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Related Concept Videos

Diversity of Antigen Receptors01:28

Diversity of Antigen Receptors

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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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T Cell Activation and Clonal Selection01:22

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T cells are integral to our adaptive immune system, recognizing and effectively responding to foreign antigens. T cell activation and clonal selection are pivotal in orchestrating this immune response. This article elucidates these mechanisms, detailing the roles of cluster of differentiation (CD) markers, major histocompatibility complex (MHC) molecules, costimulatory signals, and the process of clonal selection.
Naive T cells that have not yet encountered an antigen express two primary CD...
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TPepRet: a deep learning model for characterizing T-cell receptors-antigen binding patterns.

Meng Wang1, Wei Fan2, Tianrui Wu1

  • 1School of Computer Science and Engineering, Central South University, Changsha 410083, China.

Bioinformatics (Oxford, England)
|January 29, 2025
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Summary

TPepRet accurately deciphers T-cell receptor (TCR) and peptide binding relationships by integrating subsequence mining and semantic analysis. This novel model outperforms existing tools, advancing cancer immunotherapy and vaccine design.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • T-cell receptors (TCRs) are crucial for adaptive immunity, recognizing peptides for cancer immunotherapy, vaccine design, and autoimmune disease management.
  • Accurate characterization of TCR-peptide binding is essential but computationally challenging due to neglected directional semantics in sequence data.

Purpose of the Study:

  • To develop an innovative computational model, TPepRet, that accurately deciphers the semantic binding relationship between TCRs and peptides.
  • To address the limitations of existing tools by incorporating directional semantics and advanced sequence analysis.

Main Methods:

  • TPepRet integrates subsequence mining with semantic integration using a Bidirectional Gated Recurrent Unit (BiGRU) network and a Large Language Model framework.
  • The model analyzes both bidirectional sequence dependencies and global sequence semantics for comprehensive understanding of TCR-peptide interactions.

Main Results:

  • TPepRet demonstrated superior performance across diverse datasets and challenging scenarios, including peptide binding preference analysis and T-cell clonal expansion characterization.
  • The model successfully identified true binders in complex environments, assessed key binding sites, and validated against large-scale expression data.
  • Evaluations included screening SARS-CoV-2 TCRs, highlighting TPepRet's broad applicability and accuracy.

Conclusions:

  • TPepRet significantly outperforms existing computational tools for analyzing TCR-peptide binding.
  • The model offers a powerful new approach for understanding TCR-peptide interactions, with potential to advance clinical applications in immunotherapy and vaccine development.