ArticleBMC bioinformatics2022
aRNAque: an evolutionary algorithm for inverse pseudoknotted RNA folding inspired by Lévy flights.
Article in BMC bioinformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- De novo design of RNA pseudoknots with deep learning.bioRxiv : the preprint server for biology · 2026Article
- RNA inverse folding can be solved in linear time for structures without isolated stacks or base pairs.Algorithms for molecular biology : AMB · 2025Article
- RNAtranslator: Modeling protein-conditional RNA design as sequence-to-sequence natural language translation.PLoS computational biology · 2025Article
- Computational De Novo Design of Group II Introns Yields Highly Active Ribozymes.Chembiochem : a European journal of chemical biology · 2025Article
- DRAG: design RNAs as hierarchical graphs with reinforcement learning.Briefings in bioinformatics · 2025Article
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Abstract
backgroundWe study in this work the inverse folding problem for RNA, which is the discovery of sequences that fold into given target secondary structures.
resultsWe implement a Lévy mutation scheme in an updated version of aRNAque an evolutionary inverse folding algorithm and apply it to the design of RNAs with and without pseudoknots. We find that the Lévy mutation scheme increases the diversity of designed RNA sequences and reduces the average number of evaluations of the evolutionary algorithm. Compared to antaRNA, aRNAque CPU time is higher but more successful in finding designed sequences that fold correctly into the target structures.
conclusionWe propose that a Lévy flight offers a better standard mutation scheme for optimizing RNA design. Our new version of aRNAque is available on GitHub as a python script and the benchmark results show improved performance on both Pseudobase++ and the Eterna100 datasets, compared to existing inverse folding tools.
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