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
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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. / Wendorff, Mareike; Garcia Alvarez, Heli M.; Østerbye, Thomas; ElAbd, Hesham; Rosati, Elisa; Degenhardt, Frauke; Buus, Søren; Franke, Andre; Nielsen, Morten.
In: Frontiers in Immunology, Vol. 11, 1705, 2020.Research output: Contribution to journal › Journal article › Research › peer-review
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TY - JOUR
T1 - 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
AU - Wendorff, Mareike
AU - Garcia Alvarez, Heli M.
AU - Østerbye, Thomas
AU - ElAbd, Hesham
AU - Rosati, Elisa
AU - Degenhardt, Frauke
AU - Buus, Søren
AU - Franke, Andre
AU - Nielsen, Morten
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
KW - antigen presentation
KW - high-throughput
KW - HLA
KW - machine learning
KW - MHC class II
KW - peptide binding
KW - prediction
KW - ultra-high density peptide microarray
U2 - 10.3389/fimmu.2020.01705
DO - 10.3389/fimmu.2020.01705
M3 - Journal article
C2 - 32903714
AN - SCOPUS:85089849504
VL - 11
JO - Frontiers in Immunology
JF - Frontiers in Immunology
SN - 1664-3224
M1 - 1705
ER -
ID: 249251086