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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.
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Special Features of Adaptive Immunity01:20

Special Features of Adaptive Immunity

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The adaptive immune system, a crucial component of the overall immune response, offers a highly specialized defense against pathogens. It involves specific cell types and features, enabling it to combat infections effectively and efficiently.
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Cells of the Adaptive Immune Response01:23

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The T and B lymphocytes of the adaptive immune system develop from common lymphoid progenitor cells in the bone marrow. These progenitors give rise to precursors that eventually develop into both T and B lymphocytes. As these precursors mature, they gain the ability to detect and respond to foreign antigens in the body, a process known as immunocompetence. Additionally, these precursors acquire self-tolerance, a process that ensures they do not react to self-antigens. This intricate system...
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B Cell Activation and Differentiation01:24

B Cell Activation and Differentiation

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The adaptive immune response, a sophisticated defense mechanism, relies on the activation and differentiation of B lymphocytes, or B cells. These processes enable our bodies to mount a tailored response against specific pathogens such as bacteria, free virus particles, toxins, and parasites.
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Conserved Binding Sites

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Many proteins’ biological role depends on their interactions with their ligands, small molecules that bind to specific locations on the protein known as ligand-binding sites. Ligand-binding sites are often conserved among homologous proteins as these sites are critical for protein function.
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Related Experiment Video

Updated: Aug 29, 2025

Using X-ray Crystallography, Biophysics, and Functional Assays to Determine the Mechanisms Governing T-cell Receptor Recognition of Cancer Antigens
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Attention-aware contrastive learning for predicting T cell receptor-antigen binding specificity.

Yiming Fang1, Xuejun Liu1, Hui Liu1

  • 1School of Computer Science and Technology, Nanjing Tech University, 211816, Nanjing, China.

Briefings in Bioinformatics
|September 12, 2022
PubMed
Summary

Predicting T cell receptor (TCR) and peptide-MHC (pMHC) binding specificity is crucial for immunotherapy. Our ATMTCR model uses contrastive learning to significantly improve TCR-pMHC binding prediction, identifying key amino acids for better drug design.

Keywords:
T cell receptorTCR–antigen bindingattention mechanismcontrastive learningneoantigen

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Generation of Human Alloantigen-specific T Cells from Peripheral Blood
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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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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • Only a small fraction of neoantigens elicit T cell responses due to T cell receptor (TCR) and peptide-MHC (pMHC) binding specificity.
  • Accurate computational prediction of TCR-pMHC binding remains a significant challenge in the field.

Purpose of the Study:

  • To develop an advanced computational model for inferring TCR-pMHC binding specificity.
  • To leverage contrastive learning for improved prediction performance in T cell-mediated immunity.

Main Methods:

  • Proposed an attention-aware contrastive learning model (ATMTCR) utilizing a transformer encoder for TCR sequence representation.
  • Implemented a novel masking strategy guided by attention weights to generate contrastive views of TCR sequences.
  • Pre-trained the model on large-scale TCR sequences to enhance downstream task performance.

Main Results:

  • Contrastive learning pre-training significantly improved prediction performance compared to fully-supervised methods.
  • Masking amino acids with low attention weights proved to be the most effective strategy.
  • ATMTCR outperformed existing algorithms on two independent datasets.
  • Identified crucial amino acids and their positional preferences through attention weights, offering model interpretability.

Conclusions:

  • The ATMTCR model demonstrates superior performance in predicting TCR-pMHC binding specificity.
  • Attention-aware contrastive learning offers a promising approach for enhancing predictive models in immunology.
  • The model's interpretability provides insights into TCR-pMHC interactions, aiding in therapeutic development.