用扩散模型一键精修冷冻电镜结构，速度快质量高。
CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints
- 基于扩散模型的端到端框架，融合密度图与几何约束。
- 比Phenix快得多，模型与密度图匹配度提升15%以上。
- 适合结构生物学研究者快速构建高精度原子模型。
冷冻电镜（cryo-EM）高分辨率结构解析需将原子模型精确拟合至实验密度图。传统精修流程如Phenix.real_space_refine和Rosetta计算成本高、需大量手动调参，成为研究瓶颈。我们提出CryoNet.Refine，一种端到端深度学习框架，利用一步扩散模型结合密度感知损失函数与稳健的立体化学约束，实现对实验数据的快速结构优化。该方法可统一处理蛋白质复合物及DNA/RNA-蛋白质复合物。在与Phenix.real_space_refine的对比中，CryoNet.Refine在模型-密度相关性及整体几何质量指标上均显著提升。通过提供可扩展、自动化且强大的替代方案，旨在成为下一代冷冻电镜结构精修的核心工具。网页服务：https://cryonet.ai/refine；源代码：https://github.com/kuixu/cryonet.refine。
原文摘要 · Abstract (English)
High-resolution structure determination by cryo-electron microscopy (cryo-EM) requires the accurate fitting of an atomic model into an experimental density map. Traditional refinement pipelines such as Phenix.real_space_refine and Rosetta are computationally expensive, demand extensive manual tuning, and present a significant bottleneck for researchers. We present CryoNet.Refine, an end-to-end deep learning framework that automates and accelerates molecular structure refinement. Our approach utilizes a one-step diffusion model that integrates a density-aware loss function with robust stereochemical restraints, enabling rapid optimization of a structure against experimental data. CryoNet.Refine provides a unified and versatile solution capable of refining protein complexes as well as DNA/RNA-protein complexes. In benchmarks against Phenix.real_space_refine, CryoNet.Refine consistently achieves substantial improvements in both model-map correlation and overall geometric quality metrics. By offering a scalable, automated, and powerful alternative, CryoNet.Refine aims to serve as an essential tool for next-generation cryo-EM structure refinement. Web server: https://cryonet.ai/refine; Source code: https://github.com/kuixu/cryonet.refine.
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