Progress in cryo-electron microscopy (cryo-EM) has provided the potential for large-size protein structure determination. However, the solution rate for multi-domain proteins remains low due to the difficulty in modeling inter-domain orientations. We developed DEMO-EM, an automatic method to assemble multi-domain structures from cryo-EM maps through a progressive structural refinement procedure combining rigid-body domain fitting and flexible assembly simulations with deep neural network inter-domain distance profiles. The method was tested on a large-scale benchmark set of proteins containing up to twelve continuous and discontinuous domains with medium-to-low-resolution density maps, where DEMO-EM produced models with correct inter-domain orientations (TM-score >0.5) for 98% of cases and significantly outperformed the state-of-the-art methods. DEMO-EM was applied to SARS-CoV-2 coronavirus genome and generated models with average TM-score/RMSD of 0.97/1.4Å to the deposited structures. These results demonstrated an efficient pipeline that enables automated and reliable large-scale multi-domain protein structure modeling with atomic-level accuracy from cryo-EM maps.

中文翻译：

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根据冷冻电镜密度图逐步准确地组装多域蛋白质结构


冷冻电子显微镜（cryo-EM）的进步为大尺寸蛋白质结构测定提供了潜力。然而，由于域间方向建模困难，多域蛋白质的求解率仍然很低。我们开发了 DEMO-EM，这是一种通过将刚体域拟合和柔性装配模拟与深度神经网络域间距离剖面相结合的渐进式结构细化程序，从冷冻电磁图组装多域结构的自动方法。该方法在包含多达 12 个具有中低分辨率密度图的连续和不连续结构域的大规模蛋白质基准集上进行了测试，其中 DEMO-EM 生成了具有正确的结构域间方向的模型（TM 分数 >0） .5）适用于 98% 的案例，并且显着优于最先进的方法。 DEMO-EM 应用于 SARS-CoV-2 冠状病毒基因组，并生成沉积结构的平均 TM 分数/RMSD 为 0.97/1.4Å 的模型。这些结果证明了一种高效的流程，可以通过冷冻电镜图谱实现自动化、可靠的大规模多域蛋白质结构建模，并具有原子级精度。