New Benchmarking Shows Limited Generalization Power of TCR Antigenic Epitope Prediction Models
Authors: Yiming Liao, Yiheng Li, Ning Jiang, Bo Li, Keke Chen
Organizations: Trustworthy and Intelligent Computing Lab (TAIC), Department of Computer Science and Electrical Engineering, University of Maryland, Baltimore County, Baltimore, Maryland 21250, USA · Children’s Hospital of Philadelphia, Philadelphia, PA 19104, USA · Department of Bioengineering, University of Pennsylvania, Philadelphia, PA 19104, USA · Institute for Immunology & Immune Health, University of Pennsylvania, Philadelphia, PA 19104, USA · Institute for RNA Innovation, University of Pennsylvania, Philadelphia, PA 19104, USA · Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA · Center for Precision Engineering for Health, University of Pennsylvania, Philadelphia, PA 19104, USA · Center for Cellular Immunotherapies, University of Pennsylvania, Philadelphia, PA 19104, USA
Accurate computational prediction of T cell receptor (TCR) antigen specificity would transform the study of T cell biology and enable scalable immune engineering, yet existing models lack sufficient sensitivity and specificity for broad applications. A major limitation is the absence of rigorously defined, unseen benchmark datasets that allow unbiased evaluation of model performance and generalizability. Here, we describe two complementary classes of datasets that meet this criterion and argue that they provide both a robust framework for model assessment and a foundation for next-generation TCR-antigen prediction algorithm development.