Abstract
The identification of potential T-cell epitopes is important for development of new human or vetenary vaccines, both considering single protein/subunit vaccines, and for epitope/peptide vaccines as such. The highly diverse MHC class I alleles bind very different peptides, and accurate binding prediction methods exist only for alleles were the binding pattern have been deduced from peptide motifs. Using empirical knowledge of important anchor positions within the binding peptides dramatically reduces the number of peptides needed for reliable predictions. We here present a general method for predicting peptides binding to specific MHC class I alleles. The method combines advanced automatic scoring matrix generation with empirical position specific differential anchor weighting. The method leads to predictions with a comparable or higher accuracy than other established prediction servers, even in situations where only very limited data are available for training.
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
Adams, H.P., Koziol, J.A.: Prediction of binding to MHC class I molecules. J. Immunol. Methods 185, 181–190 (1995)
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. Nucl. Acids Res. 25, 3389–3402 (1997)
Altuvia, Y., Schueler, O., Margalit, H.: Ranking potential binding peptides to MHC molecules by a computational threading approach. J. Mol. Biol. 149, 244–250 (1995)
Bhasin, M., Singh, H., Raghava, G.P.S.: MHCBN: A comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19, 665–666 (2003)
Brusic, V., Rudy, G., Harrison, L.C.: Prediction of MHC binding peptides using artificial neural networks. In: Complex systems: mechanism of adaptation (ed. a.Y.X. Stonier RJ), pp. 253–260. IOS Press, Amsterdam (1994)
Brusic, V., Rudy, G., Harrison, L.C.: MHCPEP, a database of MHC-binding peptides: update 1997. Nucleic Acid Res. 26, 368–371 (1998)
Buus, S., Lauemøller, S.L., Worning, P., Kesmir, C., Frimurer, T., Corbet, S., Fomsgaard, A., Hilden, J., Holm, A., Brunak, S.: Sensitive quantitative predictions of peptide- MHC binding by a ’Query by Committee’ artificial neural network approach. Tissue Antigens 62, 378–384 (2003)
Christensen, J.K., Lamberth, K., Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S.L., Buus, S., Brunak, S., Lund, O.: Selecting Informative Data for Developing Peptide- MHC Binding Predictors Using a "Query By Committee" Approach. Neural Computation 15, 2931–2942 (2003)
Doytchinova, I.A., Flower, D.R.: Toward the Quantitative Prediction of T-Cell Epitopes: CoMFA and CoMSIA Studies of Peptides with Affinity for the Class I MHC Molecule HLA-A*0201. J. Med. Chem. 44, 3572–3581 (2001)
Gulukota, K., Sidney, J., Sette, A., DeLisi, C.: Two complementary methods for predicting peptides binding major histocompatibility complex molecules. Journal of Molecular Biology 267, 1258–1267 (1997)
Hebsgaard, S.M., Korning, P.G., Tolstrup, N., Engelbrecht, J., Rouze, P., Brunak, S.: Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information. Nucleic Acid Res. 24, 3439–3452 (1996)
Henikoff, S., Henikoff, J.G.: Amino acid substitution matrices from protein blocks. Proc. Natl. Acad. Sci., USA 89, 10915–10919 (1992)
Henikoff, S., Henikoff, J.G.: Position-based sequence weights. J. Mol. Biol. 243, 574–578 (1994)
Kondo, A., Sidney, J., Southwood, S., del Guercio, M.F., Appella, E., Sakamoto, H., Grey, H.M., Celis, E., Chesnut, R.W., Kubo, R.T., et al.: Two distinct HLA-A*0101- specific submotifs illustrate alternative peptide binding modes. Immunogenetics 45, 249–258 (1997)
Kubo, R.T., Sette, A., Grey, H.M., Appella, E., Sakaguchi, K., Zhu, N.Z., Arnott, D., Sherman, N., Shabanowitz, J., Michel, H.: Definition of specific peptide motifs for four major HLA-A alleles. J. Immunol. 152, 3913–3924 (1994)
Marshall, K.W., Wilson, K.J., Liang, J., Woods, A., Zaller, D., Rothbard, J.B.: Prediction of peptide affinity to HLA DRB1*0401. J. Immunol. 154, 5927–5933 (1995)
Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S.L., Lamberth, K., Buus, S., Brunak, S., Lund, O.: Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Science 12, 1007–1017 (2003)
Nielsen, M., Lundegaard, C., Worning, P., Sylvester-Hvid, C., Lamberth, K., Buus, S., Brunak, S., Lund, O.: Improved prediction of MHC class I and II epitopes using a novel Gibbs sampling approach. Bioinformatics 20, 1388–1397 (2004)
Parker, K.C., Bednarek, M.A., Coligan, J.E.: Scheme for ranking potential HLA-A2 binding peptides based on independent binding of individual peptide side-chains. J. Immunol. 152, 163–175 (1994)
Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipies in C: The Art of Scientific Computing, 2nd edn. Cambridge University Press, Cambridge (1989)
Rammensee, H., Bachmann, J., Emmerich, N., Bachor, O.A., Stevanovic, S.: SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219 (1999)
Rognan, D., Lauemøller, S.L., Holm, A., Buus, S., Tschinke, V.: 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 (1999)
Schneider, T.D., Stephens, R.M.: Sequence logos: a new way to display consensus sequences. Nucleic Acid Res. 18, 6097–6100 (1990)
Schueler-Furman, O., Altuvia, Y., Sette, A., Margalit, H.: Structure-based prediction of binding peptides to MHC class I molecules: application to a broad range of MHC alleles. Protein Science 9, 1838–1846 (2000)
Sette, A., Sidney, J.: Nine major HLA class I supertypes account for the vast preponderance of HLA-A and –B polymorphism. Immunogenetics 50, 201–212 (1999)
Stryhn, A., Pedersen, L.O., Romme, T., Holm, C.B., Holm, A., Buus, S.: Peptide binding specificity of major histocompatibility complex class I resolved into an array of apparently independent subspecificities: quantitation by peptide libraries and improved prediction of binding. Eur. J. Immunol. 26, 1911–1918 (1996)
Sweet, J.A.: Measuring the accuracy of a diagnostic systems. Science 240, 1285–1293 (1988)
Sylvester-Hvid, C., Nielsen, M., Lamberth, K., Roder, G., Justesen, S., Lundegaard, C., Worning, P., Thomadsen, H., Lund, O., Brunak, S., Buus, S.: SARS CTL vaccine candidates; HLA supertype-, genome-wide scanning and biochemical validation. Tissue Antigens 63, 395–400 (2004)
Sylvester-Hvid, C., Kristensen, N., Blicher, T., Ferré, H., Lauemøller, S.L., Wolf, X.A., Lamberth, K., Nissen, M.H., Pedersen, L.Ø., Buus, S.: Establishment of a quantitative ELISA capable of determining peptide - MHC class I interaction. Tissue Antigens 59, 251–258 (2002)
Yewdell, J.W., Bennink, J.R.: Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annual Review of Immunology 17, 51–88 (1999)
Yu, K., Petrovsky, N., Schonbach, C., Koh, J.Y., Brusic, V.: Methods for prediction of peptide binding to MHC molecules: a comparative study. Mol. Med. 8, 137–148 (2002)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2004 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lundegaard, C. et al. (2004). MHC Class I Epitope Binding Prediction Trained on Small Data Sets. In: Nicosia, G., Cutello, V., Bentley, P.J., Timmis, J. (eds) Artificial Immune Systems. ICARIS 2004. Lecture Notes in Computer Science, vol 3239. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30220-9_18
Download citation
DOI: https://doi.org/10.1007/978-3-540-30220-9_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23097-7
Online ISBN: 978-3-540-30220-9
eBook Packages: Springer Book Archive