冷冻电镜单颗粒图像处理原理
Principles of cryo-EM single-particle image processing.
作者信息
Sigworth Fred J
机构信息
Department of Cellular and Molecular Physiology, Yale University, 333 Cedar Street, New Haven, CT 06520, USA Department of Molecular Biophysics and Biochemistry, Yale University, 333 Cedar Street, New Haven, CT 06520, USA
出版信息
Microscopy (Oxf). 2016 Feb;65(1):57-67. doi: 10.1093/jmicro/dfv370. Epub 2015 Dec 24.
Abstract
Single-particle reconstruction is the process by which 3D density maps are obtained from a set of low-dose cryo-EM images of individual macromolecules. This review considers the fundamental principles of this process and the steps in the overall workflow for single-particle image processing. Also considered are the limits that image signal-to-noise ratio places on resolution and the distinguishing of heterogeneous particle populations.
摘要
单颗粒重构是从一组单个大分子的低剂量冷冻电镜图像中获取三维密度图的过程。本综述探讨了该过程的基本原理以及单颗粒图像处理总体工作流程中的各个步骤。还讨论了图像信噪比在分辨率和区分异质颗粒群体方面所带来的限制。
相似文献
1
Principles of cryo-EM single-particle image processing.
Microscopy (Oxf). 2016 Feb;65(1):57-67. doi: 10.1093/jmicro/dfv370. Epub 2015 Dec 24.
2
[A review of automatic particle recognition in Cryo-EM images].
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi. 2010 Oct;27(5):1178-82.
3
PRIME: probabilistic initial 3D model generation for single-particle cryo-electron microscopy.
Structure. 2013 Aug 6;21(8):1299-306. doi: 10.1016/j.str.2013.07.002.
4
Auto3DCryoMap: an automated particle alignment approach for 3D cryo-EM density map reconstruction.
BMC Bioinformatics. 2020 Dec 28;21(Suppl 21):534. doi: 10.1186/s12859-020-03885-9.
5
Single-particle cryo-electron microscopy of macromolecular complexes.
Microscopy (Oxf). 2016 Feb;65(1):9-22. doi: 10.1093/jmicro/dfv366. Epub 2015 Nov 25.
6
Cryo-EM and single particles.
Physiology (Bethesda). 2006 Feb;21:13-8. doi: 10.1152/physiol.00045.2005.
7
Denoising of high-resolution single-particle electron-microscopy density maps by their approximation using three-dimensional Gaussian functions.
J Struct Biol. 2016 Jun;194(3):423-33. doi: 10.1016/j.jsb.2016.04.007. Epub 2016 Apr 13.
8
DeepAlign, a 3D alignment method based on regionalized deep learning for Cryo-EM.
J Struct Biol. 2021 Jun;213(2):107712. doi: 10.1016/j.jsb.2021.107712. Epub 2021 Mar 4.
9
How cryo-EM is revolutionizing structural biology.
Trends Biochem Sci. 2015 Jan;40(1):49-57. doi: 10.1016/j.tibs.2014.10.005. Epub 2014 Nov 7.
10
[Progress in filters for denoising cryo-electron microscopy images].
Beijing Da Xue Xue Bao Yi Xue Ban. 2021 Mar 3;53(2):425-433. doi: 10.19723/j.issn.1671-167X.2021.02.033.
引用本文的文献
1
: improving synthetic cryogenic electron microscopy density maps with generative adversarial networks.
Bioinform Adv. 2025 Aug 4;5(1):vbaf179. doi: 10.1093/bioadv/vbaf179. eCollection 2025.
2
Artificial Intelligence: A New Tool for Structure-Based G Protein-Coupled Receptor Drug Discovery.
Biomolecules. 2025 Mar 17;15(3):423. doi: 10.3390/biom15030423.
3
Automated model-free analysis of cryo-EM volume ensembles with SIREn.
Structure. 2025 May 1;33(5):974-987.e4. doi: 10.1016/j.str.2025.02.004. Epub 2025 Mar 10.
4
Automated model-free analysis of cryo-EM volume ensembles with SIREn.
bioRxiv. 2024 Oct 8:2024.10.08.617123. doi: 10.1101/2024.10.08.617123.
5
A deep learning method for simultaneous denoising and missing wedge reconstruction in cryogenic electron tomography.
Nat Commun. 2024 Sep 23;15(1):8255. doi: 10.1038/s41467-024-51438-y.
6
Mutation-induced shift of the photosystem II active site reveals insight into conserved water channels.
J Biol Chem. 2024 Jul;300(7):107475. doi: 10.1016/j.jbc.2024.107475. Epub 2024 Jun 13.
7
Designing Rigid DNA Origami Templates for Molecular Visualization Using Cryo-EM.
Nano Lett. 2024 Apr 11;24(16):5031-8. doi: 10.1021/acs.nanolett.4c00915.
8
Hyper-Molecules: on the Representation and Recovery of Dynamical Structures for Applications in Flexible Macro-Molecules in Cryo-EM.
Inverse Probl. 2020 Apr;36(4). doi: 10.1088/1361-6420/ab5ede. Epub 2020 Mar 4.
9
Towards automating single-particle cryo-EM data acquisition.
IUCrJ. 2023 Jan 1;10(Pt 1):4-5. doi: 10.1107/S2052252522012039.
10
Recent advances and current trends in cryo-electron microscopy.
Curr Opin Struct Biol. 2022 Dec;77:102484. doi: 10.1016/j.sbi.2022.102484. Epub 2022 Oct 28.
本文引用的文献
1
An atomic structure of human γ-secretase.
Nature. 2015 Sep 10;525(7568):212-217. doi: 10.1038/nature14892. Epub 2015 Aug 17.
2
Directly reconstructing principal components of heterogeneous particles from cryo-EM images.
J Struct Biol. 2015 Aug;191(2):245-62. doi: 10.1016/j.jsb.2015.05.007. Epub 2015 Jun 4.
3
Measuring the optimal exposure for single particle cryo-EM using a 2.6 Å reconstruction of rotavirus VP6.
Elife. 2015 May 29;4:e06980. doi: 10.7554/eLife.06980.
4
Cryo-EM: A Unique Tool for the Visualization of Macromolecular Complexity.
Mol Cell. 2015 May 21;58(4):677-89. doi: 10.1016/j.molcel.2015.02.019.
5
CTF Challenge: Result summary.
J Struct Biol. 2015 Jun;190(3):348-59. doi: 10.1016/j.jsb.2015.04.003. Epub 2015 Apr 23.
6
A primer to single-particle cryo-electron microscopy.
Cell. 2015 Apr 23;161(3):438-449. doi: 10.1016/j.cell.2015.03.050.
7
SubspaceEM: A fast maximum-a-posteriori algorithm for cryo-EM single particle reconstruction.
J Struct Biol. 2015 May;190(2):200-14. doi: 10.1016/j.jsb.2015.03.009. Epub 2015 Mar 31.
8
Cryogenic electron microscopy and single-particle analysis.
Annu Rev Biochem. 2015;84:499-517. doi: 10.1146/annurev-biochem-060614-034226. Epub 2015 Feb 26.
9
Covariance Matrix Estimation for the Cryo-EM Heterogeneity Problem.
SIAM J Imaging Sci. 2015 Jan 22;8(1):126-185. doi: 10.1137/130935434.
10
Trajectories of the ribosome as a Brownian nanomachine.
Proc Natl Acad Sci U S A. 2014 Dec 9;111(49):17492-7. doi: 10.1073/pnas.1419276111. Epub 2014 Nov 24.
Zotero 插件