Unbiased Characterization of Peptide-HLA Class II Interactions Based on Large-Scale Peptide Microarrays; Assessment of the Impact on HLA Class II Ligand and Epitope Prediction

Mareike Wendorff*, Heli M. Garcia Alvarez, Thomas Østerbye, Hesham ElAbd, Elisa Rosati, Frauke Degenhardt, Søren Buus, Andre Franke*, Morten Nielsen

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

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Abstract

Human Leukocyte Antigen class II (HLA-II) molecules present peptides to T lymphocytes and play an important role in adaptive immune responses. Characterizing the binding specificity of single HLA-II molecules has profound impacts for understanding cellular immunity, identifying the cause of autoimmune diseases, for immunotherapeutics, and vaccine development. Here, novel high-density peptide microarray technology combined with machine learning techniques were used to address this task at an unprecedented level of high-throughput. Microarrays with over 200,000 defined peptides were assayed with four exemplary HLA-II molecules. Machine learning was applied to mine the signals. The comparison of identified binding motifs, and power for predicting eluted ligands and CD4+ epitope datasets to that obtained using NetMHCIIpan-3.2, confirmed a high quality of the chip readout. These results suggest that the proposed microarray technology offers a novel and unique platform for large-scale unbiased interrogation of peptide binding preferences of HLA-II molecules.
Original languageEnglish
Article number1705
JournalFrontiers in Immunology
Volume11
Number of pages8
ISSN1664-3224
DOIs
Publication statusPublished - 2020

Keywords

  • Ultra-high density peptide microarray
  • MHC class II
  • HLA
  • Antigen presentation
  • Prediction
  • Peptide binding
  • High-throughput
  • Machine learning

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