Modeling TCR-Epitope Recognition Specificity: What We Should Learn to Succeed
David Gfeller1,2,3, Julien Racle1,2,3, Rita Ann Roessner1,2,3
1Department of Fundamental Oncology, University of Lausanne, Lausanne, Switzerland.
Immunological Reviews
|July 10, 2026
Summary
Predicting T-cell receptor (TCR) and epitope interactions is crucial for understanding immune responses to cancer and pathogens. This review explores computational methods for predicting TCR-epitope recognition, highlighting current challenges and future directions.
Area of Science:
- Immunology
- Computational Biology
- Bioinformatics
Background:
- T-cell recognition of infected or malignant cells is vital for immune responses.
- This recognition involves T-cell receptors (TCRs) interacting with peptide-MHC epitopes.
- High diversity and structural flexibility make predicting these interactions challenging.
Purpose of the Study:
- To review strategies for predicting TCR-epitope recognition.
- To classify computational frameworks used in this field.
- To discuss data modalities, limitations, and future directions.
Main Methods:
- Literature review of computational methods for TCR-epitope prediction.
- Classification of existing computational frameworks.
- Analysis of data modalities and limitations.
Main Results:
- Identified various strategies and computational frameworks for TCR-epitope prediction.
- Examined the data requirements and limitations of current methods.
- Synthesized key insights from recent research.
Conclusions:
- Accurate prediction of TCR-epitope interactions remains a significant challenge.
- Future research should focus on integrating diverse data modalities and developing advanced computational tools.
- Improved prediction models will advance cellular immunology, cancer immunotherapy, and infectious disease research.
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