AI 中文总结
该研究针对抗体特异性表位预测任务，提出LF3DRoPE编码方法，在AsEP基准上实现最优MCC，可捕捉抗原特异性结构兼容性，提升了预测性能。
AI 中文摘要
抗体特异性表位预测旨在识别给定抗体所识别的抗原残基，该任务依赖于抗体互补决定区（CDR）与抗原表面之间的三维互补性。现有方法通常利用预训练语言模型（PLM）嵌入，并通过额外的图、表面或点云编码器注入结构信息，其中注意力内部的位置机制在很大程度上仍与一维序列顺序相关。对于蛋白质而言， token偏移的对应物不仅是序列间隔，还包括折叠后残基之间的三维位移。这引发了一个问题：折叠后的残基几何结构能否直接作为注意力的位置机制？我们提出局部框架三维旋转位置编码（LF3DRoPE），该方法在主链定义的局部框架中表达残基间位移，并将其直接注入旋转注意力。此设计保留了连续的方向几何，同时确保对全局SE(3)变换的不变性。在AsEP基准上，LF3DRoPE在比率拆分和表位组拆分上均达到了最先进的马修斯相关系数（MCC）。消融实验和刚性变换测试表明，局部三维几何提供了超越序列顺序注意力的信息，同时保留了对任意全局坐标系的不变性。突变排序结果进一步表明，LF3DRoPE能够捕捉抗原特异性结构兼容性。
英文摘要
Antibody-specific epitope prediction aims to identify which antigen residues are recognized by a given antibody, a task that depends on the three-dimensional complementarity between antibody CDRs and the antigen surface. Existing methods usually leverage PLM embeddings and inject structure through additional graph, surface, or point-cloud encoders, where the positional mechanism inside attention remains largely tied to one-dimensional sequence order. For proteins, the analogue of a token offset is not only sequence separation, but also the three-dimensional displacement between residues after folding. This raises a question, can folded residue geometry serve as the positional mechanism of attention itself? We propose Local-Frame 3D Rotary Position Encoding (LF3DRoPE), which expresses inter-residue displacements in backbone-defined local frames and injects them directly into rotary attention. This design preserves continuous directional geometry while ensuring invariance to global $\mathrm{SE}(3)$ transformations. On the AsEP benchmark, LF3DRoPE achieves state-of-the-art $\mathrm{MCC}$ on both ratio and epitope-group splits. Ablations and rigid transformation tests show that local three-dimensional geometry provides information beyond sequence-order attention while preserving invariance to arbitrary global coordinate systems. Mutation ranking results further indicate that LF3DRoPE captures antigen-specific structural compatibility.
