Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field.
@article{arxiv.2209.08171,
title = {Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions},
author = {Nabin Giri and Raj S. Roy and Jianlin Cheng},
journal= {arXiv preprint arXiv:2209.08171},
year = {2023}
}