Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structure of proteins and other macromolecular complexes at near-atomic resolution. In single particle cryo-EM, the central problem is to reconstruct the three-dimensional structure of a macromolecule from $10^{4-7}$ noisy and randomly oriented two-dimensional projections. However, the imaged protein complexes may exhibit structural variability, which complicates reconstruction and is typically addressed using discrete clustering approaches that fail to capture the full range of protein dynamics. Here, we introduce a novel method for cryo-EM reconstruction that extends naturally to modeling continuous generative factors of structural heterogeneity. This method encodes structures in Fourier space using coordinate-based deep neural networks, and trains these networks from unlabeled 2D cryo-EM images by combining exact inference over image orientation with variational inference for structural heterogeneity. We demonstrate that the proposed method, termed cryoDRGN, can perform ab initio reconstruction of 3D protein complexes from simulated and real 2D cryo-EM image data. To our knowledge, cryoDRGN is the first neural network-based approach for cryo-EM reconstruction and the first end-to-end method for directly reconstructing continuous ensembles of protein structures from cryo-EM images.

本文介绍了一种新颖的通过深度神经网络在傅里叶空间编码结构，将粒子和共同体的不断变化的结构进行建模，并通过准确的图像定向使得模型能从无标注的2D冷冻电镜图像训练出来，最终得到了一种直接对冷冻电镜图像进行连续体蛋白质结构建模的方法cryoDRGN，在模拟和真实2D冷冻电镜图像数据上实现了3D蛋白质复合物的ab initio重建。

从冷冻电子显微图像中重建3D蛋白质结构的连续分布