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

Birkir Reynisson, Carolina Barra, Saghar Kaabinejadian, William H. Hildebrand, Bjoern Peters, Morten Nielsen*

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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.
Original languageEnglish
JournalJournal of Proteome Research
Volume19
Issue number6
Pages (from-to)2304-2315
ISSN1535-3893
DOIs
Publication statusPublished - 2020

Keywords

  • Machine learning
  • Bioinformatics
  • Immunoinformatics
  • Immunology
  • MHC II
  • Antigen presentation
  • Mass spectrometry
  • Immunopeptidomics
  • Neoepitopes

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