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Solving the RNA inverse folding problem through target structure decomposition and Multiobjective Evolutionary Computation
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DOI：10.1016/j.asoc.2023.110779.png)
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Abstract
En 中文
The RNA inverse folding problem involves discovering a nucleotide sequence that folds into a desired target structure. Although numerous computational methods have been proposed over the years to tackle the problem, none have successfully solved the complete Eterna100 set. The Eterna100 set is widely recognized as a benchmark in this field. Therefore, there is still ample room for improvement in this area. This paper aims to address this challenge by introducing eM2dRNAs, an enhanced version of our previous approach called m2dRNAs, which is a multiobjective metaheuristic to design RNA sequences. By introducing eM2dRNAs, we aim to make significant advancements in RNA inverse folding. Our approach starts with the recursive decomposition of the target structure, simplifying the problem to be solved. We conducted a comparative study of our method against several published methods using the Eterna100 benchmark. The results showed that our proposal performs significantly better than the other methods across almost all metrics and categories considered, thus achieving our objective of improving the ability to solve the RNA inverse folding problem. (c) 2023 The Author(s). Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Keywords:
Bioinformatics
RNA inverse folding
Multiobjective Evolutionary Algorithm
Genetic algorithm
Recursive decomposition
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