DEEPSHARPEN: DEEP-LEARNING BASED SHARPENING OF 3D RECONSTRUCTION MAP FROM CRYO-ELECTRON MICROSCOPY

Cryo-electron microscopy (cryo-EM) has proven to be a promising tool for recovering the 3D structure of biological macromolecules. The cryo-EM map which is reconstructed from a large set of projection images, is then used for recovering the atomic model of the molecule. The accuracy of the fitted atomic model depends on the quality of the cryo-EM map. Due to current limitations during imaging or reconstruction process, the reconstructed map usually lacks interpretability and requires further quality enhancement post-processing. In this work, we present a data-driven solution to improve the quality of low-resolution cryo-EM maps. For this purpose, we generate a synthetic dataset generated from deposited protein structures in protein data bank (PDB). This dataset includes low and high-resolution map pairs in multiple resolutions. This dataset is then used to train a fully convolutional network. Our results justify the potential of our method in successfully recovering details for simulated and experimental maps. Moreover, compared to state-of-the-art cryo-EM map sharpening methods, our approach not only provides good results but is also computationally efficient.