A Multi-objective Cluster-based Biased Random-Key Genetic Algorithm with Online Parameter Control Applied to Protein Structure Prediction

Abstract

The protein structure prediction problem is one of the most important bioinformatics problems. Computational methods can be used to approach this problem and de novo methods are able to generate protein structures without the need of having known similar structures to the predicted protein. These methods transform the prediction problem into an optimization problem, using optimization models that combine different energy functions and high-level information. These models usually have only a single optimization objective. However, it is known that this single objective optimization approach may harm the optimization search due to the existence of conflicts between the different terms that compose the optimization objective. The proposed model has three objectives: energy function, secondary structure, and contact maps. A multi-objective Biased Random-Key Genetic Algorithm (BRKGA) with online parameter control, named MOBO, is proposed as the optimizer. The final predictor comprises two phases of the MOBO algorithm and selects a final structure using the MUFOLD-CL clustering method. Results obtained demonstrated that the proposed predictor generated highly competitive results with the literature.