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 language | English |
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Article number | 1705 |
Journal | Frontiers in Immunology |
Volume | 11 |
Number of pages | 8 |
ISSN | 1664-3224 |
DOIs | |
Publication status | Published - 2020 |
Keywords
- Ultra-high density peptide microarray
- MHC class II
- HLA
- Antigen presentation
- Prediction
- Peptide binding
- High-throughput
- Machine learning