RNAxplorer: Harnessing the Power of Guiding Potentials to Sample RNA Landscapes

Abstract

<jats:p>Motivation: Predicting the folding dynamics of RNAs is a computationally difficult problem, first and foremost due to the combinatorial explosion of alternative structures in the structure space. Abstractions are therefore needed to simplify downstream analyses, and make them computationally tractable. This can be achieved by various structure sampling algorithms. However, current sampling methods are still time consuming and frequently fail to represent key elements of the folding space. Method: We introduce RNAxplorer, a novel adaptive sampling method which uses dynamic programming to perform an efficient Boltzmann sampling in the presence of guiding potentials reflecting the similarity to already well-sampled structures. These potentials are accumulated into pseudo-energy terms that effectively steer sampling towards underrepresented or unexplored regions of the structure space. Results: RNAxplorer allows us to efficiently explore RNA state space. It yields rare conformations that may be inaccessible to other sampling methods. We developed and applied different measures to benchmark our sampling methods against its competitors. Most of the measures show that RNAxplorer produces more diverse structure samples and is better at finding the most relevant kinetic traps in the landscape. Thus, it produces a more representative coarse graining of the landscape that is well suited to compute better approximations of RNA folding kinetics. Availability: https://github.com/ViennaRNA/RNAxplorer/</jats:p>

DrTransformer: Heuristic cotranscriptional RNA folding using the nearest neighbor energy model

“…Finding the "right" set of nodes, the "right" set of reactions, and the "right" rates for those reactions in combination with the "right" energy function is the main challenge for this work, and there exist many different approaches to find satisfying and/or practical definitions for those terms in the context of RNA energy landscapes, e.g. Flamm et al (2000Flamm et al ( , 2002; Kucharik et al (2014); Entzian et al (2021). We follow the common assumption that the most accurate energy landscape model corresponds to elementary base-pair opening and closing steps.…”