aRNAque: an evolutionary algorithm for inverse pseudoknotted RNA folding inspired by Lévy flights

Area of Science:

• Computational Biology
• Bioinformatics
• Molecular Biology

Background:

• The inverse folding problem in RNA involves designing nucleotide sequences that adopt specific secondary structures.
• Understanding RNA folding is crucial for various biological processes and therapeutic applications.

Purpose of the Study:

• To enhance RNA sequence design by optimizing inverse folding algorithms.
• To evaluate the efficacy of a Lévy mutation scheme in an evolutionary algorithm for RNA design.

Main Methods:

• Implementation of a Lévy mutation scheme within the aRNAque evolutionary algorithm.
• Application of the updated algorithm to design RNA sequences, including those with pseudoknots.
• Comparison of aRNAque performance against the antaRNA tool and benchmark datasets (Pseudobase++ and Eterna100).

Main Results:

• The Lévy mutation scheme increased the diversity of designed RNA sequences.
• The algorithm showed reduced computational effort (fewer evaluations) compared to standard methods.
• aRNAque demonstrated higher success rates in designing functional RNA sequences than antaRNA, despite longer CPU times.

Conclusions:

• A Lévy flight-based mutation scheme is proposed as a superior method for optimizing RNA design.
• The updated aRNAque algorithm shows improved performance for RNA inverse folding, outperforming existing tools.
• The enhanced aRNAque tool is publicly available as a Python script on GitHub.