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

人工智能识别的免疫原性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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