DOI: 10.1038/s42256-024-00971-yKey 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 expensiveMUNIS Model Details:- Bimodal architecture with two components:1. Predicts peptide binding to HLAI molecules2. Models antigen processing- Trained on 650,000+ HLAI ligands- Outperforms existing prediction tools- Validated through cross-validation and real lab experimentsKey Results:- Successfully identified known and novel EBV epitopes- Triggered both effector and memory T-cell responses- Performed comparably to experimental stability assaysLimitations:- Not perfect at predicting immunogenicity- Limited to subset of HLA variants- More T-cell receptor data neededFuture Applications:- Personalized vaccine development- Autoimmune disease treatments- Preparation for emerging pathogens- More efficient vaccine design processNext Steps:- Incorporate more T-cell receptor data- Expand HLA diversity in training- Increase collaboration across fields- Develop predictive systems for future threatsImpact:- Could accelerate vaccine development- Enable more personalized treatments- Reduce experimental burden- Help prepare for future pandemics

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