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Related Concept Videos

T Cell Activation and Clonal Selection01:22

T Cell Activation and Clonal Selection

838
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...
838
Diversity of Antigen Receptors01:28

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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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Related Experiment Video

Updated: Jul 27, 2025

Generating De Novo Antigen-specific Human T Cell Receptors by Retroviral Transduction of Centric Hemichain
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Neural Network Models for Sequence-Based TCR and HLA Association Prediction.

Si Liu1, Philip Bradley1,2,3, Wei Sun1,3,4,5

  • 1Public Health Science Division, Fred Hutchinson Cancer Center, Seattle, USA.

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|June 9, 2023
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Summary

T-cell receptors (TCRs) and human leukocyte antigen (HLA) associations are crucial for understanding immune activity. A new deep learning method, DePTH, predicts these associations and reveals HLA similarities linked to cancer patient survival after immunotherapy.

Keywords:
T cell receptoramino acid sequencehuman leukocyte antigenimmune checkpoint blockadeneural network

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Area of Science:

  • Immunology
  • Bioinformatics
  • Computational Biology

Background:

  • T cells utilize T cell receptors (TCRs) to recognize foreign antigens presented by human leukocyte antigen (HLA) proteins.
  • TCRs encode an individual's immune history, with specific TCRs linked to particular HLA alleles, necessitating the study of TCR-HLA associations.
  • Understanding TCR-HLA interactions is vital for characterizing immune responses and developing targeted therapies.

Approach:

  • We developed Deep learning Prediction of TCR-HLA association (DePTH), a novel neural network method to predict TCR-HLA associations from amino acid sequences.
  • DePTH leverages deep learning to analyze the complex relationship between TCR and HLA protein sequences.
  • The method allows for the prediction and analysis of TCR-HLA binding specificities.

Key Points:

  • DePTH accurately predicts TCR-HLA associations based on sequence data.
  • The model quantifies functional similarities between different HLA alleles.
  • These quantified HLA similarities correlate with patient outcomes in cancer immunotherapy.

Conclusions:

  • DePTH provides a powerful tool for dissecting TCR-HLA associations and their functional implications.
  • Functional similarities among HLA alleles, as predicted by DePTH, are associated with patient survival following immune checkpoint blockade therapy.
  • This work bridges computational prediction with clinical relevance in cancer immunology.