74: Benchmarking TCR-epitope predictors with ePytope-TCR

Drost F et al., Cell Genomics - Drost et al. integrated 21 pre-trained sequence-based TCR-epitope predictors into ePytope-TCR and benchmarked them on a viral single-cell repertoire and deep mutational scans, revealing performance biases and limited generalization to rare and mutated epitopes. Key terms: T cell receptor, epitope prediction, ePytope-TCR, benchmarking, cross-reactivity.

Study Highlights:
The authors unified 21 sequence-based TCR-epitope predictors inside ePytope-TCR and evaluated them on two challenging datasets: a viral single-cell repertoire and deep mutational scans. Models predicted binding reliably for frequently observed epitopes but failed for less-represented targets. Strong biases in prediction scores between epitope classes were evident and most methods could not predict effects of single-residue epitope mutations. ePytope-TCR provides interoperable interfaces and a standardized benchmark to guide tool selection and method development.

Conclusion:
Pre-trained TCR-epitope predictors can annotate well-studied epitopes but do not generalize to rare or mutated epitopes; ePytope-TCR standardizes access and benchmarking to help researchers choose tools and accelerate development of improved models.

Music:
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Article title:
Benchmarking of T cell receptor-epitope predictors with ePytope-TCR

First author:
Drost F

Journal:
Cell Genomics

DOI:
10.1016/j.xgen.2025.100946

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. doi: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/

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Episode link: https://basebybase.com/episodes/epytope-tcrbenchmark-suite-corrupted-pdf

QC:
This episode was checked against the original article PDF and publication metadata for the episode release published on 2025-07-13.

QC Scope:
- article metadata and core scientific claims from the narration
- excludes analogies, intro/outro, and music
- transcript coverage: Audited transcript sections describing the Epitope-TCR benchmarking framework, data formats and ingestion, viral benchmarking dataset, deep mutational scanning dataset, performance metrics, bias observations, and practical implications.
- transcript topics: Benchmarking framework and Epitope-TCR architecture; Data format interoperability and unified interface; Categorical vs general predictors; Viral benchmarking dataset (638 TCRs, 14 epitopes, 5 MHC backgrounds); Deep mutational scanning dataset (epitope mutations VPSVWRSSL and NLVPMVATV); Performance metrics (AUC, recall@K, APS, F1) and results

QC Summary:
- factual score: 10/10
- metadata score: 10/10
- supported core claims: 8
- claims flagged for review: 0
- metadata checks passed: 4
- metadata issues found: 0

Metadata Audited:
- article_doi
- article_title
- article_journal
- license

Factual Items Audited:
- ePytope-TCR provides a unified interface ingesting six common data formats and 21 pre-trained models
- Two benchmarking datasets used:...