Springer Protocols: Abstract: Support Vector Machine-Based Prediction of MHC-Binding Peptides

Support Vector Machine-Based Prediction of MHC-Binding Peptides

By: Pierre Dönnes³

Abstract

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The use of major histocompatibility complex (MHC) class I binding peptides for immunotherapeutic purposes has shown promising results in recent years. The identification of such peptides mostly starts with predicting MHC-binding peptides, given a protein of interest. An accurate prediction method can reduce the number of peptides that needs to be tested experimentally. This protocol describes in this describes how support vector machines (SVMs) can be used for predicting MHC class I binding peptides. Focus is given on data representation, the concept of cross-validation, and how optimal SVM-specific parameters are obtained.

Affiliation(s): (3) Division for Simulation of Biological Systems, Eberhard Karls University Tübingen, Tübingen, Germany
(4) Bioinformatics, F. Hoffmann-La Roche Ltd., CH-4070 Basel, Switzerland

Book Title: Immunoinformatics: Predicting Immunogenicity In Silico

Series: Methods in Molecular Biology | Volume: 409 | Pub. Date: Jun-21-2007 | Page Range: 273-282 | DOI: 10.1007/978-1-60327-118-9_19

Subject: Immunology

Key Words: Support vector machines (SVMs) - MHC binding - immunotherapy

References

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1.	Rammensee, H.-G., Bachman, J., Philipp, N., Emmerich, N., Bachor, O. A., and Stevanovic, S. (1997) SYFPEITHI: a database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219.

2.	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.

3.	Gulukota, K., Sidney, J., Sette, A., and DeLisi, C. (1997) Two complementary methods for predicting peptides binding major histocompatibility complex molecules. J. Mol. Biol. 267, 1258–1267.

4.	Altuvia, Y., Schueler, O., and Margalit, H. (1995) Ranking potential binding peptides to MHC molecules by a computational threading approach. J. Mol. Biol. 249, 244–250.

5.	Rognan, D., Lauemoller, S., Holm, A., Buus, S., and Tschinke, V. (1999) Predicting binding affinities of protein ligands from three-dimensional models: application to peptide binding to class I major histocompatibility proteins. J. Med. Chem. 42, 4650–4658.

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

7.	Shavlik, J., Hunter, L., and Searls, D. (1995), Introduction. Mach. Learn. 21, 5–9.

8.	Vapnik, V. N. (1999) The Nature of Statistical Learning Theory, Wiley, New York, USA.

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

10.	Matthews, B. W. (1975) Comparison of predicted and observed secondary structure of T4 phage lysozyme. Biochim. Biophys. Acta 405, 442–451.

11.	Brusic, V., Rudy, G., and Harrsison, L. C. (1998) MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acids Res. 26, 368–171.

12.	Boeckmann, B., Bairoch, A., Apweiler, R., Blatter, M.-C., Estreicher, A., Gasteiger, E., Martin, M. J., Michoud, K., O’Donovan, C., Phan, I., Pilbout, S., and Schneider, M. (2003) The SWISS-PROT protein knowledgebase and its supplement TrEMBL in 2003. Nucleic Acid Res. 31, 365–370.

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

14.	Joachims, T. (1998) Making large-scale SVM learning practical, In Advances in Kernel Methods - Support Vector Learning (Schölkopf, B., Burges, C., and Smola, A. eds.), MIT Press, Cambridge, MA.

15.	Hsu, C.-H., Chang, C.-C., and Lin, C.-J. (2003) A Practical Guide to Support Vector Classification, www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf

16.	Kawashima, S., Ogata, H., and Kanehisa, M. (1999) AAindex: Amino Acid Index Database. Nucleic Acids Res. 27, 368–369.

17.	Baldi, P., Brunak, S., Chauvin, Y., Andersen, C. A., and Nielsen, H. (2000) Assessing the accuracy of prediction algorithms for classification: an overview. Bioinformatics 16, 412–424.

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