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Related Concept Videos

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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Aminoacyl-tRNA synthetases are present in both eukaryotes and bacteria. Though eukaryotes have 20 different aminoacyl-tRNA synthetases to couple to 20 amino acids, many bacteria do not have genes for all of these aminoacyl-tRNA synthetases. Despite this, they still use all 20 amino acids to synthesize their proteins. For instance, some bacteria do not have the gene encoding the enzyme that couples glutamine with its partner tRNA. In these organisms, one enzyme adds glutamic acid to all of the...

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Updated: Jun 16, 2026

T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing
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T and B Cell Receptor Immune Repertoire Analysis using Next-generation Sequencing

Published on: January 12, 2021

Deciphering small sequence differences in T cell receptor-antigen pairing.

Yi Han1, Yuqiu Yang2, James Zhu1

  • 1Department of Bioinformatics & Comp Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Nature Communications
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

A new deep learning model, pMTnet-omni, predicts T cell receptor (TCR) binding to antigens and distinguishes subtle sequence differences. This tool aids in understanding TCR-antigen interactions and designing variant TCRs for research and therapeutic applications.

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Area of Science:

  • Immunology
  • Computational Biology
  • Bioinformatics

Background:

  • T cells play crucial roles in immunity and disease via T cell receptor (TCR)-antigen interactions.
  • Existing prediction tools often struggle to discern the impact of minor sequence variations in TCRs or antigens on binding affinity.

Purpose of the Study:

  • To develop a deep learning model, pMTnet-omni, capable of predicting TCR-pMHC binding and differentiating binding affinities based on sequence similarity.
  • To utilize the model's interpretability to uncover biological rules governing TCR-antigen recognition.
  • To enable the prediction of variant TCRs with modulated binding strengths for translational applications.

Main Methods:

  • Development of a deep learning model (pMTnet-omni) for predicting TCR-pMHC binding.
  • Leveraging the model to analyze sequence variations and their effect on binding affinity.
  • Integration with a Lab-in-the-Loop (LiL) mechanism for predicting and designing variant TCRs.
  • Validation of the model's ability to predict binding for TCRs and pMHCs with sequence similarities.

Main Results:

  • pMTnet-omni accurately predicts TCR-pMHC binding and distinguishes between stronger and weaker binding TCRs with similar sequences.
  • The model provides insights into the biological determinants of TCR-antigen pairing.
  • Accurate prediction of variant TCRs with desired binding characteristics was achieved.
  • The model demonstrated efficacy in predicting TCR binding to similar pMHC molecules.

Conclusions:

  • pMTnet-omni offers a powerful and flexible toolkit for analyzing TCR-antigen interactions.
  • The model facilitates research in immunology and enables translational applications in areas like immunotherapy.
  • This approach advances the understanding and engineering of TCR-based therapeutics.