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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.
Before encountering any antigen, lymphocytes express these receptors. On B cells, the antigen receptor is a membrane-bound antibody molecule called BCR; on T cells, it is a T cell receptor or TCR. B and T cell receptors are composed of two...
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Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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T-cell receptor alpha to beta chains binding prediction.

Devora Siminovsky1, Yoram Louzoun1

  • 1Department of Mathematics, Bar-Ilan University, Ramat Gan 5290002, Israel.

Briefings in Bioinformatics
|July 8, 2026
PubMed
Summary

Predicting T-cell receptor (TCR) alpha and beta chain pairing is vital for understanding TCR-pMHC interactions. This study reveals specific associations in epitope-bound TCRs and introduces TCR-BARN, a model for predicting TCR pairing.

Keywords:
LSTMT-cell receptorTCRαTCRβmachine learning

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • T-cell receptors (TCRs) mediate adaptive immunity by recognizing peptide-major histocompatibility complex (pMHC) targets.
  • The binding specificity is determined by both TCR alpha and TCR beta chains, but their pairing is not random, especially for functional TCRs.
  • Predicting TCR alpha-beta chain pairing is essential for understanding TCR-pMHC interactions and for designing novel TCRs.

Purpose of the Study:

  • To investigate the association rules governing TCR alpha and TCR beta chain pairing within the general and pMHC-binding TCR repertoires.
  • To develop a computational model for predicting TCR alpha-beta chain pairing based on sequence and gene usage.
  • To evaluate the model's performance in predicting binding and its utility in generating de novo TCRs.

Main Methods:

  • Analysis of TCR alpha and TCR beta chain compositions and their CDR3 amino acid sequences and V/J gene usage.
  • Development of a deep learning model, TCR-BARN (TCR Beta-Alpha chains paiRing using Nlp), utilizing Long Short-Term Memory (LSTM) networks and one-hot encoding for V/J genes.
  • Performance evaluation using area under the curve (AUC) for epitope-bound TCRs.

Main Results:

  • TCR alpha and TCR beta chain compositions are independent in the general repertoire but show clear associations in pMHC-binding TCRs.
  • Distinct binding patterns were identified based on V/J gene usage, with negative correlations in charge/polarity and positive associations in molecular weight between TCR alpha and beta chains.
  • TCR-BARN achieved an AUC > 0.65 ± 0.007 for epitope-bound TCRs, demonstrating its predictive capability.

Conclusions:

  • Specific associations exist between TCR alpha and beta chains in functional TCRs, driven by sequence and V/J gene usage.
  • The developed TCR-BARN model accurately predicts TCR alpha-beta pairing and can aid in the generation of novel, functional TCRs.
  • Understanding TCR pairing rules advances TCR-pMHC interaction studies and therapeutic TCR development.