Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8(+ )T-cell epitopes.
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18 janvier 2023
- doi: 10.1016/j.cels.2022.12.002
- issn: 2405-4712
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info:eu-repo/semantics/altIdentifier/urn/urn:nbn:ch:serval-BIB_53359DC7F7C13
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Antigen determinants Epitopes Antigenic determinant groups Determinants, Antigenic EpitypesCiter ce document
D. Gfeller et al., « Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8(+ )T-cell epitopes. », Serveur académique Lausannois, ID : 10.1016/j.cels.2022.12.002
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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.