Ontology highlight
ABSTRACT:
PROVIDER: PRJNA507500 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR8259420_1.fastq.gz | Fastqsanger.gz | |||
| SRR8259420_2.fastq.gz | Fastqsanger.gz | |||
| SRR8259430_1.fastq.gz | Fastqsanger.gz | |||
| SRR8259430_2.fastq.gz | Fastqsanger.gz | |||
| SRR8259440_1.fastq.gz | Fastqsanger.gz |
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Project description:T cells targeting epitopes in infectious diseases or cancer play a central role in spontaneous and therapy-induced immune responses. T-cell epitope recognition is mediated by the binding of the T-Cell Receptor (TCR) and TCRs recognizing clinically relevant epitopes are promising for T-cell based therapies. Starting from one of the few known TCRs targeting the cancer-testis antigen NY-ESO-1 (157–165) epitope, we built large phage display libraries of TCRs with randomized Complementary Determining Region 3 of the β chain. The TCR libraries were panned against the NY-ESO-1 epitope, which enabled us to collect thousands of epitope-specific TCR sequences. We then trained a machine learning TCR-epitope interaction predictor with this data and could identify several epitope-specific TCRs directly from TCR repertoires. Cellular binding and functional assays revealed that the predicted TCRs displayed activity towards the NY-ESO-1 epitope and no detectable cross-reactivity with self-peptides. Overall, our work demonstrates how display technologies combined with machine learning models of TCR-epitope recognition can effectively leverage large TCR repertoires for TCR discovery.
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