3D reconstruction based on single-particle cryo electron microscopy images as a random signal in noise problem

2012 ◽  
Author(s):  
Qiu Wang ◽  
Yili Zheng ◽  
Peter C. Doerschuk
Keyword(s):  
Electron Microscopy ◽  
3D Reconstruction ◽  
Single Particle ◽  
Random Signal ◽  
Cryo Electron Microscopy ◽  
Microscopy Images

scholarly journals Pre-pro is a fast pre-processor for single-particle cryo-EM by enhancing 2D classification

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Szu-Chi Chung ◽  
Hsin-Hung Lin ◽  
Po-Yao Niu ◽  
Shih-Hsin Huang ◽  
I-Ping Tu ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
3D Reconstruction ◽  
Dimension Reduction ◽  
Single Particle ◽  
Minimal Cost ◽  
Initial Model ◽  
Cryo Electron Microscopy ◽  
Class Average ◽  
Microscopy Images

Abstract2D classification plays a pivotal role in analyzing single particle cryo-electron microscopy images. Here, we introduce a simple and loss-less pre-processor that incorporates a fast dimension-reduction (2SDR) de-noiser to enhance 2D classification. By implementing this 2SDR pre-processor prior to a representative classification algorithm like RELION and ISAC, we compare the performances with and without the pre-processor. Tests on multiple cryo-EM experimental datasets show the pre-processor can make classification faster, improve yield of good particles and increase the number of class-average images to generate better initial models. Testing on the nanodisc-embedded TRPV1 dataset with high heterogeneity using a 3D reconstruction workflow with an initial model from class-average images highlights the pre-processor improves the final resolution to 2.82 Å, close to 0.9 Nyquist. Those findings and analyses suggest the 2SDR pre-processor, of minimal cost, is widely applicable for boosting 2D classification, while its generalization to accommodate neural network de-noisers is envisioned.

scholarly journals Image Alignment Using 2D Interpolation in the Frequency Domain for Class Averaging in Single-particle Cryo-EM

2021 ◽  
Author(s):  
Xiangwen Wang ◽  
Yonggang Lu ◽  
Jiaxuan Liu
Keyword(s):  
Electron Microscopy ◽  
3D Reconstruction ◽  
Single Particle ◽  
Correlation Matrix ◽  
Cross Correlation ◽  
Image Alignment ◽  
Estimation Accuracy ◽  
Cryo Electron Microscopy ◽  
Maximum Value ◽  
Projection Images

Abstract Background: Three-dimensional (3D) reconstruction in single-particle cryo-electron microscopy is a significant technique for recovering the 3D structure of proteins or other biological macromolecules from their two-dimensional (2D) noisy projection images taken from unknown random directions. Class averaging in single-particle cryo-electron microscopy is an important procedure for producing high-quality initial 3D models due to the existence of a high level of noise in the projection images. Image alignment is a fundamental step in the class averaging. Results: In this paper, an efficient image alignment algorithm using 2D interpolation in the frequency domain of images is proposed to improve the estimation accuracy of alignment parameters. The proposed algorithm firstly uses the Fourier transform of two projection images to calculate a discrete cross-correlation matrix and then performs the 2D interpolation around the maximum value in the cross-correlation matrix. The alignment parameters of rotation angles and translational shifts in the x-axis and y-axis directions between the two projection images are directly determined according to the position of the maximum value in the cross-correlation matrix after interpolation. Furthermore, the proposed algorithm and the K-medoids clustering algorithm are used to compute class averages for single-particle 3D reconstruction. Conclusions: Results on simulated data set show that the proposed algorithm can be used to compute the alignment parameters efficiently, and using the 2D interpolation can improve the estimation accuracy of the alignment parameters, which usually leads to a better 3D reconstruction result.

scholarly journals Single-particle cryo-EM—How did it get here and where will it go

Science ◽  
2018 ◽  
Vol 361 (6405) ◽  
pp. 876-880 ◽  
Author(s):  
Yifan Cheng
Keyword(s):  
Electron Microscopy ◽  
Structural Biology ◽  
Single Particle ◽  
Electron Crystallography ◽  
The Past ◽  
Cryo Electron Microscopy ◽  
Technological Advances ◽  
The Future ◽  
Electron Cryotomography

Cryo–electron microscopy, or simply cryo-EM, refers mainly to three very different yet closely related techniques: electron crystallography, single-particle cryo-EM, and electron cryotomography. In the past few years, single-particle cryo-EM in particular has triggered a revolution in structural biology and has become a newly dominant discipline. This Review examines the fascinating story of its start and evolution over the past 40-plus years, delves into how and why the recent technological advances have been so groundbreaking, and briefly considers where the technique may be headed in the future.

scholarly journals In situ structure determination using single particle cryo-electron microscopy images

2020 ◽  
Author(s):  
Jing Cheng ◽  
Bufan Li ◽  
Long Si ◽  
Xinzheng Zhang
Keyword(s):  
Electron Microscopy ◽  
Structure Determination ◽  
Single Particle ◽  
Structure Refinement ◽  
Target Function ◽  
Target Protein ◽  
Particle Analysis ◽  
Cryo Electron Microscopy ◽  
Tilt Series

AbstractCryo-electron microscopy (cryo-EM) tomography is a powerful tool for in situ structure determination. However, this method requires the acquisition of tilt series, and its time consuming throughput of acquiring tilt series severely slows determination of in situ structures. By treating the electron densities of non-target protein as non-Gaussian distributed noise, we developed a new target function that greatly improves the efficiency of the recognition of the target protein in a single cryo-EM image without acquiring tilt series. Moreover, we developed a sorting function that effectively eliminates the false positive detection, which not only improves the resolution during the subsequent structure refinement procedure but also allows using homolog proteins as models to recognize the target protein. Together, we developed an in situ single particle analysis (isSPA) method. Our isSPA method was successfully applied to solve structures of glycoproteins on the surface of a non-icosahedral virus and Rubisco inside the carboxysome. The cryo-EM data from both samples were collected within 24 hours, thus allowing fast and simple structural determination in situ.

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