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De novo main-chain modeling for EM maps using MAINMAST

Author

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  • Genki Terashi

    (Purdue University)

  • Daisuke Kihara

    (Purdue University
    Purdue University)

Abstract

An increasing number of protein structures are determined by cryo-electron microscopy (cryo-EM) at near atomic resolution. However, tracing the main-chains and building full-atom models from EM maps of ~4–5 Å is still not trivial and remains a time-consuming task. Here, we introduce a fully automated de novo structure modeling method, MAINMAST, which builds three-dimensional models of a protein from a near-atomic resolution EM map. The method directly traces the protein’s main-chain and identifies Cα positions as tree-graph structures in the EM map. MAINMAST performs significantly better than existing software in building global protein structure models on data sets of 40 simulated density maps at 5 Å resolution and 30 experimentally determined maps at 2.6–4.8 Å resolution. In another benchmark of building missing fragments in protein models for EM maps, MAINMAST builds fragments of 11–161 residues long with an average RMSD of 2.68 Å.

Suggested Citation

  • Genki Terashi & Daisuke Kihara, 2018. "De novo main-chain modeling for EM maps using MAINMAST," Nature Communications, Nature, vol. 9(1), pages 1-11, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-04053-7
    DOI: 10.1038/s41467-018-04053-7
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    Cited by:

    1. Tao Li & Hong Cao & Jiahua He & Sheng-You Huang, 2024. "Automated detection and de novo structure modeling of nucleic acids from cryo-EM maps," Nature Communications, Nature, vol. 15(1), pages 1-13, December.
    2. Bintao He & Fa Zhang & Chenjie Feng & Jianyi Yang & Xin Gao & Renmin Han, 2024. "Accurate global and local 3D alignment of cryo-EM density maps using local spatial structural features," Nature Communications, Nature, vol. 15(1), pages 1-15, December.
    3. Xintao Song & Lei Bao & Chenjie Feng & Qiang Huang & Fa Zhang & Xin Gao & Renmin Han, 2024. "Accurate Prediction of Protein Structural Flexibility by Deep Learning Integrating Intricate Atomic Structures and Cryo-EM Density Information," Nature Communications, Nature, vol. 15(1), pages 1-14, December.
    4. Andrew Muenks & Samantha Zepeda & Guangfeng Zhou & David Veesler & Frank DiMaio, 2023. "Automatic and accurate ligand structure determination guided by cryo-electron microscopy maps," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    5. Jiahua He & Tao Li & Sheng-You Huang, 2023. "Improvement of cryo-EM maps by simultaneous local and non-local deep learning," Nature Communications, Nature, vol. 14(1), pages 1-16, December.
    6. Jiahua He & Peicong Lin & Ji Chen & Hong Cao & Sheng-You Huang, 2022. "Model building of protein complexes from intermediate-resolution cryo-EM maps with deep learning-guided automatic assembly," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
    7. Sheng Chen & Sen Zhang & Xiaoyu Fang & Liang Lin & Huiying Zhao & Yuedong Yang, 2024. "Protein complex structure modeling by cross-modal alignment between cryo-EM maps and protein sequences," Nature Communications, Nature, vol. 15(1), pages 1-14, December.

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