# Learning from Hetero Density for Cryo-EM Protein Reconstruction

arXiv: https://arxiv.org/abs/2610.11403 · HTML: https://mlsanity.com/paper/2610.11403/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 46 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Builds on (most cited first, 2 of 2)

- [Decoupled Weight Decay Regularization](https://mlsanity.com/paper/1711.05101/) (2017)
- [CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM](https://mlsanity.com/paper/2606.31332/) (2026)

## Abstract (arXiv)

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.