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Inverse folding an RNA secondary structure of length L can be understood as searching in space S = {A, C, G, U}L, one or many RNA sequence(s) that fold(s) into that structure. Solving this problem is a prerequisite for RNA design and is still a real challenge in bio-engineering. To improve the RNA inverse folding, we investigate an evolutionary algorithm implementing local mutations taking into account the nucleotide and canonical base pair distributions. We implement our algorithm as a simple open-source python program called aRNAque1. The empirical results show that aRNAque can solve some target structures previously unsolvable by the existing evolutionary algorithms benchmarked in this work. On the two benchmark datasets, ≈ 72% of the EteRNA100 dataset (respectively ≈ 92% using the Turner1999 energy parameter sets) and ≈ 87% of the non-EteRNA100 dataset and the designed sequences are of better quality. In comparison to existing tools, our EA implementation also shows a significant computational time improvement.

A simple evolutionary algorithm guided by local mutations for an efficient RNA design