Abstract
Major histocompatibility complex class II (MHCII) molecules play an important role in cell-mediated immunity. They present specific peptides derived from endosomal proteins for recognition by T helper cells. The identification of peptides that bind to MHCII molecules is therefore of great importance for understanding the nature of immune responses and identifying T cell epitopes for the design of new vaccines and immunotherapies. Given the large number of MHC variants, and the costly experimental procedures needed to evaluate individual peptide–MHC interactions, computational predictions have become particularly attractive as first-line methods in epitope discovery. However, only a few so-called pan-specific prediction methods capable of predicting binding to any MHC molecule with known protein sequence are currently available, and all of them are limited to HLA-DR. Here, we present the first pan-specific method capable of predicting peptide binding to any HLA class II molecule with a defined protein sequence. The method employs a strategy common for HLA-DR, HLA-DP and HLA-DQ molecules to define the peptide-binding MHC environment in terms of a pseudo sequence. This strategy allows the inclusion of new molecules even from other species. The method was evaluated in several benchmarks and demonstrates a significant improvement over molecule-specific methods as well as the ability to predict peptide binding of previously uncharacterised MHCII molecules. To the best of our knowledge, the NetMHCIIpan-3.0 method is the first pan-specific predictor covering all HLA class II molecules with known sequences including HLA-DR, HLA-DP, and HLA-DQ. The NetMHCpan-3.0 method is available at http://www.cbs.dtu.dk/services/NetMHCIIpan-3.0.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Andreatta M, Nielsen M (2012) Characterizing the binding motifs of 11 common human HLA-DP and HLA-DQ molecules using NNAlign. Immunology 136(3):306–311. doi:10.1111/j.1365-2567.2012.03579.x
Andreatta M, Schafer-Nielsen C, Lund O, Buus S, Nielsen M (2011) NNAlign: a web-based prediction method allowing non-expert end-user discovery of sequence motifs in quantitative peptide data. PLoS One 6(11):e26781. doi:10.1371/journal.pone.0026781
Berman HM, Westbrook J, Feng Z, Gilliland G, Bhat TN, Weissig H, Shindyalov IN, Bourne PE (2000) The protein data bank. Nucleic Acids Res 28(1):235–242
Castellino F, Zhong G, Germain RN (1997) Antigen presentation by MHC class II molecules: invariant chain function, protein trafficking, and the molecular basis of diverse determinant capture. Hum Immunol 54(2):159–169
Dai S, Murphy GA, Crawford F, Mack DG, Falta MT, Marrack P, Kappler JW, Fontenot AP (2010) Crystal structure of HLA-DP2 and implications for chronic beryllium disease. Proc Natl Acad Sci U S A 107(16):7425–7430
Ghosh P, Amaya M, Mellins E, Wiley DC (1995) The structure of an intermediate in class II MHC maturation: CLIP bound to HLA-DR3. Nature 378(6556):457–462. doi:10.1038/378457a0
Gonzalez-Galarza FF, Christmas S, Middleton D, Jones AR (2011) Allele frequency net: a database and online repository for immune gene frequencies in worldwide populations. Nucleic Acids Res 39(Database issue):D913–D919
Hobohm U, Scharf M, Schneider R, Sander C (1992) Selection of representative protein data sets. Protein Sci 1(3):409–417. doi:10.1002/pro.5560010313
Hoof I, Peters B, Sidney J, Pedersen LE, Sette A, Lund O, Buus S, Nielsen M (2009) NetMHCpan, a method for MHC class I binding prediction beyond humans. Immunogenetics 61(1):1–13. doi:10.1007/s00251-008-0341-z
Karosiene E, Lundegaard C, Lund O, Nielsen M (2012) NetMHCcons: a consensus method for the major histocompatibility complex class I predictions. Immunogenetics 64(3):177–186. doi:10.1007/s00251-011-0579-8
Kim CY, Quarsten H, Bergseng E, Khosla C, Sollid LM (2004) Structural basis for HLA-DQ2-mediated presentation of gluten epitopes in celiac disease. Proc Natl Acad Sci U S A 101(12):4175–4179. doi:10.1073/pnas.0306885101
Kullback S, Leibler RA (1951) On information and sufficiency. Ann Math Stat 22(1):142–143
Larkin MA, Blackshields G, Brown NP, Chenna R, McGettigan PA, McWilliam H, Valentin F, Wallace IM, Wilm A, Lopez R, Thompson JD, Gibson TJ, Higgins DG (2007) Clustal W and Clustal X version 2.0. Bioinformatics 23(21):2947–2948
Lee KH, Wucherpfennig KW, Wiley DC (2001) Structure of a human insulin peptide-HLA-DQ8 complex and susceptibility to type 1 diabetes. Nat Immunol 2(6):501–507. doi:10.1038/88694
Lund O, Nielsen M, Kesmir C, Petersen AG, Lundegaard C, Worning P, Sylvester-Hvid C, Lamberth K, Roder G, Justesen S, Buus S, Brunak S (2004) Definition of supertypes for HLA molecules using clustering of specificity matrices. Immunogenetics 55(12):797–810. doi:10.1007/s00251-004-0647-4
Nene V, Svitek N, Toye P, Golde WT, Barlow J, Harndahl M, Buus S, Nielsen M (2012) Designing bovine T cell vaccines via reverse immunology. Ticks Tick Borne Dis 3(3):188–192
Nielsen M, Justesen S, Lund O, Lundegaard C, Buus S (2010a) NetMHCIIpan-2.0—improved pan-specific HLA-DR predictions using a novel concurrent alignment and weight optimization training procedure. Immunome Res 6:9
Nielsen M, Lund O (2009) NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction. BMC Bioinforma 10:296
Nielsen M, Lund O, Buus S, Lundegaard C (2010b) MHC class II epitope predictive algorithms. Immunology 130(3):319–328. doi:10.1111/j.1365-2567.2010.03268.x
Nielsen M, Lundegaard C, Blicher T, Lamberth K, Harndahl M, Justesen S, Roder G, Peters B, Sette A, Lund O, Buus S (2007a) NetMHCpan, a method for quantitative predictions of peptide binding to any HLA-A and -B locus protein of known sequence. PLoS One 2(8):e796. doi:10.1371/journal.pone.0000796
Nielsen M, Lundegaard C, Blicher T, Peters B, Sette A, Justesen S, Buus S, Lund O (2008) Quantitative predictions of peptide binding to any HLA-DR molecule of known sequence: NetMHCIIpan. PLoS Comput Biol 4(7):e1000107. doi:10.1371/journal.pcbi.1000107
Nielsen M, Lundegaard C, Lund O (2007b) Prediction of MHC class II binding affinity using SMM-align, a novel stabilization matrix alignment method. BMC Bioinforma 8:238
Nielsen M, Lundegaard C, Worning P, Lauemoller 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(5):1007–1017. doi:10.1110/ps.0239403
Robinson J, Waller MJ, Parham P, Bodmer JG, Marsh SG (2001) IMGT/HLA Database—a sequence database for the human major histocompatibility complex. Nucleic Acids Res 29(1):210–213
Schrodinger, LLC (2010) The PyMOL molecular graphics system, version 1.3r1
Sette A, Sidney J (1999) Nine major HLA class I supertypes account for the vast preponderance of HLA-A and -B polymorphism. Immunogenetics 50(3–4):201–212
Sturniolo T, Bono E, Ding J, Raddrizzani L, Tuereci O, Sahin U, Braxenthaler M, Gallazzi F, Protti MP, Sinigaglia F, Hammer J (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat Biotechnol 17(6):555–561. doi:10.1038/9858
Thomsen M, Lundegaard C, Buus S, Lund O, Nielsen M (2013) MHCcluster, a method for functional clustering of MHC molecules. Immunogenetics. doi:10.1007/s00251-013-0714-9
Thomsen MC, Nielsen M (2012) Seq2Logo: a method for construction and visualization of amino acid binding motifs and sequence profiles including sequence weighting, pseudo counts and two-sided representation of amino acid enrichment and depletion. Nucleic Acids Res 40(Web Server issue):W281–W287
Vita R, Zarebski L, Greenbaum JA, Emami H, Hoof I, Salimi N, Damle R, Sette A, Peters B (2010) The immune epitope database 2.0. Nucleic Acids Res 38(Database issue):D854–D862
Zaitlen N, Reyes-Gomez M, Heckerman D, Jojic N (2008) Shift-invariant adaptive double threading: learning MHC II-peptide binding. J Comput Biol 15(7):927–942. doi:10.1089/cmb.2007.0183
Zhang GL, Khan AM, Srinivasan KN, August JT, Brusic V (2005) MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides. Nucleic Acids Res 33(Web Server issue):W172–W179
Zhang H, Lund O, Nielsen M (2009a) The PickPocket method for predicting binding specificities for receptors based on receptor pocket similarities: application to MHC-peptide binding. Bioinformatics 25(10):1293–1299
Zhang H, Lundegaard C, Nielsen M (2009b) Pan-specific MHC class I predictors: a benchmark of HLA class I pan-specific prediction methods. Bioinformatics 25(1):83–89. doi:10.1093/bioinformatics/btn579
Zhang L, Chen Y, Wong HS, Zhou S, Mamitsuka H, Zhu S (2012) TEPITOPEpan: extending TEPITOPE for peptide binding prediction covering over 700 HLA-DR molecules. PLoS One 7(2):e30483. doi:10.1371/journal.pone.0030483
Acknowledgments
MN is a researcher at the Argentinean national research council (CONICET). This project has been funded in whole or 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 nos. HHSN272201200010C and HHSN272200900045C.
Author information
Authors and Affiliations
Corresponding author
Electronic supplementary material
Below is the link to the electronic supplementary material.
ESM 1
(DOC 203 kb)
Rights and permissions
About this article
Cite this article
Karosiene, E., Rasmussen, M., Blicher, T. et al. 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 (2013). https://doi.org/10.1007/s00251-013-0720-y
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00251-013-0720-y