Cryo-EM resolution improvement through enhanced deep learning methods.

Abstract

Recent advancements in cryo-electron microscopy (cryo-EM) have transformed the study of biomolecular structures by enabling the determination of complex structures at near-atomic res olution. However, many cryo-EM maps remain at resolutions that challenge accurate structural modeling. This project explores the application of deep learning techniques for enhancing the resolution of cryo-EM density maps, thereby improving their suitability for downstream protein structure modeling. By testing existing state-of-the-art models such as DeepEMhancer and EM-GAN, we aim to quantify improvements in structural interpretability and assess scenarios where these models underperform. Through this study, we propose to analyze and address the limitations of current models, eventually contributing to the development of more robust tools for resolution enhancement in structural biology.