  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.
