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arXiv · 2610.11403

Learning from Hetero Density for Cryo-EM Protein Reconstruction

Xu Han·Chaozhuo Li·Xiaowei Yuan·Yuancheng Sun·Kang Liu·Qiwei Ye
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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.

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Xu Han, Chaozhuo Li, Xiaowei Yuan, Yuancheng Sun, Kang Liu, Qiwei Ye. 2026-10-08. Learning from Hetero Density for Cryo-EM Protein Reconstruction. https://arxiv.org/abs/2610.11403

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