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Improved Prediction of MHC II Antigen Presentation through Integration and Motif Deconvolution of Mass Spectrometry MHC Eluted Ligand Data

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Reynisson, Birkir, Barra, Carolina, Kaabinejadian, Saghar, Hildebrand, William H., Peters, Bjoern, Nielsen, Morten·2020·Journal of Proteome Research
DOI 10.1021/acs.jproteome.9b00874

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Abstract

Major histocompatibility complex II (MHC II) molecules play a vital role in the onset and control of cellular immunity. In a highly selective process, MHC II presents peptides derived from exogenous antigens on the surface of antigen-presenting cells for T cell scrutiny. Understanding the rules defining this presentation holds critical insights into the regulation and potential manipulation of the cellular immune system. Here, we apply the NNAlign_MA machine learning framework to analyze and integrate large-scale eluted MHC II ligand mass spectrometry (MS) data sets to advance prediction of CD4+ epitopes. NNAlign_MA allows integration of mixed data types, handling ligands with multiple potential allele annotations, encoding of ligand context, leveraging information between data sets, and has pan-specific power allowing accurate predictions outside the set of molecules included in the training data. Applying this framework, we identified accurate binding motifs of more than 50 MHC class II molecules described by MS data, particularly expanding coverage for DP and DQ beyond that obtained using current MS motif deconvolution techniques. Furthermore, in large-scale benchmarking, the final model termed NetMHCIIpan-4.0 demonstrated improved performance beyond current state-of-the-art predictors for ligand and CD4+ T cell epitope prediction. These results suggest that NNAlign_MA and NetMHCIIpan-4.0 are powerful tools for analysis of immunopeptidome MS data, prediction of T cell epitopes, and development of personalized immunotherapies.

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Research works printed but not linked: 54

  1. Unanue, 2016, Variations in MHC Class II Antigen Processing and Presentation in Health and Disease, Annu. Rev. Immunol., 34, 265, 10.1146/annurev-immunol-041015-055420 · DOI · Paper page
  2. Wieczorek, 2017, Major Histocompatibility Complex (MHC) Class I and MHC Class II Proteins: Conformational Plasticity in Antigen Presentation, Front. Immunol., 8, 292, 10.3389/fimmu.2017.00292 · DOI · Paper page
  3. Traherne, 2008, Human MHC Architecture and Evolution: Implications for Disease Association Studies, Int. J. Immunogenet., 35, 179, 10.1111/j.1744-313X.2008.00765.x · DOI
  4. Chicz, 1992, Predominant Naturally Processed Peptides Bound to HLA-DR1 Are Derived from MHC-Related Molecules and Are Heterogeneous in Size, Nature, 358, 764, 10.1038/358764a0 · DOI · Paper page
  5. Andreatta, 2017, Machine Learning Reveals a Non-Canonical Mode of Peptide Binding to MHC Class II Molecules, Immunology, 152, 255, 10.1111/imm.12763 · DOI
  6. Godkin, 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, 10.4049/jimmunol.166.11.6720 · DOI
  7. Buus, 1987, The Relation between Major Histocompatibility Complex (MHC) Restriction and the Capacity of Ia to Bind Immunogenic Peptides, Science, 235, 1353, 10.1126/science.2435001 · DOI · Paper page
  8. Gfeller, 2018, Predicting Antigen Presentation-What Could We Learn From a Million Peptides?, Front. Immunol., 9, 1716, 10.3389/fimmu.2018.01716 · DOI · Paper page
  9. Nielsen, 2010, MHC Class II Epitope Predictive Algorithms, Immunology, 130, 319, 10.1111/j.1365-2567.2010.03268.x · DOI
  10. Bassani-Sternberg, 2016, Direct Identification of Clinically Relevant Neoepitopes Presented on Native Human Melanoma Tissue by Mass Spectrometry, Nat. Commun., 7, 13404, 10.1038/ncomms13404 · DOI · Paper page
  11. Backert, 2015, Immunoinformatics and Epitope Prediction in the Age of Genomic Medicine, Genome Med., 7, 119, 10.1186/s13073-015-0245-0 · DOI · Paper page
  12. Alvarez, 2018, Computational Tools for the Identification and Interpretation of Sequence Motifs in Immunopeptidomes, Proteomics, 18, 1700252, 10.1002/pmic.201700252 · DOI
  13. Dudek, 2016, A Systems Approach to Understand Antigen Presentation and the Immune Response, Methods Mol. Biol., 1394, 189, 10.1007/978-1-4939-3341-9_14 · DOI
  14. Caron, 2015, Analysis of Major Histocompatibility Complex (MHC) Immunopeptidomes Using Mass Spectrometry, Mol. Cell. Proteomics, 14, 3105, 10.1074/mcp.O115.052431 · DOI
  15. Barra, 2018, Footprints of Antigen Processing Boost MHC Class II Natural Ligand Predictions, Genome Med., 10, 84, 10.1186/s13073-018-0594-6 · DOI
  16. Paul, 2018, Determination of a Predictive Cleavage Motif for Eluted Major Histocompatibility Complex Class II Ligands, Front. Immunol., 9, 1795, 10.3389/fimmu.2018.01795 · DOI
  17. Racle, 2019, Robust Prediction of HLA Class II Epitopes by Deep Motif Deconvolution of Immunopeptidomes, Nat. Biotechnol., 37, 1283, 10.1038/s41587-019-0289-6 · DOI · Paper page
  18. Garde, 2019, Improved Peptide-MHC Class II Interaction Prediction through Integration of Eluted Ligand and Peptide Affinity Data, Immunogenetics, 71, 445, 10.1007/s00251-019-01122-z · DOI
  19. Jurtz, 2017, NetMHCpan-4.0: Improved Peptide-MHC Class I Interaction Predictions Integrating Eluted Ligand and Peptide Binding Affinity Data, J. Immunol., 199, 3360, 10.4049/jimmunol.1700893 · DOI · Paper page
  20. Nielsen, 2018, Improved Prediction of Bovine Leucocyte Antigens (BoLA) Presented Ligands by Use of Mass-Spectrometry-Determined Ligand and in Vitro Binding Data, J. Proteome Res., 17, 559, 10.1021/acs.jproteome.7b00675 · DOI
  21. Hoof, 2009, NetMHCpan, a Method for MHC Class I Binding Prediction beyond Humans, Immunogenetics, 61, 1, 10.1007/s00251-008-0341-z · DOI · Paper page
  22. Karosiene, 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, 10.1007/s00251-013-0720-y · DOI · Paper page
  23. Nielsen, 2007, NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence, PLoS One, 2, 10.1371/journal.pone.0000796 · DOI · Paper page
  24. Abelin, 2017, Mass Spectrometry Profiling of HLA-Associated Peptidomes in Mono-Allelic Cells Enables More Accurate Epitope Prediction, Immunity, 46, 315, 10.1016/j.immuni.2017.02.007 · DOI · Paper page
  25. Abelin, 2019, Defining HLA-II Ligand Processing and Binding Rules with Mass Spectrometry Enhances Cancer Epitope Prediction, Immunity, 51, 766, 10.1016/j.immuni.2019.08.012 · DOI
  26. Andreatta, 2017, GibbsCluster: Unsupervised Clustering and Alignment of Peptide Sequences, Nucleic Acids Res., 45, W458, 10.1093/nar/gkx248 · DOI
  27. Bassani-Sternberg, 2016, Unsupervised HLA Peptidome Deconvolution Improves Ligand Prediction Accuracy and Predicts Cooperative Effects in Peptide-HLA Interactions, J. Immunol., 197, 2492, 10.4049/jimmunol.1600808 · DOI
  28. Alvarez, 2019, NNAlign_MA; MHC Peptidome Deconvolution for Accurate MHC Binding Motif Characterization and Improved T Cell Epitope Predictions, Mol. Cell. Proteomics, 18, 2459, 10.1074/mcp.TIR119.001658 · DOI
  29. Jensen, 2018, Improved Methods for Predicting Peptide Binding Affinity to MHC Class II Molecules, Immunology, 154, 394, 10.1111/imm.12889 · DOI · Paper page
  30. Nielsen, 2003, Reliable Prediction of T-Cell Epitopes Using Neural Networks with Novel Sequence Representations, Protein Sci., 12, 1007, 10.1110/ps.0239403 · DOI · Paper page
  31. Vita, 2019, The Immune Epitope Database (IEDB): 2018 Update, Nucleic Acids Res., 47, D339, 10.1093/nar/gky1006 · DOI · Paper page
  32. Heyder, 2016, Approach for Identifying Human Leukocyte Antigen (HLA)-DR Bound Peptides from Scarce Clinical Samples, Mol. Cell. Proteomics, 15, 3017, 10.1074/mcp.M116.060764 · DOI
  33. Bergseng, 2015, Different Binding Motifs of the Celiac Disease-Associated HLA Molecules DQ2.5, DQ2.2, and DQ7.5 Revealed by Relative Quantitative Proteomics of Endogenous Peptide Repertoires, Immunogenetics, 67, 73, 10.1007/s00251-014-0819-9 · DOI
  34. Sofron, 2016, High-Resolution Analysis of the Murine MHC Class II Immunopeptidome, Eur. J. Immunol., 46, 319, 10.1002/eji.201545930 · DOI
  35. Clement, 2016, The Dendritic Cell Major Histocompatibility Complex II (MHC II) Peptidome Derives from a Variety of Processing Pathways and Includes Peptides with a Broad Spectrum of HLA-DM Sensitivity, J. Biol. Chem., 291, 5576, 10.1074/jbc.M115.655738 · DOI
  36. Karunakaran, 2017, Identification of MHC-Bound Peptides from Dendritic Cells Infected with Salmonella Enterica Strain SL1344: Implications for a Nontyphoidal Salmonella Vaccine, J. Proteome Res., 16, 298, 10.1021/acs.jproteome.6b00926 · doi:10.1021/acs.jproteome.6b00926
  37. Ooi, 2017, Dominant Protection from HLA-Linked Autoimmunity by Antigen-Specific Regulatory T Cells, Nature, 545, 243, 10.1038/nature22329 · DOI · Paper page
  38. Wang, 2017, Immunogenic HLA-DR-Presented Self-Peptides Identified Directly from Clinical Samples of Synovial Tissue, Synovial Fluid, or Peripheral Blood in Patients with Rheumatoid Arthritis or Lyme Arthritis, J. Proteome Res., 16, 122, 10.1021/acs.jproteome.6b00386 · DOI
  39. Nelde, 2018, HLA Ligandome Analysis of Primary Chronic Lymphocytic Leukemia (CLL) Cells under Lenalidomide Treatment Confirms the Suitability of Lenalidomide for Combination with T-Cell-Based Immunotherapy, Oncoimmunology, 7, 10.1080/2162402X.2017.1316438 · DOI
  40. Ritz, 2018, Membranal and Blood-Soluble HLA Class II Peptidome Analyses Using Data-Dependent and Independent Acquisition, Proteomics, 18, 1700246, 10.1002/pmic.201700246 · DOI
  41. Ting, 2018, The Interplay between Citrullination and HLA-DRB1 Polymorphism in Shaping Peptide Binding Hierarchies in Rheumatoid Arthritis, J. Biol. Chem., 293, 3236, 10.1074/jbc.RA117.001013 · DOI
  42. Scally, 2013, A Molecular Basis for the Association of the HLA-DRB1 Locus, Citrullination, and Rheumatoid Arthritis, J. Exp. Med., 210, 2569, 10.1084/jem.20131241 · DOI · Paper page
  43. Khodadoust, 2017, Antigen Presentation Profiling Reveals Recognition of Lymphoma Immunoglobulin Neoantigens, Nature, 543, 723, 10.1038/nature21433 · DOI · Paper page
  44. Álvaro-Benito, 2018, Quantification of HLA-DM-Dependent Major Histocompatibility Complex of Class II Immunopeptidomes by the Peptide Landscape Antigenic Epitope Alignment Utility, Front. Immunol., 9, 872, 10.3389/fimmu.2018.00872 · DOI
  45. Nanaware, 2019, HLA-DO Modulates the Diversity of the MHC-II Self-Peptidome, Mol. Cell. Proteomics, 18, 490, 10.1074/mcp.RA118.000956 · DOI
  46. Nielsen, 2007, Prediction of MHC Class II Binding Affinity Using SMM-Align, a Novel Stabilization Matrix Alignment Method, BMC Bioinf., 8, 238, 10.1186/1471-2105-8-238 · DOI · Paper page
  47. Thomsen, 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, W281, 10.1093/nar/gks469 · DOI · Paper page
  48. Nielsen, 2008, Quantitative Predictions of Peptide Binding to Any HLA-DR Molecule of Known Sequence: NetMHCIIpan, PLoS Comput. Biol., 4, 10.1371/journal.pcbi.1000107 · DOI · Paper page
  49. Shao, 2020, High-Throughput Prediction of MHC Class I and II Neoantigens with MHCnuggets, Cancer Immunol. Res., 8, 396, 10.1158/2326-6066.CIR-19-0464 · DOI
  50. Chen, 2019, Predicting HLA Class II Antigen Presentation through Integrated Deep Learning, Nat. Biotechnol., 37, 1332, 10.1038/s41587-019-0280-2 · DOI · Paper page
  51. Karosiene, 2012, NetMHCcons: A Consensus Method for the Major Histocompatibility Complex Class I Predictions, Immunogenetics, 64, 177, 10.1007/s00251-011-0579-8 · DOI · Paper page
  52. Ciudad, 2017, Analysis of the HLA-DR Peptidome from Human Dendritic Cells Reveals High Affinity Repertoires and Nonconventional Pathways of Peptide Generation, J. Leukocyte Biol., 101, 15, 10.1189/jlb.6HI0216-069R · DOI
  53. Choe, 2006, Substrate Profiling of Cysteine Proteases Using a Combinatorial Peptide Library Identifies Functionally Unique Specificities, J. Biol. Chem., 281, 12824, 10.1074/jbc.M513331200 · DOI · Paper page
  54. Biniossek, 2011, Proteomic Identification of Protease Cleavage Sites Characterizes Prime and Non-Prime Specificity of Cysteine Cathepsins B, L, and S, J. Proteome Res., 10, 5363, 10.1021/pr200621z · DOI

Cited by

Metadata

Authors
Reynisson, B., Barra, C., Kaabinejadian, S., Hildebrand, W. H., Peters, B., & Nielsen, M.
Venue
Journal of Proteome Research
Publisher
American Chemical Society (ACS)
Published
2020-04-18
Volume
19
Issue
6
Pages
2304-2315
ISSN
  • 1535-3893 (ISSN-L)
  • 1535-3907
DOI
10.1021/acs.jproteome.9b00874
OpenAlex
W3016853422
Cited by
445
References
1
FWCI
17.22
Type
journal article
Language
en
Open access
green
Licence

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