Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions
It tackles the problem of protein structure determination for researchers in structural biology, but is incremental as it reviews existing advances.
This review addresses the challenge of automatically reconstructing accurate protein structures from cryo-EM density maps by summarizing deep learning methods, analyzing their impact, and discussing data preparation issues.
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.