Hydration-layer models for cryo-EM image simulation

Shang, Zhiguo; Sigworth, Fred J.

doi:10.1016/j.jsb.2012.04.021

Cited by 31 publications

(26 citation statements)

References 16 publications

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“…Eight rotations, uniformly spaced in the range ±20 degrees, were applied to the atomic coordinates of the antibody fragment. A density map was created for each volume by using a simulator of solvated protein (Shang 2012). Then the volumes were low pass filtered to simulate limited resolution (voxel size: 2.5 Å cubed).…”

Section: Resultsmentioning

confidence: 99%

Directly reconstructing principal components of heterogeneous particles from cryo-EM images

Tagare

Kucukelbir

Sigworth

et al. 2015

Journal of Structural Biology

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Structural heterogeneity of particles can be investigated by their three-dimensional principal components. This paper addresses the question of whether, and with what algorithm, the three-dimensional principal components can be directly recovered from cryo-EM images. The first part of the paper extends the Fourier slice theorem to covariance functions showing that the three-dimensional covariance, and hence the principal components, of a heterogeneous particle can indeed be recovered from two-dimensional cryo-EM images. The second part of the paper proposes a practical algorithm for reconstructing the principal components directly from cryo-EM images without the intermediate step of calculating covariances. This algorithm is based on maximizing the (posterior) likelihood using the Expectation-Maximization algorithm. The last part of the paper applies this algorithm to simulated data and to two real cryo-EM data sets: a data set of the 70S ribosome with and without Elongation Factor-G (EF-G), and a data set of the inluenza virus RNA dependent RNA Polymerase (RdRP). The first principal component of the 70S ribosome data set reveals the expected conformational changes of the ribosome as the EF-G binds and unbinds. The first principal component of the RdRP data set reveals a conformational change in the two dimers of the RdRP.

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Section: Resultsmentioning

confidence: 99%

Directly reconstructing principal components of heterogeneous particles from cryo-EM images

Tagare

Kucukelbir

Sigworth

et al. 2015

Journal of Structural Biology

Self Cite

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show abstract

“…Simulated cryo-EM images were generated from the atomic structure of the 50S ribosomal subunit from the Protein Data Bank (PDB ID: 1JJ2) using a hydration model [27], and was sampled at a pixel size of 3Å. The 2D particle images (inliers) were obtained from the 3D structure according to the image model in (17) and (18) by: 1) projecting the structure and applying a CTF; 2) applying the amplitude factor from a uniform distribution between 0.5 and1.5; and 3) adding Gaussian white noise.…”

Section: Resultsmentioning

confidence: 99%

Robust w-Estimators for Cryo-EM Class Means

Huang

Tagare

2016

IEEE Trans. on Image Process.

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A critical step in cryogenic electron microscopy (cryo-EM) image analysis is to calculate the average of all images aligned to a projection direction. This average, called the “class mean”, improves the signal-to-noise ratio in single particle reconstruction (SPR). The averaging step is often compromised because of outlier images of ice, contaminants, and particle fragments. Outlier detection and rejection in the majority of current cryo-EM methods is done using cross-correlation with a manually determined threshold. Empirical assessment shows that the performance of these methods is very sensitive to the threshold. This paper proposes an alternative: a “w-estimator” of the average image, which is robust to outliers and which does not use a threshold. Various properties of the estimator, such as consistency and influence function are investigated. An extension of the estimator to images with different contrast transfer functions (CTFs) is also provided. Experiments with simulated and real cryo-EM images show that the proposed estimator performs quite well in the presence of outliers.

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“…To understand how the vesicle removal affects the protein signal and how the vesicle removal performs under varying amounts of noise, we first generated simulated protein projections [38] and added Gaussian noise from increasing inverse signal-to-noise-ratio (SNR). A realistic amount of noise versus the membrane protein is about ten times in the inverse SNR [38].…”

Section: Methodsmentioning

confidence: 99%

“…A realistic amount of noise versus the membrane protein is about ten times in the inverse SNR [38]. Then we projected the simulated images, without added vesicles, onto the above trained vesicle subspace and onto its orthogonal complement.…”

Section: Methodsmentioning

confidence: 99%

Removal of Vesicle Structures From Transmission Electron Microscope Images

Jensen

Sigworth

Brandt

2016

IEEE Trans. on Image Process.

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In this paper, we address the problem of imaging membrane proteins for single-particle cryo-electron microscopy reconstruction of the isolated protein structure. More precisely, we propose a method for learning and removing the interfering vesicle signals from the micrograph, prior to reconstruction. In our approach, we estimate the subspace of the vesicle structures and project the micrographs onto the orthogonal complement of this subspace. We construct a 2d statistical model of the vesicle structure, based on higher order singular value decomposition (HOSVD), by considering the structural symmetries of the vesicles in the polar coordinate plane. We then propose to lift the HOSVD model to a novel hierarchical model by summarizing the multidimensional HOSVD coefficients by their principal components. Along with the model, a solid vesicle normalization scheme and model selection criterion are proposed to make a compact and general model. The results show that the vesicle structures are accurately separated from the background by the HOSVD model that is also able to adapt to the asymmetries of the vesicles. This is a promising result and suggests even wider applicability of the proposed approach in learning and removal of statistical structures.

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Hydration-layer models for cryo-EM image simulation

Cited by 31 publications

References 16 publications

Directly reconstructing principal components of heterogeneous particles from cryo-EM images

Directly reconstructing principal components of heterogeneous particles from cryo-EM images

Robust w-Estimators for Cryo-EM Class Means

Removal of Vesicle Structures From Transmission Electron Microscope Images

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