Machine learning for structural biology

Over the past few years, structural biology has been transformed by breakthroughs in deep learning methods for protein structure prediction. In parallel, advances in cryo-electron microscopy (cryo-EM) have produced new opportunities to study the structure and dynamics of proteins and other biomolecular complexes through imaging. In this seminar, I will overview cryoDRGN and related methods that leverage the representation power of deep neural networks for 3D reconstruction of protein structures from cryo-EM images. Extended to real datasets and released as open-source tools, these methods have been used to discover new protein structures and visualize continuous trajectories of protein motion. I will discuss various extensions of the method for scalable and robust reconstruction, analyzing the learned generative model, and visualizing dynamic protein structures in situ. Finally, I will discuss how recent advances in machine learning for protein structure prediction (e.g. AlphaFold) can complement methods for cryo-EM structure determination and what key algorithmic challenges remain to realize the next era of structural biology.