scholarly journals Learning torus PCA based classification for multiscale RNA backbone structure correction with application to SARS-CoV-2

2021 ◽  
Author(s):  
Henrik Wiechers ◽  
Benjamin Eltzner ◽  
Kanti V. Mardia ◽  
Stephan F. Huckemann
Keyword(s):  
Correction Method ◽  
Training Data ◽  
Proof Of Concept ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Rna Backbone ◽  
Backbone Structure ◽  
Mode Hunting ◽  
Structure Enhancement

Reconstructions of structure of biomolecules, for instance via X-ray crystallography or cryo-EM frequently contain clashes of atomic centers. Correction methods are usually based on simulations approximating biophysical chemistry, making them computationally expensive and often not correcting all clashes. We propose a computationally fast data-driven statistical method yielding suites free from within-suite clashes: From such a clash free training data set, devising mode hunting after torus PCA on adaptive cutting average linkage tree clustering (MINTAGE), we learn RNA suite shapes. With classification based on multiscale structure enhancement (CLEAN), for a given clash suite we determine its neighborhood on a mesoscopic scale involving several suites. As corrected suite we propose the Fréchet mean on a torus of the largest classes in this neighborhood. We validate CLEAN MINTAGE on a benchmark data set, compare it to a state of the art correction method and apply it, as proof of concept, to two exemplary suites adjacent to helical pieces of the frameshift stimulation element of SARS-CoV-2 which are difficult to reconstruct. In contrast to a recent reconstruction proposing several different structure models, CLEAN MINTAGE unanimously proposes structure corrections within the same clash free class for all suites.

Difference Fourier Analysis of Glucose Embedded and Frozen Hydrated Purple Membrane

1982 ◽  
Vol 40 ◽  
pp. 74-75
Author(s):  
Jules S. Jaffe ◽  
Robert M. Glaeser
Keyword(s):  
Purple Membrane ◽  
Data Set ◽  
High Resolution Data ◽  
X Ray ◽  
X Ray Crystallography ◽  
Fourier Techniques ◽  
Versus Protein ◽  
The Difference ◽  
Difference Fourier ◽  
Ideal Method

Although difference Fourier techniques are standard in X-ray crystallography it has only been very recently that electron crystallographers have been able to take advantage of this method. We have combined a high resolution data set for frozen glucose embedded Purple Membrane (PM) with a data set collected from PM prepared in the frozen hydrated state in order to visualize any differences in structure due to the different methods of preparation. The increased contrast between protein-ice versus protein-glucose may prove to be an advantage of the frozen hydrated technique for visualizing those parts of bacteriorhodopsin that are embedded in glucose. In addition, surface groups of the protein may be disordered in glucose and ordered in the frozen state. The sensitivity of the difference Fourier technique to small changes in structure provides an ideal method for testing this hypothesis.

scholarly journals 2D–3D reconstruction of distal forearm bone from actual X-ray images of the wrist using convolutional neural networks

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Ryoya Shiode ◽  
Mototaka Kabashima ◽  
Yuta Hiasa ◽  
Kunihiro Oka ◽  
Tsuyoshi Murase ◽  
...  
Keyword(s):  
Wrist Joint ◽  
High Accuracy ◽  
Training Data ◽  
Small Data ◽  
Data Set ◽  
Accuracy Estimation ◽  
X Ray ◽  
Learning Network ◽  
Forearm Bone ◽  
Deep Learning Network

AbstractThe purpose of the study was to develop a deep learning network for estimating and constructing highly accurate 3D bone models directly from actual X-ray images and to verify its accuracy. The data used were 173 computed tomography (CT) images and 105 actual X-ray images of a healthy wrist joint. To compensate for the small size of the dataset, digitally reconstructed radiography (DRR) images generated from CT were used as training data instead of actual X-ray images. The DRR-like images were generated from actual X-ray images in the test and adapted to the network, and high-accuracy estimation of a 3D bone model from a small data set was possible. The 3D shape of the radius and ulna were estimated from actual X-ray images with accuracies of 1.05 ± 0.36 and 1.45 ± 0.41 mm, respectively.

Design of a novel Peltier-based cooling device and its use in neutron diffraction data collection of perdeuterated yeast pyrophosphatase

2010 ◽  
Vol 43 (5) ◽  
pp. 1113-1120 ◽  
Author(s):  
Esko Oksanen ◽  
François Dauvergne ◽  
Adrian Goldman ◽  
Monika Budayova-Spano
Keyword(s):  
Data Collection ◽  
Neutron Diffraction ◽  
Diffraction Data ◽  
Catalytic Mechanism ◽  
Inorganic Pyrophosphatase ◽  
Neutron Diffraction Data ◽  
Cooling Device ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography

H atoms play a central role in enzymatic mechanisms, but H-atom positions cannot generally be determined by X-ray crystallography. Neutron crystallography, on the other hand, can be used to determine H-atom positions but it is experimentally very challenging. Yeast inorganic pyrophosphatase (PPase) is an essential enzyme that has been studied extensively by X-ray crystallography, yet the details of the catalytic mechanism remain incompletely understood. The temperature instability of PPase crystals has in the past prevented the collection of a neutron diffraction data set. This paper reports how the crystal growth has been optimized in temperature-controlled conditions. To stabilize the crystals during neutron data collection a Peltier cooling device that minimizes the temperature gradient along the capillary has been developed. This device allowed the collection of a full neutron diffraction data set.

scholarly journals A Deep Learning-Based Scatter Correction of Simulated X-ray Images

Electronics ◽  
2019 ◽  
Vol 8 (9) ◽  
pp. 944 ◽  
Author(s):  
Heesin Lee ◽  
Joonwhoan Lee
Keyword(s):  
Deep Learning ◽  
Image Quality ◽  
Imaging System ◽  
Scatter Correction ◽  
Similarity Index ◽  
Real Data ◽  
Correction Method ◽  
Training Data ◽  
X Ray ◽  
Scatter Reduction

X-ray scattering significantly limits image quality. Conventional strategies for scatter reduction based on physical equipment or measurements inevitably increase the dose to improve the image quality. In addition, scatter reduction based on a computational algorithm could take a large amount of time. We propose a deep learning-based scatter correction method, which adopts a convolutional neural network (CNN) for restoration of degraded images. Because it is hard to obtain real data from an X-ray imaging system for training the network, Monte Carlo (MC) simulation was performed to generate the training data. For simulating X-ray images of a human chest, a cone beam CT (CBCT) was designed and modeled as an example. Then, pairs of simulated images, which correspond to scattered and scatter-free images, respectively, were obtained from the model with different doses. The scatter components, calculated by taking the differences of the pairs, were used as targets to train the weight parameters of the CNN. Compared with the MC-based iterative method, the proposed one shows better results in projected images, with as much as 58.5% reduction in root-mean-square error (RMSE), and 18.1% and 3.4% increases in peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM), on average, respectively.

scholarly journals A Study on Stereoscopic X-ray Imaging Data Set on the Accuracy of Real-Time Tumor Tracking in External Beam Radiotherapy

2016 ◽  
Vol 16 (2) ◽  
pp. 167-177 ◽  
Author(s):  
Ahmad Esmaili Torshabi ◽  
Leila Ghorbanzadeh
Keyword(s):  
External Beam Radiotherapy ◽  
Training Data ◽  
Imaging Data ◽  
Data Set ◽  
X Ray ◽  
Tumor Tracking ◽  
X Ray Imaging ◽  
Additional Imaging ◽  
Data Points ◽  
Imaging Dose

At external beam radiotherapy, stereoscopic X-ray imaging system is responsible as tumor motion information provider. This system takes X-ray images intermittently from tumor position (1) at pretreatment step to provide training data set for model construction and (2) during treatment to control the accuracy of correlation model performance. In this work, we investigated the effect of imaging data points provided by this system on treatment quality. Because some information is still lacking about (1) the number of imaging data points, (2) shooting time for capturing each data point, and also (3) additional imaging dose delivered by this system. These 3 issues were comprehensively assessed at (1) pretreatment step while training data set is gathered for prediction model construction and (2) during treatment while model is tested and reconstructed using new arrival data points. A group of real patients treated with CyberKnife Synchrony module was chosen in this work, and an adaptive neuro-fuzzy inference system was considered as consistent correlation model. Results show that a proper model can be constructed while the number of imaging data points is highly enough to represent a good pattern of breathing cycles. Moreover, a trade-off between the number of imaging data points and additional imaging dose is considered in this study. Since breathing phenomena are highly variable at different patients, the time for taking some of imaging data points is very important, while their absence at that critical time may yield wrong tumor tracking. In contrast, the sensitivity of another category of imaging data points is not high, while breathing is normal and in the control range. Therefore, an adaptive supervision on the implementation of stereoscopic X-ray imaging is proposed to intelligently accomplish shooting process, based on breathing motion variations.

scholarly journals Practical Aspects of the SAMPL Challenge: Providing an Extensive Experimental Data Set for the Modeling Community

2009 ◽  
Vol 14 (10) ◽  
pp. 1245-1250 ◽  
Author(s):  
Janet Newman ◽  
Vincent J. Fazio ◽  
Tom T. Caradoc-Davies ◽  
Kim Branson ◽  
Thomas S. Peat
Keyword(s):  
Evaluation Study ◽  
X Ray Diffraction ◽  
Data Set ◽  
X Ray ◽  
Software Application ◽  
X Ray Crystallography ◽  
Software Packages ◽  
Structure Solution ◽  
Fragment Library ◽  
Extensive Experimental Data

To provide an experimental basis for a comprehensive molecular modeling evaluation study, 500 fragments from the Maybridge fragment library were soaked into crystals of bovine pancreatic trypsin and the structures determined by X-ray crystallography. The soaking experiments were performed in both single and pooled aliquots to determine if combination of fragments is an appropriate strategy. A further set of data was obtained from co-crystallizing the pooled fragments with the protein. X-ray diffraction data were collected on approximately 1000 crystals at the Australian Synchrotron, and these data were subsequently processed, and the preliminary analysis was performed with a custom software application (Jigsaw), which combines available software packages for structure solution and analysis.

Multiple CCD detector for macromolecular X-ray crystallography

2000 ◽  
Vol 33 (2) ◽  
pp. 243-251 ◽  
Author(s):  
Walter C. Phillips ◽  
Martin Stanton ◽  
Alexander Stewart ◽  
Hua Qian ◽  
Charles Ingersoll ◽  
...  
Keyword(s):  
Fiber Optic ◽  
Standard Uncertainty ◽  
Integration Time ◽  
Modulation Transfer ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Total Noise ◽  
Readout Time ◽  
A Charge

A charge-coupled device (CCD)-based detector designed for macromolecular crystallography is described. The detector has an area of 200 × 200 mm, a readout time of 1.6 s, and total noise equivalent to approximately three 12 keV X-ray photons per pixel. The detector is constructed from a 2 × 2 array of four identical units, each unit consisting of a 4.1:1 demagnifying fiber-optic taper bonded to a 1 k × 1 k, 24 µm pixel, CCD sensor. Each CCD is read out in parallel though four channels and digitized to 16 bits. A Gd2O2S phosphor X-ray-to-light converter bonded to an aluminized-plastic film is held in contact with the input surfaces of the fiber-optic tapers with an air pillow. The full width at half-maximum (FWHM) of the point response function is 120 µm, the response is linear to better than 1% over the entire range of intensity from background to nearly full well, the gain is 3.4 e per 8 keV incident X-ray photon, the noise is 12.6 e per pixel for a 10 s integration time, the modulation transfer function (MTF) is 0.35 at 5 line pairs (lp) mm−1(the Nyquest frequency), and the measured detective quantum efficiency (DQE) is 0.74 for relatively strong Bragg peaks. Data collected from crystallographic studies with synchrotron radiation are presented. In an anomalous difference Patterson map for a data set collected in 40 min on a monoclinic myoglobin crystal, the magnitude of the Fe–Fe peaks is 18 times the standard uncertainty of the map.

scholarly journals BraggNet: integrating Bragg peaks using neural networks

2019 ◽  
Vol 52 (4) ◽  
pp. 854-863 ◽  
Author(s):  
Brendan Sullivan ◽  
Rick Archibald ◽  
Jahaun Azadmanesh ◽  
Venu Gopal Vandavasi ◽  
Patricia S. Langan ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Negative Control ◽  
Protein Crystal ◽  
Data Sets ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Integrated Intensities ◽  
Neutron Crystallography

Neutron crystallography offers enormous potential to complement structures from X-ray crystallography by clarifying the positions of low-Z elements, namely hydrogen. Macromolecular neutron crystallography, however, remains limited, in part owing to the challenge of integrating peak shapes from pulsed-source experiments. To advance existing software, this article demonstrates the use of machine learning to refine peak locations, predict peak shapes and yield more accurate integrated intensities when applied to whole data sets from a protein crystal. The artificial neural network, based on the U-Net architecture commonly used for image segmentation, is trained using about 100 000 simulated training peaks derived from strong peaks. After 100 training epochs (a round of training over the whole data set broken into smaller batches), training converges and achieves a Dice coefficient of around 65%, in contrast to just 15% for negative control data sets. Integrating whole peak sets using the neural network yields improved intensity statistics compared with other integration methods, including k-nearest neighbours. These results demonstrate, for the first time, that neural networks can learn peak shapes and be used to integrate Bragg peaks. It is expected that integration using neural networks can be further developed to increase the quality of neutron, electron and X-ray crystallography data.

Ulrich Wolfgang Arndt. 23 April 1924 — 24 March 2006

2014 ◽  
Vol 60 ◽  
pp. 39-55
Author(s):  
R. A. Crowther ◽  
A. G. W. Leslie
Keyword(s):  
Protein Structures ◽  
Three Dimensional ◽  
Direct Methods ◽  
Biological Molecules ◽  
Data Generation ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Whole Process ◽  
Wide Range

Ulrich (Uli) Arndt was a physicist and engineer whose contributions to the development of a wide range of instrumentation for X-ray crystallography played an important part in our ability to solve the atomic structure of large biological molecules. Such detailed information about protein structures has for the past 50 years underpinned the huge advances in the field of molecular biology. His innovations spanned all aspects of data generation and collection, from improvements in X-ray tubes, through novel designs for diffractometers and cameras to film scanners and more direct methods of X-ray detection. When he started in the field, the intensities of individual X-ray reflections were often estimated by eye from films. By the end of his career the whole process of collecting from a crystal a three-dimensional data set, possibly comprising hundreds of thousands of measurements, was fully automated and very rapid.

scholarly journals Whole-pattern fitting technique in serial femtosecond nanocrystallography

IUCrJ ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 127-138 ◽  
Author(s):  
Ruben A. Dilanian ◽  
Sophie R. Williams ◽  
Andrew V. Martin ◽  
Victor A. Streltsov ◽  
Harry M. Quiney
Keyword(s):  
Powder Diffraction ◽  
Diffraction Data ◽  
Three Dimensional ◽  
Monte Carlo Integration ◽  
Data Set ◽  
X Ray ◽  
X Ray Crystallography ◽  
Small Crystals ◽  
Crystallite Structure ◽  
Serial Crystallography

Serial femtosecond X-ray crystallography (SFX) has created new opportunities in the field of structural analysis of protein nanocrystals. The intensity and timescale characteristics of the X-ray free-electron laser sources used in SFX experiments necessitate the analysis of a large collection of individual crystals of variable shape and quality to ultimately solve a single, average crystal structure. Ensembles of crystals are commonly encountered in powder diffraction, but serial crystallography is different because each crystal is measured individually and can be orientedviaindexing and merged into a three-dimensional data set, as is done for conventional crystallography data. In this way, serial femtosecond crystallography data lie in between conventional crystallography data and powder diffraction data, sharing features of both. The extremely small sizes of nanocrystals, as well as the possible imperfections of their crystallite structure, significantly affect the diffraction pattern and raise the question of how best to extract accurate structure-factor moduli from serial crystallography data. Here it is demonstrated that whole-pattern fitting techniques established for one-dimensional powder diffraction analysis can be feasibly extended to higher dimensions for the analysis of merged SFX diffraction data. It is shown that for very small crystals, whole-pattern fitting methods are more accurate than Monte Carlo integration methods that are currently used.

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