Deep learning for reconstructing protein structures from cryo-EM density maps: Recent advances and future directions.

Journal: Current opinion in structural biology
Published Date:

Abstract

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.

Authors

  • Nabin Giri
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Raj S Roy
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211, USA.
  • Jianlin Cheng
    Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.