B cell epitope prediction by capturing spatial clustering property of the epitopes using graph attention network.

ABSTRACT: Knowledge of B cell epitopes is critical to vaccine design, diagnostics, and therapeutics. As experimental validation for epitopes is time-consuming and costly, many in silico tools have been developed to computationally predict the B cell epitopes. While most methods show poor performance, deep learning methods in recent years have shown promising results. We developed a method called EpiGraph that outperformed previous methods, including those that showed a significant improvement in performance in recent years. Our model's performance can be attributed to the following factors: (1) a combination of structure and sequence feature embeddings obtained from pretrained ESM-IF1 and ESM-2 models could capture the structural and evolutionary features of B cell epitopes, (2) a graph attention ne

SUBMITTER: Choi S 

PROVIDER: S-EPMC11554790 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature