Cell Syst . 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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Cell Syst


. 2022 Dec 23;S2405-4712(22)00470-7.
doi: 10.1016/j.cels.2022.12.002. Online ahead of print.
Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8⁺ T-cell epitopes


David Gfeller ¹ , Julien Schmidt ² , Giancarlo Croce ³ , Philippe Guillaume ² , Sara Bobisse ⁴ , Raphael Genolet ² , Lise Queiroz ² , Julien Cesbron ² , Julien Racle ³ , Alexandre Harari ⁴



Affiliations

• PMID: 36603583
• DOI: 10.1016/j.cels.2022.12.002

Abstract

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.

Keywords: CD8(+) T cell epitopes; HLA-I peptidomics; antigen presentation; computational biology; epitope predictions; immunology; machine learning.