title: A dynamic Bayesian network approach to protein secondary structure prediction.
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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: State Key Laboratory for Turbulence and Complex Systems and Department of Biomedical Engineering, Peking University, Beijing 100871, China. yxq@ctb.pku.edu.cn
authors.forename: Xin-Qiu
authors.initials: XQ
authors.lastname: Yao
authors.forename: Huaiqiu
authors.initials: H
authors.lastname: Zhu
authors.forename: Zhen-Su
authors.initials: ZS
authors.lastname: She
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D001499:Bayes Theorem; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D010363:Pattern Recognition, Automated / Q000379:methods; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins