Abstract
Accurate prediction of peptide-T-cell receptor (TCR) binding is vital for immunotherapy, vaccine design, and diagnostics. PanPep, a meta-learning framework, was developed to generalize diverse TCR binder predictions. This study presents a comprehensive and unbiased evaluation of PanPep's reusability and practical utility. We reproduced its reported performance on original datasets and further benchmarked it against the control tools using both classification metrics and virtual screening enrichment evaluations. Leveraging a newly curated independent dataset, we have demonstrated PanPep's superior generalization to unseen antigens with few or no known TCR binders. We further extended PanPep to peptide-TCRα and peptide-TCRαβ binding prediction, demonstrating its applicability in more biologically and physiologically relevant contexts. Despite its strengths, PanPep shows limitations in early binder enrichment and reduced robustness to novel TCRs, indicating sensitivity to training data composition and negative sampling strategies. This work establishes a reproducible and extensible benchmarking framework for general peptide-TCR binding prediction and related applications. Overall, our study suggests substantial room for improvement in TCR binder prediction, particularly concerning its practical applicability.