AlphaEpi: Enhancing B Cell Epitope Prediction with AlphaFold 3

Feng Jiang, Yuzhi Guo, Hehuan Ma, Saiyang Na, Weizhi An, Bing Song, Yi Han, Jean Gao, Tao Wang, Junzhou Huang

Abstract: Accurately identifying conformational B-cell epitopes, which involve complex protein structures, is essential in modern immunology and vaccine development. Traditional methods like X-ray crystallography provide precise data but are costly and time-consuming. This has led to the rise of computational methods, especially deep learning algorithms, which offer more efficient and cost-effective solutions. A major challenge in predicting conformational epitopes based on structure is the limited availability of experimentally validated structural data. Structural prediction tools can help address this issue but still rely on provided sequence information and complex algorithms, which can introduce noise and affect accuracy. Moreover, effectively aligning and fusing structural features with evolutionary sequence information remains another challenge. To address these challenges, we introduce AlphaEpi, an innovative model that combines the advanced structure prediction capabilities of AlphaFold 3 with deep learning graph neural networks. Additionally, we propose for the first time a dynamic selector module that allows the model to adjust its processing strategy based on the reliability of the structure and sequence information, addressing the challenge posed by unreliable structural data. Moreover, a novel graph fusion module integrates structural and evolutionary information, effectively solving the disparities in the integration process of structure and sequence data. Results show that by integrating advanced structural prediction technologies AlphaFold 3 with deep learning graph neural networks, AlphaEpi significantly surpasses baseline results, establishing itself as the state-of-the-art. This advancement furthers the development of B-cell epitope prediction and has significant implications for biological research.