Nucleic Acid Secondary Structures Prediction with Planar Pseudoknots Using Genetic Algorithm.

Nucleic acid nanotechnology offers many methods to fold into a variety of complex and functional nanostructures. These self-assemble scaffolds are valuable in various applications, such as molecular programming, sensing, drug delivery and nanofabrication. However, existing algorithms are typically lacking on predicting pseudoknots structure fast and accurately. This paper proposes a novel genetic algorithm to predict nucleic acid secondary structure including pseudoknots. The length of continues base pairs stacking and free energy are considered to evaluate individuals. Furthermore, the performance of our algorithm is compared with RNAStructure using PseudoBase benchmark instances, and the results show that our algorithm outperforms on accuracy and efficiency.