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Related Concept Videos

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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.
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Related Experiment Video

Updated: Jun 11, 2025

Author Spotlight: Optimized Protocol for Detecting Antigen-Specific T Cells in Mouse Lungs Using Tetramers
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Limits on inferring T cell specificity from partial information.

James Henderson1,2, Yuta Nagano1,3, Martina Milighetti1,4

  • 1Division of Infection and Immunity, University College London, London WC1E 6BT, United Kingdom.

Proceedings of the National Academy of Sciences of the United States of America
|October 7, 2024
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Summary
This summary is machine-generated.

Understanding T cell receptor (TCR) sequence features is crucial for predicting antigen specificity. This study quantifies information in TCR sequences, revealing synergistic roles of hypervariable regions for improved prediction and cell therapy optimization.

Keywords:
Renyi informationTCRimmune repertoireinformation theoryreceptor-ligand interaction

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Area of Science:

  • Molecular Biology
  • Immunology
  • Bioinformatics

Background:

  • Mapping protein sequence to function is a central challenge in molecular biology.
  • Identifying sequence features that determine protein function, such as T cell receptor (TCR) antigen specificity, is critical.

Purpose of the Study:

  • To quantify the information provided by TCR sequence features regarding antigen specificity.
  • To identify informative sequence features by assessing their conservation in antigen-specific receptors.

Main Methods:

  • Quantified information in bits provided by TCR sequence features about antigen specificity.
  • Identified informative features based on conservation relative to null expectations.
  • Employed a coincidence-based approach to measure information and bound prediction accuracy.

Main Results:

  • TCR specificity is synergistically dependent on hypervariable regions of both receptor chains.
  • The degree of synergy is strongly influenced by the specific ligand.
  • The coincidence-based method provides a direct bound for predicting TCR specificity from partial sequence matches.

Conclusions:

  • The statistical framework can aid in developing machine learning models for TCR specificity prediction.
  • Findings support optimizing TCRs for cell therapies.
  • Coincidence-based information measures may have broader applications in pairwise classifier performance bounding.