Abstract
Major histocompatibility complex (MHC) class II antigen presentation is a key component in eliciting a CD4+ T cell response. Precise prediction of peptide-MHC (pMHC) interactions has thus become a cornerstone in defining epitope candidates for rational vaccine design. Current pMHC prediction tools have, so far, primarily focused on inference from in vitro binding affinity. In the current study, we collate a large set of MHC class II eluted ligands generated by mass spectrometry to guide the prediction of MHC class II antigen presentation. We demonstrate that models developed on eluted ligands outperform those developed on pMHC binding affinity data. The predictive performance can be further enhanced by combining the eluted ligand and pMHC affinity data in a single prediction model. Furthermore, by including ligand data, the peptide length preference of MHC class II can be accurately learned by the prediction model. Finally, we demonstrate that our model significantly outperforms the current state-of-the-art prediction method, NetMHCIIpan, on an external dataset of eluted ligands and appears superior in identifying CD4+ T cell epitopes.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Wieczorek M, Abualrous ET, Sticht J, Álvaro-Benito M, Stolzenberg S et al (2017) Major histocompatibility complex (MHC) class I and MHC class II proteins: conformational plasticity in antigen presentation. Front Immunol 8:292
Unanue ER, Turk V, Neefjes J (2016) Variations in MHC class II antigen processing and presentation in health and disease. Annu Rev Immunol 34:265–297
Jones EY (1997) MHC class I and class II structures. Curr Opin Immunol 9:75–79
Rock KL, Reits E, Neefjes J (2016) Present yourself! By MHC class I and MHC class II molecules. Trends Immunol 37:724–737
Neefjes J, Jongsma MLM, Paul P, Bakke O (2011) Towards a systems understanding of MHC class I and MHC class II antigen presentation. Nat Rev Immunol 11:823–836
Saini SK, Rekers N, Hadrup SR (2017) Novel tools to assist neoepitope targeting in personalized cancer immunotherapy. Ann Oncol 28:xii3–xii10
Rosa DS, Ribeiro SP, Cunha-Neto E (2010) CD4+ T cell epitope discovery and rational vaccine design. Arch Immunol Ther Exp 58:121–130
Olsen LR, Campos B, Barnkob MS, Winther O, Brusic V, Andersen MH (2014) Bioinformatics for cancer immunotherapy target discovery. Cancer Immunol Immunother 63:1235–1249
Andreatta M, Trolle T, Yan Z, Greenbaum JA, Peters B et al (2018) An automated benchmarking platform for MHC class II binding prediction methods. Bioinformatics 34:1522–1528
Fleri W, Paul S, Dhanda SK, Mahajan S, Xu X et al. (2017) The immune epitope database and analysis resource in epitope discovery and synthetic vaccine design. Front Immunol; 8:278
Jensen KK, Andreatta M, Marcatili P, Buus S, Greenbaum JA, Yan Z, Sette A, Peters B, Nielsen M (2018) Improved methods for predicting peptide binding affinity to MHC class II molecules. Immunology. 154:394–406
Andreatta M, Nielsen M (2015) Gapped sequence alignment using artificial neural networks: application to the MHC class i system. Bioinformatics. 32:511–517
EMBL-EBI (2018) IPD-IMGT/HLA database—statistics. Available at: https://www.ebi.ac.uk/ipd/imgt/hla/stats.html [Accessed July 4, 2018]
Andreatta M, Karosiene E, Rasmussen M, Stryhn A, Buus S, Nielsen M (2015) Accurate pan-specific prediction of peptide-MHC class II binding affinity with improved binding core identification. Immunogenetics. 67:641–650
Jurtz V, Paul S, Andreatta M, Marcatili P, Peters B, Nielsen M (2017) NetMHCpan-4.0: improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol 199:3360–3368
Andreatta M, Lund O, Nielsen M (2013) Simultaneous alignment and clustering of peptide data using a Gibbs sampling approach. Bioinformatics. 29:8–14
UniProt Consortium T (2018) UniProt: the universal protein knowledgebase. Nucleic Acids Res 46:2699–2699
Shilov IV, Seymour SL, Patel AA, Loboda A, Tang WH, Keating SP, Hunter CL, Nuwaysir LM, Schaeffer DA (2007) The paragon algorithm, a next generation search engine that uses sequence temperature values and feature probabilities to identify peptides from tandem mass spectra. Mol Cell Proteomics 6:1638–1655
Schittenhelm RB, Sian TC, Wilmann PG, Dudek NL, Purcell AW (2015) Revisiting the arthritogenic peptide theory: quantitative not qualitative changes in the peptide repertoire of HLA-B27 allotypes. Arthritis Rheumatol. (Hoboken, N.J.) 67:702–713
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
Nielsen M, Lundegaard C, Worning P, Lauemøller SL, Lamberth K, Buus S, Brunak S, Lund O (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci 12:1007–1017
Mattsson AH, Kringelum JV, Garde C, Nielsen M (2016) Improved pan-specific prediction of MHC class I peptide binding using a novel receptor clustering data partitioning strategy. HLA 88:287–292
Karosiene E, Rasmussen M, Blicher T, Lund O, Buus S, Nielsen M (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
Nielsen M, Lundegaard C, Lund O (2007) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinformatics 8:238
Abelin JG, Keskin DB, Sarkizova S, Hartigan CR, Zhang W et al (2017) Mass spectrometry profiling of HLA-associated peptidomes in mono-allelic cells enables more accurate epitope prediction. Immunity 46:315–326
Paul S, Karosiene E, Dhanda SK, Jurtz V, Edwards L, Nielsen M, Sette A, Peters B (2018) Determination of a predictive cleavage motif for eluted major histocompatibility complex class II ligands. Front Immunol 9:1795
Bentzen AK, Marquard AM, Lyngaa R, Saini SK, Ramskov S, Donia M, Such L, Furness AJS, McGranahan N, Rosenthal R, Straten P, Szallasi Z, Svane IM, Swanton C, Quezada SA, Jakobsen SN, Eklund AC, Hadrup SR (2016) Large-scale detection of antigen-specific T cells using peptide-MHC-I multimers labeled with DNA barcodes. Nat Biotechnol 34:1037–1045
Shugay M, Bagaev DV, Zvyagin IV, Vroomans RM, al CJ (2018) VDJdb: a curated database of T-cell receptor sequences with known antigen specificity. Nucleic Acids Res 46:D419–D427
Acknowledgments
ECJ was supported by a PhD stipend from the Innovation Fund Denmark. AWP was supported by a Principal Research Fellowship and project grant 1022509 from the National Health and Medical Research Council of Australia (NHMRC). SHR was supported by an Australian Postgraduate Award. MN is a researcher at the Argentinean National Research Council (CONICET).
We are grateful to the members of Prof. Purcell’s lab for their assistance in compiling the eluted ligand data. Furthermore, we thank the bioinformatics team at Evaxion Biotech for constructive feedback.
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Garde, C., Ramarathinam, S.H., Jappe, E.C. et al. Improved peptide-MHC class II interaction prediction through integration of eluted ligand and peptide affinity data. Immunogenetics 71, 445–454 (2019). https://doi.org/10.1007/s00251-019-01122-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00251-019-01122-z