Pattern Recognition for Subfamily Level Classification of GPCRs Using Motif Distillation and Distinguishing Power Evaluation

  • Ahmet Sinan Yavuz
  • Bugra Ozer
  • Osman Ugur Sezerman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7632)

Abstract

G protein coupled receptors (GPCRs) are one of the most prominent and abundant family of membrane proteins in the human genome. Since they are main targets of many drugs, GPCR research has grown significantly in recent years. However the fact that only few structures of GPCRs are known still remains as an important challenge. Therefore, the classification of GPCRs is a significant problem provoked from increasing gap between orphan GPCR sequences and a small amount of annotated ones. This work employs motif distillation using defined parameters, distinguishing power evaluation method and general weighted set cover problem in order to determine the minimum set of motifs which can cover a particular GPCR subfamily. Our results indicate that in Family A Peptide subfamily, 91% of all proteins listed in GPCRdb can be covered by using only 691 different motifs, which can be employed later as an invaluable source for developing a third level GPCR classification tool.

Keywords

g-protein coupled receptors data mining pattern recognition 

References

  1. 1.
    Filmore, D.: It’s a GPCR World. Modern Drug Discovery 7(11), 24–28 (2004)Google Scholar
  2. 2.
    Joost, P., Methner, A.: Phylogenetic analysis of 277 human G-protein- coupled receptors as a tool for the prediction of orphan receptor ligands. Genome Biology 3(11), research0063.1–research0063.16 (October 2002)Google Scholar
  3. 3.
    Davey, J., Ladds, G.: Heterologous Expression of GPCRs in Fission Yeast. Methods in Molecular Biology 746, 113–131 (2011)CrossRefGoogle Scholar
  4. 4.
    Gerber, S., Krasky, A., Rohwer, A., Lindauer, S., Closs, E., Rognan, D., Gunkel, N., Selzer, P.M., Wolf, C.: Identification and characterisation of the dopamine receptor II from the cat flea Ctenocephalides felis (CfDo- pRII). Insect Biochemistry and Molecular Biology 36(10), 749–758 (2006)CrossRefGoogle Scholar
  5. 5.
    Libert, F., Parmentier, M., Lefort, A., Dinsart, C., Van Sande, J., Maenhaut, C., Simons, M.J., Dumont, J.E., Vassart, G.: Selective amplification and cloning of four new members of the G protein-coupled receptor family. Science 244(4904), 569–572 (1989)CrossRefGoogle Scholar
  6. 6.
    Methner, A., Hermey, G., Schinke, B., Hermans-Borgmeyer, I.: A novel G protein-coupled receptor with homology to neuropeptide and chemoattractant receptors expressed during bone development. Biochemical and Biophysical Research Communications 233(2), 336–342 (1997)CrossRefGoogle Scholar
  7. 7.
    Horn, F., Bettler, E., Oliveira, L., Campagne, F., Cohen, F.E., Vriend, G.: GPCRDB information system for G protein-coupled receptors. Nucleic Acids Research 31(1), 294–297 (2003)CrossRefGoogle Scholar
  8. 8.
    Gether, U.: Uncovering molecular mechanisms involved in activation of G protein-coupled receptors. Endocrine Reviews 21(1), 90–113 (2000)CrossRefGoogle Scholar
  9. 9.
    Rosenbaum, D.M., Rasmussen, S.R.G.F., Kobilka, B.K.: The structure and function of G-protein-coupled receptors. Nature 459(7245), 356–363 (2009)CrossRefGoogle Scholar
  10. 10.
    Foord, S.M., Bonner, T.O.M.I., Neubig, R.R., Rosser, E.M., Pin, J.P., Davenport, A.P., Spedding, M., Harmar, A.J.: International Union of Pharmacology. XLVI. G Protein-Coupled Receptor List. Pharmacological Reviews 57(2), 279–288 (2005)Google Scholar
  11. 11.
    Davies, M.N., Secker, A., Halling-Brown, M., Moss, D.S., Freitas, A.A., Timmis, J., Clark, E., Flower, D.R.: GPCRTree: online hierarchical classification of GPCR function. BMC Research Notes 1, 67 (2008)CrossRefGoogle Scholar
  12. 12.
    Gaulton, A., Attwood, T.K.: Bioinformatics approaches for the classification of G-protein-coupled receptors. Current Opinion in Pharmacology 3(2), 114–120 (2003)CrossRefGoogle Scholar
  13. 13.
    Bhasin, M., Raghava, G.P.S.: GPCRpred: an SVM-based method for prediction of families and subfamilies of G-protein coupled receptors. Nucleic Acids Research 32(Web Server Issue), W383–W389 (2004)Google Scholar
  14. 14.
    Karchin, R., Karplus, K., Haussler, D.: Classifying G-protein coupled receptors with support vector machines. Bioinformatics 18(1), 147–159 (2002)CrossRefGoogle Scholar
  15. 15.
    Papasaikas, P.K., Bagos, P.G., Litou, Z.I., Promponas, V.J., Hamod- Rakas, S.J.: PRED-GPCR: GPCR recognition and family classification server. Nucleic Acids Research 32(Web Server Issue), W380–W382 (2004)Google Scholar
  16. 16.
    Yabuki Y., Muramatsu T., Hirokawa T., Mukai H., Suwa M.: GRIFFIN: a system for predicting GPCR–G-protein coupling selectivity using a support vector machine and a hidden Markov model. Nucleic Acids Research, 33(Web server issue), W148–W153 (2005)Google Scholar
  17. 17.
    Cui, J., Han, L.Y., Li, H., Ung, C.Y., Tang, Z.Q., Zheng, C.J., Cao, Z.W., Chen, Y.Z.: Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties. Molecular Immunology 44(4), 514–520 (2007)CrossRefGoogle Scholar
  18. 18.
    Atchley, W.R., Zhao, J., Fernandes, A.D., Druke, T.: Solving the protein sequence metric problem. PNAS 102(18), 6395–6400 (2005)CrossRefGoogle Scholar
  19. 19.
    Davies, M.N., Secker, A., Freitas, A.A., Mendao, M., Timmis, J., Flower, D.R.: On the hierarchical classification of G protein-coupled receptors. Bioinformatics 23(23), 3113–3118 (2007)CrossRefGoogle Scholar
  20. 20.
    Cobanoglu, M.C., Saygin, Y., Sezerman, U.: Classification of GPCRs using family specific motifs. IEEE Transactions on Computational Biology 8(6), 1495–1508 (2011)CrossRefGoogle Scholar
  21. 21.
    Davies, M.N., Secker, A., Freitas, A.A., Clark, E., Timmis, J., Flower, D.R.: Optimizing amino acid groupings for GPCR classification. Bioinformatics 24(18), 1980–1986 (2008)CrossRefGoogle Scholar
  22. 22.
    Krogh, A., Larsson, B., von Heijne, G., Sonnhammer, E.L.L.: Predicting trans- membrane protein topology with a hidden Markov model: application to complete genomes. Journal of Molecular Biology 305(3), 567–580 (2001)CrossRefGoogle Scholar
  23. 23.
    Salton, G.: Developments in automatic text retrieval. Science 253(5023), 974–980 (1991)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Sol, C.: Identification of disease related significant SNPs. M.Sc. Thesis. Faculty of Engineering and Natural Sciences. Sabanci University (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ahmet Sinan Yavuz
    • 1
  • Bugra Ozer
    • 1
  • Osman Ugur Sezerman
    • 1
  1. 1.Faculty of Engineering and Natural SciencesSabanci UniversityIstanbulTurkey

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