GTE: a graph learning framework for prediction of T-cell receptors and epitopes binding specificity.
Brief Bioinform
; 25(4)2024 May 23.
Article
en En
| MEDLINE
| ID: mdl-39007599
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 $GTE$, 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.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Receptores de Antígenos de Linfocitos T
Límite:
Humans
Idioma:
En
Año:
2024
Tipo del documento:
Article