scholarly journals An Iterative Bézier Method for Fitting Beta-sheet Component of a Cryo-EM Density Map

2017 ◽  
Vol 5 (1) ◽  
pp. 31-39 ◽  
Author(s):  
Michael Poteat ◽  
Jing He
Keyword(s):  
Shape Representation ◽  
Molecular Complexes ◽  
Geometrical Shape ◽  
3 Dimensional ◽  
Atomic Structures ◽  
Cryo Electron Microscopy ◽  
Density Maps ◽  
Medium Resolution ◽  
Density Map ◽  
Β Sheet

AbstractCryo-electron microscopy (Cryo-EM) is a powerful technique to produce 3-dimensional density maps for large molecular complexes. Although many atomic structures have been solved from cryo-EM density maps, it is challenging to derive atomic structures when the resolution of density maps is not sufficiently high. Geometrical shape representation of secondary structural components in a medium-resolution density map enhances modeling of atomic structures.We compare two methods in producing surface representation of the β-sheet component of a density map. Given a 3-dimensional volume of β-sheet that is segmented from a density map, the performance of a polynomial fitting was compared with that of an iterative Bézier fitting. The results suggest that the iterative Bézier fitting is more suitable for β-sheets, since it provides more accurate representation of the corners that are naturally twisted in a β-sheet.

scholarly journals Multi-Scale Flexible Fitting of Proteins to Cryo-EM Density Maps at Medium Resolution

2021 ◽  
Vol 8 ◽  
Author(s):  
Marta Kulik ◽  
Takaharu Mori ◽  
Yuji Sugita
Keyword(s):  
Md Simulations ◽  
Conformational Transitions ◽  
Force Constants ◽  
Coarse Grained ◽  
Implicit Solvent ◽  
Multi Scale ◽  
Cryo Electron Microscopy ◽  
Density Maps ◽  
Medium Resolution ◽  
Density Map

Structure determination using cryo-electron microscopy (cryo-EM) medium-resolution density maps is often facilitated by flexible fitting. Avoiding overfitting, adjusting force constants driving the structure to the density map, and emulating complex conformational transitions are major concerns in the fitting. To address them, we develop a new method based on a three-step multi-scale protocol. First, flexible fitting molecular dynamics (MD) simulations with coarse-grained structure-based force field and replica-exchange scheme between different force constants replicas are performed. Second, fitted Cα atom positions guide the all-atom structure in targeted MD. Finally, the all-atom flexible fitting refinement in implicit solvent adjusts the positions of the side chains in the density map. Final models obtained via the multi-scale protocol are significantly better resolved and more reliable in comparison with long all-atom flexible fitting simulations. The protocol is useful for multi-domain systems with intricate structural transitions as it preserves the secondary structure of single domains.

scholarly journals A new topology of the HK97-like fold revealed in Bordetella bacteriophage by cryoEM at 3.5 Å resolution

eLife ◽  
2013 ◽  
Vol 2 ◽  
Author(s):  
Xing Zhang ◽  
Huatao Guo ◽  
Lei Jin ◽  
Elizabeth Czornyj ◽  
Asher Hodes ◽  
...  
Keyword(s):  
Phage Display ◽  
Electrostatic Interactions ◽  
Whooping Cough ◽  
Atomic Structures ◽  
Complex Interactions ◽  
Cryo Electron Microscopy ◽  
Naturally Occurring ◽  
Feasible Alternative ◽  
Β Sheet ◽  
Cement Protein

Bacteriophage BPP-1 infects and kills Bordetella species that cause whooping cough. Its diversity-generating retroelement (DGR) provides a naturally occurring phage-display system, but engineering efforts are hampered without atomic structures. Here, we report a cryo electron microscopy structure of the BPP-1 head at 3.5 Å resolution. Our atomic model shows two of the three protein folds representing major viral lineages: jellyroll for its cement protein (CP) and HK97-like (‘Johnson’) for its major capsid protein (MCP). Strikingly, the fold topology of MCP is permuted non-circularly from the Johnson fold topology previously seen in viral and cellular proteins. We illustrate that the new topology is likely the only feasible alternative of the old topology. β-sheet augmentation and electrostatic interactions contribute to the formation of non-covalent chainmail in BPP-1, unlike covalent inter-protein linkages of the HK97 chainmail. Despite these complex interactions, the termini of both CP and MCP are ideally positioned for DGR-based phage-display engineering.

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 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.

Constrained cyclic coordinate descent for cryo-EM images at medium resolutions: beyond the protein loop closure problem

Robotica ◽  
2016 ◽  
Vol 34 (8) ◽  
pp. 1777-1790 ◽  
Author(s):  
Kamal Al Nasr ◽  
Jing He
Keyword(s):  
Protein Structure ◽  
Inverse Kinematic ◽  
Coordinate Descent ◽  
3D Images ◽  
Loop Closure ◽  
3 Dimensional ◽  
Atomic Structures ◽  
Medium Resolution ◽  
Closure Method ◽  
Cyclic Coordinate Descent

SUMMARYThe cyclic coordinate descent (CCD) method is a popular loop closure method in protein structure modeling. It is a robotics algorithm originally developed for inverse kinematic applications. We demonstrate an effective method of building the backbone of protein structure models using the principle of CCD and a guiding trace. For medium-resolution 3-dimensional (3D) images derived using cryo-electron microscopy (cryo-EM), it is possible to obtain guiding traces of secondary structures and their skeleton connections. Our new method, constrained cyclic coordinate descent (CCCD), builds α-helices, β-strands, and loops quickly and fairly accurately along predefined traces. We show that it is possible to build the entire backbone of a protein fairly accurately when the guiding traces are accurate. In a test of 10 proteins, the models constructed using CCCD show an average of 3.91 Å of backbone root mean square deviation (RMSD). When the CCCD method is incorporated in a simulated annealing framework to sample possible shift, translation, and rotation freedom, the models built with the true topology were ranked high on the list, with an average backbone RMSD100 of 3.76 Å. CCCD is an effective method for modeling atomic structures after secondary structure traces and skeletons are extracted from 3D cryo-EM images.

High Resolution Microscopy of Multi-Layered Biological Crystals

1982 ◽  
Vol 40 ◽  
pp. 80-83
Author(s):  
H.A. Cohen ◽  
T.W. Jeng ◽  
W. Chiu
Keyword(s):  
High Resolution ◽  
Low Dose ◽  
Three Dimensional ◽  
Data Interpretation ◽  
Two Dimensional ◽  
Biological Objects ◽  
Density Maps ◽  
Density Map ◽  
Complex Crystal ◽  
High Resolution Microscopy

This tutorial will discuss the methodology of low dose electron diffraction and imaging of crystalline biological objects, the problems of data interpretation for two-dimensional projected density maps of glucose embedded protein crystals, the factors to be considered in combining tilt data from three-dimensional crystals, and finally, the prospects of achieving a high resolution three-dimensional density map of a biological crystal. This methodology will be illustrated using two proteins under investigation in our laboratory, the T4 DNA helix destabilizing protein gp32*I and the crotoxin complex crystal.

Pathology image-based lung cancer subtyping using deeplearning features and cell-density maps

2020 ◽  
Vol 2020 (10) ◽  
pp. 64-1-64-5
Author(s):  
Mustafa I. Jaber ◽  
Christopher W. Szeto ◽  
Bing Song ◽  
Liudmila Beziaeva ◽  
Stephen C. Benz ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cell Density ◽  
Majority Voting ◽  
Training Set ◽  
Density Maps ◽  
Color Deconvolution ◽  
Map Generation ◽  
Density Map ◽  
Pathology Image ◽  
Whole Slide Images

In this paper, we propose a patch-based system to classify non-small cell lung cancer (NSCLC) diagnostic whole slide images (WSIs) into two major histopathological subtypes: adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC). Classifying patients accurately is important for prognosis and therapy decisions. The proposed system was trained and tested on 876 subtyped NSCLC gigapixel-resolution diagnostic WSIs from 805 patients – 664 in the training set and 141 in the test set. The algorithm has modules for: 1) auto-generated tumor/non-tumor masking using a trained residual neural network (ResNet34), 2) cell-density map generation (based on color deconvolution, local drain segmentation, and watershed transformation), 3) patch-level feature extraction using a pre-trained ResNet34, 4) a tower of linear SVMs for different cell ranges, and 5) a majority voting module for aggregating subtype predictions in unseen testing WSIs. The proposed system was trained and tested on several WSI magnifications ranging from x4 to x40 with a best ROC AUC of 0.95 and an accuracy of 0.86 in test samples. This fully-automated histopathology subtyping method outperforms similar published state-of-the-art methods for diagnostic WSIs.

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