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2,216 results for “membrane”
Pure POPE membrane simulations with the CHARMM-Drude force field (OpenMM 7.5.0)
<p>MD simulation data of a pure POPE membrane with the CHARMM-Drude force field generated with the OpenMM 7.5.0 simulation engine.</p> <p>All the input parameters are available in the *inp file. The initial structures have been obtained from CHARMM-GUI.</p> <p>Total simulation duration is 300 ns (100 ns x 3, continuing from the last frame, combined with the mdconvert). First 50 ns is discarded as equilibration. This data set contains 300 ns data with 3000 frames (saving frequency is 100 ps).</p> <p>In total, 72 POPC lipids in each leaflet (144 in total), 5040 SWM4-NPD water molecules.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "unwrapped_all_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 100 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 3 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p> </p> <p> </p>
Pure POPC Membrane with 350mM CaCl2 simulations using Drude Polarizable Force Field and OpenMM
<p>500 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 41 CaCl2, and 6400 SWM4 water molecules.</p> <p>wrapped.dcd has a frame saving frequency of 100 ps.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset.</p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the wrapped_full.dcd trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full_fixed_dt.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps. </strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 5 sub-trajectories, each of which starts from the last frame of the previous one and runs for 100 ns. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p>
Simulation data for: "Unique Amphipathic a-helix Drives Membrane Insertion and Enzymatic Activity of ATG3"
<p>Simulation data from Nishimura et al. (2023), "Unique Amphipathic a-helix Drives Membrane Insertion and Enzymatic Activity of ATG3".</p> <p>The dataset contains the MD simulations executed for the Atg3/LC3/lipid membrane system, both in the WT and 5W-mutated variants.</p> <p>More information can be found in the README file and in Table 1 of the cited paper.</p> <p> </p>
Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature
<p>This dataset contains processed data corresponding to the figures in the main text of our paper "Laser cooling a membrane-in-the-middle system close to the quantum ground state from room temperature"</p> <p>The dataset consists of 10 .csv files and a jupyter notebook for generating the figures in the main text.</p>
Data for publication "Lipid oxidation controls peptide self-assembly near membranes through a surface attraction mechanism"
<p>The data provided refer to our published article:</p> <p>T. John,* S. Piantavigna, T. J. A. Dealey, B. Abel, H. J. Risselada, L. L. Martin*, Lipid oxidation controls peptide self-assembly near<br>membranes through a surface attraction mechanism, Chem. Sci. 14 (2023), 3730-3741. <a href="https://doi.org/10.1039/d3sc00159h">https://doi.org/10.1039/d3sc00159h</a>.</p>
Pure POPC membrane simulations with 1000 mM CaCl2 with the CHARMM-Drude force field (OpenMM)
<p>400 ns MD simulation of pure POPC membrane using Charmm-Drude polarizable force field. The system contains 128 POPC lipids, 114 CaCl2, and 6400 SWM4 water molecules.</p> <p>Before running the Drude simulation, the system has been equilibriated using Charmm36 force field for 200 ns. The last frame of that simulation was used to generate Drude polarizable model. The first 100 ns of the Drude simulation has been discarded from this dataset. Total simulation time is 500 ns, included data is 397.5 ns.</p> <p> </p> <p><strong>It has been discovered that (https://github.com/NMRLipids/Databank/issues/2#issuecomment-1357871243) the previously uploaded trajectory did not have the correct timestamp: the timestep between two consecutive simulation frames was not correctly embedded into the trajectory information. Therefore, with the latest version we are uploading the "wrapped_full.xtc" which has the correct timestamp. The frame saving frequency in this trajectory is 10 ps and there are 39750 frames.</strong></p> <p><strong>This new update should not invalidate any previous calculations that did not explicitly read the timestamp information from the trajectory.</strong></p> <p><strong>This simulation consists of 4 sub-trajectories, each of which starts from the last frame of the previous one. These trajectories (originally in dcd format) were concatenated and saved in xtc format with MDAnalysis.</strong></p> <p><strong>Centering of the trajectories has been done via below MDAnalysis script</strong></p> <p><strong> ...: u = mda.Universe('../step3_charmm2omm.psf', 'step5.dcd')<br> ...: prot = u.select_atoms("resname POPC")<br> ...: ag = u.atoms<br> ...: workflow = (transformations.unwrap(ag),<br> ...: transformations.center_in_box(prot, center='mass'),<br> ...: transformations.wrap(ag, compound='fragments'))<br> ...: u.trajectory.add_transformations(*workflow)</strong></p> <p> </p>
Dataset: A Neutral pH Aqueous Biphasic System Applied to both Static and Flow Membrane-free Battery
<p>Dataset for the results shown in the publication "A Neutral pH Aqueous Biphasic System Applied to both Static and Flow Membrane-free Battery"</p>
Dynamic and membrane DAC ramp dataset storage, raw and azimuthal integrated as xy.files.
<p>The datasets are part of a project exploring the onset of phase transitions, the development of microstrain and the lattice parameters during fast compression rates, resembling those during e.g. propagation seismic shockwaves and large body impacts. The strain rate conditions achieved in our experiments are between those reached in the common static (DAC) and shock experiments.</p> <p><br> The current datasets contain a selected set of xy.files of azimuthal integrated 2D-diffraction images of either Mg0.2Fe0.8O sample or Fe (Iron) - with and without Pt (platinum) pressure marker and with and without Ne (Neon) pressure transmitting medium (PTM).<br> Diffraction images were collected at ambient temperature (in 2019 and 2020) at the ECB P02.2 Beamline, PETRA III, DESY, Germany using the piezo-driven dynamic diamond anvil cells (dDAC) in combination with fast LAMBDA GaAs 2M detectors at 25.6 keV (0.4843 Å).<br> <br> The datasets are provided as test datasets for machine learning application on spectra classification.<br> The version will be further updated once the whole work has been published and the full datasets can be made available.<br> Please find below the link towards the machine learning application.</p> <p><a href="https://github.com/European-XFEL-examples/panosc-ml-spectra-classification">https://github.com/European-XFEL-examples/panosc-ml-spectra-classification</a></p>
Describing ion transport and water splitting in an electrodialysis stack with bipolar membranes by a 2-D model: Experimental validation
<p>Electrodialysis with bipolar membranes (EDBM) has drawn attention motivated by their application in gener- ating reagents from salts. Due to the water splitting (WS) occurring at the junction of the bipolar membranes (BPMs), where the anion and cation layers are in strict contact, H+ and OH- are released from the BPM producing acid and alkali on the respective compartment. Considering this application, the interest of this work is to provide further understanding of the mechanisms of WS and transport of species in EDBM. This work develops and utilizes, for the first time, an experimentally validated two-dimensional (2-D) computational model, in which the Navier-Stokes and Nernst-Planck equations are coupled with the description of WS given by the Second Wien effect. In addition, a 1-D geometry is also proposed to perform a comparison between electroneutrality and Poisson charge conservation. The model is computationally solved using COMSOL Multiphysics. According to simulations, electroneutrality is valid for 2-D geometries. Moreover, the semipermeable characteristics of the membranes are assessed by means of evidencing a polarization effect resulting in a double-electric layer. The model proposed predicts a significant proton leakage, and facilitates the study of WS within the BPMs.</p>
Gamma-hemolysin components: computational strategies for LukF-Hlg2 dimer reconstruction on a model membrane
<p>Project files provided as supporting information to the manuscript “Gamma-Hemolysin Components: Computational Strategies for LukF-Hlg2 Dimer Reconstruction on a Model Membrane”. The data set contains the following folders:</p> <ul> <li>LukF_Hlg2_distance: files with the minimum distance between LukF and Hlg2 as a function of time for the simulated replica (Fig. S1); files with the minimum distance between each LukF residue and the Hlg2 monomer and vice versa, averaged over the last 600 ns of simulation in the replica where the spontaneous dimerization is observed (fig. S5).</li> <li>HADDOCK_dimer_crystal_pore_displacement: files with the displacement between Hlg2 residues in the HADDOCK model dimer and the same dimer in the crystal pore, after alignment on the LukF monomer (for both the HADDOCK model in presence and in absence of the LukF N-ter) (Fig. S13)</li> <li>interface_area: files with the interface area between the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed, as a function of time (total interface and contribution of LukF and Hlg2 rim domains) (Fig. S4)</li> <li>angles: files with the histograms of the angle between LukF and the axis perpendicular to the membrane, for the simulation of the single LukF monomer and for that capturing the spontaneous dimerization on the membrane (Fig. 4); file with the angle between the LukF and the Hlg2 axis as a function of time in the replica where the spontaneous dimerization is observed (Fig. S6)</li> <li>HADDOCK_scores: files with the HADDOCK scores of the predicted LukF-Hlg2 dimers and their RMSD values computed with respect to the same dimer in the crystal pore. The data are reported for the four top-scored models of each cluster ( for both the HADDOCK models in the presence and in the absence of the LukF N-ter) (Fig. 5).</li> <li>RMSD: files with the RMSD as a function of time for the LukF and the Hlg2 monomers in the replica where the spontaneous dimerization on the membrane is observed (Fig. 2, Fig. S2, Fig. S3)</li> <li>RMSF: files with the RMSF of the LukF and the Hlg2 residues in the replica where the spontaneous dimerization on the membrane is observed and in the simulations of the single monomers (Fig. 2)</li> <li>interaction_persistences: files with H-bond (side chain + backbone and backbone only atoms) salt-bridge, and hydrophobic contact persistence matrices for the single LukF monomer simulated alone (299 x 299) and for the LukF-Hlg2 dimer (299+280 x 299+280) (Fig. 4, Fig. S7 + interactions reported in the manuscript)</li> <li>distance_protein_membrane: files with the minimum distance between each monomer and the membrane, in the last 200ns of the simulation of spontaneous dimerization on the membrane (Figure S9).</li> <li>distance_residues_interface: files with the distance between functionally relevant residues measured along the simulation of the HADDOCK dimer in the absence of LukF N-terminus (Figure S14).</li> </ul>
Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"
<p>Simulation data used in the publication Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper </p>
Data for: Human atlastin-3 is a constitutive ER membrane fusion catalyst (phylogenetic and sequence analysis)
<p>Homotypic membrane fusion catalyzed by the atlastin (ATL) GTPase sustains the branched endoplasmic reticulum (ER) network in metazoans. Our recent discovery that two of the three human ATL paralogs (ATL1/2) are C-terminally autoinhibited implied that relief of autoinhibition would be integral to the ATL fusion mechanism. An alternative hypothesis is that the third paralog ATL3 promotes constitutive ER fusion with relief of ATL1/2 autoinhibition used conditionally. However, published studies suggest ATL3 is a weak fusogen at best. Contrary to expectations, we demonstrate here that purified human ATL3 catalyzes efficient membrane fusion in vitro and is sufficient to sustain the ER network in triple knockout cells. Strikingly, ATL3 lacks any detectable C-terminal autoinhibition, like the invertebrate <em>Drosophila</em> ATL ortholog. Phylogenetic analysis of ATL C-termini indicates that C-terminal autoinhibition is a recent evolutionary innovation. We suggest that ATL3 is a constitutive ER fusion catalyst and that ATL1/2 autoinhibition likely evolved in vertebrates as a means of upregulating ER fusion activity on demand.</p>
Revealing the lipidome and proteome of Arabidopsis thaliana plasma membrane
<p>This table contains peaks aera values from GC-MS, TLC-GC-MS and LC-MS for characterization of Arabidopsis thaliana plasma membrane. These data were used for Fig. 6, 7, 8, 9 and S1, S2, S3 and S4 of Bahammou et al. 2023: Revealing the lipidome and proteome of Arabidopsis thaliana plasma membrane</p>
Dataset of the Journal article "Energy duty in direct contact membrane distillation of hypersaline brines operating at the water-energy nexus"
<p>This repository contains the Dataset of the Journal article "Energy duty in direct contact membrane distillation of hypersaline brines<br> operating at the water-energy nexus" published on Journal of Membrane Science. </p>
Lipid membrane simulations with flat-bottom and double-bilayer setups, part 2/2
<p>To cite: Biriukov, D. and Javanainen, M. Efficient Simulations of Solvent Asymmetry Across Lipid Membranes Using Flat-Bottom Restraints. J. Chem. Theory Comput. 2023, 19 (18), 6332–6341. DOI: <a href="https://doi.org/10.1021/acs.jctc.3c00614">10.1021/acs.jctc.3c00614</a></p> <p>Gromacs molecular dynamics simulations to compare membrane and solvent properties from lipid membrane simulations with flat-bottom and double-bilayer setups. CHARMM36 force field was used except for simulations with peptides, where a prosECCo model was used [Nencini et al., Biophys. J. 121, 157a (2022)]</p> <p>This dataset contains only double-bilayer simulations. The flat-bottom simulations together with all topologies and mdp files can be found in part 1 : DOI: <a href="https://zenodo.org/record/7973838">10.5281/zenodo.7973838</a></p> <p>Abbreviations in the names of simulation files:</p> <ul> <li>"fb" - simulations with a flat-bottom setup</li> <li>"2m" - simulations with two lipid membranes, i.e., a double-bilayer setup</li> <li>"popc" - membrane is modeled as a POPC lipid bilayer</li> <li>"mix" - a realistic membrane with various lipids is modeled, resembling the composition from [Lorent et al., Nat. Methods 16, 644–652 (2020)]</li> <li>"nak" - only sodium and potassium cations, together with chloride anions, are present in the system</li> <li>"ext" - as "nak", but also calcium and magnesium cations are added</li> <li>"r9" - as "nak" but also R9 (nona-arginine) peptides are added on both sides of the membrane</li> <li>"r9k" - as "nak" but also R9 (nona-arginine) peptides are added on the extracellular side of the membrane</li> <li>"one" - ions are present only on one side of a lipid membrane</li> <li>"freecl" - flat-bottom simulations but without restraints on chloride anions</li> <li>"s" - simulations were performed using the scaled-charge prosECCo75 force field based on CHARMM [Nencini et al., Biophys. J. 121, 157a (2022)]</li> <li>"restr" - restraint .gro file with ionic/peptide <em>z</em> coordinates set to zero</li> </ul>
Lipid membrane simulations with flat-bottom and double-bilayer setups, part 1/2
<p>To cite: Biriukov, D. and Javanainen, M. Efficient Simulations of Solvent Asymmetry Across Lipid Membranes Using Flat-Bottom Restraints. J. Chem. Theory Comput. 2023, 19 (18), 6332–6341. DOI: <a href="https://doi.org/10.1021/acs.jctc.3c00614">10.1021/acs.jctc.3c00614</a></p> <p>Gromacs molecular dynamics simulations to compare membrane and solvent properties from lipid membrane simulations with flat-bottom and double-bilayer setups. CHARMM36 force field was used except for simulations with peptides, where a prosECCo model was used [Nencini et al., Biophys. J. 121, 157a (2022)]</p> <p>This dataset contains all the topologies and flat-bottom simulation files. The double-bilayer simulation files can be found in part 2: DOI: <a href="https://zenodo.org/record/7974633">10.5281/zenodo.7974633</a></p> <p>Abbreviations in the names of simulation files:</p> <ul> <li>"fb" - simulations with a flat-bottom setup</li> <li>"2m" - simulations with two lipid membranes, i.e., a double-bilayer setup</li> <li>"popc" - membrane is modeled as a POPC lipid bilayer</li> <li>"mix" - a realistic membrane with various lipids is modeled, resembling the composition from [Lorent et al., Nat. Methods 16, 644–652 (2020)]</li> <li>"nak" - only sodium and potassium cations, together with chloride anions, are present in the system</li> <li>"ext" - as "nak", but also calcium and magnesium cations are added</li> <li>"r9" - as "nak" but also R9 (nona-arginine) peptides are added on both sides of the membrane</li> <li>"r9k" - as "nak" but also R9 (nona-arginine) peptides are added on the extracellular side of the membrane</li> <li>"one" - ions are present only on one side of a lipid membrane</li> <li>"freecl" - flat-bottom simulations but without restraints on chloride anions</li> <li>"s" - simulations were performed using the scaled-charge prosECCo75 force field based on CHARMM [Nencini et al., Biophys. J. 121, 157a (2022)]</li> <li>"restr" - restraint .gro file with ionic/peptide <em>z</em> coordinates set to zero</li> </ul> <p> </p>
Data set for "Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing"
<p>Data set for: Kiritani T, Pala A, Gasselin C, Crochet S, Petersen CCH (2023) Membrane potential dynamics of excitatory and inhibitory neurons in mouse barrel cortex during active whisker sensing. PLOS ONE 18: e0287174. doi: 10.1371/journal.pone.0287174</p> <p>There are 2 files in this upload:</p> <p>1. The file named "2023_Kiritani_PLOSONE.pdf" is the Open Access pdf of the online publication in PLOS ONE.</p> <p>2. The file named "Kiritani_data_code.zip" (~5 GB) is a zipped version of a folder "Kiritani_data_code" (~5 GB), which contains the data analysed in the study along with the Matlab codes used to generate the published figures. To access the data and codes, first unzip the file. You need to install the Matlab 'Signal Processing' and 'Curve Fitting' Toolboxes. In Matlab, add the path of the folder 'Kiritani_data_code' and all subfolders. Directly from this folder, you should first run the codes in the folder 'Data_Analysis_Codes', sequentially executing 'Analysis_1.m' through to 'Analysis_9.m'. Note, execution of 'Analysis_9.m' can take a long time (~1 hour on a good desktop PC). You can then run the codes in the folder 'Figure_Plotting_Codes' to generate the figures published in the journal article. In the folder 'Data', you can also find a DataViewer to visualise the data sets, which you can run by executing 'DataViewer.m' directly from the subfolder ‘Data’.</p> <p> </p>
Membrane interaction and mechanism of LC3 lipidation machinery in autophagy raw GUV data
<p>Raw GUV data of fluorescent protein imaged on a Nikon A1 confocal microscope with a 63 × Plan 359 Apochromat 1.4 NA objective. Three biological replicates were performed for each experimental 360 condition. Identical laser power and gain settings were used during the course of all conditions.</p>
Figure 3 in Morphology of the arthrodial membrane gland in a Neotropical harvester (Arachnida: Opiliones)
Figure 3. Sagital sections through an arthrodial membrane of the coxa-trochanter articulation of a leg IV in a male harvester Mischonyx squalidus: (A) arthrodial membrane cuticle (AM) and cuticle (sclerite cuticle) (c); (B) arthrodial membrane and basal membrane of secretory cells; (C) secretory cells (sc) with glandular prismatic cells (black arrows), granules (gray arrows) and cuticular canals (cc) stained with hematoxylin and eosin. (bm) Basal membrane, (ci) cytoplasm, (Fo) folds, (n) nucleus.
Figure 5 in Morphology of the arthrodial membrane gland in a Neotropical harvester (Arachnida: Opiliones)
Figure 5. Interior of a prismatic cell in the arthrodial membrane of the coxa – trochanter articulation of a leg IV in a male harvester Mischonyx squalidus: (A) smooth endoplasmic reticulum (ser); (B) mitochondrion (circle) and granules (gray arrows).
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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.