Reconstructing continuous distributions of 3D protein structure from cryo-EM images

By Ellen D. Zhong, Tristan Bepler, Joseph H. Davis, Bonnie Berger. 2019

127 citations recorded by the source corpus.

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 3D structure of a macromolecule from $10^{4-7}$ noisy and randomly oriented 2D projection images. 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.

Subfields: Materials Chemistry, Radiation, Structural Biology, 3D Vision & Pose, Computational Biology & Bioinformatics

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In Materials Chemistry

Sjors H. W. Scheres

In Radiation

Sjors H. W. Scheres

In Structural Biology

In 3D Vision & Pose

Citations in the corpus

Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains (2020, methodology): “…coordinates (which they called a “positional encoding”) using sinusoids with logarithmically-spaced axis-aligned frequencies improves the performance of coordinate-based MLPs on the tasks of novel view synthesis from 2D images [27] and protein structure modeling from cryo-electron microscopy [44].”
FastNeRF: High-Fidelity Neural Rendering at 200FPS (2021, methodology): “After use in [41], this was popularized in [25, 49] and explored in greater detail by [40].”
DeepEMhancer: a deep learning solution for cryo-EM volume post-processing (2020, background): “, 2017) and since then, it has evolved to deal with other questions such as map reconstruction (Gupta et al., 2020; Zhong et al., 2019), map segmentation (Maddhuri Venkata Subramaniya et al.”
CryoDRGN: Reconstruction of heterogeneous cryo-EM structures using neural networks (2021, unknown): “We refer to this architecture as a coordinate-based neural network 41,42 as we explicitly model the volume as a function of Cartesian coordinates.”