Inverse RNA folding solution based on multi-objective genetic algorithm and Gibbs sampling method

Abstract

In living systems, RNAs play important biological functions. The functional form of an RNA frequently requires a specific tertiary structure. The scaffold for this structure is provided by secondary structural elements that are hydrogen bonds within the molecule. Here, we concentrate on the inverse RNA folding problem. In this problem, an RNA secondary structure is given as a target structure and the goal is to design an RNA sequence that its structure is the same (or very similar) to the given target structure. Different heuristic search methods have been proposed for this problem. One common feature among these methods is to use a folding algorithm to evaluate the accuracy of the designed RNA sequence during the generation process. The well known folding algorithms take O(n3) times where n is the length of the RNA sequence. In this paper, we introduce a new algorithm called GGI-Fold based on multiobjective genetic algorithm and Gibbs sampling method for the inverse RNA folding problem. Our algorithm generates a sequence where its structure is the same or very similar to the given target structure. The key feature of our method is that it never uses any folding algorithm to improve the quality of the generated sequences. We compare our algorithm with RNA-SSD for some biological test samples. In all test samples, our algorithm outperforms the RNA-SSD method for generating a sequence where its structure is more stable.