Experimental validation of immunogenic SARS-CoV-2 T cell epitopes identified by artificial intelligence - Federico, Lorenzo; Malone, Brandon; Tennøe, Simen; Chaban, Viktoriia; Osen, Julie Røkke; Gainullin, Murat; Smorodina, Eva; Kared, Hassen; Akbar, Rahmad; Greiff, Victor; Stratford, Richard; Clancy, Trevor; et al. (Frontiers in Immunology, 2023)
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.