Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions

Dens, Ceder; Bittremieux, Wout; Affaticati, Fabio; Laukens, Kris; Meysman, Pieter

doi:10.1016/j.immuno.2023.100027

Cited by 5 publications

(2 citation statements)

References 27 publications

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“…Predicting the binding of any given TCR–epitope pair benefits many advances in healthcare, such as aiding diagnostics, vaccine development, and cancer therapies [ 1 , 28 ]. Researchers have been developing models for predicting peptide immunogenicity for decades.…”

Section: Related Workmentioning

confidence: 99%

“…The interaction between T-cell receptors (TCRs) and peptides (epitopes) presented by major histocompatibility complex molecules (MHC) forms the foundation of T-cell immunity [ 1 , 2 ]. The interaction between TCRs and epitopes aids in the activation of T cells, enabling them to effectively combat pathogens within the body, such as bacteria, viruses, or tumor cells [ 3 , 4 ].…”

Section: Introductionmentioning

confidence: 99%

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GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

Jiang,

Guo,

et al. 2024

Briefings in Bioinformatics

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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 $GTE$, 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.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

Jiang,

Guo,

et al. 2024

Briefings in Bioinformatics

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T-cell receptor binding prediction: A machine learning revolution

Weber,

Pélissier,

Rodríguez Martínez

2024

ImmunoInformatics

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Development and use of machine learning algorithms in vaccine target selection

Bravi

2024

npj Vaccines

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Computer-aided discovery of vaccine targets has become a cornerstone of rational vaccine design. In this article, I discuss how Machine Learning (ML) can inform and guide key computational steps in rational vaccine design concerned with the identification of B and T cell epitopes and correlates of protection. I provide examples of ML models, as well as types of data and predictions for which they are built. I argue that interpretable ML has the potential to improve the identification of immunogens also as a tool for scientific discovery, by helping elucidate the molecular processes underlying vaccine-induced immune responses. I outline the limitations and challenges in terms of data availability and method development that need to be addressed to bridge the gap between advances in ML predictions and their translational application to vaccine design.

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Interpretable deep learning to uncover the molecular binding patterns determining TCR–epitope interaction predictions

Cited by 5 publications

References 27 publications

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity

T-cell receptor binding prediction: A machine learning revolution

Development and use of machine learning algorithms in vaccine target selection

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