SARNA-Predict: Using adaptive annealing schedule and inversion mutation operator for RNA secondary structure prediction

Ribonucleic Acid (RNA) plays a crucial role in many cellular functions including the synthesis of proteins. The structure of RNA is essential for it to serve its purposes within the cell. SARNA-Predict, which has previously been implemented using Simulated Annealing (SA), has shown excellent results predicting the secondary structure of RNA molecules. SA is effective in solving many different optimization problems and for being able to approximate global minima in a solution space. SARNA-Predict uses permutation based SA to heuristically search for RNA secondary structures with close to the minimum free energy with given constraints. A key step in the annealing process is the mutation of the predicted secondary structure in order to search for other potentially lower energy structures. The mutation changes the structure so as to avoid a local minimum and subsequently the free energy of the new structure is evaluated. The purpose of this paper is to evaluate the new inversion mutation operator and compare its use in terms of prediction accuracy to the percentage swap mutation operator previously used in SARNA-Predict. Different annealing schedules used in the SA process are also compared to find the optimal annealing schedule to use for each mutation operator.