Springer Protocols: Abstract: In Silico Prediction of Peptide

In Silico Prediction of Peptide–MHC Binding Affinity Using SVRMHC

By: Wen Liu³, Ji Wan³, Xiangshan Meng³, Darren R. Flower⁴, Tongbin Li³

Abstract

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The binding between peptide epitopes and major histocompatibility complex (MHC) proteins is a major event in the cellular immune response. Accurate prediction of the binding between short peptides and class I or class II MHC molecules is an important task in immunoinformatics. SVRMHC which is a novel method to model peptide–MHC binding affinities based on support vector machine regression (SVR) is described in this chapter. SVRMHC is among a small handful of quantitative modeling methods that make predictions about precise binding affinities between a peptide and an MHC molecule. As a kernel-based learning method, SVRMHC has rendered models with demonstrated appealing performance in the practice of modeling peptide–MHC binding.

Affiliation(s): (3) Department of Neuroscience, University of Minnesota, Minneapolis, MN
(4) The Jenner Institute, University of Oxford, Berkshire, UK

Book Title: Immunoinformatics: Predicting Immunogenicity In Silico

Series: Methods in Molecular Biology | Volume: 409 | Pub. Date: Jun-21-2007 | Page Range: 283-291 | DOI: 10.1007/978-1-60327-118-9_20

Subject: Immunology

Key Words: SVR - SVRMHC - epitope binding - modeling

References

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1.	Sette, A., Buus, S., Appella, E., Smith, J.A., Chesnut, R., Miles, C., Colon, S.M. and Grey, H.M. (1989) Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc Natl Acad Sci USA, 86, 3296–3300.

2.	Nielsen, M., Lundegaard, C., Worning, P., Hvid, C.S., Lamberth, K., Buus, S., Brunak, S. and Lund, O. (2004) Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics, 20, 1388–1397.

3.	Rammensee, H., Bachmann, J., Emmerich, N.P., Bachor, O.A. and Stevanovic, S. (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics, 50, 213–219.

4.	Parker, K.C., Bednarek, M.A. and Coligan, J.E. (1994) Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J Immunol, 152, 163–175.

5.	Reche, P.A., Glutting, J.P. and Reinherz, E.L. (2002) Prediction of MHC class I binding peptides using profile motifs. Hum Immunol, 63, 701–709.

6.	Nielsen, M., Lundegaard, C., Worning, P., Lauemoller, S.L., Lamberth, K., Buus, S., Brunak, S. and Lund, O. (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci, 12, 1007–1017.

7.	Brusic, V., Rudy, G., Honeyman, G., Hammer, J. and Harrison, L. (1998) Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics, 14, 121–130.

8.	Honeyman, M.C., Brusic, V., Stone, N.L. and Harrison, L.C. (1998) Neural network-based prediction of candidate T-cell epitopes. Nat Biotechnol, 16, 966–969.

9.	Mamitsuka, H. (1998) Predicting peptides that bind to MHC molecules using supervised learning of hidden Markov models. Proteins, 33, 460–474.

10.	Donnes, P. and Elofsson, A. (2002) Prediction of MHC class I binding peptides, using SVMHC. BMC Bioinformatics, 3, 25.

11.	Bhasin, M. and Raghava, G.P. (2004) SVM based method for predicting HLA-DRB10401 binding peptides in an antigen sequence. Bioinformatics*, 20, 421–423.

12.	Doytchinova, I.A. and Flower, D.R. (2002) Quantitative approaches to computational vaccinology. Immunol Cell Biol, 80, 270–279.

13.	Doytchinova, I.A. and Flower, D.R. (2002) A comparative molecular similarity index analysis (CoMSIA) study identifies an HLA-A2 binding supermotif. J Comput Aided Mol Des, 16, 535–544.

14.	Doytchinova, I.A. and Flower, D.R. (2001) Toward the quantitative prediction of T-cell epitopes: coMFA and coMSIA studies of peptides with affinity for the class I MHC molecule HLA-A0201. J Med Chem*, 44, 3572–3581.

15.	Hattotuwagama, C.K., Toseland, C.P., Guan, P., Taylor, D.L., Hemsley, S.L., Doytchinova, I.A. and Flower, D.R. (2005) Class II mouse major histocompatibility complex peptide binding affinity: in silico bioinformatic prediction using robust multivariate statistics. J Chem Inf Mod, 46(3), 1491–502. (2006)

16.	Doytchinova, I.A. and Flower, D.R. (2003) Towards the in silico identification of class II restricted T-cell epitopes: a partial least squares iterative self-consistent algorithm for affinity prediction. Bioinformatics, 19, 2263–2270.

17.	Doytchinova, I.A., Blythe, M.J. and Flower, D.R. (2002) Additive method for the prediction of protein-peptide binding affinity. Application to the MHC class I molecule HLA-A0201. J Proteome Res*, 1, 263–272.

18.	Hattotuwagama, C.K., Guan, P., Doytchinova, I.A. and Flower, D.R. (2004) New horizons in mouse immunoinformatics: reliable in silico prediction of mouse class I histocompatibility major complex peptide binding affinity. Org Biomol Chem, 2, 3274–3283.

19.	Vapnik, V. (1998) Statistical Learning Theory. John Wiley & Sons, New York.

20.	Vapnik, V. (1995) The Nature of Statistical Learning Theory. Springer-Verlag, New York.

21.	Cristianini, N. and Shawe-Taylor, J. (2000) An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge University Press, Cambridge, UK.

22.	Baldi, P. and Brunak, S. (2001) Bioinformatics: The Machine Learning Approach. The MIT Press, Cambridge, MA.

23.	Doytchinova, I.A. and Flower, D.R. (2002) Physicochemical explanation of peptide binding to HLA-A0201 major histocompatibility complex: a three-dimensional quantitative structure-activity relationship study. Proteins*, 48, 505–518.

24.	Liu, W., Meng, X., Xu, Q., Flower, D.R. and Li, T. (2006) Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models. BMC Bioinformatics, 7, 182.

25.	Chang, C.C. and Lin, C.J. (2004) A practical guide to SVM classification, LibSVM documentation.

26.	Cherkassky, V. and Ma, Y. (2004) Practical selection of SVM parameters and noise estimation for SVM regression. Neural Netw, 17, 113–126.

27.	Toseland, C.P., Clayton, D.J., McSparron, H., Hemsley, S.L., Blythe, M.J., Paine, K., Doytchinova, I.A., Guan, P., Hattotuwagama, C.K. and Flower, D.R. (2005) AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunol Res, 1, 4.

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