Improved probability graph model for protein secondary structure prediction

Abstract

Protein secondary structure is closely related to protein tertiary structure and function, and became a hot topic in bioinformatics. The probability graph model HMM (Hidden Markov model) is an important tool in this field. In practice, there exist problems such as: HMM training underflow, significant result differences derived from different training set, and hard process of parameter optimization. In this paper, aiming at HMM training underflow problem when predicting protein secondary structure, we put forward a method for solving the underflow problem; propose an 8-state HMM model to predict protein secondary structure for the first time; and modify parameter to be a three-dimensional parameter containing the state transition information. In order to improve the method drilling the optimal HMM, we train the initial HMM model with each sample, and get a series of new models; then average the parameters of the new models, and the obtained average parameter values are used to construct the optimal HMM model. The improved method increases the accuracy of protein secondary structure prediction, hence it is a good foundation for further improvement of HMM.

Keywords

• applied biochemistry
• probability graph
• protein secondary structure
• HMM
• underflow
• parameter optimization