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Author Spotlight: Exploring Cellular Processes by Modeling Ligands in Cryo-EM Maps
Published on: July 19, 2024
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Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic
Jiahua He1, Peicong Lin1, Ji Chen1
1School of Physics and Key Laboratory of Molecular Biophysics of MOE, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
Nature Communications
|July 13, 2022
Summary
We developed EMBuild, an automated method for building protein models from cryo-electron microscopy (cryo-EM) maps. This tool accurately constructs multi-chain protein complexes, simplifying structural analysis.
Area of Science:
- Structural Biology
- Biophysics
- Computational Biology
Background:
- Cryo-electron microscopy (cryo-EM) generates high-resolution structural data.
- Building atomic models into intermediate-resolution cryo-EM maps is challenging and time-consuming.
Purpose of the Study:
- To develop an automated method for building multi-chain protein complex models from intermediate-resolution cryo-EM maps.
- To improve the efficiency and accuracy of structural model generation in cryo-EM studies.
Main Methods:
- Integration of AlphaFold structure prediction with FFT-based global fitting.
- Application of domain-based semi-flexible refinement and graph-based iterative assembling.
- Utilizing a deep convolutional network to predict main-chain probability maps.
Main Results:
- EMBuild successfully built high-quality complex structures from diverse cryo-EM datasets.
- Performance was validated on 47 single-particle EM maps (4.0-8.0 Å) and 16 cryo-ET subtomogram averaging maps (3.7-9.3 Å).
- Results were comparable in accuracy to manually built Protein Data Bank (PDB) structures.
Conclusions:
- EMBuild provides an accurate and reliable automated solution for model building in cryo-EM.
- The method significantly reduces the labor and time required for structural model generation.
- This advancement facilitates the analysis of complex biological structures using cryo-EM data.
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