scholarly journals Outlier Profiles of Atomic Structures Derived from X-ray Crystallography and from Cryo-Electron Microscopy

Molecules ◽  
2020 ◽  
Vol 25 (7) ◽  
pp. 1540
Author(s):  
Lin Chen ◽  
Jing He
Keyword(s):  
Electron Microscopy ◽  
Potential Method ◽  
Structural Quality ◽  
Data Sets ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Atomic Structures ◽  
Cryo Electron Microscopy

Background: As more protein atomic structures are determined from cryo-electron microscopy (cryo-EM) density maps, validation of such structures is an important task. Methods: We applied a histogram-based outlier score (HBOS) to six sets of cryo-EM atomic structures and five sets of X-ray atomic structures, including one derived from X-ray data with better than 1.5 Å resolution. Cryo-EM data sets contain structures released by December 2016 and those released between 2017 and 2019, derived from resolution ranges 0–4 Å and 4–6 Å respectively. Results: The distribution of HBOS values in five sets of X-ray structures show that HBOS is sensitive distinguishing sets of X-ray structures derived from different resolution ranges-higher than 1.5 Å, 1.5–2.0 Å, 2.0–2.5 Å, 2.5–3.0 Å, and 3.0–3.5 Å. The overall quality of cryo-EM structures is likely improved, as shown in a comparison of cryo-EM structures released before the end of 2016, those between 2017 and 2018, and those between 2018 and 2019. Our investigation shows that leucine (LEU) has a significantly higher rate of HBOS outliers than that of the reference data set (X-ray-1.5) and of other residue types in the cryo-EM data sets. HBOS was able to detect outliers for those residues that are currently marked as green in PDB validation reports. Conclusions: The HBOS profile of a dataset is a potential method to characterize the overall structural quality of the set. Residue LEU deserves special attention since it has a significantly higher HBOS outlier rate in sets of cryo-EM structures and those X-ray structures derived from X-ray data of lower than 2.5 Å resolutions. Most HBOS outlier residues from the EM-0-4-2019 set are located on loops for most types of residues.

scholarly journals GPU-accelerated analysis and visualization of large structures solved by molecular dynamics flexible fitting

2014 ◽  
Vol 169 ◽  
pp. 265-283 ◽  
Author(s):  
John E. Stone ◽  
Ryan McGreevy ◽  
Barry Isralewitz ◽  
Klaus Schulten
Keyword(s):  
Electron Microscopy ◽  
Molecular Dynamics ◽  
Hybrid Structure ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cryo Electron Microscopy ◽  
Interactive Computation ◽  
Biomolecular Complexes ◽  
Quality Of Fit

Hybrid structure fitting methods combine data from cryo-electron microscopy and X-ray crystallography with molecular dynamics simulations for the determination of all-atom structures of large biomolecular complexes. Evaluating the quality-of-fit obtained from hybrid fitting is computationally demanding, particularly in the context of a multiplicity of structural conformations that must be evaluated. Existing tools for quality-of-fit analysis and visualization have previously targeted small structures and are too slow to be used interactively for large biomolecular complexes of particular interest today such as viruses or for long molecular dynamics trajectories as they arise in protein folding. We present new data-parallel and GPU-accelerated algorithms for rapid interactive computation of quality-of-fit metrics linking all-atom structures and molecular dynamics trajectories to experimentally-determined density maps obtained from cryo-electron microscopy or X-ray crystallography. We evaluate the performance and accuracy of the new quality-of-fit analysis algorithmsvis-à-visexisting tools, examine algorithm performance on GPU-accelerated desktop workstations and supercomputers, and describe new visualization techniques for results of hybrid structure fitting methods.

Using cryo-electron microscopy maps for X-ray structure determination of homologues

2020 ◽  
Vol 76 (1) ◽  
pp. 63-72
Author(s):  
Lingxiao Zeng ◽  
Wei Ding ◽  
Quan Hao
Keyword(s):  
Electron Microscopy ◽  
Hybrid Method ◽  
Model Building ◽  
Test Cases ◽  
X Ray ◽  
X Ray Crystallography ◽  
Sequence Identity ◽  
Whole Process ◽  
Cryo Electron Microscopy

The combination of cryo-electron microscopy (cryo-EM) and X-ray crystallography reflects an important trend in structural biology. In a previously published study, a hybrid method for the determination of X-ray structures using initial phases provided by the corresponding parts of cryo-EM maps was presented. However, if the target structure of X-ray crystallography is not identical but homologous to the corresponding molecular model of the cryo-EM map, then the decrease in the accuracy of the starting phases makes the whole process more difficult. Here, a modified hybrid method is presented to handle such cases. The whole process includes three steps: cryo-EM map replacement, phase extension by NCS averaging and dual-space iterative model building. When the resolution gap between the cryo-EM and X-ray crystallographic data is large and the sequence identity is low, an intermediate stage of model building is necessary. Six test cases have been studied with sequence identity between the corresponding molecules in the cryo-EM and X-ray structures ranging from 34 to 52% and with sequence similarity ranging from 86 to 91%. This hybrid method consistently produced models with reasonable R work and R free values which agree well with the previously determined X-ray structures for all test cases, thus indicating the general applicability of the method for X-ray structure determination of homologues using cryo-EM maps as a starting point.

Study of the structural quality of heteroepitaxial silicon-on-sapphire structures by high-resolution X-ray diffraction, X-ray reflectivity, and electron microscopy

2014 ◽  
Vol 59 (3) ◽  
pp. 315-322 ◽  
Author(s):  
A. E. Blagov ◽  
A. L. Vasiliev ◽  
A. S. Golubeva ◽  
I. A. Ivanov ◽  
O. A. Kondratev ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
High Resolution ◽  
Structural Quality ◽  
X Ray Diffraction ◽  
X Ray

scholarly journals The Combination of X-Ray Crystallography and Cryo-Electron Microscopy Provides Insight into the Overall Architecture of the Dodecameric Rvb1/Rvb2 Complex

PLoS ONE ◽  
2016 ◽  
Vol 11 (1) ◽  
pp. e0146457 ◽  
Author(s):  
Noella Silva-Martin ◽  
María I. Daudén ◽  
Sebastian Glatt ◽  
Niklas A. Hoffmann ◽  
Panagiotis Kastritis ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cryo Electron Microscopy ◽  
Insight Into

scholarly journals BraggNet: integrating Bragg peaks using neural networks

2019 ◽  
Vol 52 (4) ◽  
pp. 854-863 ◽  
Author(s):  
Brendan Sullivan ◽  
Rick Archibald ◽  
Jahaun Azadmanesh ◽  
Venu Gopal Vandavasi ◽  
Patricia S. Langan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Negative Control ◽  
Protein Crystal ◽  
Data Sets ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Integrated Intensities ◽  
Neutron Crystallography

Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data.

scholarly journals From X-ray crystallography to cryo-electron microscopy: computing infrastructure in structural biology

2018 ◽  
Vol 74 (a1) ◽  
pp. a224-a224
Author(s):  
Jason Key ◽  
Peter A. Meyer ◽  
Carol Herre ◽  
Michael Timony ◽  
Dimitry Filonov ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Structural Biology ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cryo Electron Microscopy ◽  
Computing Infrastructure

scholarly journals A basic introduction to single particles cryo-electron microscopy

2021 ◽  
Vol 9 (1) ◽  
pp. 5-20
Author(s):  
Vittoria Raimondi ◽  
◽  
Alessandro Grinzato ◽  
◽  
Keyword(s):  
Electron Microscopy ◽  
Structural Biology ◽  
Protein Complexes ◽  
Computational Power ◽  
Single Particles ◽  
X Ray ◽  
X Ray Crystallography ◽  
Cryo Electron Microscopy ◽  
First Choice ◽  
New Generation

<abstract> <p>In the last years, cryogenic-electron microscopy (cryo-EM) underwent the most impressive improvement compared to other techniques used in structural biology, such as X-ray crystallography and NMR. Electron microscopy was invented nearly one century ago but, up to the beginning of the last decades, the 3D maps produced through this technique were poorly detailed, justifying the term “blobbology” to appeal to cryo-EM. Recently, thanks to a new generation of microscopes and detectors, more efficient algorithms, and easier access to computational power, single particles cryo-EM can routinely produce 3D structures at resolutions comparable to those obtained with X-ray crystallography. However, unlike X-ray crystallography, which needs crystallized proteins, cryo-EM exploits purified samples in solution, allowing the study of proteins and protein complexes that are hard or even impossible to crystallize. For these reasons, single-particle cryo-EM is often the first choice of structural biologists today. Nevertheless, before starting a cryo-EM experiment, many drawbacks and limitations must be considered. Moreover, in practice, the process between the purified sample and the final structure could be trickier than initially expected. Based on these observations, this review aims to offer an overview of the principal technical aspects and setups to be considered while planning and performing a cryo-EM experiment.</p> </abstract>

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