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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从cryo-EM密度图逐步准确地组装多域蛋白质结构

低温电子显微镜（cryo-EM）的进步为确定大型蛋白质结构提供了潜力。但是，由于难以对域间方向进行建模，因此多域蛋白的溶解速度仍然很低。我们开发了DEMO-EM，这是一种自动方法，可通过将刚性体域拟合和柔性装配模拟与深层神经网络域间距离分布相结合的渐进式结构细化程序，从冷冻EM贴图组装多域结构。该方法在包含多达12个连续域和不连续域的蛋白质的大型基准蛋白质组上进行了测试，并具有中低分辨率的密度图，其中DEMO-EM产生的模型具有正确的域间方向（TM得分> 0.5） ）在98％的情况下，其效果明显优于最新方法。将DEMO-EM应用于SARS-CoV-2冠状病毒基因组，并生成沉积模型的平均TM得分/ RMSD为0.97 /1.4Å的模型。这些结果证明了一种有效的流程，该流程可实现来自cryo-EM图的原子级准确性的自动化且可靠的大规模多域蛋白质结构建模。