用结构感知多模态网络提升中分辨率冷冻电镜蛋白图谱质量
CryoSAMU: Enhancing 3D Cryo-EM Density Maps of Protein Structures at Intermediate Resolution with Structure-Aware Multimodal U-Nets
- 基于结构感知的多模态U-Net架构，融合蛋白质结构先验知识
- 在4-8Å分辨率下显著提升密度图清晰度，优于现有方法
- 处理速度更快，适合实际结构解析场景
在4-8 Å中间分辨率下增强冷冻电镜（cryo-EM）三维密度图对蛋白质结构解析至关重要。尽管深度学习已推动自动化图像增强方法的发展，但现有方法未针对中间分辨率优化，且仅依赖密度特征。为此，我们提出CryoSAMU，一种基于结构感知多模态U-Net的新方法，利用精心筛选的中间分辨率密度图进行训练。我们在多种指标下全面评估了CryoSAMU，结果表明其性能优于当前最优方法。值得注意的是，CryoSAMU展现出显著更快的处理速度，具备未来实际应用潜力。代码已开源：https://github.com/chenwei-zhang/CryoSAMU。
原文摘要 · Abstract (English)
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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