School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
Nat Commun. 2022 Jul 13;13(1):4066. doi: 10.1038/s41467-022-31748-9.
Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0-8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7-9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.
显微镜仪器和图像处理算法的进步使得越来越多的冷冻电子显微镜 (cryo-EM) 图谱得以产生。然而，将准确的模型构建到中间分辨率的 EM 图谱中仍然具有挑战性且耗费大量人力。在这里，我们提出了一种从中间分辨率冷冻电子显微镜图谱中自动构建多链蛋白质复合物模型的方法，称为 EMBuild，该方法将 AlphaFold 结构预测、基于 FFT 的全局拟合、基于域的半柔性细化以及基于图的迭代组装集成到深度卷积网络预测的主链概率图上。我们在 4.0-8.0 Å 分辨率的 47 个单颗粒 EM 图谱的不同测试集和 3.7-9.3 Å 分辨率的 16 个低温电子断层扫描数据子断层平均图谱上对 EMBuild 进行了广泛评估，并与最先进的方法进行了比较。我们证明了 EMBuild 能够从 cryo-EM 图谱中构建高质量的复杂结构，其准确性可与手动构建的 PDB 结构相媲美。这些结果证明了 EMBuild 在自动建模中的准确性和可靠性。
