️ 74: Benchmarking T Cell Receptor–Epitope Predictors with ePytope-TCR

️ Episode 74: Benchmarking T Cell Receptor–Epitope Predictors with ePytope-TCR In this episode of PaperCast Base by Base, we explore a unified computational framework that integrates and benchmarks 21 T cell receptor–epitope binding prediction models to evaluate their performance on viral epitope repertoires and deep mutational scans. Study Highlights:The authors developed ePytope-TCR, which standardizes 18 general and three categorical prediction methods into a single interoperable platform compatible with common TCR repertoire formats. They assessed model performance on 638 epitope-specific TCRs from single-cell datasets and found that only a few predictors achieved moderate accuracy, exhibiting strong biases toward frequently studied epitopes. Application to deep mutational scans of a neo-epitope and a CMV epitope revealed that current models largely fail to capture the impact of single amino acid changes, with both classification and correlation metrics near random. Analytical insights uncovered substantial disparities in predictive accuracy and score comparability across different epitope classes, highlighting the need for epitope-specific thresholds. Conclusion:This benchmark provides critical guidance for selecting suitable TCR-epitope predictors for well-characterized targets and establishes standardized datasets and metrics to drive the development of more robust and generalizable models. Reference:Drost F, Chernysheva A, Albahah M, Kocher K, Schober K, Schubert B. Benchmarking of T cell receptor–epitope predictors with ePytope-TCR. Cell Genomics. 2025;5:100946. https://doi.org/10.1016/j.xgen.2025.100946 License:This episode is based on an open-access article published under the Creative Commons Attribution 4.0 International License (CC BY 4.0) – https://creativecommons.org/licenses/by/4.0/On PaperCast Base by Base you’ll discover the latest in genomics, functional genomics, structural genomics, and proteomics.

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