Cai Yaxian, Zhang Ziying, Xu Xiangyu, Xu Liang, Chen Yu, Zhang Guijun, Zhou Xiaogen
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
School of Computer Science and Technology, Soochow University, Suzhou 215006, China.
J Chem Inf Model. 2025 Apr 14;65(7):3800-3811. doi: 10.1021/acs.jcim.5c00004. Epub 2025 Mar 28.
With the breakthroughs in protein structure prediction technology, constructing atomic structures from cryo-electron microscopy (cryo-EM) density maps through structural fitting has become increasingly critical. However, the accuracy of the constructed models heavily relies on the precision of the structure-to-map fitting. In this study, we introduce DEMO-EMfit, a progressive method that integrates deep learning-based backbone map extraction with a global-local structural pose search to fit atomic structures into density maps. DEMO-EMfit was extensively evaluated on a benchmark data set comprising both cryo-electron tomography (cryo-ET) and cryo-EM maps of protein and nucleic acid complexes. The results demonstrate that DEMO-EMfit outperforms state-of-the-art approaches, offering an efficient and accurate tool for fitting atomic structures into density maps.
随着蛋白质结构预测技术的突破，通过结构拟合从冷冻电子显微镜（cryo-EM）密度图构建原子结构变得越来越重要。然而，构建模型的准确性在很大程度上依赖于结构与图谱拟合的精度。在本研究中，我们引入了DEMO-EMfit，这是一种渐进式方法，它将基于深度学习的主干图谱提取与全局-局部结构姿态搜索相结合，以将原子结构拟合到密度图中。我们在一个包含蛋白质和核酸复合物的冷冻电子断层扫描（cryo-ET）和冷冻电镜图的基准数据集上对DEMO-EMfit进行了广泛评估。结果表明，DEMO-EMfit优于现有方法，为将原子结构拟合到密度图中提供了一种高效且准确的工具。
