Prediction of Protein Complexes Based on Protein Interaction Data and Functional Annotation Data Using Kernel Methods
Prediction of protein complexes is a crucial problem in computational biology. The increasing amount of available genomic data can enhance the identification of protein complexes. Here we describe an approach for predicting protein complexes based on integration of protein-protein interaction (PPI) data and protein functional annotation data. The basic idea is that proteins in protein complexes often interact with each other and protein complexes exhibit high functional consistency/even multiple functional consistency. We create a protein-protein relationship network (PPRN) via a kernel-based integration of these two genomic data. Then we apply the MCODE algorithm on PPRN to detect network clusters as numerically determined protein complexes. We present the results of the approach to yeast Sacchromyces cerevisiae. Comparison with well-known experimentally derived complexes and results of other methods verifies the effectiveness of our approach.
KeywordsProtein Complex Functional Annotation Kernel Method Protein Interaction Network Protein Interaction Data
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- 4.Li, X.L., Tan, S.H., Foo, C.S., Ng, S.K.: Interaction Graph Mining for Protein Complexes Using Local Clique Merging. Genome Informatics 16, 260–269 (2005)Google Scholar
- 7.Kondor, R.I., Lafferty, J.: Diffusion Kernels on Graphs and Other Discrete Input. In: Proceedings of the 19th International Conference on Machine Learning, pp. 315–322. Morgan Kaufmann, University of South Wales, Sydney, Australia (2002)Google Scholar
- 8.Ito, T., Tashiro, K., Muta, S., Ozawa, R., Chiba, T., Nishizawa, M., Yamamoto, K., Kuhara, S., Sakaki, Y.: Toward a Protein-Protein Interaction Map of the Budding Yeast: a Comprehensive System to Examine Two-hybrid Interactions in All Possible Combinations between the Yeast Proteins. Proc. Natl Acad. Sci., USA 97, 1143–1147 (2000)CrossRefGoogle Scholar
- 12.Ruepp, A., Zollner, A., Maier, D., Albermann, K., Hani, J., Mokrejs, M., Tetko, I., Guldener, U., Mannhaupt, G., Munsterkotter, M., et al.: The FunCat, a Functional Annotation Scheme for Systematic Classification of Proteins from Whole Genomes. Nucleic Acids Res. 32, 5539–5545 (2004)CrossRefGoogle Scholar