发表机构
University of Chinese Academy of Sciences; Institute of Automation, Chinese Academy of Sciences; Beijing Academy of Artificial Intelligence; Beijing University of Posts and Telecommunications; Ant Group（中国科学院大学; 中国科学院自动化研究所; 北京人工智能研究院; 北京邮电大学; 蚂蚁集团）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究针对冷冻电镜蛋白质重构中异质组分信息利用不足的问题，提出CryoCue框架，通过锚点监督检测器学习异质表示，结合多尺度异质特征优化主链定位与结构细化，提升了重构准确性。
AI 中文摘要
从冷冻电镜（cryo-EM）图中重构蛋白质结构对于理解大分子组装至关重要。尽管基于学习的方法已改进了蛋白质重构，但异质组分的信息仍未得到充分利用。我们的分析发现，异质组分附近存在错误预测和参考蛋白质位点；过滤附近候选物可改善或损害链构建。我们引入CryoCue，一个利用异质信息指导蛋白质重构的框架。锚点监督检测器学习五个组分类别的异质表示。多尺度异质特征指导主链定位，而预测的异质候选物通过其类别、置信度和帧相对几何条件化结构细化。实验表明，CryoCue改善了异质组分附近的主链定位，并实现了更准确的蛋白质结构重构。
英文摘要
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.
