Improved predictions of antigen presentation and TCR recognition with MixMHCpred2.2 and PRIME2.0 reveal potent SARS-CoV-2 CD8+ T-cell epitopes

Cell Syst. 2023 Jan 18;14(1):72-83.e5. doi: 10.1016/j.cels.2022.12.002. Epub 2023 Jan 4.

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.

MeSH terms

  • Antigen Presentation
  • CD8-Positive T-Lymphocytes*
  • COVID-19*
  • Epitopes, T-Lymphocyte
  • HLA Antigens
  • Humans
  • Ligands
  • Receptors, Antigen, T-Cell
  • SARS-CoV-2

Substances

  • Epitopes, T-Lymphocyte
  • Ligands
  • Receptors, Antigen, T-Cell
  • HLA Antigens