Publication: Recognition of the ligand-type specificity of classical and non-classical MHC I proteins.
Loading...
Full text at PDC
Publication Date
2011
Advisors (or tutors)
Editors
Journal Title
Journal ISSN
Volume Title
Publisher
Federation of European Biochemical Societies
Citations
Exportar
Abstract
Functional characterization of proteins belonging to the MHC I superfamily involves knowing their cognate ligands, which can be peptides, lipids or none. However, the experimental identification of these ligands is not an easy task and generally requires some a priori knowledge of their chemical nature (ligand-type specificity). Here, we trained k-nearest neighbor and support vector machine classifiers that predict the ligand-type specificity MHC I proteins with great accuracy. Moreover, we applied these classifiers to human and mouse MHC I proteins of uncharacterized ligands, obtaining some results that can be instrumental to unravel the function of these proteins.
Description
UCM subjects
Unesco subjects
Keywords
Citation
[1] Maenaka, K. and Jones, E.Y. (1999) MHC superfamily structure and the immune system. Curr. Opin. Struct. Biol. 9, 745–753.
[2] Townsend, A. and Bodmer, H. (1989) Antigen recognition by class I-restricted T lymphocytes. Annu. Rev. Immunol. 7, 601–624.
[3] Braud, V.M., Allan, D.S. and McMichael, A.J. (1999) Functions of nonclassical MHC and non-MHC-encoded class I molecules. Curr. Opin. Immunol. 11, 100–108.
[4] Rodgers, J.R. and Cook, R.G. (2005) MHC class Ib molecules bridge innate and acquired immunity. Nat. Rev. Immunol. 5, 459–471.
[5] Martínez-Naves, E., Lafuente, E.M. and Reche, P.A. (2011) Classification of MHC I proteins according to their ligand-type specificity in: 10th International Conference on Artificial Immune Systems (Liò, P., Nicosia, G. and Stibor, T., Eds.), pp. 55–65, Springer-Verlag Cambridge, England, UK.
[6] Frank, E., Hall, M., Trigg, L., Holmes, G. and Witten, I.H. (2004) Data mining in bioinformatics using WEKA. Bioinformatics 20, 2479–2481.
[7] Wu, X. et al. (2008) Top 10 algorithms in data mining. Knowl. Inf. Syst 14, 1–37.
[8] Dasarathy, B.V. (1991) Nearest neighbor (NN) norms: NN pattern classification techniques, IEEE Computer Society Press, Los Alamitos, California.
[9] Shatsky, M., Nussinov, R. and Wolfson, H.J. (2004) A method for simultaneous alignment of multiple protein structures. Proteins 56, 143–156.
[10] Shatsky, M., Nussinov, R. and Wolfson, H.J. (2006) Optimization of multiplesequence alignment based on multiple-structure alignment. Proteins. 62, 209–217.
[11] Notredame, C., Higgins, D.G. and Heringa, J. (2000) T-Coffee: A novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 302, 205–217.
[12] Firestine, S.M., Nixon, A.E. and Benkovic, S.J. (1996) Threading your way to protein function. Chem. Biol. 3, 779–783.
[13] Felsenstein, J. (1985) Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791.
[14] Carro, A., Tress, M., de Juan, D., Pazos, F., Lopez-Romero, P., del Sol, A., Valencia, A. and Rojas, A.M. (2006) TreeDet: a web server to explore sequence space. Nucleic Acids Res. 34, W110–W115.
[15] Bhasin, M., Reinherz, E.L. and Reche, P.A. (2006) Recognition and classification of histones using support vector machine. J. Comput. Biol. 13, 102–112.
[16] Yang, Z. and Bjorkman, P.J. (2008) Structure of UL18, a peptide-binding viral MHC mimic, bound to a host inhibitory receptor. Proc. Natl. Acad. Sci. USA 105, 10095–10100.
[17] Crowley, M.P., Fahrer, A.M., Baumgarth, N., Hampl, J., Gutgemann, I., Teyton, L. and Chien, Y. (2000) A population of murine cd T cells that recognize an inducible MHC class Ib molecule. Science 287, 314–316.
[18] Shin, S., El-Diwany, R., Schaffert, S., Adams, E.J., Garcia, K.C., Pereira, P. and Chien, Y.H. (2005) Antigen recognition determinants of cd T cell receptors. Science 308, 252–255.
[19] Koch, M. et al. (2007) Structures of an MHC class I molecule from B21 chickens illustrate promiscuous peptide binding. Immunity 27, 885–899.
[20] Hee, C.S., Gao, S., Loll, B., Miller, M.M., Uchanska-Ziegler, B., Daumke, O. and Ziegler, A. (2010) Structure of a classical MHC class I molecule that binds ‘‘non-classical’’ ligands. PLoS Biol. 8, e1000557.
[21] Loconto, J. et al. (2003) Functional expression of murine V2R pheromone receptors involves selective association with the M10 and M1 families of MHC class Ib molecules. Cell 112, 607–618.
[22] Olson, R., Huey-Tubman, K.E., Dulac, C. and Bjorkman, P.J. (2005) Structure of a pheromone receptor-associated MHC molecule with an open and empty groove. PLoS Biol. 3, e257.
[23] Treiner, E. et al. (2003) Selection of evolutionarily conserved mucosalassociated invariant T cells by MR1. Nature 422, 164–169.
[24] Huang, S., Gilfillan, S., Cella, M., Miley, M.J., Lantz, O., Lybarger, L., Fremont, D.H. and Hansen, T.H. (2005) Evidence for MR1 antigen presentation to mucosal-associated invariant T cells. J. Biol. Chem. 280, 21183–21193.
[25] Le Bourhis, L. et al. (2010) Antimicrobial activity of mucosal-associated invariant T cells. Nat. Immunol. 11, 701–708.
[26] Abos, B., Gomez Del Moral, M., Gozalbo-Lopez, B., Lopez-Relano, J., Viana, V. and Martinez-Naves, E. (2011) Human MR1 expression on the cell surface is acid sensitive, proteasome independent and increases after culturing at 26 degrees C. Biochem. Biophys. Res. Commun. 411, 632–636.
[27] Shimamura, M. et al. (2007) Modulation of Va19 NKT cell immune responses by a-mannosyl ceramide derivatives consisting of a series of modified sphingosines. Eur J Immunol. 37, 1836–1844.
[28] Huang, S. et al. (2008) MR1 uses an endocytic pathway to activate mucosalassociated invariant T cells. J. Exp. Med. 205, 1201–1211.
[29] Lafuente, E.M. and Reche, P.A. (2009) Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curr. Pharm. Des. 15, 3209–3220.