# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 1 Introduction  
B-cell epitopes are regions on the surface of antigen molecules that are specifically recognized and bound by antibodies, playing a critical role in immune responses. Accurateprediction of B-cell epitopes (BCEs) is essential for understanding antigen-antibody interactions and holds significant promise for vaccine design, immunodiagnostics, and therapeutic antibody development [1]. However, traditional experimental techniques such as X-ray crystallography and nuclear magnetic resonance (NMR) spectroscopy, which can provide precise three-dimensional structural information, are often time-consuming, expensive, and unsuitable for large-scale applications [2]. Consequently, developing efficient and accurate computational methods for B-cell epitope prediction has become a focal point of research in this field. Early computational approaches can be broadly categorized into sequence-based and structure-based methods. Sequence-based methods, such as BcePred, rely on physicochemical properties and statistical learning models trained on linear peptide sequences [3]. However, these methods are inherently limited as they fail to account for the three-dimensional structure of proteins, making them insufficient for predicting conformational epitopes. To address this limitation, structure-based methods like ElliPro utilize features such as ellipsoid approximation and protrusion index calculations to identify epitope locations [4]. While these approaches leverage spatial information, they often depend on manually designed heuristics, which restrict their generalizability across diverse antigen structures. Deep learning has recently driven substantial advancements in biomedical research [5]. Unlike traditional machine learning techniques, deep learning can automatically extract complex, nonlinear features from large datasets, offering distinct advantages for epitope prediction. Early machine learning models, such as XGBoost and logistic regression, were capable of identifying patterns in known antigen-antibody complexes but struggled with capturing the intricate spatial and nonlinear interactions between protein residues [6]. Deep learning methods such as convolutional neural networks (CNNs) and graph neural networks (GNNs) are naturally suited to handle spatial relationships in protein 3D structures, with GNNs in particular effectively capturing spatial dependencies and chemical interactions between residues, thus increasing the accuracy and generalizability of epitope prediction [7; 8]. The incorporation of deep learning, particularly graph-based models, into epitope prediction has substantially improved model performance by better representing the structural complexity of proteins [9]. Furthermore, generative models, such as VAEs, have demonstrated significant potential in representation learning. Specifically, the VQ-VAE is particularly effective in capturing the underlying structural features of proteins by learning discrete representations [10]. A similar concept of discretizing protein structures has also been explored in Foldseek, where the 3 Di alphabet encodes local structural motifs to enable rapid structural searches [11]. Building upon these advances, we propose a novel B-cell epitope prediction model--Graph-based Epitope Prediction Network (GraphEPN). This model combines the strengths of a VQ-VAE variant and a graph transformer, employing a two-stage training strategy to fully harness their capabilities. In the first stage, the VQ-VAE variant is pre-trained on a large-scale protein graph dataset, focusing on capturing high-quality representations of protein residues, encompassing both discrete and continuous features. This pre-training step enables the model to extract both discrete representations of amino acid microenvironments and continuous structural embeddings from antigen-antibody interaction data, providing a comprehensive feature set for downstream tasks.