# GraphEPN: A Deep Learning Framework for B-Cell Epitope Prediction Leveraging Graph Neural Networks
## 4 Conclusions  
In this study, we present a novel B-cell epitope prediction model, GraphEPN, which integrates the VQ-VAE and graph transformer to capture both the local and global structural features of proteins effectively. By leveraging VQ-VAE for discrete residue representations and employing a graph transformer to model complex residue interactions, GraphEPN achieves superior predictive performance across multiple datasets, significantly improving the accuracy and generalizability of epitope prediction. The experimental results indicate that GraphEPN excels in various evaluation metrics, confirming its ability to capture essential protein structural information and model intricate residue interactions. Through the incorporation of multiple features and the utilization of graph neural networks and self-attention mechanisms, the model provides a comprehensive understanding of epitope regions and their predictions. Furthermore, the two-stage training strategy enables the model to capture both local structural features and long-range dependencies within the protein structure. Nevertheless, the current predictive accuracy is still limited by the availability of experimental structural data. Given the scarcity of antigen-antibody complex structures, some of the predicted epitopes may not necessarily be false positives but rather unannotated potential true positives. Additionally, the computational cost associated with integrating VQ-VAE and graph transformers remains a challenge, necessitating further optimization to enhance efficiency. Future work will focus on improving feature representations through contrastive learning and exploring lightweight graph neural networks to reduce computational overhead. Moreover, expanding the dataset by incorporating predicted structures from AlphaFold could improve generalizability, particularly for antigens lacking experimental structural data. In conclusion, GraphEPN offers a robust and effective tool for B-cell epitope prediction, with significant potential for applications in vaccine development and antibody design. Figure 4: Visualization of GraphEPN model predictions for protein 2|88_A. (**a**) Predicted epitopes are highlighted on the protein’s 3D structure. Residues with high prediction scores are shown as cyan sticks, with secondary structure elements in green and unlabeled regions in silver. (**b**) Epitope prediction scores along the sequence, where the \(x\)-axis corresponds to the sequence positions of amino acids and the \(y\)-axis represents the predicted epitope scores. The color gradient indicates the predicted confidence, with yellow representing high-confidence predictions. The blue dashed line marks the prediction threshold. **Supplementary Materials:** The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app15042159/s1](https://www.mdpi.com/article/10.3390/app15042159/s1)]([https://www.mdpi.com/article/10.3390/app15042159/s1](https://www.mdpi.com/article/10.3390/app15042159/s1)), Figure S1: Performance of AUC for machine learning methods through 5-fold cross-validation; Figure S2: Comparison of model performance with machine learning methods; Figure S3: Metrics heatmap comparing performance across different methods; Figure S4: Sequence-based comparison of epitope predictions for PDB 6 ad8_A. The x-axis represents the residue index, while the y-axis displays the predicted epitope positions from different methods. Red stars indicate the true epitope residues. **Author Contributions:** Conceptualization, F.W. and S.C.; software, X.D. ; investigation, X.D. ; resources, L.S. ; writing--original draft preparation, X.D. ; writing--review and editing, S.C., F.W. and L.S. ; supervision, S.C.; project administration, S.C. and F.W. ; funding acquisition, F.W. and S.C. All authors have read and agreed to the published version of the manuscript. **Funding:** The project was financially supported by the National Natural Science Foundation of China (Grant No. 62373172). **Institutional Review Board Statement:** Not applicable.