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Drost, F. ; Chernysheva, A. ; Albahah, M. ; Kocher, K.* ; Schober, K.* ; Schubert, B.

Benchmarking of T cell receptor-epitope predictors with ePytope-TCR.

Cell Genom. 5:100946 (2025)
Publ. Version/Full Text Research data DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
Understanding the recognition of disease-derived epitopes through T cell receptors (TCRs) has the potential to serve as a stepping stone for the development of efficient immunotherapies and vaccines. While a plethora of sequence-based prediction methods for TCR-epitope binding exists, their pre-trained models have not been comparatively evaluated. To alleviate this shortcoming, we integrated 21 TCR-epitope prediction models into the immune-prediction framework ePytope, offering interoperable interfaces with standard TCR repertoire data formats. We showcase the applicability of ePytope-TCR by evaluating the performance of these publicly available prediction models on two challenging datasets. While novel predictors successfully predicted binding to frequently observed epitopes, all methods failed for less frequently observed epitopes. Further, we detected a strong bias in the prediction scores between different epitope classes. We envision this benchmark to guide researchers in their choice of a predictor and to accelerate the method development by defining standardized evaluation settings.
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Publication type Article: Journal article
Document type Scientific Article
Keywords T Cell Immunology ; T Cell Receptor ; Tcr-epitope Prediction ; Adaptive Immunology ; Benchmarking ; Deep Learning ; Machine Learning
Language english
Publication Year 2025
HGF-reported in Year 2025
ISSN (print) / ISBN 2666-979X
e-ISSN 2666-979X
Journal Cell Genomics
Quellenangaben Volume: 5, Issue: 8, Pages: , Article Number: 100946 Supplement: ,
Publisher Elsevier
Publishing Place 50 Hampshire St, Floor 5, Cambridge, Ma 02139 Usa
Reviewing status Peer reviewed
Institute(s) Institute of Computational Biology (ICB)
POF-Topic(s) 30205 - Bioengineering and Digital Health
Research field(s) Enabling and Novel Technologies
PSP Element(s) G-503800-001
G-503800-013
Grants De.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI)
ELIXIR-DE (Forschungszentrum Julich)
Helmholtz Associ-ation under the joint research school "Munich School for Data Science-MUDS"
Joachim Herz Stiftung
BMBF grant
DOI 10.1016/j.xgen.2025.100946
WOS ID 001583148600009
PubMed ID 40628266
Erfassungsdatum 2025-07-18
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