MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C

Clinical Snapshot

Key Findings

• P Value: Statistically significant (specific value not provided)

• Effect Size: Statistically significant improvement compared to state-of-the-art methods

• Primary Outcome: Area under the curve (AUC) for B-cell epitope prediction accuracy

• Nnt Or Sensitivity: Sensitivity and specificity values not reported in abstract

• Confidence Interval: Not reported in abstract

Clinical Application

High feasibility as computational tool requiring only protein sequences, though may need significant computational resources Highly relevant for Australian vaccine development initiatives, TGA regulatory submissions requiring epitope mapping, and research institutions developing novel therapeutics Researchers and clinicians involved in vaccine development, therapeutic antibody design, and diagnostic assay development

Abstract

Accurate prediction of B-cell epitopes plays a key role in facilitating advancements in vaccines, therapeutics, and diagnostics. In contrast to labor-intensive experimental approaches, computational strategies provide a more economical and efficient means of identifying potential epitopes. Existing methods are often limited by their reliance on experimentally resolved protein structures or by the use of lower-accuracy predicted structures. Sequence-based approaches, while fast, largely fail to capture the 3D spatial context essential for conformational epitopes. With the breakthroughs achieved by AlphaFold3 in predicting protein structures, we present MsgaBpred, the model to apply AlphaFold3-derived structures to B-cell epitope identification. Given only a protein sequence, our model employs a multi-scale graph convolutional network and additive attention to capture complex structural dependencies without relying on experimentally determined structures. The multi-scale design allows for effective modeling of both local and global contexts by aggregating information across different neighborhood ranges. Additionally, we leverage ESM-C, a more expressive protein language model than ESM-2, to enhance feature representation for B-cell epitope prediction. Extensive evaluations across multiple benchmark datasets demonstrate that MsgaBpred achieves competitive and robust performance; notably, it yields a statistically significant improvement in AUC compared to existing state-of-the-art methods. Moreover, the modular and scalable architecture of MsgaBpred holds promise for broader applications, including the structural analysis of other biomolecular entities such as nucleic acids and carbohydrates.

References

1. 1.Wang, S., Geng, A., Luo, Z., Li, Y., Xu, J., Meng, Y., Wei, L., Zou, Q., Zhang, Z., Wang, T., & Cui, F. (2026). MsgaBpred: A B-cell epitope predictor integrating AlphaFold3-predicted structures with multi-scale GCNs and pre-trained language model ESM-C. PLoS Computational Biology. https://doi.org/10.1093/nar/gkg571