Abstract
T-cell responses are activated by specific peptides, called epitopes, presented on the cell surface by MHC molecules. Binding of peptides to the MHC is the most selective step in T-cell antigen presentation and therefore an essential factor in the selection of potential epitopes. Several in-vitro methods have been developed for the determination of peptide binding to MHC molecules, but these are all costly and time-consuming. In consequence, significant effort has been dedicated to the development of in-silico methods to model this event. Here, we describe two such tools, NetMHCcons and NetMHCIIpan, for the prediction of peptide binding to MHC class I and class II molecules, respectively, involved in the activation pathways of CD8+ and CD4+ T cells.
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References
Germain RN (1994) MHC-dependent antigen processing and peptide presentation: providing ligands for T lymphocyte activation. Cell 76:287–299
Rudolph MG, Stanfield RL, Wilson IA (2006) How TCRs bind MHCs, peptides, and coreceptors. Annu Rev Immunol 24:419–466. https://doi.org/10.1146/annurev.immunol.23.021704.115658
Rudensky AY, Preston-Hurlburt P, Hong SC et al (1991) Sequence analysis of peptides bound to MHC class II molecules. Nature 353:622–627. https://doi.org/10.1038/353622a0
Lundegaard C, Hoof I, Lund O, Nielsen M (2010) State of the art and challenges in sequence based T-cell epitope prediction. Immunome Res 6:S3. https://doi.org/10.1186/1745-7580-6-S2-S3
Larsen M, Lundegaard C, Lamberth K (2005) An integrative approach to CTL epitope prediction: a combined algorithm integrating MHC class I binding, TAP transport efficiency, and proteasomal cleavage predictions. Eur J Immunol 35:2295–2303
Assarsson E, Sidney J, Oseroff C et al (2007) A quantitative analysis of the variables affecting the repertoire of T cell specificities recognized after vaccinia virus infection. J Immunol 178:7890–7901
Nielsen M, Lundegaard C, Lund O, Keşmir C (2005) The role of the proteasome in generating cytotoxic T-cell epitopes: insights obtained from improved predictions of proteasomal cleavage. Immunogenetics 57:33–41. https://doi.org/10.1007/s00251-005-0781-7
Harndahl M, Rasmussen M, Roder G, Buus S (2011) Real-time, high-throughput measurements of peptide-MHC-I dissociation using a scintillation proximity assay. J Immunol Methods 374:5–12. https://doi.org/10.1016/j.jim.2010.10.012
Harndahl M, Rasmussen M, Roder G et al (2012) Peptide-MHC class I stability is a better predictor than peptide affinity of CTL immunogenicity. Eur J Immunol 42:1405–1416. https://doi.org/10.1002/eji.201141774
Juncker AS, Larsen MV, Weinhold N et al (2009) Systematic characterisation of cellular localisation and expression profiles of proteins containing MHC ligands. PLoS One 4:e7448. https://doi.org/10.1371/journal.pone.0007448
Hoof I, van Baarle D, Hildebrand WH, Keşmir C (2012) Proteome sampling by the HLA class I antigen processing pathway. PLoS Comput Biol 8:e1002517. https://doi.org/10.1371/journal.pcbi.1002517
Frankild S, de Boer RJ, Lund O et al (2008) Amino acid similarity accounts for T cell cross-reactivity and for “holes” in the T cell repertoire. PLoS One 3:e1831. https://doi.org/10.1371/journal.pone.0001831
Yewdell JW, Bennink JR (1999) Immunodominance in major histocompatibility complex class I-restricted T lymphocyte responses. Annu Rev Immunol 17:51–88. https://doi.org/10.1146/annurev.immunol.17.1.51
Tenzer S, Peters B, Bulik S et al (2005) Modeling the MHC class I pathway by combining predictions of proteasomal cleavage, TAP transport and MHC class I binding. Cell Mol Life Sci CMLS 62:1025–1037. https://doi.org/10.1007/s00018-005-4528-2
Stranzl T, Larsen MV, Lundegaard C, Nielsen M (2010) NetCTLpan: pan-specific MHC class I pathway epitope predictions. Immunogenetics 62:357–368. https://doi.org/10.1007/s00251-010-0441-4
Doytchinova IA, Guan P, Flower DR (2006) EpiJen: a server for multistep T cell epitope prediction. BMC Bioinformatics 7:131. https://doi.org/10.1186/1471-2105-7-131
Karosiene E, Lundegaard C, Lund O, Nielsen M (2012) NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64:177–186. https://doi.org/10.1007/s00251-011-0579-8
Lundegaard C, Lamberth K, Harndahl M et al (2008) NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8-11. Nucleic Acids Res 36:W509–W512. https://doi.org/10.1093/nar/gkn202
Hoof I, Peters B, Sidney J et al (2009) NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61:1–13. https://doi.org/10.1007/s00251-008-0341-z
Zhang H, Lund O, Nielsen M (2009) The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinforma Oxf Engl 25:1293–1299. https://doi.org/10.1093/bioinformatics/btp137
Karosiene E, Rasmussen M, Blicher T et al (2013) NetMHCIIpan-3.0, a common pan-specific MHC class II prediction method including all three human MHC class II isotypes, HLA-DR, HLA-DP and HLA-DQ. Immunogenetics 65:711–724. https://doi.org/10.1007/s00251-013-0720-y
Andreatta M, Karosiene E, Rasmussen M et al (2015) Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics 67(11-12):641–650
Nielsen M, Lund O (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinformatics 10:296
Andreatta M, Schafer-Nielsen C, Lund O et al (2011) NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data. PLoS One 6:e26781. https://doi.org/10.1371/journal.pone.0026781
Godkin AJ, Smith KJ, Willis A et al (2001) Naturally processed HLA class II peptides reveal highly conserved immunogenic flanking region sequence preferences that reflect antigen processing rather than peptide-MHC interactions. J Immunol 166:6720–6727
Carson RT, Vignali KM, Woodland DL, Vignali DA (1997) T cell receptor recognition of MHC class II-bound peptide flanking residues enhances immunogenicity and results in altered TCR V region usage. Immunity 7:387–399
Moutaftsi M, Peters B, Pasquetto V et al (2006) A consensus epitope prediction approach identifies the breadth of murine T(CD8+)-cell responses to vaccinia virus. Nat Biotechnol 24:817–819. https://doi.org/10.1038/nbt1215
Rao X, Costa AI, van Baarle D, Kesmir C (2009) A comparative study of HLA binding affinity and ligand diversity: implications for generating immunodominant CD8+ T cell responses. J Immunol 182:1526–1532
Paul S, Weiskopf D, Angelo MA et al (2013) HLA class I alleles are associated with peptide-binding repertoires of different size, affinity, and immunogenicity. J Immunol 191:5831–5839. https://doi.org/10.4049/jimmunol.1302101
Erup Larsen M, Kloverpris H, Stryhn A et al (2011) HLArestrictor--a tool for patient-specific predictions of HLA restriction elements and optimal epitopes within peptides. Immunogenetics 63:43–55. https://doi.org/10.1007/s00251-010-0493-5
Braendstrup P, Mortensen BK, Justesen S et al (2014) Identification and HLA-tetramer-validation of human CD4+ and CD8+ T cell responses against HCMV proteins IE1 and IE2. PLoS One 9:e94892. https://doi.org/10.1371/journal.pone.0094892
Andreatta M, Nielsen M (2012) Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlign. Immunology 136:306–311. https://doi.org/10.1111/j.1365-2567.2012.03579.x
Vita R, Overton JA, Greenbaum JA et al (2015) The immune epitope database (IEDB) 3.0. Nucleic Acids Res 43:D405–D412. https://doi.org/10.1093/nar/gku938
Lundegaard C, Lund O, Nielsen M (2008) Accurate approximation method for prediction of class I MHC affinities for peptides of length 8, 10 and 11 using prediction tools trained on 9mers. Bioinformatics 24:1397–1398. https://doi.org/10.1093/bioinformatics/btn128
Trolle T, Nielsen M (2014) NetTepi: an integrated method for the prediction of T cell epitopes. Immunogenetics 66:449–456. https://doi.org/10.1007/s00251-014-0779-0
Jørgensen KW, Rasmussen M, Buus S, Nielsen M (2014) NetMHCstab - predicting stability of peptide-MHC-I complexes; impacts for cytotoxic T lymphocyte epitope discovery. Immunology 141:18–26. https://doi.org/10.1111/imm.12160
Acknowledgments
This study was supported in part with Federal funds from the National Institutes of Allergy and Infectious Diseases, National Institutes of Health, Department of Health and Human Services, under Contract No. HHSN272201200010C. This work was partially funded by the Agencia Nacional de Promoción Científica y Tecnológica, Argentina (PICT-2012-0115). MN is a researcher at the Argentinean national research council (CONICET).
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Andreatta, M., Nielsen, M. (2018). Bioinformatics Tools for the Prediction of T-Cell Epitopes. In: Rockberg, J., Nilvebrant, J. (eds) Epitope Mapping Protocols. Methods in Molecular Biology, vol 1785. Humana Press, New York, NY. https://doi.org/10.1007/978-1-4939-7841-0_18
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DOI: https://doi.org/10.1007/978-1-4939-7841-0_18
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