De novo main-chain modeling for EM maps using MAINMAST
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DOI: 10.1038/s41467-018-04053-7
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Cited by:
- 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.
- 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.
- 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.
- 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.
- 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.
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