Fast and automated protein-DNA/RNA macromolecular complex modeling from cryo-EM maps

被引:10
|
作者
Nakamura, Andrew
Meng, Hanze [1 ]
Zhao, Minglei [2 ]
Wang, Fengbin [3 ]
Hou, Jie [4 ]
Cao, Renzhi [5 ]
Si, Dong [6 ,7 ]
机构
[1] Duke Univ, Comp Sci, Durham, NC USA
[2] Univ Chicago, Dept Biochem & Mol Biophys, Chicago, IL USA
[3] Univ Alabama Birmingham, Dept Biochem & Mol Genet, Birmingham, AL USA
[4] St Louis Univ, Dept Comp Sci, St Louis, MO USA
[5] Pacific Lutheran Univ, Dept Comp Sci, Tacoma, WA USA
[6] Washington Univ, Div Comp & Software Syst, Bothell, WA USA
[7] Washington Univ, eSci, Bothell, WA USA
关键词
cryo-EM; macromolecular modeling; protein-DNA/RNA; machine learning; artificial intelligence; RNA;
D O I
10.1093/bib/bbac632
中图分类号
Q5 [生物化学];
学科分类号
071010 ; 081704 ;
摘要
Cryo-electron microscopy (cryo-EM) allows a macromolecular structure such as protein-DNA/RNA complexes to be reconstructed in a three-dimensional coulomb potential map. The structural information of these macromolecular complexes forms the foundation for understanding the molecular mechanism including many human diseases. However, the model building of large macromolecular complexes is often difficult and time-consuming. We recently developed DeepTracer-2.0, an artificial-intelligence-based pipeline that can build amino acid and nucleic acid backbones from a single cryo-EM map, and even predict the best-fitting residues according to the density of side chains. The experiments showed improved accuracy and efficiency when benchmarking the performance on independent experimental maps of protein-DNA/RNA complexes and demonstrated the promising future of macromolecular modeling from cryo-EM maps. Our method and pipeline could benefit researchers worldwide who work in molecular biomedicine and drug discovery, and substantially increase the throughput of the cryo-EM model building. The pipeline has been integrated into the web portal https://deeptracer.uw.edu/.
引用
收藏
页数:10
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