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

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

doi:10.3389/fimmu.2023.1265044

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

Front Immunol. 2023 Nov 17:14:1265044. doi: 10.3389/fimmu.2023.1265044. eCollection 2023.

Authors

Lorenzo Federico^{1

2}, Brandon Malone³, Simen Tennøe³, Viktoriia Chaban^{1

2}, Julie Røkke Osen^{1

2}, Murat Gainullin^{1

2}, Eva Smorodina^{1

4}, Hassen Kared^{1

2}, Rahmad Akbar^{1

4}, Victor Greiff^{1

4}, Richard Stratford³, Trevor Clancy³, Ludvig Andre Munthe^{1

2}

Affiliations

¹ Department of Immunology, Oslo University Hospital, Oslo, Norway.
² KG Jebsen Centre for B cell Malignancies, Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
³ NEC OncoImmunity AS, Oslo, Norway.
⁴ Institute of Clinical Medicine, Oslo University Hospital, Oslo, Norway.

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.

Keywords: CD4+ lymphocytes; CD8+ lymphocytes; COVID-19; SARS-CoV-2; T cell; antigen (Ag); artificial intelligence; vaccine.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Artificial Intelligence
COVID-19*
Epitopes, T-Lymphocyte
Humans
Leukocytes, Mononuclear
Pandemics / prevention & control
Peptides
SARS-CoV-2
Viral Vaccines*

Substances

Epitopes, T-Lymphocyte
Viral Vaccines
Peptides

Grants and funding

The author(s) declare financial support was received for the research, authorship, and/or publication of this article. This work was supported by The Coalition for Epidemic Preparedness Innovations (2021-N-1), The Research Council of Norway (316277), The University of Oslo (KGJ-19), NEC Oncoimmunity AS, and The South-Eastern Norway Regional Health Authority/Oslo University Hospital (HSØ-29286).