Using principal component analysis and support vector machine to predict protein structural class for low-similarity sequences via PSSM

Abstract

The accurate identification of protein structure class solely using extracted information from protein sequence is a complicated task in the current computational biology. Prediction of protein structural class for low-similarity sequences remains a challenging problem. In this study, the new computational method has been developed to predict protein structural class by fusing the sequence information and evolution information to represent a protein sample. To evaluate the performance of the proposed method, jackknife cross-validation tests are performed on two widely used benchmark data-sets, 1189 and 25PDB with sequence similarity lower than 40 and 25%, respectively. Comparison of our results with other methods shows that the proposed method by us is very promising and may provide a cost-effective alternative to predict protein structural class in particular for low-similarity data-sets.

Keywords:

• protein structural class
• principal component analysis
• support vector machine
• transition probability matrix
• PSSM

Acknowledgements

The authors thank the anonymous referees for many valuable suggestions that have improved this manuscript. The authors also thank Dr Taigang Liu for providing the data-sets used in this paper. The work was supported in part by Scientific Research Startup Foundation of Xidian University and the Fundamental Research Funds for the Central Universities.