNA-based nanotechnology. Future applications will also be discussed.