Machine Learning for Reconstructing Dynamic Protein Structures from Cryo-EM Images

Abstract

Proteins and other biomolecules form dynamic macromolecular machines that carry out essential biological processes responsible for life. However, studying the mechanisms of these biomolecular complexes at relevant atomic-scale resolutions is an extraordinarily challenging task in structural biology. This thesis presents new algorithms that address the computational bottlenecks at the frontier of structure determination of dynamic biomolecular complexes via cryo-electron microscopy (cryo-EM).

In single particle cryo-EM, the central problem is to reconstruct the 3D structure of a target biomolecular complex from a set of noisy and randomly oriented 2D projection images, a challenging inverse problem especially when instances of the imaged biomolecular complex exhibit structural heterogeneity.

The main contribution of this thesis is a machine learning system, cryoDRGN, for reconstructing continuous distributions of biomolecular structures from cryo-EM images. Underpinning the cryoDRGN method is a deep generative model parameterized by a new neural representation of cryo-EM volumes and a learning algorithm to optimize this representation from unlabeled 2D cryo-EM images. Released as an open source software tool, cryoDRGN has been applied on real datasets to uncover heterogeneity in high resolution datasets, discover new conformations of large macromolecular machines and visualize continuous trajectories of their motion. This thesis also describes an extension, cryoDRGN2, for learning this model from unposed images, i.e. ab initio reconstruction. Finally, this thesis presents emerging directions in analyzing the learned manifold of cryo-EM structures and in incorporating atomic model priors into cryo-EM reconstruction.