局部框架旋转注意力 LFRA 用于抗体特异性表位预测
Local-Frame Rotary Attention for Antibody-Specific Epitope Prediction
AI 导读
一项 arXiv 研究提出局部框架旋转注意力（LFRA）方法，用于抗体特异性表位预测，即识别给定抗体识别哪些抗原残基。LFRA 将残基间位移表达在主链定义的局部框架中并直接注入旋转注意力，保持连续方向几何的同时保证对独立 SE(3) 变换的不变性。代码和数据集已公开。
正文 · 原文
Abstract: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 Rotary Attention (LFRA), 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 independent $\mathrm{SE}(3)$ transformations. The code and dataset are available at this https URL.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2608.01092 [cs.AI] |
| (or arXiv:2608.01092v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2608.01092 arXiv-issued DOI via DataCite |
Submission history
From: Chuanliu Fan [view email]
[v1]
Sun, 2 Aug 2026 08:48:07 UTC (4,231 KB)
[v2]
Thu, 5 Nov 2026 05:35:13 UTC (1,115 KB)
来源：arXiv cs.AI · arxiv.org