ArticleBMC bioinformatics2012
Predicting folding pathways between RNA conformational structures guided by RNA stacks.
Article in BMC bioinformatics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 11 citations in OpenAlex.
- Finding the direct optimal RNA barrier energy and improving pathways with an arbitrary energy model.Bioinformatics (Oxford, England) · 2020Article
- RNA folding pathways and kinetics using 2D energy landscapes.Journal of mathematical biology · 2015Article
- Memory-efficient RNA energy landscape exploration.Bioinformatics (Oxford, England) · 2014Article
- Analysing RNA-kinetics based on folding space abstraction.BMC bioinformatics · 2014Article
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Authors and funding
2 authors at 1 institution in 1 country.
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Abstract
backgroundAccurately predicting low energy barrier folding pathways between conformational secondary structures of an RNA molecule can provide valuable information for understanding its catalytic and regulatory functions. Most existing heuristic algorithms guide the construction of folding pathways by free energies of intermediate structures in the next move during the folding. However due to the size and ruggedness of RNA energy landscape, energy-guided search can become trapped in local optima.
resultsIn this paper, we propose an algorithm that guides the construction of folding pathways through the formation and destruction of RNA stacks. Guiding the construction of folding pathways by coarse grained movements of RNA stacks can help reduce the search space and make it easier to jump out of local optima. RNAEAPath is able to find lower energy barrier folding pathways between secondary structures of conformational switches and outperforms the existing heuristic algorithms in most test cases.
conclusionsRNAEAPath provides an alternate approach for predicting low-barrier folding pathways between RNA conformational secondary structures. The source code of RNAEAPath and the test data sets are available at http://genome.ucf.edu/RNAEAPath.
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