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EMProt improves structure determination from cryo-EM maps
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AI
DOI：10.1038/s41594-025-01723-1.png)
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Abstract
En 中文
Cryo-electron microscopy (cryo-EM) has become the mainstream technique for macromolecular structure determination. However, because of intrinsic resolution heterogeneity, accurate modeling of all-atom structure from cryo-EM maps remains challenging even for maps at near-atomic resolution. Addressing the challenge, we present EMProt, a fully automated method for accurate protein structure determination from cryo-EM maps by efficiently integrating map information and structure prediction with a three-track attention network. EMProt is extensively evaluated on a diverse test set of 177 experimental cryo-EM maps with up to 54 chains in a case at <4-Å resolution, and compared to state-of-the-art methods including DeepMainmast, ModelAngelo, phenix.dock_and_rebuild and AlphaFold3. It is shown that EMProt greatly outperforms the existing methods in recovering the protein structure and building the complete structure. In addition, the built models by EMrot exhibit a high accuracy in model-to-map fit and structure validations. Here the authors present an artificial-intelligence-based automated method for improved protein structure determination from cryo-EM density maps by efficiently integrating map information and structure prediction.
Keywords:
cryo-electron microscopy
protein structure determination
deep learning
map-to-structure fitting
attention network
Journal
N
IF:
10.1
Papers:
4.8K
Citations:
2.8W