arXiv：cs.LG· Xu Han, Chaozhuo Li, Xiaowei Yuan, Yuancheng Sun, Kang Liu, Qiwei Ye·· 3 小时前
CryoCue：利用异质密度信息引导冷冻电镜蛋白质结构重建
Learning from Hetero Density for Cryo-EM Protein Reconstruction
AI 导读
针对冷冻电镜蛋白质重建中异质组分信息利用不足的问题，研究者提出 CryoCue 框架，用异质信息引导蛋白质重建。其锚点监督检测器可跨五类组分学习异质表示，多尺度异质特征引导骨架定位，预测的异质候选通过类别、置信度和帧相对几何条件约束结构精修。实验显示 CryoCue 改善了异质组分附近的骨架定位，并获得更准确的蛋白质结构重建。
正文
Abstract:Reconstructing protein structures from cryo-electron microscopy (cryo-EM) maps is essential for understanding macromolecular assemblies. Although learning-based methods have improved protein reconstruction, information from hetero components remains underused. Our analysis finds both false predictions and reference protein sites near hetero components; filtering nearby candidates can improve or impair chain construction. We introduce CryoCue, a framework that uses hetero information to guide protein reconstruction. An anchor-supervised detector learns hetero representations across five component classes. Multiscale hetero features guide backbone localization, while predicted hetero candidates condition structure refinement through their class, confidence, and frame-relative geometry. Experiments show that CryoCue improves backbone localization near hetero components and achieves more accurate protein structure reconstruction.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2610.11403 [cs.LG] |
| (or arXiv:2610.11403v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2610.11403 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Xu Han [view email]
[v1]
Thu, 8 Oct 2026 07:34:58 UTC (5,980 KB)
来源：arXiv：cs.LG · arxiv.org