scholarly journals CryoDRGN: Reconstruction of heterogeneous structures from cryo-electron micrographs using neural networks

2020 ◽  
Author(s):  
Ellen D. Zhong ◽  
Tristan Bepler ◽  
Bonnie Berger ◽  
Joseph H. Davis
Keyword(s):  
Neural Networks ◽  
Conformational Changes ◽  
Deep Neural Networks ◽  
Protein Complexes ◽  
Structural Heterogeneity ◽  
Particle Analysis ◽  
Single Particle Analysis ◽  
Current Analysis ◽  
Heterogeneous Structures ◽  
First Time

AbstractCryo-EM single-particle analysis has proven powerful in determining the structures of rigid macromolecules. However, many protein complexes are flexible and can change conformation and composition as a result of functionally-associated dynamics. Such dynamics are poorly captured by current analysis methods. Here, we present cryoDRGN, an algorithm that for the first time leverages the representation power of deep neural networks to efficiently reconstruct highly heterogeneous complexes and continuous trajectories of protein motion. We apply this tool to two synthetic and three publicly available cryo-EM datasets, and we show that cryoDRGN provides an interpretable representation of structural heterogeneity that can be used to identify discrete states as well as continuous conformational changes. This ability enables cryoDRGN to discover previously overlooked structural states and to visualize molecules in motion.

scholarly journals Electron microscopy snapshots of single particles from single cells

2018 ◽  
Vol 294 (5) ◽  
pp. 1602-1608 ◽  
Author(s):  
Xiunan Yi ◽  
Eric J. Verbeke ◽  
Yiran Chang ◽  
Daniel J. Dickinson ◽  
David W. Taylor
Keyword(s):  
Electron Microscopy ◽  
Single Particle ◽  
Protein Complexes ◽  
Signal To Noise Ratio ◽  
Single Cells ◽  
Particle Analysis ◽  
Biological Macromolecules ◽  
Single Particle Analysis ◽  
Whole Cells ◽  
Potential Applications

Cryo-electron microscopy (cryo-EM) has become an indispensable tool for structural studies of biological macromolecules. Two additional predominant methods are available for studying the architectures of multiprotein complexes: 1) single-particle analysis of purified samples and 2) tomography of whole cells or cell sections. The former can produce high-resolution structures but is limited to highly purified samples, whereas the latter can capture proteins in their native state but has a low signal-to-noise ratio and yields lower-resolution structures. Here, we present a simple, adaptable method combining microfluidic single-cell extraction with single-particle analysis by EM to characterize protein complexes from individual Caenorhabditis elegans embryos. Using this approach, we uncover 3D structures of ribosomes directly from single embryo extracts. Moreover, we investigated structural dynamics during development by counting the number of ribosomes per polysome in early and late embryos. This approach has significant potential applications for counting protein complexes and studying protein architectures from single cells in developmental, evolutionary, and disease contexts.

scholarly journals Zernike Phase Plate Cryoelectron Microscopy Facilitates Single Particle Analysis of Unstained Asymmetric Protein Complexes

Structure ◽  
2010 ◽  
Vol 18 (1) ◽  
pp. 17-27 ◽  
Author(s):  
Wei-Hau Chang ◽  
Michael T.-K. Chiu ◽  
Chin-Yu Chen ◽  
Chi-Fu Yen ◽  
Yen-Cheng Lin ◽  
...  
Keyword(s):  
Single Particle ◽  
Protein Complexes ◽  
Cryoelectron Microscopy ◽  
Particle Analysis ◽  
Phase Plate ◽  
Single Particle Analysis

scholarly journals Technical note: AI-Track-tive: automated fission track recognition using computer vision (Artificial Intelligence)

2020 ◽  
Author(s):  
Simon Nachtergaele ◽  
Johan De Grave
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computer Vision ◽  
Deep Neural Networks ◽  
Fission Track ◽  
Detection Algorithm ◽  
Technical Note ◽  
Success Rates ◽  
Automatic Counting ◽  
First Time

Abstract. Artificial intelligence techniques such as deep neural networks and computer vision are developed for fission track recognition and included in a computer program for the first time. These deep neural networks use the Yolov3 object detection algorithm, which is currently one of the most powerful and fastest object recognition algorithms. These deep neural networks can be used in new software called AI-Track-tive. The developed program successfully finds most of the fission tracks in the microscope images, however, the user still needs to supervise the automatic counting. The success rates of the automatic recognition range from 70 % to 100 % depending on the areal track densities in apatite and (muscovite) external detector. The success rate generally decreases for images with high areal track densities, because overlapping tracks are less easily recognizable for computer vision techniques.

scholarly journals Cryo-EM analysis of PIP2 regulation in mammalian GIRK channels

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Yiming Niu ◽  
Xiao Tao ◽  
Kouki K Touhara ◽  
Roderick MacKinnon
Keyword(s):  
Conformational Changes ◽  
Cytoplasmic Membrane ◽  
Membrane Surface ◽  
Cytoplasmic Domain ◽  
Inward Rectifier ◽  
Particle Analysis ◽  
Single Particle Analysis ◽  
Girk Channels ◽  
Binding Surface ◽  
To Receive

G-protein-gated inward rectifier potassium (GIRK) channels are regulated by G proteins and PIP2. Here, using cryo-EM single particle analysis we describe the equilibrium ensemble of structures of neuronal GIRK2 as a function of the C8-PIP2 concentration. We find that PIP2 shifts the equilibrium between two distinguishable structures of neuronal GIRK (GIRK2), extended and docked, towards the docked form. In the docked form the cytoplasmic domain, to which Gβγ binds, becomes accessible to the cytoplasmic membrane surface where Gβγ resides. Furthermore, PIP2 binding reshapes the Gβγ binding surface on the cytoplasmic domain, preparing it to receive Gβγ. We find that cardiac GIRK (GIRK1/4) can also exist in both extended and docked conformations. These findings lead us to conclude that PIP2 influences GIRK channels in a structurally similar manner to Kir2.2 channels. In Kir2.2 channels, the PIP2-induced conformational changes open the pore. In GIRK channels, they prepare the channel for activation by Gβγ.

scholarly journals Uncertainty estimation on road safety analysis using bayesian deep neural networks

2021 ◽  
Author(s):  
Guangyuan Pan ◽  
Chen Qili ◽  
Fu Liping ◽  
Yu Ming ◽  
Muresan Matthew
Keyword(s):  
Neural Networks ◽  
Traffic Engineering ◽  
Road Safety ◽  
Deep Neural Networks ◽  
Surface Condition ◽  
Uncertainty Estimation ◽  
Public Confidence ◽  
Accurate Information ◽  
Crash Prediction ◽  
First Time

Deep neural networks have been successfully used in many different areas of traffic engineering, such as crash prediction, intelligent signal optimization and real-time road surface condition monitoring. The benefits of deep neural networks are often uniquely suited to solve certain problems and can offer improvements in performance when compared to traditional methods. In collision prediction, uncertainty estimation is a critical area that can benefit from their application, and accurate information on the reliability of a model’s predictions can increase public confidence in those models. Applications of deep neural networks to this problem that consider these effects have not been studied previously. This paper develops a Bayesian deep neural network for crash prediction and examines the reliability of the model based on three key methods: layer-wise greedy unsupervised learning, Bayesian regularization and adapted marginalization. An uncertainty equation for the model is also proposed for this domain for the first time. To test the performance, eight years of car collision data collected from Highway 401, Canada, is used, and three experiments are designed.

scholarly journals Deep learning or interpolation for inverse modelling of heat and fluid flow problems?

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Rainald Löhner ◽  
Harbir Antil ◽  
Hamid Tamaddon-Jahromi ◽  
Neeraj Kavan Chakshu ◽  
Perumal Nithiarasu
Keyword(s):  
Neural Networks ◽  
Heat Conduction ◽  
Deep Neural Networks ◽  
Nonlinear Behaviour ◽  
Content Type ◽  
Flow Problems ◽  
Interpolation Algorithms ◽  
Transfer Problems ◽  
First Time ◽  
Heat And Fluid Flow

Purpose The purpose of this study is to compare interpolation algorithms and deep neural networks for inverse transfer problems with linear and nonlinear behaviour. Design/methodology/approach A series of runs were conducted for a canonical test problem. These were used as databases or “learning sets” for both interpolation algorithms and deep neural networks. A second set of runs was conducted to test the prediction accuracy of both approaches. Findings The results indicate that interpolation algorithms outperform deep neural networks in accuracy for linear heat conduction, while the reverse is true for nonlinear heat conduction problems. For heat convection problems, both methods offer similar levels of accuracy. Originality/value This is the first time such a comparison has been made.

scholarly journals Harnessing protein folding neural networks for peptide–protein docking

2022 ◽  
Vol 13 (1) ◽  
Author(s):  
Tomer Tsaban ◽  
Julia K. Varga ◽  
Orly Avraham ◽  
Ziv Ben-Aharon ◽  
Alisa Khramushin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Conformational Changes ◽  
Protein Interactions ◽  
Protein Complexes ◽  
Protein Docking ◽  
Great Promise ◽  
Learning Approaches ◽  
Multiple Sequence ◽  
Starting Point ◽  
Wide Range

AbstractHighly accurate protein structure predictions by deep neural networks such as AlphaFold2 and RoseTTAFold have tremendous impact on structural biology and beyond. Here, we show that, although these deep learning approaches have originally been developed for the in silico folding of protein monomers, AlphaFold2 also enables quick and accurate modeling of peptide–protein interactions. Our simple implementation of AlphaFold2 generates peptide–protein complex models without requiring multiple sequence alignment information for the peptide partner, and can handle binding-induced conformational changes of the receptor. We explore what AlphaFold2 has memorized and learned, and describe specific examples that highlight differences compared to state-of-the-art peptide docking protocol PIPER-FlexPepDock. These results show that AlphaFold2 holds great promise for providing structural insight into a wide range of peptide–protein complexes, serving as a starting point for the detailed characterization and manipulation of these interactions.

scholarly journals Raspberry PI for Kill Mosquitoes by Laser

2021 ◽  
Author(s):  
Ildar Rakhmatulin
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Raspberry Pi ◽  
Classification Problems ◽  
Power Laser ◽  
The World ◽  
Aerial Vehicle ◽  
First Time ◽  
Laser Installation

More than 700 thousand human deaths from mosquito bites are observed annually in the world. It is more than 2 times the number of annual murders in the world. In this regard, the invention of new more effective methods of protection against mosquitoes is necessary. In this article for the first time, comprehensive studies of mosquito neutralization using machine vision and a 1 W power laser are considered. Developed laser installation with Raspberry Pi that changing the direction of the laser with a galvanometer. We developed a program for mosquito tracking in real. The possibility of using deep neural networks, Haar cascades, machine learning for mosquito recognition was considered. We considered in detail the classification problems of mosquitoes in images. A recommendation is given for the implementation of this device based on a microcontroller for subsequent use as part of an unmanned aerial vehicle. Any harmful insects in the fields can be used as objects for control.

scholarly journals Survey of the analysis of continuous conformational variability of biological macromolecules by electron microscopy

2019 ◽  
Vol 75 (1) ◽  
pp. 19-32 ◽  
Author(s):  
C. O. S. Sorzano ◽  
A. Jiménez ◽  
J. Mota ◽  
J. L. Vilas ◽  
D. Maluenda ◽  
...  
Keyword(s):  
Electron Microscopy ◽  
Image Processing ◽  
High Resolution ◽  
Conformational Changes ◽  
Three Dimensional ◽  
Particle Analysis ◽  
Biological Macromolecules ◽  
Single Particle Analysis ◽  
Conformational Variability ◽  
Established Technique

Single-particle analysis by electron microscopy is a well established technique for analyzing the three-dimensional structures of biological macromolecules. Besides its ability to produce high-resolution structures, it also provides insights into the dynamic behavior of the structures by elucidating their conformational variability. Here, the different image-processing methods currently available to study continuous conformational changes are reviewed.

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