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

Article · Jiahua He, Peicong Lin, Ji Chen, Hong Cao, Sheng‐You Huang

- Archive id: doi:10.1038/s41467-022-31748-9
- Published: 2022-07-13
- Venue: Nature Communications
- DOI: https://doi.org/10.1038/s41467-022-31748-9
- PMID: 35831370
- Citations: 96
- Source URL: https://doi.org/10.1038/s41467-022-31748-9
- PDF: https://www.nature.com/articles/s41467-022-31748-9.pdf
- Provenance: openalex

## 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.

_Abstract reproduced under CC-BY (publisher license, via OpenAlex)._

Topics: [AI Protein Design & Structure Prediction](https://space-kitty.com/topics/protein-design)

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