MeasureIce: Accessible on-the-fly measurement of ice thickness in cryo-electron microscopy

Alternative Techniques ◽

Map Resolution

Ice thickness is arguably one of the most important factors limiting the resolution of protein structures determined by cryo electron microscopy. The amorphous atomic structure of the ice that stabilizes and protects biological samples in cryo-EM grids also imprints some addition noise in the TEM images. Ice that is too thick jeopardizes the success of particle picking and reconstruction of the biomolecule in the worst case and, at best, deteriorates eventual map resolution. Minimizing the thickness of the ice layer and thus the magnitude of its noise contribution is thus imperative in cryo-EM grid preparation. In this paper we introduce MeasureIce, a simple, easy to use ice thickness measurement tool for screening and selecting acquisition areas of cryo-EM grids. We show that it is possible to simulate thickness-image intensity look-up tables using elementary scattering physics and thereby adapt the tool to any microscope without time consuming experimental calibration. We benchmark our approach using two alternative techniques: the "ice-channel" technique and tilt-series tomography. We also demonstrate the utility of ice thickness measurement for selecting holes in gold grids containing an Equine apoferritin sample, achieving a 1.88 Ångstrom resolution in subsequent refinement of the atomic map.

Development of Thin-Ice Tem Grids to Control the Ice Thickness for Cryo-Electron Microscopy

Biophysical Journal ◽

10.1016/j.bpj.2016.11.3100 ◽

2017 ◽

Vol 112 (3) ◽

pp. 576a

Author(s):

Liguo Wang ◽

Lige Tonggu

Keyword(s):

Electron Microscopy ◽

Ice Thickness ◽

Accurate Flexible Fitting of High-Resolution Protein Structures into Cryo-Electron Microscopy Maps Using Coarse-Grained Pseudo-Energy Minimization

Biophysical Journal ◽

10.1016/j.bpj.2010.12.3680 ◽

2011 ◽

Vol 100 (2) ◽

pp. 478-488 ◽

Cited By ~ 51

Author(s):

Wenjun Zheng

Keyword(s):

Electron Microscopy ◽

High Resolution ◽

Energy Minimization ◽

Protein Structures ◽

Coarse Grained ◽

2.8 Å resolution reconstruction of the Thermoplasma acidophilum 20S proteasome using cryo-electron microscopy

eLife ◽

10.7554/elife.06380 ◽

2015 ◽

Vol 4 ◽

Cited By ~ 117

Author(s):

Melody G Campbell ◽

David Veesler ◽

Anchi Cheng ◽

Clinton S Potter ◽

Bridget Carragher

Keyword(s):

Electron Microscopy ◽

Drug Design ◽

Single Particle ◽

Protein Structures ◽

Rational Drug Design ◽

20S Proteasome ◽

Thermoplasma Acidophilum ◽

Structure Based Drug Design ◽

Pharmaceutical Industries

Recent developments in detector hardware and image-processing software have revolutionized single particle cryo-electron microscopy (cryoEM) and led to a wave of near-atomic resolution (typically ∼3.3 Å) reconstructions. Reaching resolutions higher than 3 Å is a prerequisite for structure-based drug design and for cryoEM to become widely interesting to pharmaceutical industries. We report here the structure of the 700 kDa Thermoplasma acidophilum 20S proteasome (T20S), determined at 2.8 Å resolution by single-particle cryoEM. The quality of the reconstruction enables identifying the rotameric conformation adopted by some amino-acid side chains (rotamers) and resolving ordered water molecules, in agreement with the expectations for crystal structures at similar resolutions. The results described in this manuscript demonstrate that single particle cryoEM is capable of competing with X-ray crystallography for determination of protein structures of suitable quality for rational drug design.

Protein modeling by homology using the example of tick-borne encephalitis serine protease NS3

10.33920/med-13-2003-05 ◽

2020 ◽

pp. 59-66

Author(s):

Nikita Ilment ◽

Ekaterina Zinina

Keyword(s):

Electron Microscopy ◽

Homology Modeling ◽

Homology Modelling ◽

Protein Structures ◽

3D Structure ◽

Free Software ◽

Homologous Proteins ◽

Tick Borne Encephalitis ◽

Sequences Analysis

Homology modeling is a process of obtaining a 3D structure of a protein using various algorithms based on already known structures of homologous proteins. The spatial structure of protein is required for in silico protein evaluation. 3D structures can be obtained using different methods: NMR, Xray crystallography (XRC), and cryo-electron microscopy (cryo-EM), but these methods require a lot of time and money. At the same time, the speed of nucleotide sequences analysis is increasing, thereby creating a mismatch between the number of decoded genomes and the investigated 3D protein structures that are encoded by these sequences. Also, homology modeling is the easiest and fastest way to obtain the model of the desired protein. This review describes free software for homology modelling — SWISS-MODEL and MODELLER, how to use it and how to evaluate the results.

Structure Determination by Single-Particle Cryo-Electron Microscopy: Only the Sky (and Intrinsic Disorder) is the Limit

International Journal of Molecular Sciences ◽

10.3390/ijms20174186 ◽

2019 ◽

Vol 20 (17) ◽

pp. 4186 ◽

Cited By ~ 11

Author(s):

Emeka Nwanochie ◽

Vladimir N. Uversky

Keyword(s):

Electron Microscopy ◽

Nmr Spectroscopy ◽

Structure Determination ◽

Single Particle ◽

Protein Structures ◽

X Ray ◽

X Ray Crystallography ◽

Intrinsically Disordered ◽

Small Proteins ◽

Traditionally, X-ray crystallography and NMR spectroscopy represent major workhorses of structural biologists, with the lion share of protein structures reported in protein data bank (PDB) being generated by these powerful techniques. Despite their wide utilization in protein structure determination, these two techniques have logical limitations, with X-ray crystallography being unsuitable for the analysis of highly dynamic structures and with NMR spectroscopy being restricted to the analysis of relatively small proteins. In recent years, we have witnessed an explosive development of the techniques based on Cryo-electron microscopy (Cryo-EM) for structural characterization of biological molecules. In fact, single-particle Cryo-EM is a special niche as it is a technique of choice for the structural analysis of large, structurally heterogeneous, and dynamic complexes. Here, sub-nanometer atomic resolution can be achieved (i.e., resolution below 10 Å) via single-particle imaging of non-crystalline specimens, with accurate 3D reconstruction being generated based on the computational averaging of multiple 2D projection images of the same particle that was frozen rapidly in solution. We provide here a brief overview of single-particle Cryo-EM and show how Cryo-EM has revolutionized structural investigations of membrane proteins. We also show that the presence of intrinsically disordered or flexible regions in a target protein represents one of the major limitations of this promising technique.

Residue Specific Radiation Damage of Protein Structures using High-Resolution Cryo-Electron Microscopy

Biophysical Journal ◽

10.1016/j.bpj.2014.11.1052 ◽

2015 ◽

Vol 108 (2) ◽

pp. 190a

Author(s):

Doreen Matthies ◽

Alberto Bartesaghi ◽

Alan Merk ◽

Soojay Banerjee ◽

Sriram Subramaniam

Keyword(s):

Electron Microscopy ◽

High Resolution ◽

Radiation Damage ◽

Protein Structures ◽

Accurate Flexible Fitting of High-Resolution Protein Structures into cryo-Electron Microscopy Maps Using Coarse-Grained Pseudo-Energy Minimization

Biophysical Journal ◽

10.1016/j.bpj.2010.12.1960 ◽

2011 ◽

Vol 100 (3) ◽

pp. 322a

Author(s):

Wenjun Zheng

Keyword(s):

Electron Microscopy ◽

High Resolution ◽

Energy Minimization ◽

Protein Structures ◽

Coarse Grained ◽

Self-Blotting Nanowire Grids for Cryo-EM Sample Preparation

Microscopy and Microanalysis ◽

10.1017/s1431927617004901 ◽

2017 ◽

Vol 23 (S1) ◽

pp. 848-849

Author(s):

Hui Wei ◽

Venkat Dandey ◽

Zhening Zhang ◽

Ashleigh Raczkowski ◽

Bridget Carragher ◽

...

Keyword(s):

Thin Film ◽

Electron Microscopy ◽

Sample Preparation ◽

Filter Paper ◽

Ice Thickness ◽

New Approach ◽

Piezo Electric

AbstractAlmost every aspect of cryo electron microscopy (CryoEM) has been automated over the last few decades. One of the challenges that remains to be addressed is the robust and reliable preparation of vitrified specimens of suitable ice thickness. The development of a new self-blotting nanowire (Zhang et al., 2013) grid in conjunction with a piezo electric dispensing robot called Spotiton (Jain et al., 2012) enables spreading a sample to a thin film without the use of externally applied filter paper. This new approach has the advantage of using small amounts of protein material, resulting in large areas of ice of a well- defined thickness containing evenly distributed particles (Razinkov et al., 2016).

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

Molecules ◽

10.3390/molecules24061181 ◽

2019 ◽

Vol 24 (6) ◽

pp. 1181 ◽

Cited By ~ 10

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 ◽

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.

An overview of the recent advances in cryo-electron microscopy for life sciences

Emerging Topics in Life Sciences ◽

10.1042/etls20200295 ◽

2021 ◽

Author(s):

Anshul Assaiya ◽

Ananth Prasad Burada ◽

Surbhi Dhingra ◽

Janesh Kumar

Keyword(s):

Electron Microscopy ◽

High Resolution ◽

Structure Determination ◽

Electron Tomography ◽

Protein Structures ◽

Particle Analysis ◽

X Ray Crystallography ◽

Recent Advances ◽

Cellular Environment

Cryo-electron microscopy (CryoEM) has superseded X-ray crystallography and NMR to emerge as a popular and effective tool for structure determination in recent times. It has become indispensable for the characterization of large macromolecular assemblies, membrane proteins, or samples that are limited, conformationally heterogeneous, and recalcitrant to crystallization. Besides, it is the only tool capable of elucidating high-resolution structures of macromolecules and biological assemblies in situ. A state-of-the-art electron microscope operable at cryo-temperature helps preserve high-resolution details of the biological sample. The structures can be determined, either in isolation via single-particle analysis (SPA) or helical reconstruction, electron diffraction (ED) or within the cellular environment via cryo-electron tomography (cryoET). All the three streams of SPA, ED, and cryoET (along with subtomogram averaging) have undergone significant advancements in recent times. This has resulted in breaking the boundaries with respect to both the size of the macromolecules/assemblies whose structures could be determined along with the visualization of atomic details at resolutions unprecedented for cryoEM. In addition, the collection of larger datasets combined with the ability to sort and process multiple conformational states from the same sample are providing the much-needed link between the protein structures and their functions. In overview, these developments are helping scientists decipher the molecular mechanism of critical cellular processes, solve structures of macromolecules that were challenging targets for structure determination until now, propelling forward the fields of biology and biomedicine. Here, we summarize recent advances and key contributions of the three cryo-electron microscopy streams of SPA, ED, and cryoET.