Springer Protocols: Abstract: Artificial Intelligence Methods for Predicting T-Cell Epitopes

Artificial Intelligence Methods for Predicting T-Cell Epitopes

By: Yingdong Zhao³, Myong-Hee Sung⁴, Richard Simon⁵

Abstract

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Identifying epitopes that elicit a major histocompatibility complex (MHC)-restricted T-cell response is critical for designing vaccines for infectious diseases and cancers. We have applied two artificial intelligence approaches to build models for predicting T-cell epitopes. We developed a support vector machine to predict T-cell epitopes for an MHC class I-restricted T-cell clone (TCC) using synthesized peptide data. For predicting T-cell epitopes for an MHC class IIrestricted TCC, we built a shift model that integrated MHC-binding data and data from T-cell proliferation assay against a combinatorial library of peptide mixtures

Affiliation(s): (3) National Cancer Institute, National Institutes of Health, Rockville, MD
(4) Laboratory of Receptor Biology and Gene Expression, Staff Scientist National Cancer Institute, Bethesda, MD
(5) Biometric Research Branch, National Cancer Institute, National Institutes of Health, Rockville, MD

Book Title: Immunoinformatics: Predicting Immunogenicity In Silico

Series: Methods in Molecular Biology | Volume: 409 | Pub. Date: Jun-21-2007 | Page Range: 217-225 | DOI: 10.1007/978-1-60327-118-9_15

Subject: Immunology

Key Words: T-cell receptors - MHC binding - epitope prediction - support vector machine - artificial intelligence - combinatorial peptide library - vaccine design

References

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

2.	Zhao, Y., Pinilla, C., Valmori, D., Martin, R., and Simon, R. (2003) Application of support vector machines in T-cell epitope prediction. Bioinformatics 19, 1978–1984.

3.	Sung, M.H., Zhao, Y., Martin, R., and Simon, R. (2002) T-cell epitope prediction with combinatorial peptide libraries. J. Comput. Biol. 9, 527–539.

4.	Houghten, R.A. (1985) General method for the rapid solid-phase synthesis of large numbers of peptides: specificity of antigen-antibody interaction at the level of individual amino acids. Proc. Natl. Acad. Sci. U.S.A. 82, 5131–5135.

5.	Zhao, Y., Gran, B., Pinilla, C., Markovic-Plese, S., Hemmer, B., Tzou, A., Whitney, L. W., Biddison, W. E., Martin, R., and Simon, R. (2001) Combinatorial peptide libraries and biometric score matrices permit the quantitative analysis of specific and degenerate interactions between clonotypic T-cell receptors and MHC-peptide ligands. J. Immunol. 167, 2130–2141.

6.	Rubio-Godoy, V., Dutoit, V., Zhao, Y., Simon, R., Guillaume, P., Houghten, R.A., Romero, P., Cerottini, J.C., Pinilla, C., and Valmori, D. (2002) Positional scanning-synthetic peptide library-based analysis of self- and pathogen-derived peptide cross-reactivity with tumor-reactive Melan-A-specific CTL. J. Immunol. 169, 5696–5707.

7.	Hammer, J., Belunis, C., Bolin, D., Papadopoulos, J., Walsky, R., Higelin, J., Danho, W., Sinigaglia, F., and Nagy, Z.A. (1994) High-affinity binding of short peptides to major histocompatibility complex class II molecules by anchor combinations. Proc. Natl. Acad. Sci. U.S.A. 91, 4456–4460.

8.	Hammer, J., Bono, E., Gallazzi, F., Belunis, C., Nagy, Z., and Sinigaglia, F. (1994) Precise prediction of major histocompatibility complex class II-peptide interaction based on peptide side chain scanning. J. Exp. Med. 180, 2353–2358.

9.	Sturniolo, T., Bono, E., Ding, J., Raddrizzani, L., Tuereci, O., Sahin, U., Braxenthaler, M., Gallazzi, F., Protti, M.P., Sinigaglia, F., and Hammer, J. (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices . Nat. Biotechnol. 17, 555–561.

10.	Kidera, A., Konishi, Y., Oka, M., Ooi, T., and Scheraga, H.A. (1985) Statistical analysis of the physical properties of the 20 naturally occuring amino acids. J. Protein Chem. 4, 23–55.

11.	Joachim, T. (1999) Making large scale SVM learning practical, In Advances in Kernel Methods-Support Vector Learning. MIT press, Cambridge.

12.	Swets, J. (1988) Measuring the accuracy of diagnostic systems. Science 240, 1285–1293.

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