Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy
Abstract
:1. Introduction
2. The Cryo-EM Workflow
3. Structural Heterogeneity in Cryo-EM Samples
4. Probing Short-Lived Conformational Changes by Time-Resolved Cryo-EM
5. 3D Classification & Refinement: Approaches to Modeling Discrete Heterogeneity
6. Masking-Based Approaches to Resolve Discrete Structural States and Continuous Flexibility
7. Focused Classification and Multi-Body Refinement of Ribosomal Complexes
8. Approaches to Modeling Continuous Heterogeneity
Method | Advantages | Disadvantages | Reference |
---|---|---|---|
ManifoldEM | Generates free-energy landscape of the system | Fine tuning of hyperspace parameters | Frank & Ourmazd, 2016 [18] |
AlphaCryo4D | Applicable to small proteins | Requires large dataset Oversamples conformational space | Wu et al., 2022 [162] |
CryoDRGN | New version [159] does not require initial model or pose information Resolves discrete and continuous conformations | Long training time Empirical optimization of latent space | Zhong et al., 2021 [19] |
CryoGAN | Does not require initial model or pose information Resolves discrete and continuous conformations | Limited resolution of reconstructions | Gupta et al., 2021 [154] |
e2gmm | Reduces parameters needed to represent particles Intuitive interpretation by Gaussian parameters | Requires large amount of GPU memory Limited to small proteins for high resolution | Chen & Ludtke, 2021 [156] |
3DVA | Resolves discrete and continuous motion No fine-tuning of parameters Applicable to small proteins | Not applicable to systems with nonlinear geometry Artifact of appearing/disappearing densities | Punjani et al., 2021 [17] |
3DFlex | Models motion directly instead of 3D volume | Auto-decoder is computationally expensive | Punjani et al., 2022 [157] |
Multi-body refinement | Automated implementation in RELION [44] Improves subdomain resolution | Interfaces between bodies poorly resolved Size limitation for densities < 150 kDa | Nakane et al., 2018 [15] |
9. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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DiIorio, M.C.; Kulczyk, A.W. Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy. Micromachines 2023, 14, 118. https://doi.org/10.3390/mi14010118
DiIorio MC, Kulczyk AW. Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy. Micromachines. 2023; 14(1):118. https://doi.org/10.3390/mi14010118
Chicago/Turabian StyleDiIorio, Megan C., and Arkadiusz W. Kulczyk. 2023. "Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy" Micromachines 14, no. 1: 118. https://doi.org/10.3390/mi14010118
APA StyleDiIorio, M. C., & Kulczyk, A. W. (2023). Exploring the Structural Variability of Dynamic Biological Complexes by Single-Particle Cryo-Electron Microscopy. Micromachines, 14(1), 118. https://doi.org/10.3390/mi14010118