biostudies-literatureGfeller DHorizon 2020 Marie Skłodowska-Curie ActionsSwiss Cancer Research FoundationSwiss Cancer LeagueMarie Curie72-83.e5https://www.ebi.ac.uk/biostudies/studies/S-EPMC9811684biostudies-literatureUnknown14(1)The recognition of pathogen or cancer-specific epitopes by CD8⁺ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8⁺ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.Cell systemsImproved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8⁺ T-cell epitopes.PMC9811684101027973KFS-4961-02-2020H2020-MSCA-IF-2020Gfeller DBobisse SGenolet RHarari ACesbron JQueiroz LSchmidt JCroce GRacle JGuillaume PfalseImproved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8⁺ T-cell epitopes.The recognition of pathogen or cancer-specific epitopes by CD8⁺ T cells is crucial for the clearance of infections and the response to cancer immunotherapy. This process requires epitopes to be presented on class I human leukocyte antigen (HLA-I) molecules and recognized by the T-cell receptor (TCR). Machine learning models capturing these two aspects of immune recognition are key to improve epitope predictions. Here, we assembled a high-quality dataset of naturally presented HLA-I ligands and experimentally verified neo-epitopes. We then integrated these data in a refined computational framework to predict antigen presentation (MixMHCpred2.2) and TCR recognition (PRIME2.0). The depth of our training data and the algorithmic developments resulted in improved predictions of HLA-I ligands and neo-epitopes. Prospectively applying our tools to SARS-CoV-2 proteins revealed several epitopes. TCR sequencing identified a monoclonal response in effector/memory CD8⁺ T cells against one of these epitopes and cross-reactivity with the homologous peptides from other coronaviruses.2023-01-01T00:00:00Z2023 Jan2026-05-27T23:10:37.047Z2025-04-04T13:53:28.11ZS-EPMC98116843660358310.1016/j.cels.2022.12.002