Machine learning reveals limited contribution of trans-only encoded variants to the HLA-DQ immunopeptidome.

Human leukocyte antigen (HLA) class II antigen presentation is key for controlling and triggering T cell immune responses. HLA-DQ molecules, which are believed to play a major role in autoimmune diseases, are heterodimers that can be formed as both cis and trans variants depending on whether the α- and ß-chains are encoded on the same (cis) or opposite (trans) chromosomes. So far, limited progress has been made for predicting HLA-DQ antigen presentation. In addition, the contribution of trans-only variants (i.e. variants not observed in the population as cis) in shaping the HLA-DQ immunopeptidome remains largely unresolved. Here, we seek to address these issues by integrating state-of-the-art immunoinformatics data mining models with large volumes of high-quality HLA-DQ specific mass spectrometry immunopeptidomics data. The analysis demonstrates highly improved predictive power and molecular coverage for models trained including these novel HLA-DQ data. More importantly, investigating the role of trans-only HLA-DQ variants reveals a limited to no contribution to the overall HLA-DQ immunopeptidome. In conclusion, this study furthers our understanding of HLA-DQ specificities and casts light on the relative role of cis versus trans-only HLA-DQ variants in the HLA class II antigen presentation space. The developed method, NetMHCIIpan-4.2, is available at https://services.healthtech.dtu.dk/services/NetMHCIIpan-4.2 .

PopCover-2.0. Improved Selection of Peptide Sets With Optimal HLA and Pathogen Diversity Coverage.

The use of minimal peptide sets offers an appealing alternative for design of vaccines and T cell diagnostics compared to conventional whole protein approaches. T cell immunogenicity towards peptides is contingent on binding to human leukocyte antigen (HLA) molecules of the given individual. HLA is highly polymorphic, and each variant typically presents a different repertoire of peptides. This polymorphism combined with pathogen diversity challenges the rational selection of peptide sets with broad immunogenic potential and population coverage. Here we propose PopCover-2.0, a simple yet highly effective method, for resolving this challenge. The method takes as input a set of (predicted) CD8 and/or CD4 T cell epitopes with associated HLA restriction and pathogen strain annotation together with information on HLA allele frequencies, and identifies peptide sets with optimal pathogen and HLA (class I and II) coverage. PopCover-2.0 was benchmarked on historic data in the context of HIV and SARS-CoV-2. Further, the immunogenicity of the selected SARS-CoV-2 peptides was confirmed by experimentally validating the peptide pools for T cell responses in a panel of SARS-CoV-2 infected individuals. In summary, PopCover-2.0 is an effective method for rational selection of peptide subsets with broad HLA and pathogen coverage. The tool is available at https://services.healthtech.dtu.dk/service.php?PopCover-2.0.