A dynamic Bayesian network approach to protein secondary structure prediction

Authors

Xin-Qiu Joe Yao, University of Nebraska at OmahaFollow
Huaigiu Zhu, Peking University
Zhen-Su She, Peking University

Author ORCID Identifier

Yao - https://orcid.org/0000-0003-2706-2028

Document Type

Article

Publication Date

1-25-2008

Publication Title

BMC Bioinformatics

Volume

9

Issue

49

DOI

https://doi.org/10.1186/1471-2105-9-49

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.

Comments

This is an open access article licensed under Creative Commons Attribution License ( https://creativecommons.org/licenses/by/2.0 ),

Recommended Citation

Yao, Xin-Qiu Joe; Zhu, Huaigiu; and She, Zhen-Su, "A dynamic Bayesian network approach to protein secondary structure prediction" (2008). Chemistry Faculty Publications. 78.
https://digitalcommons.unomaha.edu/chemfacpub/78

Creative Commons License

This work is licensed under a Creative Commons Attribution 2.0 License.