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Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

Antigen receptors are essential components of the immune system crucial in defending the body against foreign invaders. These receptors are present on the surface of B and T cells, enabling them to recognize antigens and mount an appropriate immune response.
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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.

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|July 10, 2026
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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.

Keywords:
T‐cell epitope recognitionT‐cell receptorcomputational immunologymachine learning

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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.