Benchmarking predictions of MHC class I restricted T cell epitopes in a comprehensively studied model system.

ABSTRACT: T cell epitope candidates are commonly identified using computational prediction tools in order to enable applications such as vaccine design, cancer neoantigen identification, development of diagnostics and removal of unwanted immune responses against protein therapeutics. Most T cell epitope prediction tools are based on machine learning algorithms trained on MHC binding or naturally processed MHC ligand elution data. The ability of currently available tools to predict T cell epitopes has not been comprehensively evaluated. In this study, we used a recently published dataset that systematically defined T cell epitopes recognized in vaccinia virus (VACV) infected C57BL/6 mice (expressing H-2Db and H-2Kb), considering both peptides predicted to bind MHC or experimentally eluted from infect

SUBMITTER: Paul S 

PROVIDER: S-EPMC7274474 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature