CryoDRGN2: Ab initio neural reconstruction of 3D protein structures from real cryo-EM images

• Published in: Proceedings / IEEE International Conference on Computer Vision pp. 4046 - 4055
• Main Authors: Zhong, Ellen D., Lerer, Adam, Davis, Joseph H., Berger, Bonnie
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.10.2021
• Subjects: 3D from multiview and other sensors; Adaptation models; and cell microscopy; biological; Computational modeling; Computer vision; Efficient training and inference methods; Medical; Neural generative models; Proteins; Reconstruction algorithms; Representation learning; Stereo; Task analysis; Three-dimensional displays; Vision applications and systems
• ISSN: 2380-7504
• DOI: 10.1109/ICCV48922.2021.00403

Protein structure determination from cryo-EM data requires reconstructing a 3D volume (or distribution of volumes) from many noisy and randomly oriented 2D projection images. While the standard homogeneous reconstruction task aims to recover a single static structure, recently-proposed neural and non-neural methods can reconstruct distributions of structures, thereby enabling the study of protein complexes that possess intrinsic structural or conformational heterogeneity. These heterogeneous reconstruction methods, however, require fixed image poses, which are typically estimated from an upstream homogeneous reconstruction and are not guaranteed to be accurate under highly heterogeneous conditions.In this work we describe cryoDRGN2, an ab initio reconstruction algorithm, which can jointly estimate image poses and learn a neural model of a distribution of 3D structures on real heterogeneous cryo-EM data. To achieve this, we adapt search algorithms from the traditional cryo-EM literature, and describe the optimizations and design choices required to make such a search procedure computationally tractable in the neural model setting. We show that cryoDRGN2 is robust to the high noise levels of real cryo-EM images, trains faster than earlier neural methods, and achieves state-of-the-art performance on real cryo-EM datasets.