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2,216 results for “membrane”
Role of internal loop dynamics in antibiotic permeability of outer membrane porins
<p>Representative structures for Markov State models for wild type and mutants of OmpF "Coupling of internal loop dynamics and antibiotic permeation in outer membrane porins"</p>
Simulation Input Data for "Molecular simulation of lignin-related aromatic compound permeation through Gram-negative bacterial outer membranes"
<p>This is the reduced data behind an upcoming manuscript investigating permeability across the outer membranes of Gram-negative bacteria. The data is taken directly from the directory structure that contains both the simulation and analysis, with excluded trajectory files and intermediate products to fit within the zenodo upload limit. The tar command used to generate this tarball was:</p> <pre><code class="language-bash">tar --exclude="*BAK" --exclude="*dcd" --exclude="*xsc" --exclude="*vel" --exclude="*coor" --exclude="*csv" --exclude="*old" --exclude="*log" --exclude="*state" --exclude="*watpos/*npz" --exclude="*new*png" --exclude="*frame*png" --exclude="*ppm" --exclude="*mp4" --exclude="*bayesdata*npy" --exclude="*run.npy" -zcvf OM.tgz OuterMembrane</code></pre> <p>Within the OuterMembrane directory, there are 3 primary subdirectories.</p> <ul> <li><strong>Build </strong>contains the scripts and files to build the simulation systems, including the CHARMM-GUI output</li> <li><strong>Equilibrium</strong> contains the equilibrium simulation inputs and the analysis scripts (subdirectory <strong>Analysis</strong>)</li> <li><strong>REUS2</strong>, which has the replica exchange inputs and essential output. It also contains an <strong>Analysis</strong> subdirectory that carries out the analysis within the text.</li> </ul>
In-operando visualization of redox flow battery in membrane-free microfluidic platform
<p>Images are raw data of main figure 1.(B-D) from "In-operando visualization of redox flow battery in membrane-free microfluidic platform"</p>
Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"
<p><strong>Project files provided as supporting information to the manuscript "Membrane binding of pore-forming gamma-hemolysin components studied at different lipid compositions"</strong></p> <p>The dataset contains the following folders:</p> <p>- number_of_contacts: files with the number of contacts between the rim domains of LukF and Hlg2 and the membrane, for different bilayer compositions (Fig. 2).</p> <p>- binding_events: files with the duration of the time interavals in which LukF and Hlg2 are bound to the membrane, and the scripts used to compute for each system the number of binding/unbinding events and the average membrane residence time (Fig. 3).</p> <p>- electrostatic_potential: files of the surface electrostatic potential produced with the adaptive Poisson-Boltzmann solver and used for visualization with Chimera (Fig. 4).</p> <p>- angles: files with the angle values computed between the protein axis and the z-axis of the simulation box (Fig. 5).</p> <p>- contacts_per_residue: files with the number of frames in which each protein residue is in contact with the membrane, with respect to the total number of frames in which the rim domain interacts with the bilayer (Fig. 5).</p> <p>- distance_protein_membrane: files with the minimum distance between the protein and the membrane (Fig. 6).</p> <p>- binding_sites: file produced by PyLipid with relevant information on the main DOPC binding sites identified in LukF.</p> <p>- min_distance_per_residue: files with the minimum distance between each protein residue and the membrane, computed at the binding steps (Fig. S5).</p>
Source molecular simulation data for calculating energy and friction profiles and permeability coefficients through model lipid membranes
<p>Energy files from GROMACS molecular dynamics simulations with enhanced free energy sampling contain time-dependent evolution of the free energy profiles and friction profiles (and other energies and simulation properties) that were used for calculating permeability coefficients in the publication https://www.biorxiv.org/content/10.1101/2021.07.16.452599v1</p> <p>Simulation system contains a lipid POPC or DPPC bilayer with a varying amount of cholesterol (specified as mol% in the file name). Hydrophobic level of the permeating particle is specified as "level-I", "level-II" etc. When unspecified in the file name, the particle is hydrophobic level "III". Lipids D-C14-PC denote PC lipids with both tails monounsaturated of length 14 carbon atoms. DOPC is equivalent to D-C18-PC. (Detailed description in the publication)</p> <p>Adaptive Weighted Histogram (AWH) method was used to sample the free energy profile of translocating small molecule through the lipid bilayer.</p> <p>GROMACS tool `gmx awh` reads the files and provides the described profiles.</p> <p>Files were generated by GROMACS `mdrun` simulation engine version 2019.3.</p> <p> </p> <p>Coarse-grained MARTINI 3.0 model was used for modeling the biomolecular interactions.</p> <p>Scripts to perform the simulations and the files with initial configurations and simulation settings are stored in a public GitHub repository depozited on Zenodo.org: <a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p> <p> </p> <p>Abraham, M. J. et al. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1–2, 19–25 (2015).</p> <p>Lindahl, V., Lidmar, J. & Hess, B. Accelerated weight histogram method for exploring free energy landscapes. J. Chem. Phys. 141, 044110 (2014).</p> <p>Souza, P. C. T. et al. Martini 3: a general purpose force field for coarse-grained molecular dynamics. Nat. Methods 18, 382–388 (2021).</p> <p>Melcr, J. Git repository with analysis scripts for MD simulations of permeability through lipid membranes. (2021) doi:<a href="https://doi.org/10.5281/zenodo.5082249">https://doi.org/10.5281/zenodo.5082249</a>.</p>
Raw Data for the article: Atrial Septostomy for Left Ventricular Unloading During Extracorporeal Membrane Oxygenation for Cardiogenic Shock: Animal Model
<p><strong>Objectives: </strong>The aim of this study was to quantify and understand the unloading effect of percutaneous balloon atrial septostomy (BAS) in acute cardiogenic shock (CS) treated with venoarterial (VA) extracorporeal membranous oxygenation (ECMO).</p> <p><strong>Background: </strong>In CS treated with VA ECMO, increased left ventricular (LV) afterload is observed that commonly interferes with myocardial recovery or even promotes further LV deterioration. Several techniques for LV unloading exist, but the optimal strategy and the actual extent of such procedures have not been fully disclosed.</p> <p><strong>Methods: </strong>In a porcine model (n = 11; weight 56 kg [53-58 kg]), CS was induced by coronary artery balloon occlusion (57 minutes [53-64 minutes]). Then, a step-up VA ECMO protocol (40-80 mL/kg/min) was run before and after percutaneous BAS was performed. LV pressure-volume loops and multiple hemoglobin saturation data were evaluated. The Wilcoxon rank sum test was used to assess individual variable differences.</p> <p><strong>Results: </strong>Immediately after BAS while on VA ECMO support, LV work decreased significantly: pressure-volume area, end-diastolic pressure, and stroke volume to ∼78% and end-systolic pressure to ∼86%, while superior vena cava and tissue oximetry did not change. During elevating VA ECMO support (40-80 mL/kg/min) with BAS vs without BAS, we observed 1) significantly less mechanical work increase (122% vs 172%); 2) no end-diastolic volume increase (100% vs 111%); and 3) a considerable increase in end-systolic pressure (134% vs 144%).</p> <p><strong>Conclusions: </strong>In acute CS supported by VA ECMO, atrial septostomy is an effective LV unloading tool. LV pressure is a key component of LV work load, so whenever LV work reduction is a priority, arterial pressure should carefully be titrated low while maintaining organ perfusion.</p>
Raw Data for the article: Apneic Tracheostomy in COVID-19 Patients on Veno-Venous Extracorporeal Membrane Oxygenation
<p>COVID-19 creates an impressive burden for intensive care units in terms of need for advanced respiratory care, with a huge number of acute respiratory distress syndromes (ARDS) requiring prolonged mechanical ventilation. In some cases, this proves to be insufficient, with a refractory respiratory failure calling for an extracorporeal approach (veno-venous ECMO). In this scenario, most of these patients need an early tracheostomy procedure to be carried out, which creates the risk of distribution of aerosol particles, possibly leading to personnel infection. The use of apneic tracheostomy has been proposed for COVID-19 patients, but in case of ECMO it may produce lung derecruitment, severe hypoxemia, and sudden worsening of respiratory mechanics. We developed an apneic tracheostomy technique and applied it in over 32 patients supported by veno-venous ECMO. We present data showing the safety and feasibility of this technique in terms of patient care and personnel protection. Gas exchange and pH did not show statistically significant changes after the tracheostomy, nor did respiratory mechanics data or the need for inspiratory pressure and FiO<sub>2</sub>. The use of apneic tracheostomy was a safe option for patient care during ECMO and reduced the possibility of virus spreading.</p>
Dataset for the paper "High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells", DOI:10.1038/s41929-022-00772-9
<p>The data in this spreadsheet was used to produce the figures in the paper </p> <p>Authors: Asad Mehmood, Mengjun Gong, Frédéric Jaouen, Aaron Roy, Andrea Zitolo, Anastassiya Khan, Moulay-Tahar Sougrati, Mathias Primbs, Alex Martinez Bonastre, Dash Fongalland, Goran Drazic, Peter Strasser, Anthony Kucernak</p> <p>Title: High Loading of Single Atomic Iron Sites in Pyrolysed Fe-NC Oxygen Reduction Catalysts for Proton Exchange Membrane Fuel Cells</p> <p>Journal: Nature Materials</p> <p>DOI: 10.1038/s41929-022-00772-9</p> <p>Please cite the above reference if you wish to use this data </p> <p> </p> <p>DOI of data: 10.5281/zenodo.6411262</p>
Input files for publication: Binding of DEP Domain to Phospholipid Membranes: More Than Just Electrostatics
<p>Input files for publication: Francesco L. Falginella, Marek Kravec, Martina Drabinová, Petra Paclíková, Vítězslav Bryja, and Robert Vácha: Binding of DEP Domain to Phospholipid Membranes: More Than Just Electrostatics</p>
Conserved structural elements specialize ATAD1 as a membrane protein extraction machine
<p>The mitochondrial AAA protein ATAD1 (in humans; Msp1 in yeast) removes mislocalized membrane proteins, as well as stuck import substrates from the mitochondrial outer membrane, facilitating their re-insertion into their cognate organelles and maintaining mitochondria's protein import capacity. In doing so, it helps to maintain proteostasis in mitochondria. How ATAD1 tackles the energetic challenge to extract hydrophobic membrane proteins from the lipid bilayer and what structural features adapt ATAD1 for its particular function has remained a mystery. Previously, we determined the structure of Msp1 in complex with a peptide substrate (Wang et al., 2020). The structure showed that Msp1's mechanism follows the general principle established for AAA proteins while adopting several structural features that specialize it for its function. Among these features in Msp1 was the utilization of multiple aromatic amino acids to firmly grip the substrate in the central pore. However, it was not clear whether the aromatic nature of these amino acids were required, or if they could be functionally replaced by aliphatic amino acids. In this work, we determined the cryo-EM structures of the human ATAD1 in complex with a peptide substrate at near atomic resolution. The structures show that phylogenetically conserved structural elements adapt ATAD1 for its function while generally adopting a conserved mechanism shared by many AAA proteins. We developed a microscopy-based assay reporting on protein mislocalization, with which we directly assessed ATAD1's activity in live cells and showed that both aromatic amino acids in pore-loop 1 are required for ATAD1's function and cannot be substituted by aliphatic amino acids. A short α-helix at the C-terminus strongly facilitates ATAD1's oligomerization, a structural feature that distinguishes ATAD1 from its closely related proteins.</p>
Selectivity improvement of polysulfone-zeolite templated carbon membrane by annealing and coating treatment for CO2/CH4 and H2/CH4 separation
<p>Natural gas, which majorly consists of methane (CH4), is a renewable energy source widely used as fuel, as well as hydrogen (H2). However, in its production process of each gas containing other impurity gasses. Therefore, membrane technology is needed to separate the gasses from the impurities. Mixed Matrix Membrane (MMM) is a more promising membrane compared to the others. Zeolite Templated Carbon-based MMM offers a good separation performance because of ZTC's large surface area and well-defined pore structure. However, the interfacial void in MMM is challenging to be prevented, which contributes to the reduced separation performance. This study aims to enhance separation performance by modifying membrane surfaces using various methods. MMM PSF/ZTC modified by annealing at 120, 150, and 190 °C; coating using 0.01, 0.03, and 0.05 mol tetramethylorthosilicate (TMOS); and both combinations annealing at 190 °C and coating using 0.03 mol TMOS. MMM PSF/ZTC was successfully significantly improved CO2/CH4 selectivity by both combinations of annealed at 190 °C and coated 0.03 mol TMOS from 1.37 to 5.90 (331%) and H2/CH4 selectivity by coating with 0.03 mol TMOS from 4.58 to 65.76 (1378%). The enhancement of selectivity was due to structural changes of the membrane that was denser and smoother, which SEM and AFM observed. In this study, annealing and coating treatment are the methods that can use for improving the polymer matrix and filler particle adhesion.</p>
A feed-forward pathway drives LRRK2 kinase membrane recruitment and apparent activation
<p>Activating mutations in the Leucine Rich Repeat Kinase 2 (LRRK2) cause Parkinson's disease and activated LRRK2 phosphorylates a subset of Rab GTPases. Moreover, Golgi-associated Rab29 can recruit LRRK2 to the surface of the Golgi and activate it there for both auto- and Rab substrate phosphorylation. Here we define the precise Rab29 binding region of the LRRK2 Armadillo domain between residues 360-450 and show that this site, termed "Site #1", can also bind additional LRRK2 substrates, Rab8A and Rab10. Moreover, we identify a distinct, N-terminal, higher affinity interaction interface between LRRK2 phosphorylated Rab8 and Rab10 termed "Site #2", that can retain LRRK2 on membranes in cells to catalyze multiple, subsequent phosphorylation events. Kinase inhibitor washout experiments and mutation analysis demonstrate that rapid recovery of kinase activity in cells depends on the ability of LRRK2 to associate with phosphorylated Rab reaction products. Reconstitution of purified LRRK2 recruitment onto planar lipid bilayers decorated with Rab10 protein demonstrates cooperative association of only active LRRK2 with phospho-Rab10-containing membrane surfaces. These experiments reveal a feed-forward pathway that provides spatial control and apparent membrane activation of LRRK2 kinase activity.</p>
Hyperactive antifreeze protein from the beetle Rhagium mordax stabilizes model lipid membranes during temperature dependent phase transition
<p>Data from the study submitted in the paper Hyperactive antifreeze protein from the beetle Rhagium mordax stabilises model lipid membranes during temperature-dependent phase transition</p> <p>Data Includes;</p> <p>1. DSC results of RmAFPs interactions with liposomes showing Tm, ΔHcal and Full width at half maximum (FWHM) as well as Phase transitions thermographs by DSC on 1.5mg/ml SUV liposomes either with 60μM (or 0.75mg/ml) RmAFPs or without RmAFPs as control.</p> <p>2. fluorescence spectroscopy data, a complete compilation</p>
Operational parameters of lab-scale anaerobic membrane bioreactors amended with graphene oxide
<p>The file contains the logbook with all the monitored parameters of a 5 L lab-scale anaerobic membrane bioreactor (AnMBR) fed with raw municipal wastewater collected from Girona’s wastewater treatment plant (Spain).</p> <p>The reactor was inoculated with anaerobic sludge and operated without modification until day 85, where a mix of organic micropollutants at 0.20 µM were dosed. Furthermore, on day 216, graphene oxide (GO) at 5 mg of GO per g of volatile solids (VS) was added to the AnMBR.</p>
Figure 3–Figure Supplement 4 of the paper 'A Feed-forward Pathway Drives LRRK2 kinase Membrane Recruitment and Activation'
<p>Raw immunoblotting and quantitation data for Figure 3–Figure Supplement 4 of the paper "A Feed-forward Pathway Drives LRRK2 kinase Membrane Recruitment and Activation" (Edmundo G. Vides, Ayan Adhikar, Claire Y. Chiang, Pawel Lis, Elena Purlyte, Charles Limouse, Justin L. Shumate, Elena Spínola-Lasso, Herschel S. Dhekne, Dario R. Alessi, and Suzanne R. Pfeffer)</p> <p><strong>File descriptions:</strong></p> <p><strong>Figure 3–Figure Supplement 4A</strong><br> <strong>Fig.3.S4A_700(tRab10;tLRRK2)_High.tif </strong>- Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; high contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4A_700(tRab10;tLRRK2)_Low.tif</strong> - Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; low contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4A_800(pRab10;pLRRK2)_High.tif</strong> - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4A_800(pRab10;pLRRK2)_Low.tif</strong> - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4A_HA.tif </strong>- ChemiDoc Scan (ECL) showing HA blot</p> <p><strong>Figure 3–Figure Supplement 4B</strong></p> <p><strong>Fig.3.S4B_700(tRab10;tLRRK2)_High.tif </strong>- Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; high contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4B_700(tRab10;tLRRK2)_Low.tif</strong> - Licor scan (channel 700) showing total Rab10 and total LRRK2 blots; low contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4B_800(pRab10;pLRRK2)_High.tif</strong> - Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4B_800(pRab10;pLRRK2)_Low.tif </strong>- Licor scan (channel 800) showing Rab10 pT73 and LRRK2 pS935 blots; high contrast<br> Panel 1 - top left membrane<br> Panel 2 - top right membrane<br> Panel 3 - bottom left membrane<br> Panel 4 - bottom right membrane</p> <p><strong>Fig.3.S4B_HA.tif </strong>- ChemiDoc Scan (ECL) showing HA blot</p> <p><strong>Fig.3.S4_immunoblotting_numerical_data.xlsx</strong> - Excel sheet containing raw quatitation values from Licor scans as well as calculated pRab10/Rab10 ratios (values used in Figure 3C and 3D)</p>
Characterization of the nanoparticles and modified ceramic membranes
<p>The membranes were coated with one layer of CeO<sub>2</sub> solution and one layer of CeTiOx. Consequently, three different coating layers will be evaluated</p> <ul> <li>One layer of CeO<sub>2</sub></li> <li>One layer of CeTiO<sub>x</sub></li> <li>One layer of CeO2 and one layer of CeTiO<sub>x</sub></li> </ul> <p>The ceramics membranes, as well as, their modified surfaces were characterized in order to determine its chemical and phase composition, crystallite size, structure and morphology, using the following analytical techniques: Scanning Electron Microscopy (SEM) with Energy Dispersive X-ray Spectroscopy (EDS); X-ray Diffraction Analysis (XRD); Fourier Transform Infrared Spectroscopy (FTIR) and Atomic Force Microscopy (AFM). Mercury Intrusion Porosimetry (MIP) was used to investigate the surface area and pore size distribution. Zetasizer was used to identify the zeta potential of the nanoparticles used in the suspension experiments. </p>
Matrimid-JUC-62 and Matrimid-PCN-250 mixed matrix membranes displaying light-responsive gas separation and beneficial ageing characteristics for CO2/N2 separation
<p>FTIR, TGA, XRD and gas permeation results for our study of Matrimid-JUC-62 and Matrimid-PCN-250 mixed matrix membranes, including light-responsive properties.</p> <p>Published in: Prasetya, N., Teck, A.A., Ladewig, B.P., <em>Matrimid-JUC-62 and Matrimid-PCN-250 mixed matrix membranes displaying light-responsive gas separation and beneficial ageing characteristics for CO<sub>2</sub>/N<sub>2</sub> separation</em>, (2018) <strong>Scientific Reports</strong>, 8 (1), 2944. </p> <p>doi:<a href="http://dx.doi.org/10.1038/s41598-018-21263-7">10.1038/s41598-018-21263-7</a></p>
Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback
<p>Data for all the plots from original paper "Membrane Fluctuation Model to Understand the Effect of the Receptor Nanoclustering on the Activation of Natural Killer Cells through Biomechanical Feedback".</p>
Data supporting: "Interaction of MRI Contrast Agent [Gd(DOTA)]− with Lipid Membranes: A Molecular Dynamics Study"
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Nucleotide sequence database of Copper-containing membrane monooxygenases genes for analysing primer pairs targeting the ammonia monooxygenase subunit A gene of complete ammonia oxidising Nitrospira
<p>Nucleotide sequences of 487 Cu-mmo genes, including amoA comammox clade A and clade B, amoA ammonia oxidizing bacteria as well as other Cu-mmo genes.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
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.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.