24 results on '"Bell EW"'
Search Results
2. I-TASSER-MTD: a deep-learning-based platform for multi-domain protein structure and function prediction.
- Author
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Zhou X, Zheng W, Li Y, Pearce R, Zhang C, Bell EW, Zhang G, and Zhang Y
- Subjects
- Algorithms, Cryoelectron Microscopy, Databases, Protein, Models, Molecular, Protein Conformation, Proteins chemistry, Computational Biology methods, Deep Learning
- Abstract
Most proteins in cells are composed of multiple folding units (or domains) to perform complex functions in a cooperative manner. Relative to the rapid progress in single-domain structure prediction, there are few effective tools available for multi-domain protein structure assembly, mainly due to the complexity of modeling multi-domain proteins, which involves higher degrees of freedom in domain-orientation space and various levels of continuous and discontinuous domain assembly and linker refinement. To meet the challenge and the high demand of the community, we developed I-TASSER-MTD to model the structures and functions of multi-domain proteins through a progressive protocol that combines sequence-based domain parsing, single-domain structure folding, inter-domain structure assembly and structure-based function annotation in a fully automated pipeline. Advanced deep-learning models have been incorporated into each of the steps to enhance both the domain modeling and inter-domain assembly accuracy. The protocol allows for the incorporation of experimental cross-linking data and cryo-electron microscopy density maps to guide the multi-domain structure assembly simulations. I-TASSER-MTD is built on I-TASSER but substantially extends its ability and accuracy in modeling large multi-domain protein structures and provides meaningful functional insights for the targets at both the domain- and full-chain levels from the amino acid sequence alone., (© 2022. Springer Nature Limited.)
- Published
- 2022
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3. PEPPI: Whole-proteome Protein-protein Interaction Prediction through Structure and Sequence Similarity, Functional Association, and Machine Learning.
- Author
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Bell EW, Schwartz JH, Freddolino PL, and Zhang Y
- Subjects
- Bayes Theorem, COVID-19, Humans, Proteomics, SARS-CoV-2, Machine Learning, Protein Interaction Mapping, Proteome chemistry, Software
- Abstract
Proteome-wide identification of protein-protein interactions is a formidable task which has yet to be sufficiently addressed by experimental methodologies. Many computational methods have been developed to predict proteome-wide interaction networks, but few leverage both the sensitivity of structural information and the wide availability of sequence data. We present PEPPI, a pipeline which integrates structural similarity, sequence similarity, functional association data, and machine learning-based classification through a naïve Bayesian classifier model to accurately predict protein-protein interactions at a proteomic scale. Through benchmarking against a set of 798 ground truth interactions and an equal number of non-interactions, we have found that PEPPI attains 4.5% higher AUROC than the best of other state-of-the-art methods. As a proteomic-scale application, PEPPI was applied to model the interactions which occur between SARS-CoV-2 and human host cells during coronavirus infection, where 403 high-confidence interactions were identified with predictions covering 73% of a gold standard dataset from PSICQUIC and demonstrating significant complementarity with the most recent high-throughput experiments. PEPPI is available both as a webserver and in a standalone version and should be a powerful and generally applicable tool for computational screening of protein-protein interactions., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)
- Published
- 2022
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4. Protein structure prediction using deep learning distance and hydrogen-bonding restraints in CASP14.
- Author
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Zheng W, Li Y, Zhang C, Zhou X, Pearce R, Bell EW, Huang X, and Zhang Y
- Subjects
- Computational Biology, Protein Conformation, Protein Folding, Software, Deep Learning, Hydrogen Bonding, Models, Molecular, Proteins chemistry, Proteins metabolism, Sequence Alignment methods, Sequence Analysis, Protein methods
- Abstract
In this article, we report 3D structure prediction results by two of our best server groups ("Zhang-Server" and "QUARK") in CASP14. These two servers were built based on the D-I-TASSER and D-QUARK algorithms, which integrated four newly developed components into the classical protein folding pipelines, I-TASSER and QUARK, respectively. The new components include: (a) a new multiple sequence alignment (MSA) collection tool, DeepMSA2, which is extended from the DeepMSA program; (b) a contact-based domain boundary prediction algorithm, FUpred, to detect protein domain boundaries; (c) a residual convolutional neural network-based method, DeepPotential, to predict multiple spatial restraints by co-evolutionary features derived from the MSA; and (d) optimized spatial restraint energy potentials to guide the structure assembly simulations. For 37 FM targets, the average TM-scores of the first models produced by D-I-TASSER and D-QUARK were 96% and 112% higher than those constructed by I-TASSER and QUARK, respectively. The data analysis indicates noticeable improvements produced by each of the four new components, especially for the newly added spatial restraints from DeepPotential and the well-tuned force field that combines spatial restraints, threading templates, and generic knowledge-based potentials. However, challenges still exist in the current pipelines. These include difficulties in modeling multi-domain proteins due to low accuracy in inter-domain distance prediction and modeling protein domains from oligomer complexes, as the co-evolutionary analysis cannot distinguish inter-chain and intra-chain distances. Specifically tuning the deep learning-based predictors for multi-domain targets and protein complexes may be helpful to address these issues., (© 2021 Wiley Periodicals LLC.)
- Published
- 2021
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5. Protein inter-residue contact and distance prediction by coupling complementary coevolution features with deep residual networks in CASP14.
- Author
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Li Y, Zhang C, Zheng W, Zhou X, Bell EW, Yu DJ, and Zhang Y
- Subjects
- Computational Biology, Protein Conformation, Sequence Analysis, Protein, Deep Learning, Models, Molecular, Proteins chemistry, Proteins metabolism, Sequence Alignment methods, Software
- Abstract
This article reports and analyzes the results of protein contact and distance prediction by our methods in the 14th Critical Assessment of techniques for protein Structure Prediction (CASP14). A new deep learning-based contact/distance predictor was employed based on the ensemble of two complementary coevolution features coupling with deep residual networks. We also improved our multiple sequence alignment (MSA) generation protocol with wholesale meta-genome sequence databases. On 22 CASP14 free modeling (FM) targets, the proposed model achieved a top-L/5 long-range precision of 63.8% and a mean distance bin error of 1.494. Based on the predicted distance potentials, 11 out of 22 FM targets and all of the 14 FM/template-based modeling (TBM) targets have correctly predicted folds (TM-score >0.5), suggesting that our approach can provide reliable distance potentials for ab initio protein folding., (© 2021 Wiley Periodicals LLC.)
- Published
- 2021
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6. Folding non-homologous proteins by coupling deep-learning contact maps with I-TASSER assembly simulations.
- Author
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Zheng W, Zhang C, Li Y, Pearce R, Bell EW, and Zhang Y
- Subjects
- Humans, Protein Conformation, Algorithms, Models, Molecular, Computational Biology methods, SARS-CoV-2 genetics, Proteins genetics, Deep Learning, COVID-19
- Abstract
Structure prediction for proteins lacking homologous templates in the Protein Data Bank (PDB) remains a significant unsolved problem. We developed a protocol, C-I-TASSER, to integrate interresidue contact maps from deep neural-network learning with the cutting-edge I-TASSER fragment assembly simulations. Large-scale benchmark tests showed that C-I-TASSER can fold more than twice the number of non-homologous proteins than the I-TASSER, which does not use contacts. When applied to a folding experiment on 8,266 unsolved Pfam families, C-I-TASSER successfully folded 4,162 domain families, including 504 folds that are not found in the PDB. Furthermore, it created correct folds for 85% of proteins in the SARS-CoV-2 genome, despite the quick mutation rate of the virus and sparse sequence profiles. The results demonstrated the critical importance of coupling whole-genome and metagenome-based evolutionary information with optimal structure assembly simulations for solving the problem of non-homologous protein structure prediction., Competing Interests: DECLARATION OF INTERESTS The authors declare no competing interests.
- Published
- 2021
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7. Deducing high-accuracy protein contact-maps from a triplet of coevolutionary matrices through deep residual convolutional networks.
- Author
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Li Y, Zhang C, Bell EW, Zheng W, Zhou X, Yu DJ, and Zhang Y
- Subjects
- Computational Biology, Protein Conformation, Protein Folding, Reproducibility of Results, Neural Networks, Computer, Proteins chemistry, Proteins metabolism, Sequence Analysis, Protein methods
- Abstract
The topology of protein folds can be specified by the inter-residue contact-maps and accurate contact-map prediction can help ab initio structure folding. We developed TripletRes to deduce protein contact-maps from discretized distance profiles by end-to-end training of deep residual neural-networks. Compared to previous approaches, the major advantage of TripletRes is in its ability to learn and directly fuse a triplet of coevolutionary matrices extracted from the whole-genome and metagenome databases and therefore minimize the information loss during the course of contact model training. TripletRes was tested on a large set of 245 non-homologous proteins from CASP 11&12 and CAMEO experiments and outperformed other top methods from CASP12 by at least 58.4% for the CASP 11&12 targets and 44.4% for the CAMEO targets in the top-L long-range contact precision. On the 31 FM targets from the latest CASP13 challenge, TripletRes achieved the highest precision (71.6%) for the top-L/5 long-range contact predictions. It was also shown that a simple re-training of the TripletRes model with more proteins can lead to further improvement with precisions comparable to state-of-the-art methods developed after CASP13. These results demonstrate a novel efficient approach to extend the power of deep convolutional networks for high-accuracy medium- and long-range protein contact-map predictions starting from primary sequences, which are critical for constructing 3D structure of proteins that lack homologous templates in the PDB library., Competing Interests: The authors have declared that no competing interests exist.
- Published
- 2021
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8. Recombinant Penicillium oxalicum 16 β-Glucosidase 1 Displays Comprehensive Inhibitory Resistance to Several Lignocellulose Pretreatment Products, Ethanol, and Salt.
- Author
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Li H, Yi S, Bell EW, Huang Q, and Zhao X
- Subjects
- Enzyme Activation drug effects, Enzyme Stability, Gene Expression Regulation, Fungal, Hydrolysis, Kinetics, Potassium Chloride, Saccharomycetales genetics, Sequence Analysis, Sodium Chloride pharmacology, beta-Glucosidase chemistry, Ethanol antagonists & inhibitors, Lignin antagonists & inhibitors, Penicillium genetics, Salts antagonists & inhibitors, beta-Glucosidase drug effects, beta-Glucosidase genetics, beta-Glucosidase metabolism
- Abstract
β-Glucosidase (BGL) is a rate-limiting enzyme of lignocellulose hydrolysis for second-generation bioethanol production, but its inhibition by lignocellulose pretreatment products, ethanol, and salt is apparent. Here, the recombinant Penicillium oxalicum 16 BGL 1 (rPO16BGL1) from Pichia pastoris GS115 kept complete activity at 0.2-1.4 mg/mL furan derivatives and phenolic compounds, 50 mg/mL sodium chloride (potassium chloride), or 100 mg/mL ethanol at 40 °C. rPO16BGL1 retained above 50% residual activity at 30 mg/mL organic acid sodium, and 60% residual activity at 40 °C with 300 mg/mL ethanol. Sodium chloride and potassium chloride had a complicated effect on rPO16BGL1, which resulted in activation or inhibition. The inhibition kinetics of the enzyme reaction demonstrated that organic acids and organic acid sodium were non-competitive inhibitors and that ethanol was a competitive inhibitor at < 1.5 mg/mL salicin. Moreover, substrate inhibition of the enzyme was found at > 2 mg/mL salicin, and the K
m /KI and Km /KSI average values revealed that the inhibitory strength was ranked as salicin-organic acids > organic acids > salicin-organic acid sodium salt > organic acid sodium salt > salicin > salicin-KCl > salicin-NaCl > salicin-ethanol > ethanol.- Published
- 2020
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9. EDock: blind protein-ligand docking by replica-exchange monte carlo simulation.
- Author
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Zhang W, Bell EW, Yin M, and Zhang Y
- Abstract
Protein-ligand docking is an important approach for virtual screening and protein function annotation. Although many docking methods have been developed, most require a high-resolution crystal structure of the receptor and a user-specified binding site to start. This information is, however, not available for the majority of unknown proteins, including many pharmaceutically important targets. Developing blind docking methods without predefined binding sites and working with low-resolution receptor models from protein structure prediction is thus essential. In this manuscript, we propose a novel Monte Carlo based method, EDock, for blind protein-ligand docking. For a given protein, binding sites are first predicted by sequence-profile and substructure-based comparison searches with initial ligand poses generated by graph matching. Next, replica-exchange Monte Carlo (REMC) simulations are performed for ligand conformation refinement under the guidance of a physical force field coupled with binding-site distance constraints. The method was tested on two large-scale datasets containing 535 protein-ligand pairs. Without specifying binding pockets on the experimental receptor structures, EDock achieves on average a ligand RMSD of 2.03 Å, which compares favorably with state-of-the-art docking methods including DOCK6 (2.68 Å) and AutoDock Vina (3.92 Å). When starting with predicted models from I-TASSER, EDock still generates reasonable docking models, with a success rate 159% and 67% higher than DOCK6 and AutoDock Vina, respectively. Detailed data analyses show that the major advantage of EDock lies in reliable ligand binding site predictions and extensive REMC sampling, which allows for the implementation of multiple van der Waals weightings to accommodate different levels of steric clashes and cavity distortions and therefore enhances the robustness of low-resolution docking with predicted protein structures.
- Published
- 2020
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10. Protein Structure and Sequence Reanalysis of 2019-nCoV Genome Refutes Snakes as Its Intermediate Host and the Unique Similarity between Its Spike Protein Insertions and HIV-1.
- Author
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Zhang C, Zheng W, Huang X, Bell EW, Zhou X, and Zhang Y
- Subjects
- Amino Acid Sequence, Animals, Betacoronavirus classification, COVID-19, Eutheria virology, HIV-1 genetics, Humans, Metagenome, Pandemics, Protein Structure, Tertiary, SARS-CoV-2, Sequence Alignment, Sequence Analysis, Protein, Snakes virology, Betacoronavirus genetics, Coronavirus Infections virology, Genome, Viral genetics, Host-Pathogen Interactions, Models, Molecular, Pneumonia, Viral virology, Spike Glycoprotein, Coronavirus chemistry, Spike Glycoprotein, Coronavirus genetics
- Abstract
As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, the careful analysis of its transmission and cellular mechanisms is sorely needed. In this Communication, we first analyzed two recent studies that concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions share a unique similarity to HIV-1. However, the reimplementation of the analyses, built on larger scale data sets using state-of-the-art bioinformatics methods and databases, presents clear evidence that rebuts these conclusions. Next, using metagenomic samples from Manis javanica , we assembled a draft genome of the 2019-nCoV-like coronavirus, which shows 73% coverage and 91% sequence identity to the 2019-nCoV genome. In particular, the alignments of the spike surface glycoprotein receptor binding domain revealed four times more variations in the bat coronavirus RaTG13 than in the Manis coronavirus compared with 2019-nCoV, suggesting the pangolin as a missing link in the transmission of 2019-nCoV from bats to human.
- Published
- 2020
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11. Protein structure and sequence re-analysis of 2019-nCoV genome does not indicate snakes as its intermediate host or the unique similarity between its spike protein insertions and HIV-1.
- Author
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Zhang C, Zheng W, Huang X, Bell EW, Zhou X, and Zhang Y
- Abstract
As the infection of 2019-nCoV coronavirus is quickly developing into a global pneumonia epidemic, careful analysis of its transmission and cellular mechanisms is sorely needed. In this report, we re-analyzed the computational approaches and findings presented in two recent manuscripts by Ji et al . (https://doi.org/10.1002/jmv.25682) and by Pradhan et al . (https://doi.org/10.1101/2020.01.30.927871), which concluded that snakes are the intermediate hosts of 2019-nCoV and that the 2019-nCoV spike protein insertions shared a unique similarity to HIV-1. Results from our re-implementation of the analyses, built on larger-scale datasets using state-of-the-art bioinformatics methods and databases, do not support the conclusions proposed by these manuscripts. Based on our analyses and existing data of coronaviruses, we concluded that the intermediate hosts of 2019-nCoV are more likely to be mammals and birds than snakes, and that the "novel insertions" observed in the spike protein are naturally evolved from bat coronaviruses.
- Published
- 2020
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12. Ensembling multiple raw coevolutionary features with deep residual neural networks for contact-map prediction in CASP13.
- Author
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Li Y, Zhang C, Bell EW, Yu DJ, and Zhang Y
- Subjects
- Algorithms, Databases, Protein, Machine Learning, Metagenome genetics, Models, Molecular, Neural Networks, Computer, Proteins chemistry, Proteins genetics, Sequence Alignment, Sequence Analysis, Protein methods, Computational Biology, Protein Conformation, Proteins ultrastructure
- Abstract
We report the results of residue-residue contact prediction of a new pipeline built purely on the learning of coevolutionary features in the CASP13 experiment. For a query sequence, the pipeline starts with the collection of multiple sequence alignments (MSAs) from multiple genome and metagenome sequence databases using two complementary Hidden Markov Model (HMM)-based searching tools. Three profile matrices, built on covariance, precision, and pseudolikelihood maximization respectively, are then created from the MSAs, which are used as the input features of a deep residual convolutional neural network architecture for contact-map training and prediction. Two ensembling strategies have been proposed to integrate the matrix features through end-to-end training and stacking, resulting in two complementary programs called TripletRes and ResTriplet, respectively. For the 31 free-modeling domains that do not have homologous templates in the PDB, TripletRes and ResTriplet generated comparable results with an average accuracy of 0.640 and 0.646, respectively, for the top L/5 long-range predictions, where 71% and 74% of the cases have an accuracy above 0.5. Detailed data analyses showed that the strength of the pipeline is due to the sensitive MSA construction and the advanced strategies for coevolutionary feature ensembling. Domain splitting was also found to help enhance the contact prediction performance. Nevertheless, contact models for tail regions, which often involve a high number of alignment gaps, and for targets with few homologous sequences are still suboptimal. Development of new approaches where the model is specifically trained on these regions and targets might help address these problems., (© 2019 Wiley Periodicals, Inc.)
- Published
- 2019
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13. I-TASSER gateway: A protein structure and function prediction server powered by XSEDE.
- Author
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Zheng W, Zhang C, Bell EW, and Zhang Y
- Abstract
There is an increasing gap between the number of known protein sequences and the number of proteins with experimentally characterized structure and function. To alleviate this issue, we have developed the I-TASSER gateway, an online server for automated and reliable protein structure and function prediction. For a given sequence, I-TASSER starts with template recognition from a known structure library, followed by full-length atomic model construction by iterative assembly simulations of the continuous structural fragments excised from the template alignments. Functional insights are then derived from comparative matching of the predicted model with a library of proteins with known function. The I-TASSER pipeline has been recently integrated with the XSEDE Gateway system to accommodate pressing demand from the user community and increasing computing costs. This report summarizes the configuration of the I-TASSER Gateway with the XSEDE-Comet supercomputer cluster, together with an overview of the I-TASSER method and milestones of its development.
- Published
- 2019
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14. DockRMSD: an open-source tool for atom mapping and RMSD calculation of symmetric molecules through graph isomorphism.
- Author
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Bell EW and Zhang Y
- Abstract
Comparison of ligand poses generated by protein-ligand docking programs has often been carried out with the assumption of direct atomic correspondence between ligand structures. However, this correspondence is not necessarily chemically relevant for symmetric molecules and can lead to an artificial inflation of ligand pose distance metrics, particularly those that depend on receptor superposition (rather than ligand superposition), such as docking root mean square deviation (RMSD). Several of the commonly-used RMSD calculation algorithms that correct for molecular symmetry do not take into account the bonding structure of molecules and can therefore result in non-physical atomic mapping. Here, we present DockRMSD, a docking pose distance calculator that converts the symmetry correction to a graph isomorphism searching problem, in which the optimal atomic mapping and RMSD calculation are performed by an exhaustive and fast matching search of all isomorphisms of the ligand structure graph. We show through evaluation of docking poses generated by AutoDock Vina on the CSAR Hi-Q set that DockRMSD is capable of deterministically identifying the minimum symmetry-corrected RMSD and is able to do so without significant loss of computational efficiency compared to other methods. The open-source DockRMSD program can be conveniently integrated with various docking pipelines to assist with accurate atomic mapping and RMSD calculations, which can therefore help improve docking performance, especially for ligand molecules with complicated structural symmetry.
- Published
- 2019
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15. Identification of inhibitors of the E. coli chaperone SurA using in silico and in vitro techniques.
- Author
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Bell EW, Zheng EJ, and Ryno LM
- Subjects
- Alanine analogs & derivatives, Alanine metabolism, Amino Acid Sequence, Binding Sites, Carrier Proteins metabolism, Escherichia coli Proteins metabolism, Fluorescence Polarization, Molecular Docking Simulation, Peptides chemistry, Peptides metabolism, Peptidylprolyl Isomerase metabolism, Protein Structure, Tertiary, Carrier Proteins antagonists & inhibitors, Escherichia coli metabolism, Escherichia coli Proteins antagonists & inhibitors, Peptidylprolyl Isomerase antagonists & inhibitors
- Abstract
SurA is a gram-negative, periplasmic chaperone protein involved in the proper folding of outer membrane porins (OMPs), which protect bacteria against toxins in the extracellular environment by selectively regulating the passage of nutrients into the cell. Previous studies demonstrated that deletion of SurA renders bacteria more sensitive to toxins that compromise the integrity of the outer membrane. Inhibitors of SurA will perturb the folding of OMPs, leading to disruption of the outer membrane barrier and making the cell more vulnerable to toxic insults. The discovery of novel SurA inhibitors is therefore of great importance for developing alternative strategies to overcome antibiotic resistance. Our laboratory has screened over 10,000,000 compoundsin silicoby computationally docking these compounds onto the crystal structure of SurA. Through this screen and a screen of fragment compounds (molecular weight less than 250 g/mol), we found twelve commercially readily available candidate compounds that bind to the putative client binding site of SurA. We confirmed binding to SurA by developing and employing a competitive fluorescence anisotropy-based binding assay. Our results show that one of these compounds, Fmoc-β-(2-quinolyl)-d-alanine, binds the client binding site with high micromolar affinity. Using this compound as a lead, we also discovered that Fmoc-l-tryptophan and Fmoc-l-phenylalanine, but not Fmoc-l-tyrosine, bind SurA with similar micromolar affinity. To our knowledge, this is the first report of a competitive fluorescence anisotropy assay developed for the identification of inhibitors of the chaperone SurA, and the identification of three small molecules that bind SurA at its client binding site., (Copyright © 2018 Elsevier Ltd. All rights reserved.)
- Published
- 2018
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16. Coaxial-resonator-driven rf (Paul) trap for strong confinement.
- Author
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Jefferts SR, Monroe C, Bell EW, and Wineland DJ
- Published
- 1995
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17. Merged-beams energy-loss technique for electron-ion excitation: Absolute total cross sections for O5+(2s-->2p).
- Author
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Bell EW, Guo XQ, Forand JL, Rinn K, Swenson DR, Thompson JS, Dunn GH, Bannister ME, Gregory DC, Phaneuf RA, Smith AC, Müller A, Timmer CA, Wåhlin EK, DePaola BD, and Belic DS
- Published
- 1994
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18. Electron-impact ionization of In+ and Xe+
- Author
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Bell EW, Djuric N, and Dunn GH
- Published
- 1993
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19. Absolute cross sections for electron-impact single ionization of Si+ and Si2+
- Author
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Djuric N, Bell EW, Guo XQ, Dunn GH, Phaneuf RA, Bannister ME, Pindzola MS, and Griffin DC
- Published
- 1993
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20. Electron-impact ionization of Si6+ and Si7+ ions.
- Author
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Zeijlmans van Emmichoven PA, Bannister ME, Gregory DC, Havener CC, Phaneuf RA, Bell EW, Guo XQ, Thompson JS, and Sataka M
- Published
- 1993
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21. Evidence for significant backscattering in near-threshold electron-impact excitation of Ar7+(3s-->3p).
- Author
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Guo XQ, Bell EW, Thompson JS, Dunn GH, Bannister ME, Phaneuf RA, and Smith AC
- Published
- 1993
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22. Absolute cross-section measurements for electron-impact ionization of Cl+
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Djuric N, Bell EW, Daniel E, and Dunn GH
- Published
- 1992
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23. Effects of dietary fiber from wheat, corn, and soy hull bran on excretion of fecal bile acids in humans.
- Author
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Bell EW, Emken EA, Klevay LM, and Sandstead HH
- Subjects
- Bile Acids and Salts isolation & purification, Chenodeoxycholic Acid metabolism, Cholic Acids metabolism, Deoxycholic Acid metabolism, Humans, Lithocholic Acid metabolism, Glycine max, Specimen Handling, Triticum, Zea mays, Bile Acids and Salts metabolism, Cellulose pharmacology, Dietary Fiber pharmacology, Feces analysis
- Abstract
Effects of dietary fiber on bile acid excretion and fecal bile acid concentration have been studied for seven subjects fed 26 g of either soft white wheat bran, corn bran, soybean hulls, or hard red spring wheat bran. Results indicate that even in a controlled study using a metabolic word, individual subject variation has a major impact on fecal bile acid excretion. This observation has not been fully appreciated in previous human studies. No significant change in the composition of fecal bile acids could be associated with the decrease in serum lipid levels previously reported. A method for the isolation and quantitation of fecal bile acids is described which does not require purification by thin-layer chromatography. A preliminary study of lyophilized fecal samples stored at -10 to -30 degrees C showed very little or no change in bile acid content. Samples stored at room temperatures for 11 months showed a substantial reduction in bile acid content.
- Published
- 1981
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24. Potential juvenile hormone activity: preparation of fatty beta-methylcrotonyl and 3,4-methylenedioxyphenyl derivatives.
- Author
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Bell EW, Gast LE, Cowan JC, Friedrich JP, and Bowers WS
- Subjects
- Acrylates pharmacology, Animals, Benzene Derivatives pharmacology, Crotonates pharmacology, Linseed Oil, Pupa drug effects, Structure-Activity Relationship, Butyrates pharmacology, Dioxoles pharmacology, Juvenile Hormones pharmacology, Tenebrio drug effects
- Published
- 1973
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