Functional Classification of G-Protein Coupled Receptors, Based on Their Specific Ligand Coupling Patterns
Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research. Although thousands of GPCR sequences are known, many of them remain as orphan sequences (the activating ligand is unknown). Therefore, classification methods for automated characterization of orphan GPCRs are imperative. In this study, for predicting Level 2 subfamilies of Amine GPCRs, a novel method for obtaining fixed-length feature vectors, based on the existence of activating ligand specific patterns, has been developed and utilized for a Support Vector Machine (SVM)-based classification. Exploiting the fact that there is a non-promiscuous relationship between the specific binding of GPCRs into their ligands and their functional classification, our method classifies Level 2 subfamilies of Amine GPCRs with a high predictive accuracy of 97.02% in a ten-fold cross validation test. The presented machine learning approach, bridges the gulf between the excess amount of GPCR sequence data and their poor functional characterization.
KeywordsSupport Vector Machine Feature Vector Radial Basis Function Grid Search Radial Basis Function Kernel
Unable to display preview. Download preview PDF.
- 1.Altshul, S., et al.: Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)Google Scholar
- 4.Brazma, A., et al.: Discovering patterns and subfamilies in biosequences. In: Proceedings of the Fourth International Conference on Intellignent Systems for Molecular Biology (ISMB 1996), pp. 34–43. AAAI Press, Menlo Park (1996), Pratt 2.1 software is available at www.ebi.ac.uk/pratt
- 5.Byvatov, E., Schneider, G.: Support vector machine applications in bioinformatics. Appl. Bioinformatics 2, 67–77 (2003)Google Scholar
- 7.Chang, C.C. and Lin, C.J.: LIBSVM : a library for support vector machines. (2001) LIBSVM software is available at http://www.csie.ntu.edu.tw/ cjlin/libsvmGoogle Scholar
- 10.Horn, F., et al.: GPCRDB: an information system for G protein coupled receptors. Nucleic Acids Res. 26, 275–279 (1998), Available at http://www.gpcr.org/7tm
- 11.Hsu, C.W., et al.: A Practical Guide to Support Vector Classification. Image, Speech and Intelligent Systems (ISIS) Seminars (2004)Google Scholar
- 15.Krzysztof, P., et al.: Crystal Structure of Rhodopsin: A G- Protein-Coupled Receptor. Science 4, 739–745 (2000)Google Scholar
- 16.Lin, H.T., Lin, C.J.: A study on sigmoid kernels for SVM and the train ing of nonPSDkernels by SMO type methods. Technical report, Department of Computer Science and Information Engineering, National Taiwan University cjlin/papers/tanh.pdf (2003), Available at http://www.csie.ntu.edu.tw/
- 21.Quinlan, J.R.: C4.5; Programs for Machine Learning. Morgan Kauffman Publishers, San Francisco (1988)Google Scholar
- 23.Schoneberg, T., et al.: The structural basis of g-protein-coupled receptor function and dysfunction in human diseases. Rev. Physiol. Biochem. Pharmacol. 144, 143–227 (2002)Google Scholar
- 28.Vert, J.P.: Introduction to Support Vector Machines and applications to computational biology (2001)Google Scholar