Deep learning enhances the prediction of HLA class I-presented CD8+ T cell epitopes in foreign pathogens

DOI: 10.1038/s42256-024-00971-y

Key Topics:

- New deep learning model MUNIS for predicting CD8+ T-cell epitopes

- Implications for vaccine development and personalized medicine

- Real-world validation using Epstein-Barr virus (EBV)

Background Science:

- HLAI molecules display protein fragments (epitopes) on cell surfaces

- CD8+ T-cells recognize foreign epitopes to trigger immune response

- Traditional lab identification of epitopes is time-consuming and expensive

MUNIS Model Details:

- Bimodal architecture with two components:

1. Predicts peptide binding to HLAI molecules

2. Models antigen processing

- Trained on 650,000+ HLAI ligands

- Outperforms existing prediction tools

- Validated through cross-validation and real lab experiments