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arXiv · 2209.08171

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

Nabin Giri·Raj S. Roy·Jianlin Cheng
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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.

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Nabin Giri, Raj S. Roy, Jianlin Cheng. 2022-09-16. Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions. https://doi.org/10.1016/j.sbi.2023.102536

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