Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly

Jiahua He, Peicong Lin, Ji Chen, Hong Cao and Sheng-You Huang ()
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Jiahua He: Huazhong University of Science and Technology
Peicong Lin: Huazhong University of Science and Technology
Ji Chen: Huazhong University of Science and Technology
Hong Cao: Huazhong University of Science and Technology
Sheng-You Huang: Huazhong University of Science and Technology

Nature Communications, 2022, vol. 13, issue 1, 1-16

Abstract: Abstract Advances in microscopy instruments and image processing algorithms have led to an increasing number of cryo-electron microscopy (cryo-EM) maps. However, building accurate models into intermediate-resolution EM maps remains challenging and labor-intensive. Here, we propose an automatic model building method of multi-chain protein complexes from intermediate-resolution cryo-EM maps, named EMBuild, by integrating AlphaFold structure prediction, FFT-based global fitting, domain-based semi-flexible refinement, and graph-based iterative assembling on the main-chain probability map predicted by a deep convolutional network. EMBuild is extensively evaluated on diverse test sets of 47 single-particle EM maps at 4.0–8.0 Å resolution and 16 subtomogram averaging maps of cryo-ET data at 3.7–9.3 Å resolution, and compared with state-of-the-art approaches. We demonstrate that EMBuild is able to build high-quality complex structures that are comparably accurate to the manually built PDB structures from the cryo-EM maps. These results demonstrate the accuracy and reliability of EMBuild in automatic model building.

Date: 2022
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DOI: 10.1038/s41467-022-31748-9

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