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SageTCR: a structure-based model integrating residue- and atom-level representations for enhanced TCR-pMHC binding

Xiangyi Li1, Chuance Sun1, Weiran Huang1

  • 1Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University, Dongchuan Road, Minhang District, Shanghai 200240, China.

Briefings in Bioinformatics
|September 23, 2025
PubMed
Summary

SageTCR, a novel graph neural network, accurately predicts T-cell receptor (TCR) and peptide-MHC (pMHC) interactions using structural data. This framework enhances TCR-based therapies by improving the understanding of immune responses.

Keywords:
TCR-antigenbinding predictiondeep learninggraph sample and aggregate networksimmunology

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Area of Science:

  • Immunology
  • Computational Biology
  • Structural Biology

Background:

  • T-cell receptors (TCRs) are crucial for adaptive immunity, recognizing peptide-MHC (pMHC) complexes.
  • The inherent diversity and cross-reactivity of TCRs present significant challenges for predicting TCR-epitope interactions and developing TCR-based therapies.

Purpose of the Study:

  • To introduce SageTCR, a bi-level graph neural network (GNN) framework designed for predicting TCR-pMHC binding possibilities.
  • To leverage structural data and pretrained language models for enhanced prediction accuracy.

Main Methods:

  • SageTCR employs a GNN architecture to encode structural information at both residue and atomic levels.
  • Attention mechanisms integrate bimodal representations, and data augmentation strategies address the scarcity of experimental structures.
  • The framework preserves the characteristic diagonal binding mode of TCR-pMHC interactions.

Main Results:

  • SageTCR significantly outperforms six other deep learning methods in predicting TCR-pMHC binding.
  • The model demonstrates interpretability by identifying key contact residues and their conformational features at the interface.

Conclusions:

  • SageTCR provides a powerful and accurate method for predicting TCR-pMHC interactions, advancing TCR-related therapies.
  • The framework's interpretability offers valuable insights for TCR engineering and the design of immunotherapies.