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Open Access Research article

A dynamic Bayesian network approach to protein secondary structure prediction

Xin-Qiu Yao12, Huaiqiu Zhu12 and Zhen-Su She123*

Author Affiliations

1 State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, Peking University, Beijing, 100871, China

2 Center for Theoretical Biology, Peking University, Beijing, 100871, China

3 Department of Mathematics, University of California, Los Angeles, Los Angeles, CA 90095, USA

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BMC Bioinformatics 2008, 9:49  doi:10.1186/1471-2105-9-49

Published: 25 January 2008

Abstract

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 improvement in the accuracy compared to other pure HMM-type methods. Further improvement is achieved by combining the DBN with an NN, a method called DBNN, which shows better Q3 accuracy than many popular methods and is competitive to the current state-of-the-arts. The most interesting feature of DBN/DBNN is that a significant improvement in the prediction accuracy is achieved when combined with other methods by a simple consensus.

Conclusion

The DBN method using a Gaussian distribution for the PSI-BLAST profile and a high-ordered dependency between profiles of neighboring residues produces significantly better prediction accuracy than other HMM-type probabilistic methods. Owing to their different nature, the DBN and NN combine to form a more accurate method DBNN. Future improvement may be achieved by combining DBNN with a method of SVM type.