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

• URL: http://arxiv.org/abs/2503.20291v2
• Date: Thu, 15 May 2025 15:06:46 GMT
• Title: CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets
• Authors: Chenwei Zhang, Khanh Dao Duc,
• Abstract summary: Enhancing cryogenic electron microscopy (cryo-EM) 3D density maps at intermediate resolution is crucial in protein structure determination.<n>Recent advances in deep learning have led to the development of automated approaches for enhancing experimental cryo-EM density maps.<n>We propose CryoSAMU, a novel method designed to enhance 3D cryo-EM density maps of protein structures using structure-aware multimodal U-Nets.
• Score: 10.433861497458212
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Enhancing cryogenic electron microscopy (cryo-EM) 3D density maps at intermediate resolution (4-8 {\AA}) 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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