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149 results for “glycan”
Supplementary Data for "Substrate-Assisted Mechanism for the Degradation of N-glycans by a Gut Bacterial Mannoside Phosphorylase"
<p>This dataset contains atomic coordinates of the molecular dynamics simulations described in "Substrate-Assisted Mechanism for the Degradation of N-glycans by a Gut Bacterial Mannoside Phosphorylase" by M. Alfonso-Prieto, I. Cuxart, G. Potocki-Véronèse, I. André and C. Rovira, published in ACS Catalysis (https://doi.org/10.1021/acscatal.3c00451). Further details on the setup of the simulations can be found in the Supplementary Information of the article. </p> <p>If you use this dataset, please cite this zenodo upload (https://doi.org/10.5281/zenodo.7704778), as well as the the original journal article (https://doi.org/10.1021/acscatal.3c00451). </p> <p>This dataset is organized in the following folders:</p> <p><strong>Snapshots_Figures_Main_Text.zip</strong>, that contains a README.txt file and:</p> <p><strong>- Figure_3</strong> contains representative structures (atomic coordinates) of the hexameric form of UhgbMP in complex with 3 different disaccharide molecules, Man-b-(1,4)-GlcNAc, Man-b-(1,4)-Glc and Man-b-(1,4)-Man.</p> <p><strong>- Figure_4</strong> contains representative structures (atomic coordinates) of the hexameric form of UhgbMP at the three minima observed along the reaction coordinate corresponding to phosphorolysis of the disaccharide Man-b-(1,4)-GlcNAc: Michaelis complex (MC), transition state (TS) and product (P) complex.</p> <p>Files in this dataset are in PDB format. For all structures, the solvation box (water and ions) has been stripped to reduce file size. See README.txt inside <a href="https://zenodo.org/api/files/f3836540-b7b6-4820-87b3-7fa5dff7840c/Snapshots_Figures_Main_Text.zip">Snapshots_Figures_Main_Text.zip </a>for more information.</p>
Glycosylated models for: The diversity of the glycan shield of sarbecoviruses closely related to SARS-CoV-2
<p>Glycosylated models (as PDB files) of the sarbecovirus spike proteins used in the study: The diversity of the glycan shield of sarbecoviruses closely related to SARS-CoV-2.</p>
Datasets used in: "Correcting for sparsity and interdependence in glycomics by accounting for glycan biosynthesis"
<p>Datasets included in: Bokan Bao+, Benjamin P. Kellman+, Austin W. T. Chiang, Austin K. York, Mahmoud A. Mohammad, Morey W. Haymond, Lars Bode, and Nathan E. Lewis. 2019. “<strong>Correcting for Sparsity and Non-Independence in Glycomic Data through a System Biology Framework.</strong>” bioRxiv. <a href="https://doi.org/10.1101/693507">https://doi.org/10.1101/693507</a></p> <p><strong>Central Datasets</strong><br> - Github_Yang2019_EPO<br> - paper_hmo<br> These are the HMO and EPO datasets used throughout the majority of the manuscript. They are formatted consistent with the github code repository: https://github.com/LewisLabUCSD/GlyCompare</p> <p><strong>Additional Datasets</strong><br> - Webapp_Jin2017_Mucin<br> - Webapp_Riley2019_SiteSpecN<br> - Webapp_Sibile2016_Glycolipid<br> These are additional datasets explored in the final figure and supplement of the manuscript formatted for the webapp: https://glycompare.herokuapp.com/</p> <p>All datasets but Riley2019 have structural data, Riley2019 only contains compositional data</p> <p><strong>Detailed Descriptions</strong></p> <ul> <li>Github_Yang2019_EPO <ul> <li>Sixteen MALDI-TOF glycoprofiles of EPO, where each EPO glycoprofile was produced in a different glycoengineered</li> </ul> </li> <li>CHO cell line- paper_hmo <ul> <li>Forty-eight HPLC glycoprofiles of HMO from six mothers22.</li> </ul> </li> <li>Webapp_Jin2017_Mucin <ul> <li>Mucin-type O-glycans from tumor and normal samples from gastrointestinal cancers</li> </ul> </li> <li>Webapp_Riley2019_SiteSpecN <ul> <li>Site-specific N-glycosylation in mouse brain</li> </ul> </li> <li>Webapp_Sibile2016_Glycolipid <ul> <li>Glycolipid abundance in Rat eye, brain and blood<br> </li> </ul> </li> </ul>
HDX-MS dataset for: "Glycan-induced structural activation softens the human papillomavirus capsid for entry through reduction of intercapsomere flexibility"
<p>Hydrogen/deuterium exchange mass spectrometry dataset used in: <strong>Glycan-induced structural activation softens the human papillomavirus capsid for entry through reduction of intercapsomere flexibility.</strong> Yuzhen Feng*, Dominik van Bodegraven*, Alan Kádek*, Ignacio L.B. Munguira, Laura Soria-Martinez, Sarah Nentwich, Sreedeepa Saha, Florian Chardon, Daniel Kavan, Charlotte Uetrecht#, Mario Schelhaas#, Wouter H. Roos#. <em>Nature Communications</em> 10076 (2024). doi: 10.1038/s41467-024-54373-0</p> <p>* - authors contributing equally</p> <p># - corresponding authors</p> <p><strong>Description:</strong></p> <p>Hydrogen/deuterium exchange mass spectrometry (HXMS) analysis of the effect of heparin on the conformational dynamics of human papillomavirus 16 pseudovirus (PsV).</p> <p><strong>Sample processing:</strong></p> <p>HPV16 PsV were prepared according to (Buck & Thompson: Current Protocols in Cell Biology 2007). In short, p16Shell and pClneo-EGFP were transfected into HEK293TT cells. After 48 h, cells were harvested and lysed followed by maturation of the virus particles for 24 h. For purification, the particles were purified using a CsCl step gradient (27 % w/V and 38.8 % w/V CsCl in 10 mM Tris-HCl pH 7.4, 207570 x g, 3 h 50 min, 4 °C) followed by dialysis in Float-A-Lyzer devices (1 mL, Spectra/Por) against a total of 3 L HPV virion buffer (1x PBS, 635 mM NaCl, 0.9 mM CaCl2, 0.5 mM MgCl2, 2.1 mM KCl, pH 7.4).</p> <p>PsV were pre-incubated for 1 h either with or without heparin (H4784, Sigma-Aldrich) at room temperature. To initiate deuterium labelling the samples were 6-fold diluted with the virion buffer they were obtained in, only made of 99.9% D2O (150 mM NaCl, 4.8 mM KCl, 10 mM Na2HPO4, 1.8 mM KH2PO4, 0.9 mM CaCl2, 0.5 mM MgCl2, pD 7.2). This resulted in a final concentration of 0.5 µM L1 monomer in the form of PsV with or without 1 mg/ml heparin during deuterium labelling. The exchange reaction was left to proceed at room temperature until aliquots of 45 µl were removed at predetermined time points (1 min, 5 min, 15 min, 1 h and 4 h). In the aliquots, the exchange was immediately stopped by twofold dilution with ice-cold quench buffer (0.25 M glycine, 100 mM TCEP, 8 M urea, indicated pH 2.7), resulting in final pH 2.5. For samples with heparin, the quench buffer additionally contained 1 mg/ml protamine sulphate (P4020, Sigma-Aldrich). After 30 s incubation on ice, the samples were centrifuged at 10.000 x g for 1 min at 0 °C. Each supernatant was transferred to a fresh tube and flash frozen in liquid nitrogen. Low binding microtubes and low binding pipette tips (both Axygen) were used throughout for all handling of viral particles.</p> <p>The frozen samples were quickly thawed and injected into a refrigerated (1°C) HPLC system (Infinity 1260, Agilent Technologies), through a porcine pepsin column (≥ 3200 units/mg, Sigma-Aldrich) in-house immobilized onto POROS-20AL perfusion resin (Thermo Scientific) as described previously (Wang et al.: Molecular & Cellular Proteomics 2002), which was kept at 4°C. Pepsin digestion was performed at isocratic 200 µl/min flow rate (0.4 % formic acid in water). After the digestion, peptides were online desalted for 3 min on a peptide microtrap (OPTI-TRAP, Optimize Technologies) and then eluted on a reversed-phase analytical column (ZORBAX 300SB-C18, 0.5 x 35 mm, 3.5 µm, 300Å, Agilent Technologies). There LC separation proceeded at 25 µl/min flow rate through an 8 min gradient of 8–30% solvent B, followed by a 3 min gradient of 30-90 % solvent B (solvent A: 0.4 % formic acid in water, solvent B: 0.4 % formic acid in acetonitrile). The outlet of the HPLC system was connected to an electrospray ionization (ESI) source of an Orbitrap Fusion Tribrid Mass Spectrometer (Thermo Scientific). The instrument was operated in positive ESI MS-only mode for deuterated samples, scan range 300-2000 m/z, using 4 microscans at resolving power setting 120,000. In a separate measurement on non-deuterated sample, the instrument was used in positive data-dependent ESI MS/MS mode with 30% HCD dissociation, 1 microscan and 240,000 resolving power setting for the identification of all peptides produced by non-specific pepsin cleavage.</p> <p>In total 22 pmol and 50 pmol L1 protein were injected per MS and MS/MS analysis, respectively. To minimize sample carry-over on the protease column, two washing solutions were always injected between sample injections modified from Majumdar et al. 69 (wash solution 1: 5% acetonitrile, 5% isopropanol, 20% acetic acid; wash solution 2: 4 M Urea, 1 M glycine, pH 2.5). All HDX samples were analysed in technical triplicates, except for the 15 min time point for PsV without heparin, which was measured in duplicate.</p> <p><br><strong>Data processing:</strong></p> <p>Peptides were identified from the MS/MS data by the Andromeda search algorithm implemented in MaxQuant (version 1.6.5.0) using a custom protein database containing the sequences of HPV16 L1 and L2 proteins. Deuterium uptake for the identified peptides was calculated with DeutEx (in-house developed), manually inspected and the statistical significance of the observed differences in deuteration was evaluated by applying an unpaired two-tailed Student’s T-test with single pooled variance evaluated with alpha ≤ 0.05 using the Holm-Šidák correction for multiple comparisons in Prism 8.0.1 (GraphPad Software). The processed data were visualized using MSTools (https://peterslab.org/MSTools/, Kavan & Man: International Journal of Mass Spectrometry 2011) and open-source PyMol 2.6.0a0 (Schrödinger, Inc).</p> <p>For ZENODO the datafiles were deposited as native Thermo .raw files (including instrumental parameters metadata) while all peaks in the spectra were additionally exported into plain m/z vs intensity .txt files per each scan in the LC-MS analysis as also used for the DeutEx HDX-MS processing.</p>
Predicting glycan structure from tandem mass spectrometry via deep learning
<p>Curated set of LC-MS/MS data from glycomics studies. Used for training and applying CandyCrunch, a deep learning model to predict glycan structure from LC-MS/MS data, described in Urban et al., Nat Methods, 2024 and https://github.com/BojarLab/CandyCrunch.</p> <p>Files:</p> <p>full_dataset.xlsx: Full dataset with all annotated LC-MS/MS glycan spectra</p> <p>X_train.pkl: spectra and metadata from our training set</p> <p>y_train.pkl: labels from our training set</p> <p>X_test.pkl: spectra and metadata from our independent test set</p> <p>y_test.pkl: labels from our independent test set</p> <p>glycans.pkl: glycans in IUPAC-condensed nomenclature in the same order as the label-encoding</p>
Dataset associated to Bioinspired electro-permeable glycans on carbon: Fouling control for sensing in complex matrices
<p>This dataset is associated to the publication "Bioinspired electro-permeable glycans on carbon: Fouling control for sensing in complex matrices" performed in Trinity College, Dublin, Ireland. The dataset contains raw data associated to the measures contained in the article: X ray photoelectron spectroscopy, Atomic force microscopy, cyclic voltammetries. This publication has emanated from research conducted with the financial support of Science Foundation Ireland (SFI) grant No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319311455#gs1">13/CDA/2213</a>. AM and JAB gratefully acknowledge support from the School of Chemistry and the Irish Research Council Grant No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319311455#gs2">GOIPG/2014/399</a>, respectively. EW is grateful for support by the Undergraduate Research Bursary Program of the Royal Society of Chemistry and Nuffield Foundation. Use of the XPS of Prof. I. V. Shvets and C. McGuinness provided under SFI Equipment Infrastructure funds. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. <a href="https://www.sciencedirect.com/science/article/pii/S0008622319311455#gs6">799175</a> (HiBriCarbon). The results of this publication reflect only the authors’ view and the Commission is not responsible for any use that may be made of the information it contains.</p>
Dataset for the Manuscript: Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut (in revision)
<p>This dataset provides the classical and QM/MM MD simulation trajectory data to the manuscript:</p> <p><strong>Structural and mechanistic insights into the cleavage of clustered O-glycan patches-containing glycoproteins by mucinases of the human gut</strong></p> <p>The data set contains classical MD simulations of AM0627 with three substrate peptides P1, P2, P9, and BT4244 with glycopeptides, as well as QM/MM metadynamics simulations for our manuscript. PDB files for Figures 4,5 and Figure S5-8,10 are also included.</p>
Supplementary Data and Figures for "Myomedin replicas of gp120 V3 loop glycan epitopes of PGT121 and PGT126 antibodies as non-cognate antigens for HIV-1 broadly neutralizing antibodies"
<p>Supplementary pymol sessions for "Myomedin replicas of gp120 V3 loop glycan epitopes of PGT121 and PGT126 antibodies as non-cognate antigens for HIV-1 broadly neutralizing antibodies". Pymol sessions contain the source data for Figure 3, panels A-K.</p>
Training dataset: MALDI imaging of N-glycans in murine kidney sections
<p>The files provided here are all adopted from the <a href="http://www.ebi.ac.uk/pride/archive/projects/PXD009808">PRIDE PXD009808 datasets</a> and the corresponding publication: Ove J. R. Gustafsson, Matthew T. Briggs, Mark R. Condina, Lyron J. Winderbaum, Matthias Pelzing, Shaun R. McColl, Arun V. Everest-Dass, Nicolle H. Packer, Peter Hoffmann. “MALDI imaging mass spectrometry of N-linked glycans on formalin-fixed paraffin-embedded murine kidney.” Analytical and Bioanalytical Chemistry (2015) 407: 2127. <a href="https://doi.org/10.1007/s00216-014-8293-7">https://doi.org/10.1007/s00216-014-8293-7</a></p> <p><br> Three 6µm sections of formalin-fixed paraffin-embedded murine kidney tissue specimens were prepared for MALDI imaging. To release N-linked glycans, PNGase F was printed onto two kidney sections. In the third section one area was printed with buffer to serve as a control and another area was covered with N-glycan calibrants (Gustafsson et al., Figure 4 a-c). 2,5-DHB matrix was sprayed onto the tissue sections and MALDI imaging was performed with 100 µm spatial resolution using a MALDI-TOF/TOF instrument.</p> <p><br> We processed the original imzML files to make them concise but meaningful as training data sets in the Galaxy training network (https://galaxyproject.github.io/training-material/).<br> We reduced the m/z range to 1250 – 2310 and resampled the m/z values with a step size of 0.1. The main part of the training is based on the control and first treated kidney file for which we selected representative pixels to further decrease file size (files: ‘control’, ‘treated1’). To test the results on the complete dataset we also provide a file in which both treated kidney sections, the control and the calibrant files are combined after decreasing and resampling the m/z range as described above. The combined file was normalized to the total ion current (TIC) (file: ‘all_files’). All processing steps were performed on<a href="http://https://usegalaxy.eu"> https://usegalaxy.eu</a> with the tools ‘MSI filtering’, ‘MSI combine’ and ‘MSI preprocessing’ in version 1.12.1.3).<br> Additionally, the LC-MS/MS results were extracted from table S2 of the publication by Gustafsson et al. and are provided as tabular file to enable the N-glycan identification (file: 'Glycan_IDs').</p>
Dissolved storage glycans shaped the community composition of abundant bacterioplankton clades during a North Sea spring phytoplankton bloom
<p>In 2020 we sampled a complete spring bloom in the German Bight over a 90-day period. Bacterioplankton metagenomes from 30 time-points allowed reconstruction of 251 metagenome-assembled genomes (MAGs). Corresponding metatranscriptomes highlighted 50 particularly active MAGs of the most abundant clades. Saccharide measurements together with bacterial polysaccharide utilization loci (PUL) expression data identified β-glucans (diatom laminarin) and α-glucans as the most prominent dissolved polysaccharide substrates metabolized by the bacterioplankton. Here we are depositing all supporting environmental data for the analyzed 2020 Helgoland spring algal bloom. This includes physicochemical data, data on algal abundances and biovolumes, data on copepod and flagellate abundances, data on monosaccharide and antibody-based polysaccharide measurements, and 16S rRNA-based bacterial diversity data. The corresponding metagenome, metatranscriptome and MAG sequence data of this project are available from the European Nucleotide Archive (accession PRJEB52999).</p>
Information transfer in mammalian glycan-based communication
<p>Glycan-binding proteins, so-called lectins, are exposed on mammalian cell surfaces and decipher the information encoded within glycans translating it into biochemical signal transduction pathways in the cell. These glycan-lectin communication pathways are complex and difficult to analyze. However, quantitative data with single <span class="ins cts-3">-</span>cell resolution provide means to disentangle the associated signaling cascades. We chose C-type lectin receptors (<span class="jrnlPatterns">CTLs</span>) expressed on immune cells as a model system to study their capacity to transmit information encoded in glycans of incoming particles. In particular, we used <span class="del cts-4">NF-κB</span><span class="ins cts-4">nuclear factor kappa-B</span>-reporter cell lines expressing <span class="ins cts-4">DC-specific ICAM-3–grabbing nonintegrin<span class="ins cts-7"> <span class="ins cts-7">(DC-SIGN)</span></span></span><span class="del cts-4">DC-SIGN</span>, <span class="ins cts-4">macrophage C-type lectin (</span>MCL<span class="ins cts-4">)</span>, dectin-1, dectin-2, and <span class="del cts-4">mincle</span><span class="ins cts-4">macrophage-inducible C-type lectin<span class="ins cts-6"> (MINCLE)</span></span>, as well as TNFαR and TLR-1<span class="jrnlPatterns">&</span><span class="jrnlPatterns">2</span> in monocytic cell lines and compared their transmission of glycan-encoded information. All receptors <span class="del cts-7">did </span>transmit information with similar signaling capacity, except dectin-2. This lectin was identified to <span class="ins cts-3">be </span>less efficient in information transmission compared to the other CTLs<span class="ins cts-3">,</span> and even wh<span class="del cts-7">ile </span><span class="ins cts-7">en </span>the sensitivity of the dectin-2 pathway was enhanced by overexpression of its co-receptor FcRγ, its transmitted information was not. Next, we expanded our investigation toward<span class="del cts-3">s</span> the integration of multiple signal transduction pathways including synergistic lectins, which is crucial during pathogen recognition. We show how the signaling capacity of lectin receptors using a similar signal transduction pathway (dectin-1 and dectin-2) <span class="del cts-3">are</span><span class="ins cts-3">is</span> being integrated by compromising between the lectins. In contrast, co-expression of MCL synergistically enhanced the dectin-2 signaling capacity, particularly at low <span class="ins cts-3">-</span>glycan stimulant concentration. By using dectin-2 and other lectins as examples, we demonstrate how signaling capacity of dectin-2 is modulated in the presence of other lectins<span class="ins cts-3">,</span> and therefore<span class="ins cts-3">,</span> the findings provide insight into how immune cells translate glycan information using</p>
An Open Label Study of IgG Fc Glycan Composition in Human Immunity
ClinicalTrials.gov study NCT01967238. IPD Sharing: NO. Countries: 1. Publications: 1.
Information transfer in mammalian glycan-based communication
Open the record for dataset details and reuse information.
Data from: Mucus-derived glycans are inhibitory signals for <em>Salmonella</em> Typhimurium SPI-1-mediated invasion
Open the record for dataset details and reuse information.
Supplementary data for: Ligand-specific tuning of CLEC10A signalling strength and dendritic cell responses through engagement of different GalNAc-containing glycan structures
Open the record for dataset details and reuse information.
Exploration, representation and rationalization of the conformational phase-space of N-glycans
<p>Collection of structure and trajectory files of free N-glycans simulated in solution using either the CHARMM36m or GLYCAM06j force field. Simulations are either plain MD or enhanced sampled via the combination of replica exchange methods REST-RECT.</p>
GWAS summary statistics pertaining to the publication "Mapping of the gene network that regulates glycan clock of ageing"
<p>Dataset pertaining to the publication "Mapping of the gene network that regulates IgG galactosylation". If you use this dataset, please cite the manuscript in order to fairly acknowledge the contribution of all participating studies and their sponsors.</p> <p>Data consists of three gzipped files corresponding to genome-wide association meta-analysis (GWAMA) of three IgG N-glycome traits describing the percentage of galactosylation: G0, G1 and G2. The files are tab-separated and contain genome-wide association meta-analysis data for the discovery studies. Summary data are given for the meta-analyses of over 15 million directly genotyped or imputed single variant polymorphisms corresponding to the HRC (Haplotype Reference Consortium) r1.1 reference panel. Meta-analysis estimates are corrected for inflation of test statistics using genomic control at the individual study level.</p>
Navigating the Maze of Mass Spectra: A Machine-Learning Guide to Identifying Diagnostic Ions in O-Glycan Analysis
<p>FragmentFactory_dataset is a pickled pandas DataFrame and should be loaded using the following code:</p> <pre><code>import pandas as pd FF_data = pd.read_pickle('/my_directory/FragmentFactory_dataset.pkl')</code></pre> <p> </p>
Bacteroides cellulosilyticus-directed glycans relieve colitis
<p>Bacteroides cellulosilyticus correlates with inflammatory bowel disease (IBD). Targeting an increase in abundance of B. cellulosilyticus is a feasible approach to treating IBD. Although B. cellulosilyticus is responsive to dietary components, untargeted manipulation cannot focus on target microbe and can lead to an increase in harmful bacteria in the microbiota. Despite breakthroughs in methods for regulating specific microbes, the protocols are expensive, time-consuming, and difficult to follow. Glycans based on microbial-carbohydrate-active enzymes (CAZymes) that target a specific microbiota would provide a potential solution.</p>
Fig. 6 in TIM barrel fold and glycan moieties in the structure of ICChI, a protein with chitinase and lysozyme activity
Fig. 6. (A) Three-dimensional (3D) structural model of the ICChI- NAG complex illustrating by Docking. (B) DIMPLOT result revels the interacting amino acid residue during formation of complex.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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