Abstract
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.
Highlights
T cell based vaccines and diagnostics offer an appealing alternative to their antibody based counterpart
Schubert et al compared the performance of these different methods and demonstrated that methods that simultaneously aim to optimize pathogen and human leukocyte antigen (HLA) coverage (i.e., OptiTope and PopCover) significantly outperformed methods focusing on pathogen coverage alone (i.e., Mosaic) [10]
These can either be pasted into the input field or uploaded in a text file. Submitting these will run PopCover-2.0 in an alternative ‘mode’ where peptides of a user-defined length are extracted from the sequences, onto which HLA binders are mapped. This approach is more thorough as the number of contained binders per peptide can be potentially much larger than when only the input HLA binders are considered, and more sequence information can go into the epitope selection
Summary
T cell based vaccines and diagnostics offer an appealing alternative to their antibody based counterpart. PopCover-2.0 variants of the HLA genes currently annotated in the IMGTHLA database [1] This immense diversity imposes a major challenge when designing T cell based vaccines and diagnostics, since each HLA molecule has a unique peptide binding specificity resulting in a potential to bind and present a unique set of antigenic peptides to the T cells of the host [reviewed in [2]]. None of the current peptide driven tools allow an automatic and in depth approach to satisfy the important aspects of vaccine design associated with peptide redundancy and integration of CD4 and CD8 immunity We resolve these limitations by proposing an updated version 2.0 of the PopCover method allowing identification of peptide subsets from large data set(s) of predicted HLA class I and/or HLA class II binders with optimal HLA and pathogen genotype coverage, and showcase the power of this method on large protein data sets from HIV and SARS-CoV-2
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