A dynamic Bayesian network approach to protein secondary structure prediction

Background: Protein secondary structure prediction method based on probabilistic models such as hidden Markov model (HMM) appeals to many because it provides meaningful information relevant to sequence-structure relationship. However, at present, the prediction accuracy of pure HMM-type methods is much lower than that of machine learning-based methods such as neural networks (NN) or support vector machines (SVM). Results: In this paper, we report a new method of probabilistic nature for protein secondary structure prediction, based on dynamic Bayesian networks (DBN). The new method models the PSI-BLAST profile of a protein sequence using a multivariate Gaussian distribution, and simultaneously takes into account the dependency between the profile and secondary structure and the dependency between profiles of neighboring residues. In addition, a segment length distribution is introduced for each secondary structure state. Tests show that the DBN method has made a significant improvemen...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Xin-Qiu Yao, Huaiqiu Zhu, Zhen-Su She
• Keywords: BMCBI 2008, Prediction Accuracy, Protein Secondary Structure, Secondary Structure