Protein structure prediction (PSP) has important applications in different fields, such as drug design, disease prediction, and so on. In protein structure prediction, there are two important issues. The first one is the design of the structure model and the second one is the design of the optimization technology. Because of the complexity of the realistic protein structure, the structure model adopted in this paper is a simplified model, which is called off-lattice AB model. After the structure model is assumed, optimization technology is needed for searching the best conformation of a protein sequence based on the assumed structure model. However, PSP is an NP-hard problem even if the simplest model is assumed. Thus, many algorithms have been developed to solve the global optimization problem. In this paper, a hybrid algorithm, which combines genetic algorithm based on matrix coding (GAMC) and tabu search (TS) algorithm, is developed to complete this task. Experiments are performed with Fibonacci sequences and real protein sequences. Results show that the lowest energy obtained by the proposed GTAMC algorithm is lower than that obtained by previous methods. Our algorithm has better performance in global optimization and can predict 3D protein structure more effectively.
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