Learning from Hetero Density for Cryo-EM Protein Reconstruction

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.

Machine Learning (cs.LG)

arXiv:2610.11403 [cs.LG]

(or arXiv:2610.11403v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2610.11403 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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