scholarly journals SegmA: Residue Segmentation of cryo-EM density maps

2021 ◽  
Author(s):  
Haim J. Wolfson ◽  
Mark Rozanov
Keyword(s):  
De Novo ◽  
Protein Structures ◽  
Amino Acid Type ◽  
Learning Tools ◽  
Residue Type ◽  
Convolutional Network ◽  
Density Maps ◽  
Acid Type ◽  
Density Map ◽  
Structure Patterns

The cryo-EM resolution revolution enables the development of algorithms for direct de-novo modelling of protein structures from given cryo-EM density maps. Deep Learning tools have been applied to locate structure patterns, such as rotamers, secondary structures and Cα atoms. We present a deep neural network (nicknamed SegmA) for the residue type segmentation of a cryo-EM density map. The network labels voxels in a cryo-EM map by the residue type (amino acid type or nucleic acid) of the sampled macromolecular structure. It also provides a visual representation of the density map by coloring the different types of voxels by their assgned colors. SegmA's algorithm combines a rotation equivariant group convolutional network with a traditional U-net network in a cascade. In addition SegmA estimates the labeling accuracy and reports only labels assigned with high confidence. At resolution of 3 Å SegmAs accuracy is 80% for nucleotides. Amino acids which can be seen by eye, such as LEU, ARG and PHE, are detected by Segma with about 70% accuracy. A web server of the application is under development at https://dev.dcsh7cbr3o89e.amplifyapp.com. The SegmA open code is available at https://github.com/Mark-Rozanov/SegmA_3A/ tree/master .

scholarly journals Multiscale Aggregate Networks with Dense Connections for Crowd Counting

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Pengfei Li ◽  
Min Zhang ◽  
Jian Wan ◽  
Ming Jiang
Keyword(s):  
Mean Squared Error ◽  
Absolute Error ◽  
Image Features ◽  
Convolutional Network ◽  
Crowd Counting ◽  
Squared Error ◽  
Crowd Density ◽  
Density Maps ◽  
Density Map ◽  
Map Decoder

The most advanced method for crowd counting uses a fully convolutional network that extracts image features and then generates a crowd density map. However, this process often encounters multiscale and contextual loss problems. To address these problems, we propose a multiscale aggregation network (MANet) that includes a feature extraction encoder (FEE) and a density map decoder (DMD). The FEE uses a cascaded scale pyramid network to extract multiscale features and obtains contextual features through dense connections. The DMD uses deconvolution and fusion operations to generate features containing detailed information. These features can be further converted into high-quality density maps to accurately calculate the number of people in a crowd. An empirical comparison using four mainstream datasets (ShanghaiTech, WorldExpo’10, UCF_CC_50, and SmartCity) shows that the proposed method is more effective in terms of the mean absolute error and mean squared error. The source code is available at https://github.com/lpfworld/MANet.

scholarly journals Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Jingfan Tang ◽  
Meijia Zhou ◽  
Pengfei Li ◽  
Min Zhang ◽  
Ming Jiang
Keyword(s):  
Receptive Field ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Crowd Counting ◽  
Dilated Convolution ◽  
Perspective Distortion ◽  
Contact Information ◽  
Crowd Density ◽  
Density Maps ◽  
Density Map

The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance. However, due to the crowd occlusion and perspective distortion in the image, the directly generated density map usually neglects the scale information and spatial contact information. To solve it, we proposed MDPDNet (Multiresolution Density maps and Parallel Dilated convolutions’ Network) to reduce the influence of occlusion and distortion on crowd estimation. This network is composed of two modules: (1) the parallel dilated convolution module (PDM) that combines three dilated convolutions in parallel to obtain the deep features on the larger receptive field with fewer parameters while reducing the loss of multiscale information; (2) the multiresolution density map module (MDM) that contains three-branch networks for extracting spatial contact information on three different low-resolution density maps as the feature input of the final crowd density map. Experiments show that MDPDNet achieved excellent results on three mainstream datasets (ShanghaiTech, UCF_CC_50, and UCF-QNRF).

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 Machine learning to estimate the local quality of protein crystal structures

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ikuko Miyaguchi ◽  
Miwa Sato ◽  
Akiko Kashima ◽  
Hiroyuki Nakagawa ◽  
Yuichi Kokabu ◽  
...  
Keyword(s):  
Electron Density ◽  
Protein Structures ◽  
Three Dimensional ◽  
Protein Crystal ◽  
Low Resolution ◽  
X Ray Crystallography ◽  
Density Maps ◽  
Local Quality ◽  
Density Map ◽  
Electron Density Map

AbstractLow-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, called quality assessment based on an electron density map (QAEmap), which evaluates local protein structures determined by X-ray crystallography and could be applied to correct structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and putative high-resolution experimental electron density map. This correlation could be used as a metric to modify the structure. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

scholarly journals QAEmap: A Novel Local Quality Assessment Method for Protein Crystal Structures Using Machine Learning

2021 ◽  
Author(s):  
Ikuko Miyaguchi ◽  
Miwa Sato ◽  
Akiko Kashima ◽  
Hiroyuki Nakagawa ◽  
Yuichi Kokabu ◽  
...  
Keyword(s):  
High Resolution ◽  
Quality Assessment ◽  
Electron Density ◽  
Protein Structures ◽  
Assessment Method ◽  
Protein Crystal ◽  
Low Resolution ◽  
Density Maps ◽  
Density Map ◽  
Electron Density Map

Abstract Low-resolution electron density maps can pose a major obstacle in the determination and use of protein structures. Herein, we describe a novel method, quality assessment based on an electron density map (QAEmap), that evaluates local protein structures determined by X-ray crystallography and corrects structural errors using low-resolution maps. QAEmap uses a three-dimensional deep convolutional neural network with electron density maps and their corresponding coordinates as input and predicts the correlation between the local structure and the putative high-resolution experimental electron density map. This estimates how well the structure fits the high-resolution map. Further, we propose that this method may be applied to evaluate ligand binding, which can be difficult to determine at low resolution.

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.

scholarly journals De novo design of a reversible phosphorylation-dependent switch for membrane targeting

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Leon Harrington ◽  
Jordan M. Fletcher ◽  
Tamara Heermann ◽  
Derek N. Woolfson ◽  
Petra Schwille
Keyword(s):  
Protein Interactions ◽  
Lipid Membrane ◽  
De Novo ◽  
Protein Localization ◽  
Protein Structures ◽  
Spatiotemporal Pattern ◽  
Membrane Targeting ◽  
Protein Protein Interactions ◽  
Reversible Phosphorylation ◽  
Potential Applications

AbstractModules that switch protein-protein interactions on and off are essential to develop synthetic biology; for example, to construct orthogonal signaling pathways, to control artificial protein structures dynamically, and for protein localization in cells or protocells. In nature, the E. coli MinCDE system couples nucleotide-dependent switching of MinD dimerization to membrane targeting to trigger spatiotemporal pattern formation. Here we present a de novo peptide-based molecular switch that toggles reversibly between monomer and dimer in response to phosphorylation and dephosphorylation. In combination with other modules, we construct fusion proteins that couple switching to lipid-membrane targeting by: (i) tethering a ‘cargo’ molecule reversibly to a permanent membrane ‘anchor’; and (ii) creating a ‘membrane-avidity switch’ that mimics the MinD system but operates by reversible phosphorylation. These minimal, de novo molecular switches have potential applications for introducing dynamic processes into designed and engineered proteins to augment functions in living cells and add functionality to protocells.

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