CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets

Chenwei Zhang, Khanh Dao Duc
3/26/2025
cs.CVcs.AIcs.LG

Official code: chenwei-zhang/CryoSAMU (10 stars, MIT, last commit 2025-03-08).

Abstract

Enhancing cryogenic electron microscopy (cryo-EM) 3D density maps at intermediate resolution (4-8 Å) is crucial in protein structure determination. Recent advances in deep learning have led to the development of automated approaches for enhancing experimental cryo-EM density maps. Yet, these methods are not optimized for intermediate-resolution maps and rely on map density features alone. To address this, we propose CryoSAMU, a novel method designed to enhance 3D cryo-EM density maps of protein structures using structure-aware multimodal U-Nets and trained on curated intermediate-resolution density maps. We comprehensively evaluate CryoSAMU across various metrics and demonstrate its competitive performance compared to state-of-the-art methods. Notably, CryoSAMU achieves significantly faster processing speed, showing promise for future practical applications. Our code is available at https://github.com/chenwei-zhang/CryoSAMU.

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Code Implementations(1)

chenwei-zhang/CryoSAMUOfficial100%
MIT

CryoSAMU: Enhancing Low-Resolution 3D Cryo-EM Maps with Structure-Aware Multimodal U-Nets

101PythonFeb 27, 20251 years agoMIT

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Cite this paper

@article{zhang2025cryosamu,
  title  = {CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets},
  author = {Chenwei Zhang and Khanh Dao Duc},
  year   = {2025},
  eprint = {2503.20291},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2503.20291v2}
}

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