Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence

人工智能鉴定的免疫原性SARS-CoV-2 T细胞表位的实验验证

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作者:Lorenzo Federico ,Brandon Malone ,Simen Tennøe ,Viktoriia Chaban ,Julie Røkke Osen ,Murat Gainullin ,Eva Smorodina ,Hassen Kared ,Rahmad Akbar ,Victor Greiff ,Richard Stratford ,Trevor Clancy ,Ludvig Andre Munthe

Abstract

During the COVID-19 pandemic we utilized an AI-driven T cell epitope prediction tool, the NEC Immune Profiler (NIP) to scrutinize and predict regions of T cell immunogenicity (hotspots) from the entire SARS-CoV-2 viral proteome. These immunogenic regions offer potential for the development of universally protective T cell vaccine candidates. Here, we validated and characterized T cell responses to a set of minimal epitopes from these AI-identified universal hotspots. Utilizing a flow cytometry-based T cell activation-induced marker (AIM) assay, we identified 59 validated screening hits, of which 56% (33 peptides) have not been previously reported. Notably, we found that most of these novel epitopes were derived from the non-spike regions of SARS-CoV-2 (Orf1ab, Orf3a, and E). In addition, ex vivo stimulation with NIP-predicted peptides from the spike protein elicited CD8+ T cell response in PBMC isolated from most vaccinated donors. Our data confirm the predictive accuracy of AI platforms modelling bona fide immunogenicity and provide a novel framework for the evaluation of vaccine-induced T cell responses.

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