Tools for Simulating and Analyzing RNA Folding Kinetics

It has recently been found that some RNA functions are determined by the actual folding process itself and not just the RNA’s nucleotide sequence or its native structure. In this paper, we present new computational tools that can be used to study kinetics-based functions for RNA such as population kinetics, folding rates, and the folding of particular subsequences. Previously, these properties could only be studied for small RNA whose conformation space could be enumerated (e.g., RNA with 30-40 nucleotides) or for RNA whose kinetics were restricted in some way. Our method follows our previous work by first building an approximate map (or model) of the RNA’s folding energy landscape. Next, we use our new analysis technique, called Map-based Monte Carlo (MMC) simulation, to stochastically extract folding pathways from the map. MMC, in conjunction with an improved sampling strategy for building the map, enables us to study kinetics-based functions for larger RNA than we could before, e.g., RNA with 200+ nucleotides. We validate our method against known experimental data and analyze two case studies in detail. First, we compare simulated folding rates for ColE1 RNAII and its mutants against experimental rates and show that our method identifies the same relative folding order as seen in experiment. Second, we predict the gene expression rates of wild-type MS2 phage RNA and three of its mutants and and show that our approach computes the same relative functional rates as seen in experiment.