Protein Structure Prediction Based on HP Model Using an Improved Hybrid EDA

Abstract

Protein structure prediction (PSP) is one of the most important problems in computational biology. This chapter introduces a novel hybrid Estimation of Distribution Algorithm (EDA) to solve the PSP problem on HP model. Firstly, a composite fitness function containing the information of folding structure core (H-Core) is introduced to replace the traditional fitness function of HP model. The new fitness function is expected to select better individuals for probabilistic model of EDA. Secondly, local search with guided operators is utilized to refine found solutions for improving efficiency of EDA. Thirdly, an improved backtracking-based repairing method is introduced to repair invalid individuals sampled by the probabilistic model of EDA. It can significantly reduce the number of backtracking searching operation and the computational cost for long sequence protein. Experimental results demonstrate that the new method outperforms the basic EDAs method. At the same time, it is very competitive with other existing algorithms for the PSP problem on lattice HP models.