Protein secondary structure prediction is one of the most important and challenging problems in structural bioinformatics, which has been an essential task in determining the structure and function of the proteins. Despite significant progress made in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. A novel probability revise table based hidden Markov model (PRT-HMM) method is presented in this paper with considering the dependencies among the state transitions. We revise the initial predicted protein structure through looking up the probability revise table, which is learned from the dataset. Theoretical analysis and experiment results indicate that the proposed method is reasonable and the accuracy of protein secondary structure prediction is increased compared to the original hidden Markov model (HMM).
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