Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps
Abstract
1. Introduction
2. Rigid Fitting Methods
3. Flexible Fitting Methods
4. De Novo Modeling Methods
5. Machine Learning Approaches
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Methods | Strengths | Limitations |
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EM-Fold [70] |
|
|
Gorgon [71] |
|
|
Rosetta [73] |
|
|
Pathwalking [74] |
|
|
Phenix [75] |
|
|
MAINMAST [76] |
|
|
Methods | Strengths | Limitations |
---|---|---|
RENNSH [102] |
|
|
SSELearner [103] |
|
|
CNN by Li. et al. [104] |
|
|
Emap2sec [101] |
|
|
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Alnabati, E.; Kihara, D. Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps. Molecules 2020, 25, 82. https://doi.org/10.3390/molecules25010082
Alnabati E, Kihara D. Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps. Molecules. 2020; 25(1):82. https://doi.org/10.3390/molecules25010082
Chicago/Turabian StyleAlnabati, Eman, and Daisuke Kihara. 2020. "Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps" Molecules 25, no. 1: 82. https://doi.org/10.3390/molecules25010082
APA StyleAlnabati, E., & Kihara, D. (2020). Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps. Molecules, 25(1), 82. https://doi.org/10.3390/molecules25010082