Zhang Ziying, Xu Liang, Zhang Shuai, Peng Chunxiang, Zhang Guijun, Zhou Xiaogen
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Department of Biological Chemistry, University of Michigan, Ann Arbor, MI 48109, United States.
Nucleic Acids Res. 2025 Jul 7;53(W1):W228-W237. doi: 10.1093/nar/gkaf416.
Atomic structure modeling is a crucial step in determining the structures of protein complexes using cryo-electron microscopy (cryo-EM). This work introduces DEMO-EMol, an improved server that integrates deep learning-based map segmentation and chain fitting to accurately assemble protein-nucleic acid (NA) complex structures from cryo-EM density maps. Starting from a density map and independently modeled chain structures, DEMO-EMol first segments protein and NA regions from the density map using deep learning. The overall complex is then assembled by fitting protein and NA chain models into their respective segmented maps, followed by domain-level fitting and optimization for protein chains. The output of DEMO-EMol includes the final assembled complex model along with overall and residue-level quality assessments. DEMO-EMol was evaluated on a comprehensive benchmark set of cryo-EM maps with resolutions ranging from 1.96 to 12.77 Å, and the results demonstrated its superior performance over the state-of-the-art methods for both protein-NA and protein-protein complex modeling. The DEMO-EMol web server is freely accessible at https://zhanggroup.org/DEMO-EMol/.
原子结构建模是使用冷冻电子显微镜（cryo-EM）确定蛋白质复合物结构的关键步骤。这项工作引入了DEMO-EMol，这是一个经过改进的服务器，它整合了基于深度学习的图谱分割和链拟合，以从冷冻电子显微镜密度图中准确组装蛋白质-核酸（NA）复合物结构。从密度图和独立建模的链结构开始，DEMO-EMol首先使用深度学习从密度图中分割出蛋白质和NA区域。然后，通过将蛋白质和NA链模型拟合到各自分割的图谱中，接着进行蛋白质链的结构域水平拟合和优化，来组装整个复合物。DEMO-EMol的输出包括最终组装的复合物模型以及整体和残基水平的质量评估。在一组分辨率范围为1.96至12.77 Å的冷冻电子显微镜图谱综合基准集上对DEMO-EMol进行了评估，结果表明，在蛋白质-NA和蛋白质-蛋白质复合物建模方面，它的性能优于现有最先进的方法。可通过https://zhanggroup.org/DEMO-EMol/免费访问DEMO-EMol网络服务器。
