用物理启发的滑动注意力预测抗体抗原结合位点
ABConformer: Physics-inspired Sliding Attention for Antibody-Antigen Interface Prediction
- 基于Conformer架构，引入物理启发的滑动注意力捕捉序列特征
- 无需三维结构即可精准预测抗原表位，准确率优于现有方法
- 适合疫苗设计与治疗抗体开发，支持无抗体条件下的表位预测
准确预测抗体-抗原（Ab-Ag）界面对于疫苗设计、免疫诊断和治疗性抗体开发至关重要。然而，仅凭序列实现可靠预测仍是挑战。本文提出ABConformer，基于Conformer骨干网络，捕捉生物序列的局部与全局特征。为精确建模Ab-Ag相互作用，引入物理启发的滑动注意力机制，实现残基级接触恢复，且不依赖三维结构数据。ABConformer可仅凭抗体与抗原序列预测互补位与表位，并在无抗体信息时预测抗原上的泛表位。在近期SARS-CoV-2 Ab-Ag数据集上，其性能达到当前最优，显著优于广泛使用的基于序列的方法。消融实验表明，相比传统交叉注意力，滑动注意力显著提升表位预测精度。代码将在论文接受后开源。
原文摘要 · Abstract (English)
Accurate prediction of antibody-antigen (Ab-Ag) interfaces is critical for vaccine design, immunodiagnostics, and therapeutic antibody development. However, achieving reliable predictions from sequences alone remains a challenge. In this paper, we present ABCONFORMER, a model based on the Conformer backbone that captures both local and global features of a biosequence. To accurately capture Ab-Ag interactions, we introduced the physics-inspired sliding attention, enabling residue-level contact recovery without relying on three-dimensional structural data. ABConformer can accurately predict paratopes and epitopes given the antibody and antigen sequence, and predict pan-epitopes on the antigen without antibody information. In comparison experiments, ABCONFORMER achieves state-of-the-art performance on a recent SARS-CoV-2 Ab-Ag dataset, and surpasses widely used sequence-based methods for antibody-agnostic epitope prediction. Ablation studies further quantify the contribution of each component, demonstrating that, compared to conventional cross-attention, sliding attention significantly enhances the precision of epitope prediction. To facilitate reproducibility, we will release the code under an open-source license upon acceptance.
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