用原子为中心方法，一键生成高精度冷冻电镜蛋白结构，还能解析动态变化。
CryoACE: An Atom-centric Framework for Accurate and Automated Model Building in Cryo-EM
- 以原子坐标为起点，直接采样密度特征并迭代优化结构
- 在真实数据上首次揭示复杂结构的原子级动态构象
- 无需训练，依靠局部分辨率先验解决构象模糊问题
从冷冻电镜密度图自动构建蛋白质模型面临物理化学合理性与构象异质性双重挑战。现有方法多局限于静态预测或需高成本启发式搜索。我们提出CryoACE，一种端到端框架，可重建均质与非均质结构的精确原子图。方法核心创新包括：原子为中心的重构范式——密度特征直接在原子坐标处采样，并迭代回流以优化结构，替代耗时的体素卷积，实现高效多模态融合；以及无训练的引导机制，利用预测的局部分辨率先验解决动态歧义。在新构建的高质量数据集上验证，CryoACE在静态基准上显著优于现有基线，并首次在真实世界数据集EMPIAR-10345上揭示原子级动态构象，且不依赖预建静态结构。
原文摘要 · Abstract (English)
Protein automodeling from cryo-EM density maps faces unique challenges in enforcing physicochemical validity and managing conformational heterogeneity. Current solvers are often limited to static predictions or require computationally intensive heuristic searches. We present CryoACE, an end-to-end framework that reconstructs precise atomic graphs for both homogeneous and heterogeneous structures. Our method features two key innovations: an atom-centric reconstruction paradigm, where density features are sampled directly at atomic coordinates and iteratively recycled to refine structures, replacing expensive voxel convolutions for efficient multimodal fusion; and a training-free guidance mechanism that leverages predicted local resolution priors to resolve dynamic ambiguity. Validated on a newly constructed high-quality dataset, CryoACE significantly outperforms existing baselines on static benchmarks and, for the first time, unveils atomic-level dynamic conformations on complex real-world datasets like EMPIAR-10345 without relying on pre-built static structures.
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