You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
HLA Class II Specificity Assessed by High-Density Peptide Microarray Interactions
Contributor:
Osterbye, Thomas;
Nielsen, Morten;
Dudek, Nadine L.;
Ramarathinam, Sri H.;
Purcell, Anthony W.;
Schafer-Nielsen, Claus;
Buus, Soren
imprint:
The American Association of Immunologists, 2020
Published in:
The Journal of Immunology, 205 (2020) 1, Seite 290-299
Language:
English
DOI:
10.4049/jimmunol.2000224
ISSN:
0022-1767;
1550-6606
Origination:
Footnote:
Description:
<jats:title>Abstract</jats:title>
<jats:p>The ability to predict and/or identify MHC binding peptides is an essential component of T cell epitope discovery, something that ultimately should benefit the development of vaccines and immunotherapies. In particular, MHC class I prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC class I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Because MHC-II restricted CD4+ T cells control and orchestrated most immune responses, this shortcoming severely hampers the development of effective immunotherapies. The ability to generate large panels of peptides and subsequently large bodies of peptide–MHC-II interaction data are key to the solution of this problem, a solution that also will support the improvement of bioinformatics predictors, which critically relies on the availability of large amounts of accurate, diverse, and representative data. In this study, we have used rHLA-DRB1*01:01 and HLA-DRB1*03:01 molecules to interrogate high-density peptide arrays, in casu containing 70,000 random peptides in triplicates. We demonstrate that the binding data acquired contains systematic and interpretable information reflecting the specificity of the HLA-DR molecules investigated, suitable of training predictors able to predict T cell epitopes and peptides eluted from human EBV-transformed B cells. Collectively, with a cost per peptide reduced to a few cents, combined with the flexibility of rHLA technology, this poses an attractive strategy to generate vast bodies of MHC-II binding data at an unprecedented speed and for the benefit of generating peptide–MHC-II binding data as well as improving MHC-II prediction tools.</jats:p>