CoChat
Sign Up
Cited by 68vaccines and immunoinformatics approachesChemical Synthesis and AnalysisImmunotherapy and Immune Responses

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Mass Spectrometry Based Immunopeptidomics Leads to Robust Predictions of Phosphorylated HLA Class I Ligands

Open access
Solleder, Marthe, Guillaume, Philippe, Racle, Julien, Michaux, Justine, Pak, Hui-Song, Müller, Markus, Coukos, George, Bassani-Sternberg, Michal, Gfeller, David·2020·Molecular & Cellular Proteomics
DOI 10.1074/mcp.tir119.001641PMC PMC7000122

Published version of a preprint

Open this paper in CoChat — free account

Abstract

Abstract The presentation of peptides on class I human leukocyte antigen (HLA-I) molecules plays a central role in immune recognition of infected or malignant cells. In cancer, non-self HLA-I ligands can arise from many different alterations, including non-synonymous mutations, gene fusion, cancer-specific alternative mRNA splicing or aberrant post-translational modifications. Identifying HLA-I ligands remains a challenging task that requires either heavy experimental work for in-vivo identification or optimized bioinformatics tools for accurate predictions. To date, no HLA-I ligand predictor includes post-translational modifications. To fill this gap, we curated phosphorylated HLA-I ligands from several immunopeptidomics studies (including six newly measured samples) covering 72 HLA-I alleles, and retrieved a total of 2,066 unique phosphorylated peptides. We then expanded our motif deconvolution tool to identify precise binding motifs of phosphorylated HLA-I ligands. Our results reveal a clear enrichment of phosphorylated peptides among HLA-C ligands and demonstrate a prevalent role of both HLA-I motifs and kinase motifs on the presentation of phosphorylated peptides. This data further enabled us to develop and validate the first predictor of interactions between HLA-I molecules and phosphorylated peptides.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1074/mcp.tir119.001641

Cites

3 of 3 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 54

  1. Robinson, 2015, The IPD and IMGT/HLA database: Allele variant databases, Nucleic Acids Res, 43, D423, 10.1093/nar/gku1161 · DOI
  2. Gfeller, 2018, Predicting antigen presentation — what could we learn from a million peptides?, Front. Immunol, 9, 1, 10.3389/fimmu.2018.01716 · DOI
  3. Andreatta, 2015, Gapped sequence alignment using artificial neural networks: Application to the MHC class I system, Bioinformatics, 32, 511, 10.1093/bioinformatics/btv639 · DOI
  4. 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
  5. Gfeller, 2018, The length distribution and multiple specificity of naturally presented HLA-I ligands, J. Immunol, 202, 1
  6. Donnell, 2018, MHCflurry: Open-source class I MHC binding affinity prediction, Cell Syst, 7, 129, 10.1016/j.cels.2018.05.014 · DOI
  7. Cho, 2018, Post-translational modification of OCT4 in breast cancer tumorigenesis, Cell Death Differ, 25, 1781, 10.1038/s41418-018-0079-6 · DOI
  8. Jarrold, 2019, PRMTs and arginine methylation: cancer's best-kept secret?, Trends Mol. Med, 25, 1, 10.1016/j.molmed.2019.05.007 · DOI
  9. Krueger, 2006, Posttranslational protein modifications, Mol. Cell. Proteomics, 5, 1799, 10.1074/mcp.R600009-MCP200 · DOI
  10. Archer, 2018, Proteomics, post-translational modifications, and integrative analyses reveal molecular heterogeneity within medulloblastoma subgroups, Cancer Cell, 34, 396, 10.1016/j.ccell.2018.08.004 · DOI
  11. Hanahan, 2000, The hallmarks of cancer, Cell, 100, 57, 10.1016/S0092-8674(00)81683-9 · DOI · Paper page
  12. Humphrey, 2015, Protein Phosphorylation: A Major Switch Mechanism for Metabolic Regulation, Trends Endocrinol. Metab, 26, 676, 10.1016/j.tem.2015.09.013 · DOI
  13. Hunter, 1995, Protein kinases and phosphatases: the yin and yang of protein phosphorylation and signaling, Cell, 80, 225, 10.1016/0092-8674(95)90405-0 · DOI
  14. Ardito, 2017, The crucial role of protein phosphorylation in cell signaling and its use as targeted therapy, Int. J. Mol. Med, 40, 271, 10.3892/ijmm.2017.3036 · DOI
  15. Reimand, 2013, The mutational landscape of phosphorylation signaling in cancer, Sci. Rep, 3, 1, 10.1038/srep02651 · DOI
  16. Krug, 2019, A curated resource for phosphosite-specific signature analysis, Mol. Cell. Proteomics, 18, 576, 10.1074/mcp.TIR118.000943 · DOI
  17. Andersen, 1999, Phosphorylated peptides can be transported by TAP molecules, presented by class I MHC molecules, and recognized by phosphopeptide-specific CTL, J. Immunol, 163, 3812, 10.4049/jimmunol.163.7.3812 · DOI
  18. Zarling, 2000, Phosphorylated peptides are naturally processed and presented by major histocompatibility complex class I molecules in vivo, J. Exp. Med, 192, 1755, 10.1084/jem.192.12.1755 · DOI · Paper page
  19. Zarling, 2006, Identification of class I MHC-associated phosphopeptides as targets for cancer immunotherapy, Proc. Natl. Acad. Sci, 103, 14889, 10.1073/pnas.0604045103 · DOI
  20. Meyer, 2009, Identification of natural MHC class II presented phosphopeptides and tumor-derived MHC class I phospholigands, J. Proteome Res, 8, 3666, 10.1021/pr800937k · DOI
  21. Petersen, 2009, Phosphorylated self-peptides alter human leukocyte antigen class I-restricted antigen presentation and generate tumor-specific epitopes, Proc. Natl. Acad. Sci, 106, 2776, 10.1073/pnas.0812901106 · DOI
  22. Cobbold, 2013, MHC class I-associated phosphopeptides are the targets of memory-like immunity in leukemia, Sci. Transl. Med, 5, 1, 10.1126/scitranslmed.3006061 · DOI
  23. Marcilla, 2014, Increased diversity of the HLA-B40 ligandome by the presentation of peptides phosphorylated at their main anchor residue, Mol. Cell. Proteomics, 13, 462, 10.1074/mcp.M113.034314 · DOI
  24. Bassani-Sternberg, 2016, Direct identification of clinically relevant neoepitopes presented on native human melanoma tissue by mass spectrometry, Nat. Commun, 7, 1, 10.1038/ncomms13404 · DOI · Paper page
  25. 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
  26. Alpízar, 2017, A molecular basis for the presentation of phosphorylated peptides by HLA-B antigens, Mol. Cell. Proteomics, 16, 181, 10.1074/mcp.M116.063800 · DOI
  27. Mohammed, 2017, The antigenic identity of human class I MHC phosphopeptides is critically dependent upon phosphorylation status, Oncotarget, 8, 54160, 10.18632/oncotarget.16952 · DOI
  28. Olsson, 2018, T-cell immunopeptidomes reveal cell subtype surface markers derived from intracellular proteins, Proteomics, 18, e1700410, 10.1002/pmic.201700410 · DOI
  29. Lin, 2019, Immunological evaluation of a novel HLA-A2 restricted phosphopeptide of tumor associated antigen, TRAP1, on cancer therapy, Vaccine X, 100017
  30. Mohammed, 2008, Phosphorylation-dependent interaction between antigenic peptides and MHC class I: a molecular basis for the presentation of transformed self, Nat. Immunol, 9, 1236, 10.1038/ni.1660 · DOI · Paper page
  31. Mommen, 2014, Expanding the detectable HLA peptide repertoire using electron-transfer/higher-energy collision dissociation (EThcD), Proc. Natl. Acad. Sci, 111, 4507, 10.1073/pnas.1321458111 · DOI
  32. Chong, 2017, High-throughput and sensitive immunopeptidomics platform reveals profound IFNγ-mediated remodeling of the HLA ligandome, Mol. Cell. Proteomics, 17, 533, 10.1074/mcp.TIR117.000383 · DOI
  33. Cox, 2008, MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification, Nat. Biotechnol, 26, 1367, 10.1038/nbt.1511 · DOI · Paper page
  34. Savitski, 2011, Confident phosphorylation site localization using the mascot delta score, Mol. Cell. Proteomics, 10, 1, 10.1074/mcp.M110.003830 · DOI
  35. Bassani-Sternberg, 2017, Deciphering HLA-I motifs across HLA peptidomes improves neo-antigen predictions and identifies allostery regulating HLA specificity, PLoS Comput. Biol, 13, e1005725, 10.1371/journal.pcbi.1005725 · DOI
  36. Bassani-Sternberg, 2015, Mass spectrometry of human leukocyte antigen class I peptidomes reveals strong effects of protein abundance and turnover on antigen presentation, Mol. Cell. Proteomics, 14, 658, 10.1074/mcp.M114.042812 · DOI
  37. Di Marco, 2017, Unveiling the peptide motifs of HLA-C and HLA-G from naturally presented peptides and generation of binding prediction matrices, J. Immunol, 199, 2639, 10.4049/jimmunol.1700938 · DOI
  38. Ostrov, 2012, Drug hypersensitivity caused by alteration of the MHC-presented self-peptide repertoire, Proc. Natl. Acad. Sci, 109, 9959, 10.1073/pnas.1207934109 · DOI
  39. Schittenhelm, 2015, Revisiting the arthritogenic peptide theory: quantitative not qualitative changes in the peptide repertoire of HLA-B27 allotypes, Arthritis Rheumatol, 67, 702, 10.1002/art.38963 · DOI
  40. Giam, 2015, A comprehensive analysis of peptides presented by HLA-A1, Tissue Antigens, 85, 492, 10.1111/tan.12565 · DOI
  41. Ramarathinam, 2018, Identification of Native and Posttranslationally Modified HLA-B*57:01-Restricted HIV Envelope Derived Epitopes Using Immunoproteomics, Proteomics, 18, 1, 10.1002/pmic.201700253 · DOI
  42. 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
  43. Vita, 2015, The immune epitope database (IEDB) 3.0, Nucleic Acids Res, 43, D405, 10.1093/nar/gku938 · DOI
  44. Wagih, 2017, Ggseqlogo: A versatile R package for drawing sequence logos, Bioinformatics, 33, 3645, 10.1093/bioinformatics/btx469 · DOI
  45. Sharma, 2014, Resource ultradeep human phosphoproteome reveals a distinct regulatory nature, Cell Rep, 8, 1583, 10.1016/j.celrep.2014.07.036 · DOI
  46. Diella, 2008, Phospho.ELM: A database of phosphorylation sites - Update 2008, Nucleic Acids Res, 36, 240, 10.1093/nar/gkm772 · DOI
  47. Nielsen, 2004, Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach, Bioinformatics, 20, 1388, 10.1093/bioinformatics/bth100 · DOI
  48. Henikoff, 1992, Amino acid substitution matrices from protein blocks, Proc. Natl. Acad. Sci, 89, 10915, 10.1073/pnas.89.22.10915 · DOI · Paper page
  49. Pedregosa, 2011, Scikit-learn: machine learning in python, J. Mach. Learn. Res, 12, 2825
  50. Ullah, 2016, dbPAF: An integrative database of protein phosphorylation in animals and fungi, Sci. Rep, 6, 1, 10.1038/srep23534 · DOI
  51. Dinkel, 2011, Phospho.ELM: a database of phosphorylation sites — update 2011, Nucleic Acids Res, 39, 261, 10.1093/nar/gkq1104 · DOI
  52. Hornbeck, 2015, PhosphoSitePlus, 2014: mutations, PTMs and recalibrations, Nucleic Acids Res, 43, 512, 10.1093/nar/gku1267 · DOI · Paper page
  53. Andreatta, 2019, MS-rescue: a computational pipeline to increase the quality and yield of immunopeptidomics experiments, Proteomics, 19, e1800357, 10.1002/pmic.201800357 · DOI
  54. Perez-Riverol, 2019, The PRIDE database and related tools and resources in 2019: improving support for quantification data, Nucleic Acids Res, 47, 442, 10.1093/nar/gky1106 · DOI

Metadata

Authors
Solleder, M., Guillaume, P., Racle, J., Michaux, J., Pak, H.-S., Müller, M., Coukos, G., Bassani-Sternberg, M., & Gfeller, D.
Affiliations
  • SIB Swiss Institute of Bioinformatics (CH)
  • Ludwig Cancer Research (CH)
  • Centre Hospitalier Universitaire Vaudois (CH)
Corresponding author
Gfeller, David
ORCID
Venue
Molecular & Cellular Proteomics
Publisher
Elsevier BV
Published
2020-02
Volume
19
Issue
2
Pages
390-404
ISSN
1535-9476 (ISSN-L)
DOI
10.1074/mcp.tir119.001641
PMC
PMC7000122
OpenAlex
W2996241254
Cited by
68
References
3
Citations by year
2026: 10 · 2025: 5 · 2024: 8 · 2023: 12 · 2022: 11 · 2021: 15 · 2020: 6
FWCI
2.62
Type
journal article
Language
en
Open access
Licence
Locations
  • Molecular & Cellular Proteomics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • Europe PMC (PubMed Central) (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
  • IRIS (submittedVersion)
  • SERVAL (Université de Lausanne) (submittedVersion)
Funders
Université de Lausanne
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › vaccines and immunoinformatics approaches
  • Physical Sciences › Chemistry › Organic Chemistry › Chemical Synthesis and Analysis
  • Life Sciences › Immunology and Microbiology › Immunology › Immunotherapy and Immune Responses
Keywords
immunopeptidomics, post-translational modification, HLA-C, peptide binding motifs, antigen presentation
MeSH terms
Humans, Ligands, Peptides, Phosphorylation, Mass Spectrometry, Histocompatibility Antigens Class I, Proteomics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review