scholarly journals REdiii: a pipeline for automated structure solution

2015 ◽  
Vol 71 (5) ◽  
pp. 1059-1067 ◽  
Author(s):  
Markus-Frederik Bohn ◽  
Celia A. Schiffer
Keyword(s):  
High Throughput ◽  
Scientific Discovery ◽  
Workflow Engine ◽  
Quality Data ◽  
Crystallographic Structure ◽  
Molecular Replacement ◽  
High Quality Data ◽  
Adaptive Workflow ◽  
Structure Solution ◽  
Integrated Software

High-throughput crystallographic approaches require integrated software solutions to minimize the need for manual effort.REdiiiis a system that allows fully automated crystallographic structure solution by integrating existing crystallographic software into an adaptive and partly autonomous workflow engine. The program can be initiated after collecting the first frame of diffraction data and is able to perform processing, molecular-replacement phasing, chain tracing, ligand fitting and refinement without further user intervention. Preset values for each software component allow efficient progress with high-quality data and known parameters. The adaptive workflow engine can determine whether some parameters require modifications and choose alternative software strategies in case the preconfigured solution is inadequate. This integrated pipeline is targeted at providing a comprehensive and efficient approach to screening for ligand-bound co-crystal structures while minimizing repetitiveness and allowing a high-throughput scientific discovery process.

scholarly journals High-throughput UHPLC-MS to screen metabolites in feces for gut metabolic health

2021 ◽  
Author(s):  
Andressa de Zawadzki ◽  
Maja Thiele ◽  
Tommi Suvitaival ◽  
Asger Wretlind ◽  
Min Kim ◽  
...  
Keyword(s):  
High Throughput ◽  
Metabolic Diseases ◽  
Quality Data ◽  
High Quality Data ◽  
Fecal Samples ◽  
Heterogeneous Samples ◽  
Relative Standard ◽  
High Throughput Method ◽  
Related Liver Disease ◽  
Metabolite Detection

(1) Background: Feces are the product of our diets and have been linked to diseases of the gut, including Crohn's disease and metabolic diseases such as diabetes. For screening metabolites in heterogeneous samples such as feces, it is necessary to use fast and reproducible analytical methods that maximize metabolite detection. (2) Methods: As sample preparation is crucial to obtain high quality data in MS-based clinical metabolomics, we developed a novel, efficient and robust method for preparing fecal samples for analysis with a focus in reducing aliquoting and detecting both polar and non-polar metabolites. Fecal samples (n= 475) from patients with alcohol-related liver disease and healthy controls were prepared according to the proposed method and analyzed in an UHPLC-QQQ targeted platform in order to obtain a quantitative profile of compounds that impact liver-gut axis metabolism. (3) Results: MS analyses of the prepared fecal samples have shown reproducibility and coverage of n=28 metabolites, mostly comprising bile acids and amino acids. We report metabolite-wise relative standard deviation (RSD) in quality control samples, inter-day repeatability, LOD, LOQ and range of linearity. The average concentrations for 135 healthy participants are reported here for clinical applications. (4) Conclusions: our high-throughput method provides an efficient tool for investigating gut-liver axis metabolism in liver-related diseases using a noninvasive collected sample.

scholarly journals Shift-field refinement of macromolecular atomic models

2020 ◽  
Vol 76 (12) ◽  
pp. 1192-1200
Author(s):  
K. Cowtan ◽  
S. Metcalfe ◽  
P. Bond
Keyword(s):  
Diffraction Pattern ◽  
Crystallographic Structure ◽  
Model Parameters ◽  
Molecular Replacement ◽  
Macromolecular Crystallography ◽  
Initial Model ◽  
New Approach ◽  
Structure Solution ◽  
Uncertainty Parameters ◽  
Key Parameter

The aim of crystallographic structure solution is typically to determine an atomic model which accurately accounts for an observed diffraction pattern. A key step in this process is the refinement of the parameters of an initial model, which is most often determined by molecular replacement using another structure which is broadly similar to the structure of interest. In macromolecular crystallography, the resolution of the data is typically insufficient to determine the positional and uncertainty parameters for each individual atom, and so stereochemical information is used to supplement the observational data. Here, a new approach to refinement is evaluated in which a `shift field' is determined which describes changes to model parameters affecting whole regions of the model rather than individual atoms only, with the size of the affected region being a key parameter of the calculation which can be changed in accordance with the resolution of the data. It is demonstrated that this approach can improve the radius of convergence of the refinement calculation while also dramatically reducing the calculation time.

scholarly journals Rapid Optical Clearing for High-Throughput Analysis of Tumour Spheroids

2021 ◽  
Author(s):  
Gency Gunasingh ◽  
Alexander Browning ◽  
Nikolas Haass
Keyword(s):  
High Throughput ◽  
Three Dimensional ◽  
Quality Data ◽  
High Quality ◽  
Size Distortion ◽  
High Throughput Analysis ◽  
High Quality Data ◽  
Tumour Spheroids ◽  
High Throughput Method ◽  
Significant Size

Tumour spheroids are fast becoming commonplace in basic cancer research and drug development. Obtaining high-quality data relating to the inner structure of spheroids is important for analysis, yet existing techniques often use equipment that is not commonly available, are expensive, laborious, cause significant size distortion, or are limited to relatively small spheroids. We present a high-throughput method of mounting, clearing, and imaging tumour spheroids that causes minimal size distortion. Spheroids are mounted in an agarose gel to prevent movement, cleared using a solution prepared from commonly available materials, and imaged using confocal microscopy. We find that our method yields high quality two- and three-dimensional images that provide information about the inner structure of spheroids.

scholarly journals Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

2020 ◽  
Author(s):  
Natalie Eyke ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Learning Algorithm ◽  
Quality Data ◽  
Experiment Planning ◽  
Planning Tool ◽  
High Quality Data ◽  
Test Set ◽  
Screening Experiments ◽  
Selection Of

High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.

scholarly journals E9: The Fine Resolution Powder Diffractometer (FIREPOD) at BER II

2017 ◽  
Vol 3 ◽  
Author(s):  
Alexandra Franz ◽  
Andreas Hoser
Keyword(s):  
Rietveld Analysis ◽  
Quality Data ◽  
Data Sets ◽  
Ambient Conditions ◽  
High Quality Data ◽  
Elastic Neutron ◽  
Powder Diffractometer ◽  
Structure Solution ◽  
Fine Resolution

The E9 (FIREPOD) is an upgraded fine resolution powder diffractometer for elastic neutron scattering, obtaining high quality data sets for Rietveld analysis, structure solution and phase analysis under ambient conditions as well as in situ at low / high temperatures, magnetic fields, gas pressure and various atmospheres.

scholarly journals Low-dose fixed-target serial synchrotron crystallography

2017 ◽  
Vol 73 (4) ◽  
pp. 373-378 ◽  
Author(s):  
Robin L. Owen ◽  
Danny Axford ◽  
Darren A. Sherrell ◽  
Anling Kuo ◽  
Oliver P. Ernst ◽  
...  
Keyword(s):  
High Throughput ◽  
Low Dose ◽  
Quality Data ◽  
Translation System ◽  
Fixed Target ◽  
High Quality Data ◽  
X Ray ◽  
Serial Sampling ◽  
Serial Data ◽  
Serial Crystallography

The development of serial crystallography has been driven by the sample requirements imposed by X-ray free-electron lasers. Serial techniques are now being exploited at synchrotrons. Using a fixed-target approach to high-throughput serial sampling, it is demonstrated that high-quality data can be collected from myoglobin crystals, allowing room-temperature, low-dose structure determination. The combination of fixed-target arrays and a fast, accurate translation system allows high-throughput serial data collection at high hit rates and with low sample consumption.

About the variable-counting-time techniques in powder diffraction data: their use in EXPO2004

2007 ◽  
Vol 222 (12/2007) ◽  
Author(s):  
Iván da Silva ◽  
Javier González-Platas ◽  
Carmelo Giacovazzo ◽  
Angela Altomare
Keyword(s):  
Diffraction Data ◽  
Direct Methods ◽  
Optimization Techniques ◽  
Quality Data ◽  
Powder Diffraction Data ◽  
High Quality Data ◽  
X Ray ◽  
Structure Solution ◽  
Process Decomposition ◽  
Counting Time

High quality data are essential for crystal structure solution, particularly when Direct Methods instead of Global Optimization Techniques are used. In this work we study the performances of the variable-counting-time techniques in the two crucial steps of the phasing process: decomposition of the full diffraction pattern and direct phasing. The experimental data were collected by a conventional X-ray laboratory diffractometer: they were then submitted to the program EXPO2004 for assessing their usefulness in the phasing process. The results are compared with those obtained by using experimental diffraction data collected

scholarly journals Iterative Experimental Design Based on Active Machine Learning Reduces the Experimental Burden Associated with Reaction Screening

2020 ◽  
Author(s):  
Natalie Eyke ◽  
William H. Green ◽  
Klavs F. Jensen
Keyword(s):  
Machine Learning ◽  
High Throughput ◽  
Learning Algorithm ◽  
Quality Data ◽  
Experiment Planning ◽  
Planning Tool ◽  
High Quality Data ◽  
Test Set ◽  
Screening Experiments ◽  
Selection Of

High-throughput reaction screening has emerged as a useful means of rapidly identifying the influence of key reaction variables on reaction outcomes. We show that active machine learning can further this objective by eliminating dependence on complete screens through iterative selection of maximally informative experiments from the subset of all possible experiments in the domain. To demonstrate our approach, we conduct retrospective analyses of the preexisting results of high-throughput reaction screening experiments. We compare the test set errors of models trained on actively-selected reactions to models trained on reactions selected at random from the same domain. We find that the degree to which models trained on actively-selected data outperform models trained on randomly-selected data depends on the domain being modeled, with it being possible to achieve very low test set errors when the dataset is heavily skewed in favor of low- or zero-yielding reactions. Our results confirm that the active learning algorithm is a useful experiment planning tool that can change the reaction screening paradigm, by allowing discovery and process chemists to focus their reaction screening efforts on the generation of a small amount of high-quality data.

scholarly journals Approaches toab initiomolecular replacement of α-helical transmembrane proteins

2017 ◽  
Vol 73 (12) ◽  
pp. 985-996 ◽  
Author(s):  
Jens M. H. Thomas ◽  
Felix Simkovic ◽  
Ronan Keegan ◽  
Olga Mayans ◽  
Chengxin Zhang ◽  
...  
Keyword(s):  
Ab Initio ◽  
Transmembrane Protein ◽  
Protein Structures ◽  
Transmembrane Proteins ◽  
Crystallographic Structure ◽  
Molecular Replacement ◽  
Search Models ◽  
Contact Information ◽  
Structure Solution ◽  
The Ideal

α-Helical transmembrane proteins are a ubiquitous and important class of proteins, but present difficulties for crystallographic structure solution. Here, the effectiveness of theAMPLEmolecular replacement pipeline in solving α-helical transmembrane-protein structures is assessed using a small library of eight ideal helices, as well as search models derived fromab initiomodels generated both with and without evolutionary contact information. The ideal helices prove to be surprisingly effective at solving higher resolution structures, butab initio-derived search models are able to solve structures that could not be solved with the ideal helices. The addition of evolutionary contact information results in a marked improvement in the modelling and makes additional solutions possible.

What Can Digitization Do For Formulated Product Innovation and Development

2020 ◽  
Author(s):  
James McDonagh ◽  
William Swope ◽  
Richard L. Anderson ◽  
Michael Johnston ◽  
David J. Bray
Keyword(s):  
High Performance ◽  
Quality Data ◽  
High Quality Data ◽  
Base Level ◽  
Hybrid Approaches ◽  
Digital Ecosystem ◽  
Recent Developments ◽  
Chemical Simulation ◽  
High Level ◽  
Physical And Chemical

Digitization offers significant opportunities for the formulated product industry to transform the way it works and develop new methods of business. R&D is one area of operation that is challenging to take advantage of these technologies due to its high level of domain specialisation and creativity but the benefits could be significant. Recent developments of base level technologies such as artificial intelligence (AI)/machine learning (ML), robotics and high performance computing (HPC), to name a few, present disruptive and transformative technologies which could offer new insights, discovery methods and enhanced chemical control when combined in a digital ecosystem of connectivity, distributive services and decentralisation. At the fundamental level, research in these technologies has shown that new physical and chemical insights can be gained, which in turn can augment experimental R&D approaches through physics-based chemical simulation, data driven models and hybrid approaches. In all of these cases, high quality data is required to build and validate models in addition to the skills and expertise to exploit such methods. In this article we give an overview of some of the digital technology demonstrators we have developed for formulated product R&D. We discuss the challenges in building and deploying these demonstrators.<br>

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