scholarly journals A methodology using Gaussian-based density map approximation to assess sets of cryo-electron microscopy density maps

2018 ◽  
Vol 204 (2) ◽  
pp. 344-350 ◽  
Author(s):  
Slavica Jonić
Keyword(s):  
Electron Microscopy ◽  
Cryo Electron Microscopy ◽  
Density Maps ◽  
Density Map

scholarly journals Deep Learning for Validating and Estimating Resolution of Cryo-Electron Microscopy Density Maps †

Molecules ◽  
2019 ◽  
Vol 24 (6) ◽  
pp. 1181 ◽  
Author(s):  
Todor Avramov ◽  
Dan Vyenielo ◽  
Josue Gomez-Blanco ◽  
Swathi Adinarayanan ◽  
Javier Vargas ◽  
...  
Keyword(s):  
Neural Network ◽  
Electron Microscopy ◽  
Deep Learning ◽  
Protein Structures ◽  
Imaging Method ◽  
Classification Models ◽  
Cryo Electron Microscopy ◽  
Density Maps ◽  
Density Map ◽  
3D Cnn

Cryo-electron microscopy (cryo-EM) is becoming the imaging method of choice for determining protein structures. Many atomic structures have been resolved based on an exponentially growing number of published three-dimensional (3D) high resolution cryo-EM density maps. However, the resolution value claimed for the reconstructed 3D density map has been the topic of scientific debate for many years. The Fourier Shell Correlation (FSC) is the currently accepted cryo-EM resolution measure, but it can be subjective, manipulated, and has its own limitations. In this study, we first propose supervised deep learning methods to extract representative 3D features at high, medium and low resolutions from simulated protein density maps and build classification models that objectively validate resolutions of experimental 3D cryo-EM maps. Specifically, we build classification models based on dense artificial neural network (DNN) and 3D convolutional neural network (3D CNN) architectures. The trained models can classify a given 3D cryo-EM density map into one of three resolution levels: high, medium, low. The preliminary DNN and 3D CNN models achieved 92.73% accuracy and 99.75% accuracy on simulated test maps, respectively. Applying the DNN and 3D CNN models to thirty experimental cryo-EM maps achieved an agreement of 60.0% and 56.7%, respectively, with the author published resolution value of the density maps. We further augment these previous techniques and present preliminary results of a 3D U-Net model for local resolution classification. The model was trained to perform voxel-wise classification of 3D cryo-EM density maps into one of ten resolution classes, instead of a single global resolution value. The U-Net model achieved 88.3% and 94.7% accuracy when evaluated on experimental maps with local resolutions determined by MonoRes and ResMap methods, respectively. Our results suggest deep learning can potentially improve the resolution evaluation process of experimental cryo-EM maps.

scholarly journals Cylindrical Similarity Measurement for Helices in Medium-Resolution Cryo-Electron Microscopy Density Maps

2020 ◽  
Vol 60 (5) ◽  
pp. 2644-2650 ◽  
Author(s):  
Salim Sazzed ◽  
Peter Scheible ◽  
Maytha Alshammari ◽  
Willy Wriggers ◽  
Jing He
Keyword(s):  
Electron Microscopy ◽  
Similarity Measurement ◽  
Cryo Electron Microscopy ◽  
Density Maps ◽  
Medium Resolution

scholarly journals Opportunities and challenges for assigning cofactors in cryo-EM density maps of chlorophyll-containing proteins

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Christopher J. Gisriel ◽  
Jimin Wang ◽  
Gary W. Brudvig ◽  
Donald A. Bryant
Keyword(s):  
Electron Microscopy ◽  
Electrostatic Potential ◽  
Protein Function ◽  
Protein Complexes ◽  
Chemical Environment ◽  
Cryo Electron Microscopy ◽  
Functional Differences ◽  
Density Maps ◽  
Structural Differences

AbstractThe accurate assignment of cofactors in cryo-electron microscopy maps is crucial in determining protein function. This is particularly true for chlorophylls (Chls), for which small structural differences lead to important functional differences. Recent cryo-electron microscopy structures of Chl-containing protein complexes exemplify the difficulties in distinguishing Chl b and Chl f from Chl a. We use these structures as examples to discuss general issues arising from local resolution differences, properties of electrostatic potential maps, and the chemical environment which must be considered to make accurate assignments. We offer suggestions for how to improve the reliability of such assignments.

scholarly journals Advances in Structure Modeling Methods for Cryo-Electron Microscopy Maps

Molecules ◽  
2019 ◽  
Vol 25 (1) ◽  
pp. 82 ◽  
Author(s):  
Eman Alnabati ◽  
Daisuke Kihara
Keyword(s):  
Electron Microscopy ◽  
De Novo ◽  
Molecular Structures ◽  
Structure Modeling ◽  
Rapid Progress ◽  
Macromolecular Complexes ◽  
Modeling Methods ◽  
Cryo Electron Microscopy ◽  
Density Maps

Cryo-electron microscopy (cryo-EM) has now become a widely used technique for structure determination of macromolecular complexes. For modeling molecular structures from density maps of different resolutions, many algorithms have been developed. These algorithms can be categorized into rigid fitting, flexible fitting, and de novo modeling methods. It is also observed that machine learning (ML) techniques have been increasingly applied following the rapid progress of the ML field. Here, we review these different categories of macromolecule structure modeling methods and discuss their advances over time.

scholarly journals Fibril structure of amyloid-β(1–42) by cryo–electron microscopy

Science ◽  
2017 ◽  
Vol 358 (6359) ◽  
pp. 116-119 ◽  
Author(s):  
Lothar Gremer ◽  
Daniel Schölzel ◽  
Carla Schenk ◽  
Elke Reinartz ◽  
Jörg Labahn ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Senile Plaques ◽  
Amyloid Β ◽  
Amyloid Β Protein ◽  
Dimer Interface ◽  
Cryo Electron Microscopy ◽  
Fibril Structure ◽  
Β Protein ◽  
Density Map ◽  
Main Component

Amyloids are implicated in neurodegenerative diseases. Fibrillar aggregates of the amyloid-β protein (Aβ) are the main component of the senile plaques found in brains of Alzheimer’s disease patients. We present the structure of an Aβ(1–42) fibril composed of two intertwined protofilaments determined by cryo–electron microscopy (cryo-EM) to 4.0-angstrom resolution, complemented by solid-state nuclear magnetic resonance experiments. The backbone of all 42 residues and nearly all side chains are well resolved in the EM density map, including the entire N terminus, which is part of the cross-β structure resulting in an overall “LS”-shaped topology of individual subunits. The dimer interface protects the hydrophobic C termini from the solvent. The characteristic staggering of the nonplanar subunits results in markedly different fibril ends, termed “groove” and “ridge,” leading to different binding pathways on both fibril ends, which has implications for fibril growth.

scholarly journals PRISM-EM: template interface-based modelling of multi-protein complexes guided by cryo-electron microscopy density maps. Corrigendum

2018 ◽  
Vol 74 (1) ◽  
pp. 65-66
Author(s):  
Guray Kuzu ◽  
Ozlem Keskin ◽  
Ruth Nussinov ◽  
Attila Gursoy
Keyword(s):  
Electron Microscopy ◽  
Protein Complexes ◽  
Acta Cryst ◽  
Cryo Electron Microscopy ◽  
Density Maps

A revised Table 6 and Supporting Information are provided for the article by Kuzuet al.[(2016),Acta Cryst.D72, 1137–1148].

scholarly journals A WAVELET BASED ALTERNATIVE ITERATION METHOD FOR THE ORIENTATION REFINEMENT OF CRYO-ELECTRON MICROSCOPY 3D RECONSTRUCTION

2015 ◽  
Vol 20 (3) ◽  
pp. 396-408 ◽  
Author(s):  
Zhucui Jing ◽  
Ming Li
Keyword(s):  
Electron Microscopy ◽  
Iteration Method ◽  
Three Dimensional ◽  
Particle Method ◽  
Biological Macromolecules ◽  
Orthonormal Bases ◽  
Cryo Electron Microscopy ◽  
Marquardt Algorithm ◽  
Density Map ◽  
Particle Images

Cryo-electron microscopy (cryo-EM) single particle method (SPM) reconstructs the three-dimensional (3D) density map of biological macromolecules using 2D particle images with estimated orientations. The estimated orientations have errors which result in the decrease in resolution of the reconstructed map. We propose a wavelet orthonormal bases based iteration method by refining alternatively the orientations and the map using Levenberg–Marquardt algorithm and soft-thresholding, respectively. The convergence analysis of the proposed algorithm is provided and numerical experiments for simulated particle images show its good performance.

Sign in / Sign up

Export Citation Format

Share Document