Enhancing Mass Spectrometry-Based MHC-I Peptide Identification Through a Targeted Database Search Approach

  • Prathyusha Konda
  • J. Patrick Murphy
  • Morten Nielsen
  • Shashi GujarEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2024)


MHC-bound peptide ligands dictate the activation and specificity of CD8+ T- cells-based and thus are important for devising T-cell immunotherapies. In recent times, advances in mass spectrometry (MS) have enabled the precise identification of these peptides, wherein MS/MS spectra are compared against a reference proteome. Unfortunately, matching immunopeptide MS/MS to reference proteome databases is hindered by inflated search spaces attributed to the number of matches that need to be considered due to a lack of enzyme restriction. These large search spaces limit the efficiency with which MHC-I peptides are identified. Here we offer a solution to this problem whereby we describe a targeted database search approach and accompanying tool SpectMHC that is based on a priori predicted MHC-I peptides (Murphy et al., J Proteome Res 16:1806–1816, 2017).

Key words

MHC ligandome Mass spectrometry Bioinformatics Immuno-informatics MHC peptides NetMHC SpectMHC 



We gratefully acknowledge Dr. Stefan Stevanovic, Dr. Dan Kowalewski, and Dr. Heiko Schuster (Department of Immunology, Institute for Cell Biology, University of Tubingen) for helpful discussions in devising the targeted database search approach. We also acknowledge Ms. Youra Kim for her support with editing of this document. We acknowledge financial support from the Canadian Institutes of Health Research (CIHR), Canadian Cancer Society Research Institute (CCSRI), the Beatrice Hunter Cancer Research Institute (BHCRI), and the Dalhousie Medical Research Foundation (DMRF).


  1. 1.
    Rock KL, Reits E, Neefjes J (2016) Present yourself! By MHC class I and MHC class II molecules. Trends Immunol 37(11):724–737CrossRefGoogle Scholar
  2. 2.
    Blum JS, Wearsch PA, Cresswell P (2013) Pathways of antigen processing. Annu Rev Immunol 31:443–473CrossRefGoogle Scholar
  3. 3.
    Caron E, Kowalewski DJ, Koh CC et al (2015) Analysis of major histocompatibility complex (MHC) immunopeptidomes using mass spectrometry. Mol Cell Proteomics 14(12):3105–3117CrossRefGoogle Scholar
  4. 4.
    Andreatta M, Nielsen M (2015) Gapped sequence alignment using artificial neural networks: application to the MHC class I system. Bioinformatics 32(4):511–517CrossRefGoogle Scholar
  5. 5.
    Jurtz V, Paul S, Andreatta M et al (2017) NetMHCpan-4.0: improved peptide–MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data. J Immunol 199(9):3360–3368CrossRefGoogle Scholar
  6. 6.
    Murphy JP, Konda P, Kowalewski DJ et al (2017) MHC-I ligand discovery using targeted database searches of mass spectrometry data: implications for T-cell immunotherapies. J Proteome Res 16:1806–1816CrossRefGoogle Scholar
  7. 7.
    Boegel S, Scholtalbers J, Löwer M et al (2015) In silico HLA typing using standard RNA-Seq sequence reads. Methods Mol Biol 1310:247–258CrossRefGoogle Scholar
  8. 8.
    Huang Y, Yang J, Ying D et al (2015) HLA reporter: a tool for HLA typing from next generation sequencing data. Genome Med 7(1):25CrossRefGoogle Scholar
  9. 9.
    Warren RL, Choe G, Freeman DJ et al (2012) Derivation of HLA types from shotgun sequence datasets. Genome Med 4(12):95CrossRefGoogle Scholar
  10. 10.
    Elias JE, Gygi SP (2007) Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods 4(3):207–214CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Prathyusha Konda
    • 1
  • J. Patrick Murphy
    • 2
  • Morten Nielsen
    • 3
    • 4
  • Shashi Gujar
    • 1
    • 2
    • 5
    • 6
    Email author
  1. 1.Department of Microbiology and ImmunologyDalhousie UniversityHalifaxCanada
  2. 2.Department of PathologyDalhousie UniversityHalifaxCanada
  3. 3.Department of Bio and Health InformaticsTechnical University of DenmarkLyngbyDenmark
  4. 4.Instituto de Investigaciones BiotecnológicasUniversidad Nacional de San MartínSan Martín, Buenos AiresArgentina
  5. 5.Department of BiologyDalhousie UniversityHalifaxCanada
  6. 6.Centre for Innovative and Collaborative Health Services ResearchIWK Health CentreHalifaxCanada

Personalised recommendations