Amino Acid Features for Prediction of Protein-Protein Interface Residues with Support Vector Machines
Knowledge of protein-protein interaction sites is vital to determine proteins’ function and involvement in different pathways. Support Vector Machines (SVM) have been proposed over the recent years to predict protein-protein interface residues, primarily based on single amino acid sequence inputs. We investigate the features of amino acids that can be best used with SVM for predicting residues at protein-protein interfaces. The optimal feature set was derived from investigation into features such as amino acid composition, hydrophobic characters of amino acids, secondary structure propensity of amino acids, accessible surface areas, and evolutionary information generated by PSI-BLAST profiles. Using a backward elimination procedure, amino acid composition, accessible surface areas, and evolutionary information generated by PSI-BLAST profiles gave the best performance. The present approach achieved overall prediction accuracy of 74.2% for 77 individulal proteins collected from the Protein Data Bank, which is better than the previously reported accuracies.
Unable to display preview. Download preview PDF.
- 9.Yan, C., Dobbs, D., Honavar, V.: Identification of residues involved in protein-protein interaction from amino acid sequencea support vector machine approach. In: Abraham, A., Franke, K., Köppen, M. (eds.) Intelligent Systems Design and Applications, pp. 53–62. Springer, Berlin (2003)Google Scholar
- 11.Cristianini, N., Shawe-Taylor, J.: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, New York (2000)Google Scholar
- 13.Nguyen, M.N., Rajapakse, J.C.: Two-stage multi-class SVMs for protein secondary structure prediction. Pacific Symposium on Biocomputing (PSB), Hawaii (2005)Google Scholar
- 17.Rajapakse, J.C., Duan, K.-B., Yeo, W.K.: Proteomic cancer classification with mass spectra data. American Journal of Pharmacology 5(5), 228–234 (2005)Google Scholar
- 19.Thornton, J., Taylor, W.R.: Structure prediction. In: Findlay, J.B.C., Geisow, M.J. (eds.) Protein Sequencing, pp. 147–190. IRL Press, Oxford (1989)Google Scholar
- 23.Chakrabarti, P., Janin, J.: Dissecting protein-protein recognition sites. J. Mol. Biol. 272, 132–143 (2002)Google Scholar