In silico prediction of Ara h 2 T cell epitopes in peanut‐allergic children

Abstract

Despite the frequency and severity of peanut allergy, the only approved treatment is strict avoidance. Different types of immunotherapy with crude peanut extract are not universally effective and have been associated with relatively high adverse reaction rates. We sought to determine whether in silico predictive algorithms were useful in identifying candidate peptides for an Ara h 2 peptide-based vaccine using peanut-allergic patients' peripheral blood mononuclear cells (PBMCs) in vitro. A human leucocyte antigen (HLA) distribution analysis was also performed. Major histocompatibility complex (MHC)-class II-binding peptides were predicted using NetMHCIIpan-2.0 and NetMHCII-2.2 algorithms. PBMCs from 80 peanut-allergic patients were stimulated with overlapping 20-mer Ara h 2 peptides. Cell supernatant cytokine profiles were evaluated by multiplex assays. HLA-DRB1* and HLA-DQB1* typing were performed. Four regions of overlapping sequences induced PBMC proliferation and predominant Th2 cytokine production. HLA genotyping showed 30 different DRB1* allele specificities and eight DQ serological specificities. The in silico analysis revealed similar relevant regions and predicted identical or similar core 9-mer epitopes to those identified in vitro. If relevant peptides, as determined by either in vitro or in silico analysis (15 peptides and 9 core epitopes respectively), were used in a peptide-based vaccine, they would cover virtually all subjects in the cohort studied. Four dominant regions in Ara h 2 have been identified, containing sequences that could serve as potential candidates for peptide-based immunotherapy. MHC-class II-based T cell epitope prediction algorithms for HLA-DR and -DQ loci accurately predicted Ara h 2 T cell epitopes in peanut-allergic subjects, suggesting their potential utility in a peptide-based vaccine design for food allergy.