scholarly journals Lessons from ten years of crystallization experiments at the SGC

2016 ◽  
Vol 72 (2) ◽  
pp. 224-235 ◽  
Author(s):  
Jia Tsing Ng ◽  
Carien Dekker ◽  
Paul Reardon ◽  
Frank von Delft
Keyword(s):  
Large Scale ◽  
Sparse Matrix ◽  
Protein Crystallization ◽  
Data Sets ◽  
Mixing Ratios ◽  
Protein Sample ◽  
Screening Experiments ◽  
Crystallization Experiments ◽  
Practical Guidelines ◽  
Incubation Temperatures

Although protein crystallization is generally considered more art than science and remains significantly trial-and-error, large-scale data sets hold the promise of providing general learning. Observations are presented here from retrospective analyses of the strategies actively deployed for the extensive crystallization experiments at the Oxford site of the Structural Genomics Consortium (SGC), where comprehensive annotations by SGC scientists were recorded on a customized database infrastructure. The results point to the importance of using redundancy in crystallizing conditions, specifically by varying the mixing ratios of protein sample and precipitant, as well as incubation temperatures. No meaningful difference in performance could be identified between the four most widely used sparse-matrix screens, judged by the yield of crystals leading to deposited structures; this suggests that in general any comparison of screens will be meaningless without extensive cross-testing. Where protein sample is limiting, exploring more conditions has a higher likelihood of being informative by yielding hits than does redundancy of either mixing ratio or temperature. Finally, on the logistical question of how long experiments should be stored, 98% of all crystals that led to deposited structures appeared within 30 days. Overall, these analyses serve as practical guidelines for the design of initial screening experiments for new crystallization targets.

scholarly journals Characterization of salt interferences in second-harmonic generation detection of protein crystals

2013 ◽  
Vol 46 (6) ◽  
pp. 1903-1906 ◽  
Author(s):  
R. G. Closser ◽  
E. J. Gualtieri ◽  
J. A. Newman ◽  
G. J. Simpson
Keyword(s):  
Second Harmonic Generation ◽  
Harmonic Generation ◽  
Sparse Matrix ◽  
Second Harmonic ◽  
Protein Crystallization ◽  
Potassium Dihydrogen Phosphate ◽  
Protein Crystals ◽  
Dihydrogen Phosphate ◽  
Crystal Formation ◽  
Crystallization Experiments

Studies were undertaken to assess the merits and limitations of second-harmonic generation (SHG) for the selective detection of protein and polypeptide crystal formation, focusing on the potential for false positives from SHG-active salts present in crystallization media. The SHG activities of salts commonly used in protein crystallization were measured and quantitatively compared with reference samples. Out of 19 salts investigated, six produced significant background SHG and 15 of the 96 wells of a sparse-matrix screen produced SHG upon solvent evaporation. SHG-active salts include phosphates, hydrated sulfates, formates and tartrates, while chlorides, acetates and anhydrous sulfates resulted in no detectable SHG activity. The identified SHG-active salts produced a range of signal intensities spanning nearly three orders of magnitude. However, even the weakest SHG-active salt produced signals that were several orders of magnitude greater than those produced by typical protein crystals. In general, SHG-active salts were identifiable through characteristically strong SHG and negligible two-photon-excited ultraviolet fluorescence (TPE-UVF). Exceptions included trials containing either potassium dihydrogen phosphate or ammonium formate, which produced particularly strong SHG, but with residual weak TPE-UVF signals that could potentially complicate discrimination in crystallization experiments using these precipitants.

Adaptive Hashing with Sparse Modification for Scalable Image Retrieval

2017 ◽  
Vol 31 (06) ◽  
pp. 1754011
Author(s):  
Lifang Zhang ◽  
Qi Shen ◽  
Defang Li ◽  
Guocan Feng ◽  
Xin Tang ◽  
...  
Keyword(s):  
Large Scale ◽  
Nearest Neighbor ◽  
Sparse Matrix ◽  
Hash Functions ◽  
Original Data ◽  
Data Sets ◽  
Compact Binary ◽  
Large Scale Data ◽  
Scale Data ◽  
Ann Search

Approximate Nearest Neighbor (ANN) search is a challenging problem with the explosive high-dimensional large-scale data in recent years. The promising technique for ANN search include hashing methods which generate compact binary codes by designing effective hash functions. However, lack of an optimal regularization is the key limitation of most of the existing hash functions. To this end, a new method called Adaptive Hashing with Sparse Modification (AHSM) is proposed. In AHSM, codes consist of vertices on the hypercube and the projection matrix is divided into two separate matrices. Data is rotated through a orthogonal matrix first and modified by a sparse matrix. Here the sparse matrix needs to be learned as a regularization item of hash function which is used to avoid overfitting and reduce quantization distortion. Totally, AHSM has two advantages: improvement of the accuracy without any time cost increasement. Furthermore, we extend AHSM to a supervised version, called Supervised Adaptive Hashing with Sparse Modification (SAHSM), by introducing Canonical Correlation Analysis (CCA) to the original data. Experiments show that the AHSM method stably surpasses several state-of-the-art hashing methods on four data sets. And at the same time, we compare three unsupervised hashing methods with their corresponding supervised version (including SAHSM) on three data sets with labels known. Similarly, SAHSM outperforms other methods on most of the hash bits.

scholarly journals Structure of glycerol dehydrogenase (GldA) fromEscherichia coli

2019 ◽  
Vol 75 (3) ◽  
pp. 176-183 ◽  
Author(s):  
Jun Zhang ◽  
Ankanahalli N. Nanjaraj Urs ◽  
Lianyun Lin ◽  
Yan Zhou ◽  
Yiling Hu ◽  
...  
Keyword(s):  
Active Site ◽  
Electrostatic Interactions ◽  
Large Scale ◽  
Sparse Matrix ◽  
Protein Crystallization ◽  
High Yield ◽  
Glycerol Dehydrogenase ◽  
Glycerol Metabolism ◽  
Scale Production ◽  
K 12

Escherichia coli(strain K-12, substrain MG1655) glycerol dehydrogenase (GldA) is required to catalyze the first step in fermentative glycerol metabolism. The protein was expressed and purified to homogeneity using a simple combination of heat-shock and chromatographic methods. The high yield of the protein (∼250 mg per litre of culture) allows large-scale production for potential industrial applications. Purified GldA exhibited a homogeneous tetrameric state (∼161 kDa) in solution and relatively high thermostability (Tm= 65.6°C). Sitting-drop sparse-matrix screens were used for protein crystallization. An optimized condition with ammonium sulfate (2 M) provided crystals suitable for diffraction, and a binary structure containing glycerol in the active site was solved at 2.8 Å resolution. Each GldA monomer consists of nine β-strands, thirteen α-helices, two 310-helices and several loops organized into two domains, the N- and C-terminal domains; the active site is located in a deep cleft between the two domains. The N-terminal domain contains a classic Rossmann fold for NAD+binding. The O1and O2atoms of glycerol serve as ligands for the tetrahedrally coordinated Zn2+ion. The orientation of the glycerol within the active site is mainly stabilized by van der Waals and electrostatic interactions with the benzyl ring of Phe245. Computer modeling suggests that the glycerol molecule is sandwiched by the Zn2+and NAD+ions. Based on this, the mechanism for the relaxed substrate specificity of this enzyme is also discussed.

Potential use of ultrasound to promote protein crystallization

2010 ◽  
Vol 43 (6) ◽  
pp. 1419-1425 ◽  
Author(s):  
Rosa Crespo ◽  
Pedro M. Martins ◽  
Luís Gales ◽  
Fernando Rocha ◽  
Ana M. Damas
Keyword(s):  
Protein Crystallization ◽  
Crystal Formation ◽  
Data Sets ◽  
Resolution Limit ◽  
X Ray Diffraction ◽  
Promoting Effect ◽  
X Ray ◽  
X Ray Crystallography ◽  
Egg White Lysozyme ◽  
Crystallization Experiments

This work shows promising applications of ultrasound in promoting protein crystallization, which is important for structure determination by X-ray crystallography. It was observed that ultrasound can be used as a nucleation promoter as it decreases the energy barrier for crystal formation. Crystallization experiments on egg-white lysozyme were carried out with and without ultrasonic irradiation using commercial crystallization plates placed in temperature-controlled water baths. The nucleation-promoting effect introduced by ultrasound is illustrated by the reduction of the metastable zone width, as measured by the isothermal microbatch technique. The same effect was confirmed by the increased number of conditions leading to the formation of crystals when vapour diffusion techniques were carried out in the presence of ultrasound. By inducing faster nucleation, ultrasound leads to protein crystals grown at low supersaturation levels, which are known to have better diffraction properties. In fact, X-ray diffraction data sets collected using 13 lysozyme crystals (seven grown with ultrasound and six without) show an average 0.1 Å improvement in the resolution limit when ultrasound was used (p< 0.10). Besides the immediate application of ultrasound in nucleation promotion, the preliminary diffraction results also suggest a promising application in crystal quality enhancement.

Beamlet migration using local cosine basis

Geophysics ◽  
2008 ◽  
Vol 73 (5) ◽  
pp. S207-S217 ◽  
Author(s):  
Ru-Shan Wu ◽  
Yongzhong Wang ◽  
Mingqiu Luo
Keyword(s):  
Large Scale ◽  
Heterogeneous Media ◽  
High Efficiency ◽  
Sparse Matrix ◽  
Synthetic Data ◽  
Small Scale ◽  
Local Perturbation ◽  
Data Sets ◽  
Lateral Velocity ◽  
Local Perturbations

We have developed the theoretical foundation and technical details of a migration method using a local-cosine-bases (LCB) beamlet propagator. A beamlet propagator for heterogeneous media based on local perturbation theory is derived, and a fast implementation method is constructed. The use of local background velocities and local perturbations results in a two-scale decomposition of beamlet propagators: a background propagator for large-scale structures and a local phase-screen correction for small-scale local perturbations. Because of its locally adaptive nature, the beamlet propagator can handle strong lateral velocity variations with improved accuracy. For high-efficiency migration, we use a table-driven method and apply some techniques of sparse matrix operations. Compared with the Fourier finite-dif-ference and generalized screen propagator methods, the image quality and computational efficiency are similar. In some cases, we see fewer migration artifacts around and inside salt bodies with our method. We attribute this to the better high-angle accuracy of beamlet propagators in strong-contrast media. Numerical tests using synthetic data sets of the SEG-EAGE 2D salt model, Marmousi model, and Sigsbee 2A model demonstrate its high accuracy and reasonable efficiency. Another special feature of LCB beamlet migration is the availability of information in the local wavenumber domain during migration, which can be used to correct acquisition aperture effect and for other processing. Compared to beamlet migration using the Gabor-Daubechies frame (GDF) propagator, LCB migration is much more efficient because LCB is an orthonormal basis, whereas GDF has redundancy (usually greater than two) in the decomposition.

scholarly journals Galaxy spin direction distribution in HST and SDSS show similar large-scale asymmetry

2020 ◽  
Vol 37 ◽  
Author(s):  
Lior Shamir
Keyword(s):  
Large Scale ◽  
Spiral Galaxies ◽  
Hubble Space Telescope ◽  
Gravitational Interaction ◽  
Large Data ◽  
Sloan Digital Sky Survey ◽  
Data Sets ◽  
Dipole Axis ◽  
Data Set ◽  
The Asymmetry

Abstract Several recent observations using large data sets of galaxies showed non-random distribution of the spin directions of spiral galaxies, even when the galaxies are too far from each other to have gravitational interaction. Here, a data set of $\sim8.7\cdot10^3$ spiral galaxies imaged by Hubble Space Telescope (HST) is used to test and profile a possible asymmetry between galaxy spin directions. The asymmetry between galaxies with opposite spin directions is compared to the asymmetry of galaxies from the Sloan Digital Sky Survey. The two data sets contain different galaxies at different redshift ranges, and each data set was annotated using a different annotation method. The results show that both data sets show a similar asymmetry in the COSMOS field, which is covered by both telescopes. Fitting the asymmetry of the galaxies to cosine dependence shows a dipole axis with probabilities of $\sim2.8\sigma$ and $\sim7.38\sigma$ in HST and SDSS, respectively. The most likely dipole axis identified in the HST galaxies is at $(\alpha=78^{\rm o},\delta=47^{\rm o})$ and is well within the $1\sigma$ error range compared to the location of the most likely dipole axis in the SDSS galaxies with $z>0.15$ , identified at $(\alpha=71^{\rm o},\delta=61^{\rm o})$ .

scholarly journals Accelerating In-Transit Co-Processing for Scientific Simulations Using Region-Based Data-Driven Analysis

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 154
Author(s):  
Marcus Walldén ◽  
Masao Okita ◽  
Fumihiko Ino ◽  
Dimitris Drikakis ◽  
Ioannis Kokkinakis
Keyword(s):  
Large Scale ◽  
Data Driven ◽  
Data Sets ◽  
Output Constraints ◽  
Data Driven Approach ◽  
Scientific Simulations ◽  
Multiple Metrics ◽  
In Transit ◽  
Multiple Compression ◽  
Large Scale Simulations

Increasing processing capabilities and input/output constraints of supercomputers have increased the use of co-processing approaches, i.e., visualizing and analyzing data sets of simulations on the fly. We present a method that evaluates the importance of different regions of simulation data and a data-driven approach that uses the proposed method to accelerate in-transit co-processing of large-scale simulations. We use the importance metrics to simultaneously employ multiple compression methods on different data regions to accelerate the in-transit co-processing. Our approach strives to adaptively compress data on the fly and uses load balancing to counteract memory imbalances. We demonstrate the method’s efficiency through a fluid mechanics application, a Richtmyer–Meshkov instability simulation, showing how to accelerate the in-transit co-processing of simulations. The results show that the proposed method expeditiously can identify regions of interest, even when using multiple metrics. Our approach achieved a speedup of 1.29× in a lossless scenario. The data decompression time was sped up by 2× compared to using a single compression method uniformly.

scholarly journals Compartment and hub definitions tune metabolic networks for metabolomic interpretations

GigaScience ◽  
2020 ◽  
Vol 9 (1) ◽  
Author(s):  
T Cameron Waller ◽  
Jordan A Berg ◽  
Alexander Lex ◽  
Brian E Chapman ◽  
Jared Rutter
Keyword(s):  
Large Scale ◽  
Metabolic Networks ◽  
Shortest Paths ◽  
Large Data ◽  
Differential Regulation ◽  
Large Data Sets ◽  
Data Sets ◽  
Human Metabolism ◽  
Experimental Conditions ◽  
Systemic Model

Abstract Background Metabolic networks represent all chemical reactions that occur between molecular metabolites in an organism’s cells. They offer biological context in which to integrate, analyze, and interpret omic measurements, but their large scale and extensive connectivity present unique challenges. While it is practical to simplify these networks by placing constraints on compartments and hubs, it is unclear how these simplifications alter the structure of metabolic networks and the interpretation of metabolomic experiments. Results We curated and adapted the latest systemic model of human metabolism and developed customizable tools to define metabolic networks with and without compartmentalization in subcellular organelles and with or without inclusion of prolific metabolite hubs. Compartmentalization made networks larger, less dense, and more modular, whereas hubs made networks larger, more dense, and less modular. When present, these hubs also dominated shortest paths in the network, yet their exclusion exposed the subtler prominence of other metabolites that are typically more relevant to metabolomic experiments. We applied the non-compartmental network without metabolite hubs in a retrospective, exploratory analysis of metabolomic measurements from 5 studies on human tissues. Network clusters identified individual reactions that might experience differential regulation between experimental conditions, several of which were not apparent in the original publications. Conclusions Exclusion of specific metabolite hubs exposes modularity in both compartmental and non-compartmental metabolic networks, improving detection of relevant clusters in omic measurements. Better computational detection of metabolic network clusters in large data sets has potential to identify differential regulation of individual genes, transcripts, and proteins.

Use of a crystallization robot to set up sitting-drop vapor-diffusion crystallization andin situcrystallization screens

2000 ◽  
Vol 33 (2) ◽  
pp. 344-349 ◽  
Author(s):  
Christopher F. Snook ◽  
Michael D. Purdy ◽  
Michael C. Wiener
Keyword(s):  
Membrane Proteins ◽  
Protein Crystallization ◽  
Integral Membrane Proteins ◽  
Vapor Diffusion ◽  
Liquid Handling ◽  
General Utility ◽  
Crystallization Experiments ◽  
Automated Screening ◽  
Set Up

A commercial crystallization robot has been modified for use in setting up sitting-drop vapor-diffusion crystallization experiments, and for setting up protein crystallization screensin situ. The primary aim of this effort is the automated screening of crystallization of integral membrane proteins in detergent-containing solutions. However, the results of this work are of general utility to robotic liquid-handling systems. Sources of error that can prevent the accurate dispensing and mixing of solutions have been identified, and include local environmental, machine-specific and solution conditions. Solutions to each of these problems have been developed and implemented.

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