Protein Structure Prediction Based on Profile HMM and DMQPSO
Keywords:
Protein structure prediction, protein secondary structure, fold recognition, profile HMM, DMQPSO.Abstract
Protein structure prediction is a challenging field
strongly associated with protein function and evolution
determination, which is crucial for biologists and the
pharmaceutical industry. Despite significant process made in recent years, protein structure prediction maintains its status as one of the prime unsolved problems in computational biology. In this study, we have developed a method for protein structure
prediction based on profile Hidden Markov Model (HMM) and Quantum Particle Swarm Optimization (QPSO) with diversity-maintained algorithm (DMQPSO). The profile HMM can reduce the number of states using secondary structure information about proteins for each fold, which is called a 7-state HMM. The DMQPSO is an efficient optimization algorithm which is used to train profile HMM. Experiment results show that the proposed method is reasonable and the accuracy of protein secondary structure prediction is increased.
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