CryoNet.Refine: 一种用于利用冷冻电镜密度图约束快速优化结构模型的一步扩散模型
CryoNet.Refine: A One-step Diffusion Model for Rapid Refinement of Structural Models with Cryo-EM Density Map Restraints
AI总结：
CryoNet.Refine通过一步扩散模型实现快速结构模型优化，结合密度感知损失和立体化学约束，提供自动化且高效的冷冻电镜结构精修解决方案。
摘要：
冷冻电镜（cryo-EM）高分辨率结构确定需要将原子模型准确地拟合到实验密度图中。传统的精修流程，如Phenix.real_space_refine和Rosetta，计算成本高，需要大量的手动调整，并成为研究人员的瓶颈。我们提出了CryoNet.Refine，一种端到端的深度学习框架，该框架自动化并加速了分子结构的精修。我们的方法利用一种一步扩散模型，结合了密度感知损失函数和稳健的立体化学约束，能够快速优化结构以适应实验数据。CryoNet.Refine提供了一种统一且灵活的解决方案，能够精修蛋白质复合物以及DNA/RNA-蛋白质复合物。在与Phenix.real_space_refine的基准测试中，CryoNet.Refine在模型-图相关性和整体几何质量指标上均实现了显著的改进。通过提供一个可扩展、自动化和强大的替代方案，CryoNet.Refine旨在成为下一代冷冻电镜结构精修的重要工具。网页服务器：https://cryonet.ai/refine；源代码：https://github.com/kuixu/cryonet.refine。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
