Abstract
Major histocompatibility complex class I (MHC I) molecules belong to a large and diverse protein superfamily whose families can be divided in three groups according to the type of ligands that they can accommodate (ligand-type specificity): peptides, lipids or none. Here, we assembled a dataset of MHC I proteins of known ligand-type specificity (MHCI556 dataset) and trained k-nearest neighbor and support vector machine algorithms. In cross-validation, the resulting classifiers predicted the ligand-type specificity of MHC I molecules with an accuracy ≥ 99%, using solely their amino acid composition. By holding out entire MHC I families prior to model building, we proved that ML-based classifiers trained on amino acid composition are capable of predicting the ligand-type specificity of MHC I molecules unrelated to those used for model building. Moreover, they are superior to BLAST at predicting the class of MHC I molecules that do not bind any ligand.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Maenaka, K., Jones, E.Y.: MHC superfamily structure and the immune system. Curr. Opin. Struct. Biol. 9, 745–753 (1999)
Townsend, A., Bodmer, H.: Antigen recognition by class I-restricted T lymphocytes. Annual Review of Immunology 7, 601–624 (1989)
Braud, V.M., Allan, D.S., McMichael, A.J.: Functions of nonclassical MHC and non-MHC-encoded class I molecules. Curr. Opin. Immunol. 11, 100–108 (1999)
Clements, C.S., Kjer-Nielsen, L., Kostenko, L., Hoare, H.L., Dunstone, M.A., Moses, E., Freed, K., Brooks, A.G., Rossjohn, J., McCluskey, J.: Crystal structure of HLA-G: a nonclassical MHC class I molecule expressed at the fetal-maternal interface. Proc. Natl. Acad. Sci. USA 102, 3360–3365 (2005)
He, X., Tabaczewski, P., Ho, J., Stroynowski, I., Garcia, K.C.: Promiscuous antigen presentation by the nonclassical MHC Ib Qa-2 is enabled by a shallow, hydrophobic groove and self-stabilized peptide conformation. Structure 9, 1213–1224 (2001)
Lu, L., Werneck, M.B., Cantor, H.: The immunoregulatory effects of Qa-1. Immunological Reviews 212, 51–59 (2006)
Hoare, H.L., Sullivan, L.C., Pietra, G., Clements, C.S., Lee, E.J., Ely, L.K., Beddoe, T., Falco, M., Kjer-Nielsen, L., Reid, H.H., McCluskey, J., Moretta, L., Rossjohn, J., Brooks, A.G.: Structural basis for a major histocompatibility complex class Ib-restricted T cell response. Nat. Immunol. 7, 256–264 (2006)
Rodgers, J.R., Cook, R.G.: MHC class Ib molecules bridge innate and acquired immunity. Nature Reviews Immunology 5, 459–471 (2005)
Barral, D.C., Brenner, M.B.: CD1 antigen presentation: how it works. Nature Reviews Immunology 7, 929–941 (2007)
Kennedy, M.W., Heikema, A.P., Cooper, A., Bjorkman, P.J., Sanchez, L.M.: Hydrophobic ligand binding by Zn-alpha 2-glycoprotein, a soluble fat-depleting factor related to major histocompatibility complex proteins. The Journal of Biological Chemistry 276, 35008–35013 (2001)
Esmon, C.T.: The endothelial protein C receptor. Current Opinion in Hematology 13, 382–385 (2006)
Sanchez, L.M., Chirino, A.J., Bjorkman, P.: Crystal structure of human ZAG, a fat-depleting factor related to MHC molecules. Science 283, 1914–1919 (1999)
Oganesyan, V., Oganesyan, N., Terzyan, S., Qu, D., Dauter, Z., Esmon, N.L., Esmon, C.T.: The crystal structure of the endothelial protein C receptor and a bound phospholipid. J. Biol. Chem. 277, 24851–24854 (2002)
Liu, Y., Xiong, Y., Naidenko, O.V., Liu, J.H., Zhang, R., Joachimiak, A., Kronenberg, M., Cheroutre, H., Reinherz, E.L., Wang, J.H.: The crystal structure of a TL/CD8alphaalpha complex at 2.1 A resolution: implications for modulation of T cell activation and memory. Immunity 18, 205–215 (2003)
Wingren, C., Crowley, M.P., Degano, M., Chien, Y., Wilson, I.A.: Crystal structure of a gammadelta T cell receptor ligand T22: a truncated MHC-like fold. Science 287, 310–314 (2000)
Bahram, S., Inoko, H., Shiina, T., Radosavljevic, M.: MIC and other NKG2D ligands: from none to too many. Current Opinion in Immunology 17, 505–509 (2005)
Roopenian, D.C., Akilesh, S.: FcRn: the neonatal Fc receptor comes of age. Nature Reviews Immunology 7, 715–725 (2007)
Feder, J.N., Gnirke, A., Thomas, W., Tsuchihashi, Z., Ruddy, D.A., Basava, A., Dormishian, F., Domingo, R., Ellis Jr, M.C., Fullan, A., Hinton, L.M., Jones, N.L., Kimmel, B.E., Kronmal, G.S., Lauer, P., Lee, V.K., Loeb, D.B., Mapa, F.A., McClelland, E., Meyer, N.C., Mintier, G.A., Moeller, N., Moore, T., Morikang, E., Prass, C.E., Quintana, L., Starnes, S.M., Schatzman, R.C., Brunke, K.J., Drayna, D.T., Risch, N.J., Bacon, B.R., Wolff, R.K.: A novel MHC class I-like gene is mutated in patients with hereditary haemochromatosis. Nature Genetics 13, 399–408 (1996)
Burmeister, W.P., Huber, A.H., Bjorkman, P.J.: Crystal structure of the complex of rat neonatal Fc receptor with Fc. Nature 372, 379–383 (1994)
Lebron, J.A., Bennett, M.J., Vaughn, D.E., Chirino, A.J., Snow, P.M., Mintier, G.A., Feder, J.N., Bjorkman, P.J.: Crystal structure of the hemochromatosis protein HFE and characterization of its interaction with transferrin receptor. Cell 93, 111–123 (1998)
Wistrand, M., Sonnhammer, E.L.: Improved profile HMM performance by assessment of critical algorithmic features in SAM and HMMER. BMC Bioinformatics 6, 99 (2005)
Sonnhammer, E.L., Eddy, S.R., Durbin, R.: Pfam: a comprehensive database of protein domain families based on seed alignments. Proteins 28, 405–420 (1997)
Neuwald, A.F., Liu, J.S., Lawrence, C.E.: Gibbs motif sampling detection of bacterial outer membrane protein repeats. Prot. Sci. 4, 1618–1632 (1995)
EMBOSS: the European Molecular Biology Open Software Suite. Trends Genet. 16, 276–277 (2000)
Frank, E., Hall, M., Trigg, L., Holmes, G., Witten, I.H.: Data mining in bioinformatics using Weka. Bioinformatics 20, 2479–2481 (2004)
Wu, X., Kumar, V., Quilan, J.R., Ghosh, J., Yang, Q., Motoda, H., McLachlan, G.J., Ng, A., Liu, B., Yu, P.S., Zhou, Z.-H., Steinbach, M., Hand, D.J., Steinberg, D.: Top 10 algorithms in data mining Knowledge and Information Systems. Springer, Heidelberg (2008)
Dasarathy, B.V.: Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. McGraw-Hill Computer Science Series. IEEE Computer Society Press, Los Alamitos, California (1991)
Platt, J.C.: Fast Training of Support Vector Machines using Sequential Minimal Optimization Advances in Kernel Methods - Support Vector Learning. In: Schölkopf, B., Burges, C., Smola, A.J. (eds.), pp. 185–208. MIT Press, Cambridge (1999)
Diez-Rivero, C.M., Chenlo, B., Zuluaga, P., Reche, P.A.: Quantitative modeling of peptide binding to TAP using support vector machine. Proteins 14, 14 (2009)
Bhasin, M., Reinherz, E.L., Reche, P.A.: Recognition and classification of histones using support vector machine. J. Comput. Biol. 13, 102–112 (2006)
Chen, K., Kurgan, L.A., Ruan, J.: Prediction of protein structural class using novel evolutionary collocation-based sequence representation. J. Comput. Chem. 29, 1596–1604 (2008)
Saha, S., Zack, J., Singh, B., Raghava, G.P.: VGIchan: prediction and classification of voltage-gated ion channels. Genomics Proteomics Bioinformatics 4, 253–258 (2006)
Altschul, S.F., Madden, T.L., Schaffer, A.A., Zhang, J., Zhang, Z., Miller, W., Lipman, D.J.: Gapped BLAST and PSI-BLAST: a new generation of protein database search programs. Nucleic Acids Res. 25, 3389–3402 (1997)
Lafuente, E.M., Reche, P.A.: Prediction of MHC-peptide binding: a systematic and comprehensive overview. Curent Pharmaceutical Design 15, 3209–3220 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Martínez-Naves, E., Lafuente, E.M., Reche, P.A. (2011). Classification of MHC I Proteins According to Their Ligand-Type Specificity. In: Liò, P., Nicosia, G., Stibor, T. (eds) Artificial Immune Systems. ICARIS 2011. Lecture Notes in Computer Science, vol 6825. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22371-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-22371-6_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-22370-9
Online ISBN: 978-3-642-22371-6
eBook Packages: Computer ScienceComputer Science (R0)