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

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成果类型：

期刊论文

作者：

Fu, Xiangzheng;Peng, Li;Chen, Haowen;Rong, Mingqiang;Chen, Yifan;...

通讯作者：

Chen, YF;Lu, AP

作者机构：

[Chen, Yifan; Fu, Xiangzheng] Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Inst Artificial Intelligence Applicat, 498 Shaoshan South Rd,Tianxin Dist, Changsha 410004, Hunan, Peoples R China.

[Lu, Aiping; Fu, Xiangzheng] Hong Kong Baptist Univ, Sch Chinese Med, Kowloon Tong, Kowloon, 15 Baptist Univ Rd, Hong Kong 999077, Peoples R China.

[Chen, Haowen] Hunan Univ, Coll Sci & Elect Engn, 2 Lushan South Rd, Changsha 410082, Hunan, Peoples R China.

[Peng, Li] Hunan Univ Sci & Technol, Coll Comp Sci & Engn, 1 Taoyuan Rd, Xiangtan 411201, Hunan, Peoples R China.

[Rong, Mingqiang] Hunan Normal Univ, Coll Life Sci, Natl & Local Joint Engn Lab Anim Peptide Drug Dev, 36 Lushan Rd, Changsha 410081, Hunan, Peoples R China.

通讯机构：

[Chen, YF ] C

[Lu, AP ] H

Cent South Univ Forestry & Technol, Coll Comp & Informat Engn, Inst Artificial Intelligence Applicat, 498 Shaoshan South Rd,Tianxin Dist, Changsha 410004, Hunan, Peoples R China.

Hong Kong Baptist Univ, Sch Chinese Med, Kowloon Tong, Kowloon, 15 Baptist Univ Rd, Hong Kong 999077, Peoples R China.

Changsha Med Univ, Sch Informat Engn, 1501 Leifeng Rd, Changsha 410219, Peoples R China.

语种：

英文

关键词：

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

期刊：

BRIEFINGS IN BIOINFORMATICS

ISSN：

1467-5463

年：

2025

卷：

期：

DOI：

10.1093/bib/bbaf522

基金类别：

National Natural Science Foundation of China [62372158, 62402533, 62572178, 62472165]; Natural Science Foundation of Hunan Province [2025JJ60400]; Educational Commission of Hunan Province [23B0237]

机构署名：

本校为第一且通讯机构

院系归属：

计算机与信息工程学院

摘要：

T-cell receptor (TCR)-epitope binding prediction is critical for immunotherapies but remains challenged by sparse interaction networks and severe class imbalance in training data. Current graph neural network (GNN) approaches for predicting TCR-epitope binding (TEB) fail to address two key limitations: over-smoothing during message propagation in sparse TCR-epitope graphs and biased predictions toward dominant epitope-TCR pairs. Here, we present GRAPE (Graph-Regularized Attentive Protein Embeddings), a framework unifying spectral graph regularization and imbalance-aware learning. GRAPE first l...

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

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