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 (MHC-I) prediction tools have matured to a point where accurate selection of optimal peptide epitopes is possible for virtually all MHC-I allotypes; in comparison, current MHC class II (MHC-II) predictors are less mature. Since MHC-II restricted CD4+ T cells control and orchestrate 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 is 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. Here, we have used recombinant HLA-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. Collectively, with a cost per peptide reduced to a few cents combined with the flexibility of recombinant HLA 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.

Binding of HLA-DRB1*01:01 and HLA-DRB1*03:01 to high density peptide arrays containing 63k random 13-mer peptides in triplicate. The binding data was used to train artificial neural networks (ANN) capable of predicting HLA-DR binding and specificity. 

HLA-DRB1*01:01 and HLA-DRB1*03:01 were re-folded with peptides synthesized on the peptide array and subsequently stained with mAb clone L243 and a anti-mouse-Cy3 before scanning with a laser scanner at one micron resolution. 

The obtained signals were used to train artifcial neural networks (ANNs) where the final network ensamble were able to predict the binding of unknown peptides to the respective HLAs.