Supervised Deep Learning for Efficient Cryo-EM Image Alignment in Drug Discovery with cryoPARES

Abstract

Cryo-Electron Microscopy (cryo-EM) is a pivotal tool for determining 3D structures of biological macromolecules. Current workflows are computationally demanding and require manual intervention, creating bottlenecks for high-throughput applications like structure-based drug discovery. In such contexts, where all protein samples can be assumed to be equivalent at resolutions relevant for image alignment, information about particle poses from previous refinements could be reused. Existing methods, however, ignore this prior knowledge, aligning each dataset from scratch. We present cryoPARES, a deep learning pose estimation method trained on pre-aligned datasets. Our method not only provides accurate angular predictions significantly faster than traditional approaches but also introduces automated particle pruning capabilities that eliminate manual intervention. Together with its single-pass operation, these features enable near real-time reconstructions that provide feedback during data acquisition. We demonstrate cryoPARESs effectiveness through rapid structural determination of seven ligand-bound complexes across four distinct protein targets. We also release three fragment-bound cryo-EM datasets.