Super-Resolution Cryo-EM Maps With 3D Deep Generative Networks

An increasing number of biological macromolecules have been solved with cryo-electron microscopy (cryo-EM). Over the past few years, the resolutions of density maps determined by cryo-EM have largely improved in general. However, there are still many cases where the resolution is not high enough to model molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3-4 Angstroms), a small improvement of resolution will significantly facilitate structure modeling. Here, we report SuperEM, a novel deep learning-based method that uses a three-dimensional generative adversarial network for generating an improved-resolution EM map from an experimental EM map. SuperEM is designed to work with EM maps in the resolution range of 3 Angstroms to 6 Angstroms and has shown an average resolution improvement of 1.0 Angstrom on a test dataset of 36 experimental maps. The generated super-resolution maps are shown to result in better structure modelling of proteins.