Uniqueness of the macromolecular crystallographic phase problem

2015 ◽  
Vol 71 (6) ◽  
pp. 592-598 ◽  
Author(s):  
Rick P. Millane ◽  
Romain D. Arnal
Keyword(s):  
Ab Initio ◽  
Single Particle ◽  
Real Space ◽  
Unit Cell ◽  
Macromolecular Crystallography ◽  
Phase Problem ◽  
Particle Imaging ◽  
Single Particle Imaging ◽  
Molecular Envelope ◽  
Constraint Ratio

Uniqueness of the phase problem in macromolecular crystallography, and its relationship to the case of single particle imaging, is considered. The crystallographic problem is characterized by a constraint ratio that depends only on the size and symmetry of the molecule and the unit cell. The results are used to evaluate the effect of various real-space constraints. The case of an unknown molecular envelope is considered in detail. The results indicate the quite wide circumstances under whichab initiophasing should be possible.

scholarly journals Evaluation of the performance of classification algorithms for XFEL single-particle imaging data

IUCrJ ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 331-340 ◽  
Author(s):  
Yingchen Shi ◽  
Ke Yin ◽  
Xuecheng Tai ◽  
Hasan DeMirci ◽  
Ahmad Hosseinizadeh ◽  
...  
Keyword(s):  
Single Particle ◽  
Three Dimensional ◽  
Real Space ◽  
Classification Algorithms ◽  
Imaging Data ◽  
Network Graph ◽  
Manifold Embedding ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

Using X-ray free-electron lasers (XFELs), it is possible to determine three-dimensional structures of nanoscale particles using single-particle imaging methods. Classification algorithms are needed to sort out the single-particle diffraction patterns from the large amount of XFEL experimental data. However, different methods often yield inconsistent results. This study compared the performance of three classification algorithms: convolutional neural network, graph cut and diffusion map manifold embedding methods. The identified single-particle diffraction data of the PR772 virus particles were assembled in the three-dimensional Fourier space for real-space model reconstruction. The comparison showed that these three classification methods lead to different datasets and subsequently result in different electron density maps of the reconstructed models. Interestingly, the common dataset selected by these three methods improved the quality of the merged diffraction volume, as well as the resolutions of the reconstructed maps.

scholarly journals An encryption–decryption framework to validating single-particle imaging

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Zhou Shen ◽  
Colin Zhi Wei Teo ◽  
Kartik Ayyer ◽  
N. Duane Loh
Keyword(s):  
Conceptual Framework ◽  
Single Particle ◽  
Latent Variables ◽  
Ground Truth ◽  
Resolving Power ◽  
Validation Measure ◽  
Encryption Decryption ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

AbstractWe propose an encryption–decryption framework for validating diffraction intensity volumes reconstructed using single-particle imaging (SPI) with X-ray free-electron lasers (XFELs) when the ground truth volume is absent. This conceptual framework exploits each reconstructed volumes’ ability to decipher latent variables (e.g. orientations) of unseen sentinel diffraction patterns. Using this framework, we quantify novel measures of orientation disconcurrence, inconsistency, and disagreement between the decryptions by two independently reconstructed volumes. We also study how these measures can be used to define data sufficiency and its relation to spatial resolution, and the practical consequences of focusing XFEL pulses to smaller foci. This conceptual framework overcomes critical ambiguities in using Fourier Shell Correlation (FSC) as a validation measure for SPI. Finally, we show how this encryption-decryption framework naturally leads to an information-theoretic reformulation of the resolving power of XFEL-SPI, which we hope will lead to principled frameworks for experiment and instrument design.

scholarly journals Comparison of EMC and CM methods for orienting diffraction images in single-particle imaging experiments

IUCrJ ◽  
2021 ◽  
Vol 8 (6) ◽  
Author(s):  
Miklós Tegze ◽  
Gábor Bortel
Keyword(s):  
Intensity Distribution ◽  
Single Particle ◽  
Free Electron ◽  
Free Electron Laser ◽  
Biological Molecules ◽  
Crucial Step ◽  
X Ray ◽  
Diffraction Patterns ◽  
Particle Imaging ◽  
Single Particle Imaging

In single-particle imaging (SPI) experiments, diffraction patterns of identical particles are recorded. The particles are injected into the X-ray free-electron laser (XFEL) beam in random orientations. The crucial step of the data processing of SPI is finding the orientations of the recorded diffraction patterns in reciprocal space and reconstructing the 3D intensity distribution. Here, two orientation methods are compared: the expansion maximization compression (EMC) algorithm and the correlation maximization (CM) algorithm. To investigate the efficiency, reliability and accuracy of the methods at various XFEL pulse fluences, simulated diffraction patterns of biological molecules are used.

scholarly journals Virus detection and identification in minutes using single-particle imaging and deep learning

2020 ◽  
Author(s):  
Nicolas Shiaelis ◽  
Alexander Tometzki ◽  
Leon Peto ◽  
Andrew McMahon ◽  
Christof Hepp ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Single Particle ◽  
Virus Detection ◽  
Diagnostic Methods ◽  
Clinical Samples ◽  
Promising Alternative ◽  
Detection And Identification ◽  
Particle Imaging ◽  
Single Particle Imaging

AbstractThe increasing frequency and magnitude of viral outbreaks in recent decades, epitomized by the current COVID-19 pandemic, has resulted in an urgent need for rapid and sensitive viral diagnostic methods. Here, we present a methodology for virus detection and identification that uses a convolutional neural network to distinguish between microscopy images of single intact particles of different viruses. Our assay achieves labeling, imaging and virus identification in less than five minutes and does not require any lysis, purification or amplification steps. The trained neural network was able to differentiate SARS-CoV-2 from negative clinical samples, as well as from other common respiratory pathogens such as influenza and seasonal human coronaviruses, with high accuracy. Single-particle imaging combined with deep learning offers a promising alternative to traditional viral diagnostic methods, and has the potential for significant impact.

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