In Frontiers in immunology ; h5-index 100.0
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.
Wendorff Mareike, Garcia Alvarez Heli M, Østerbye Thomas, ElAbd Hesham, Rosati Elisa, Degenhardt Frauke, Buus Søren, Franke Andre, Nielsen Morten
HLA, MHC class II, antigen presentation, high-throughput, machine learning, peptide binding, prediction, ultra-high density peptide microarray