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GRAPE: graph-regularized protein language modeling unlocks TCR-epitope binding specificity.

Xiangzheng Fu^1,2, Li Peng³, Haowen Chen⁴

¹Institute of Artificial Intelligence Application, College of Computer and Information Engineering, Central South University of Forestry and Technology, No. 498 Shaoshan South Road, Tianxin District, Changsha, Hunan 410004, China.

Briefings in Bioinformatics

|October 6, 2025

Summary

This summary is machine-generated.

GRAPE enhances T-cell receptor (TCR)-epitope binding prediction by integrating graph regularization and imbalance-aware learning. This novel framework improves accuracy for immunotherapies by addressing limitations in current graph neural network models.

Keywords:

AUC-maximization TCR-epitope binding graph regularization protein language models

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Immunology
Computational Biology
Bioinformatics

Background:

T-cell receptor (TCR)-epitope binding (TEB) prediction is crucial for developing immunotherapies.
Existing methods using graph neural networks (GNNs) struggle with sparse data and imbalanced predictions.

Purpose of the Study:

To develop a robust framework, GRAPE (Graph-Regularized Attentive Protein Embeddings), for accurate TEB prediction.
To address over-smoothing and prediction bias in GNNs for TEB.

Main Methods:

Utilized protein language models (ESM-2) for evolutionary-informed TCR/epitope embeddings.
Implemented spectral graph regularization to prevent over-smoothing in sparse graphs.
Introduced dynamic edge reweighting and a differentiable AUC-maximization objective for imbalance resilience.

Main Results:

GRAPE significantly outperforms state-of-the-art methods on public TEB prediction datasets.
The framework effectively mitigates over-smoothing and prediction bias.
Demonstrated improved accuracy in predicting TCR-epitope interactions.

Conclusions:

GRAPE provides a powerful and robust framework for understanding TCR-epitope interactions.
This approach has broad applications in immunology research and the design of novel therapeutics.