Predictions of immunogenicity reveal potent SARS-CoV-2 CD8+ T-cell epitopes
Gfeller, D.; Schmidt, J.; Croce, G.; Guillaume, P.; Bobisse, S.; Genolet, R.; Queiroz, L.; Cesbron, J.; Racle, J.; Harari, A.
Show 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 with new algorithmic developments to improve predictions of both antigen presentation and TCR recognition. Applying our tool to SARS-CoV-2 proteins enabled us to uncover 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 SARS-CoV-1 peptide.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Deep learning predictions of TCR-epitope interactions reveal epitope-specific chains in dual alpha T cells 95%
- APMAT analysis reveals the association between CD8 T cell receptors, cognate antigen, and T cell phenotype and persistence 95%
- scifAI: Explainable machine learning for profiling the immunological synapse and functional characterization of therapeutic antibodies 94%
Similar papers in this journal
- TCR meta-clonotypes for biomarker discovery with tcrdist3: identification of public, HLA-restricted SARS-CoV-2 associated TCR features 97%
- T cells discriminate between groups C1 and C2 HLA-C 96%
- SARS-CoV-2-specific CD4+ and CD8+ T cell responses can originate from cross-reactive CMV-specific T cells 95%
Similar papers in this journal
- SARS-CoV-2 genome-wide mapping of CD8 T cell recognition reveals strong immunodominance and substantial CD8 T cell activation in COVID-19 patients 96%
- Computational prediction of MHC anchor locations guide neoantigen identification and prioritization 96%
- Innate receptors with high specificity for HLA class I-peptide complexes 95%
Similar papers in this journal
- The mutational landscape of SARS-CoV-2 variants diversifies T cell targets in an HLA supertype-dependent manner 96%
- Integrated single-cell analyses of affinity-tested B-cells enable the identification of a gene signature to predict antibody affinity. 94%
- Machine learning analysis of the T cell receptor repertoire identifies sequence features that predict self-reactivity 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.