A Simple Yet Effective Greedy Evolutionary Strategy for RNA Design

Ribonucleic acid (RNA) design, also known as the RNA inverse folding problem, involves discovering a nucleotide sequence that folds into a target structure. This problem has been addressed from a wide number of approaches, improving the ability to solve it in a reasonable time over time. Despite all these efforts, today no method has completely solved the problem. We present GREED-RNA, a new RNA design algorithm, based on a simple greedy evolutionary strategy. The main feature is the use of several objective functions (base-pair distance, Hamming distance, probability over ensemble, partition function, ensemble defect, and GC-content) to select the best solution in each iteration, changing their weight according to the problem-solving conditions. The performance of GREED-RNA was tested using the Eterna100 benchmark, widely used in this area and never fully solved by any method. In addition, a comparative analysis against several published RNA design methods considering three metrics (solved structures, success rate, and execution time), allowed us to verify that GREED-RNA performs better than previously developed methods, thus successfully improving the current ability to solve this problem. This tool also allows users to select a range within which the GC-content of the solution sequences must fall. Source code and results are available at https://github.com/iARN-unex/GREED-RNA.