GTE: A Graph Learning Framework for Prediction of T-Cell Receptors and Epitopes Binding Specificity

Authors

Feng Jiang
Yuzhi Guo
Hehuan Ma
Saiyang Na
Wenliang Zhong
Yi Han
Tao Wang
Junzhou Huang

Language

English

Publication Date

5-23-2024

Journal

Briefings in Bioinformatics

DOI

10.1093/bib/bbae343

PMID

39007599

PMCID

PMC11247411

PubMedCentral® Posted Date

7-15-2024

PubMedCentral® Full Text Version

Post-print

Abstract

The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) is fundamental to the immune response. Accurate prediction of TCR–epitope interactions is crucial for advancing the understanding of various diseases and their prevention and treatment. Existing methods primarily rely on sequence-based approaches, overlooking the inherent topology structure of TCR–epitope interaction networks. In this study, we present , a novel heterogeneous Graph neural network model based on inductive learning to capture the topological structure between TCRs and Epitopes. Furthermore, we address the challenge of constructing negative samples within the graph by proposing a dynamic edge update strategy, enhancing model learning with the nonbinding TCR–epitope pairs. Additionally, to overcome data imbalance, we adapt the Deep AUC Maximization strategy to the graph domain. Extensive experiments are conducted on four public datasets to demonstrate the superiority of exploring underlying topological structures in predicting TCR–epitope interactions, illustrating the benefits of delving into complex molecular networks. The implementation code and data are available at https://github.com/uta-smile/GTE.

Keywords

Receptors, Antigen, T-Cell, Humans, Epitopes, T-Lymphocyte, Neural Networks, Computer, Computational Biology, Protein Binding, Epitopes, Algorithms, Software, T cell receptor, epitope specificity, immunoinformatics, heterogeneous graph neural networks, inductive learning, deep AUC maximization

Published Open-Access

yes

Recommended Citation

Jiang, Feng; Guo, Yuzhi; Ma, Hehuan; et al., "GTE: A Graph Learning Framework for Prediction of T-Cell Receptors and Epitopes Binding Specificity" (2024). Faculty, Staff and Student Publications. 6738.
https://digitalcommons.library.tmc.edu/uthgsbs_docs/6738