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

Cryo-Electron Microscopy (cryo-EM) is a pivotal tool for determining the 3D structures of biological macromolecules. Current cryo-EM workflows, while effective, are computationally demanding and require manual intervention, creating bottlenecks for use in high-throughput scenarios such as structure-based drug discovery. Often in structure-based drug discovery, one can assume that all instances of a protein are equivalent at the resolutions needed for alignment and it therefore should be possible to harness information about particle poses from previous refinements. Current methods, however, do not leverage this form of prior knowledge, instead 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. These features, together with its single-pass operation, can enable real-time reconstructions that provide feedback during data acquisition. We demonstrate cryoPARES’s effectiveness through the rapid structural determination of six ligand-bound complexes across three distinct protein targets and release three new fragment-bound cryo-EM datasets.