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47 results for “membrane dynamics”
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
Data set for "State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice"
<p>Data set for: Pala A, Petersen CCH (2018) State-dependent cell-type-specific membrane potential dynamics and unitary synaptic inputs in awake mice. eLife 7: e35869. DOI: https://doi.org/10.7554/eLife.35869.</p> <p>There are 12 files in this data upload:</p> <p>1. '2018_Pala_eLife.pdf' - this is a pdf version of the online publication: Pala & Petersen (2018).</p> <p>2. 'data.mat' - this is a Matlab data structure, which contains all the data for the publication.</p> <p>3. 'DataViewer.m' - this is a Matlab code for viewing the data.</p> <p>4. 'DataViewer.fig' - this is a Matlab figure file, which is the GUI layout for 'DataViewer.m'.</p> <p>5. 'PalaPetersen_Plot.m' - this is a Matlab code, which plots the figures for Pala & Petersen (2018).</p> <p>6. 'PalaPetersen_Analysis.m' - this is a Matlab code, which analyses the data for the figures of Pala & Petersen (2018).</p> <p>7. 'blankAPs.m' - this is a Matlab code, which blanks action potentials from the membrane potential trace.</p> <p>8. 'lowpassfilt.m' - this is a Matlab code, which low pass filters the LFP.</p> <p>9. 'medianFiltAPs.m' - this is a Matlab code, which median filters the membrane potential trace to remove action potentials.</p> <p>10. 'remTrialswithAPs.m' - this is a Matlab code, which removes trials with action potentials.</p> <p>11. 'retrieveSegDur.m' - this is a Matlab code, which retrieves chunks of the recording of a given length.</p> <p>12. 'suptitleAP.m' - this is a Matlab code, which puts titles above subplots.</p>
Polymer Electrolyte Membrane Water Electrolyzer Oxygen Bubble Evolution Optical Video Recording For Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli
<p>Dataset used for the training of the segmentation model employed in the work "Deep Learning-Enhanced Characterization of Bubble Dynamics in Proton Exchange Membrane Water Electrolyzer" by André Colliard-Granero, Keusra A. Gompou, Christian Rodenbücher, Kourosh Malek, Michael H. Eikerling, and Mohammad J. Eslamibidgoli. This dataset consists in 35 images and the corresponding manual annotated masks of diverse bubbly scenarios extracted from the optical video recording of a PEMWE with a transparent flow field.</p>
Outputs of molecular dynamics simulations of two NS1 ZIKV variants in the membrane presence
<p>Files corresponding to outputs obtained through Molecular Dynamics (MD) simulations of two Non-structural (NS) proteins 1 of the Zika virus from Uganda (ZIKV-UG) and Brazil (ZIKV-BR). Simulations were performed using GROMACS 5.1.5 or later versions. Systems were built based on atomistic models (https://zenodo.org/record/5608521#.YvDNZTlBzJw) and converted to a coarse-grained representation employing MARTINI 2.2p ElNeDyn. It was assumed to be NS1 systems in <em>apo</em> and <em>holo</em> forms (<em>i.e.</em>, in the absence and presence of a lipid bilayer, respectively). The membrane model tries to reproduce a lipid concentration of an endoplasmic reticulum lipid bilayer. <em>Holo</em> and <em>apo</em> systems were simulated until they reached 20 and 10 µs, respectively. Trajectories do not include water molecules. For the specific case of <em>holo</em> systems, frames were skipped every 5 frames, which means that processed trajectories are equivalent to simulations when it is recorded every 1000 ps. More details can be found at <a href="https://doi.org/10.1021/acs.jcim.2c01461">https://doi.org/10.1021/acs.jcim.2c01461</a></p> <p>Note: Some topology and index files important for MD analysis are also present.</p>
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex ("pore water" added over membrane re-entrant loop)
<p>Simulation originally starting with "pore water" above the membrane re-entrant loop.</p> <p>Found here are all files needed to reproduce or visualize the results of molecular dynamics simulation of the SpoIVFB intramembrane protease bound to the transcription factor Pro-sigmaK. The protein complex was embedded in a POPE_POPG_DAG_CL bilayer using CHARMM-GUI, and the "generate pore water" feature was used to initially fill the area above the membrane re-entrant loop with water (as opposed to lipids initially being placed in this vicinity). The system was equilibrated and and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p><br>Individual output (.out) and trajectory (.dcd) files are provided for each checkpoint of the simulation. A combined trajectory containing 250 ns of unrestrained simulation is also provided (combined_250ns_traj.dcd). Together with the step5_input.psf file, this combined dcd file can be used with common software such as VMD to visualize the molecular dynamics trajectory.</p>
trajectories for: Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations
<p>Coarse-grained trajectories produced and analysed for publication: </p> <p>----------------------</p> <p>Membrane-binding mechanism of the EEA1 FYVE domain revealed by multi-scale molecular dynamics simulations</p> <p>Andreas Haahr Larsen*, Lilya Tata*, Laura John & Mark S.P. Sansom</p> <p>Department of Biochemistry, University of Oxford, Oxford, United Kingdom, OX1 3QU</p> <p>PLOS comp biol (in press) </p> <p>-------------------------</p> <p> </p> <p>** file overview**</p> <p>md_X.xtc: (X=0..14) 15 repeated CG simulations (1500 ns each) of the FYVE domain from EEA1 binding to POPC:POP1 bilayer. The repeats differ in the rotation of the initial frame.<br> </p> <p>final_cg2at_aligned.pdb: initial frame for AT (after CG2AT)</p> <p>prod_cym_cent_repX.xtc (X=1,2,3) 3 repeated AT sims (500 ns each) of the FYVE domain from EEA1 binding to POPC:POP1 bilayer. </p> <p>** scripts for reproduction at GitHub**</p> <p>scripts and files for reproduction are available at: https://github.com/andreashlarsen/Larsen-Tata2021-FYVE</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>
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>
The GET insertase exhibits conformational plasticity and induces membrane thinning - The Molecular Dynamics Dataset
<p>The molecular dynamics simulation systems.</p> <p>List of files: </p> <ol> <li><strong>SimulationSystems.pdf</strong>: List of all simulation systems reported and their compositions</li> <li><strong>ProteinComplex.pdb</strong>: The initial model for the hsGet2ΔN-Get1/Get3 complex used in simulations was constructed based on the cryo-EM structure (PDB accession 6SO5). Missing residues (except the terminal ones) were modeled using Modeller.</li> <li><strong>prod.mdp</strong>: The GROMACS molecular dynamics parameters (mdp) file used for all simulations</li> <li><strong>toppar.zip</strong>: The Charmm36(m) force field parameter and topology set used for all simulations generated by CHARMM-GUI.</li> <li>Compressed (zip) files containing simulations inputs and trajectories</li> </ol> <p><strong>1-PC.zip<br> 2-1:4_PI:PC.zip<br> 3-1:4_PE:PC.zip<br> 4-1:4_PS:PC.zip<br> 5-1:4_CL:PC.zip<br> 6-1:4_chol:PC.zip<br> 7-1:1:1:1_PC:PI:PS:PE.zip<br> 8-1:1:1:1:1:1_PC:PDPC:PS:PI:PE:chol.zip</strong></p> <p>Each zip file contains the following files:</p> <ol> <li><strong>System_0ns.pdb</strong>: The initial configuration used for the simulations generated using CHARMM-GUI and equilibrated following the CHARMM-GUI equilibration protocol</li> <li><strong>index.ndx</strong>: GROMACS index file</li> <li><strong>topol.top</strong>: GROMACS topology file</li> <li><strong>prod0.tpr, prod1.tpr, prod2.tpr</strong>: GROMACS run topology files (tpr) for each repeat</li> <li><strong>prod0.gro, prod1.gro, prod2.gro</strong>: The final configuration after 3 μs production runs for each repeat</li> <li><strong>noW_0ns.pdb</strong>: The initial configuration without the water molecules.</li> <li><strong>noW_0.xtc, noW_1.xtc, noW_2.xtc</strong>: The 3 μs processed production trajectories. The water molecules were removed, and the trajectories were subsampled at 1 ns intervals.</li> </ol> <p> </p> <p> </p>
Raw data for the manuscript under the title "Mitochondrial RNA granules are fluid condensates, positioned by membrane dynamics".
<p><strong>This is the data-repository</strong> to contain all relevant raw-data used and referred to in the manuscript entitled:<br> "Mitochondrial RNA granules are fluid condensates, positioned by membrane dynamics"<br> [manuscript under revision, and thus not citable as published article]</p> <p>The repository is structured analogous to the manuscript. Find a more detailed description in the README.</p>
Multifaceted Activity of Fabimycin: Insights from Molecular Dynamics Studies on Bacterial Membrane models
<p>This dataset presents a comprehensive collection of input data for Molecular Dynamics (MD) simulations performed using the GROMACS simulation software. The included systems cover various membrane environments, each with distinctive properties. The systems consist of:</p> <ol> <li><strong>IM (Inner Membrane):</strong> Simulations involving the bacteral mimicking inner membrane environment.</li> <li><strong>IM_OM (Inner Membrane and Outer Membrane Complex):</strong> Complex systems encompassing both inner and outer bacterial membrane models.</li> <li><strong>OM_D (Double Symmetric Outer Membrane):</strong> Simulations featuring a symmetric outer membrane structure.</li> <li><strong>OM (Asymmetric Outer Membrane):</strong> Simulations with an asymmetric outer membrane configuration.</li> <li><strong>PC Membrane (Phosphatidylcholine Membrane):</strong> Simulations involving membranes composed of phosphatidylcholine.</li> </ol> <p>For each membrane type, the dataset provides three replicas. The dataset includes initial and final structures (.gro files), simulation parameter files (.mdp), index files (.ndx), and topology files (.itp and .top) applicable to all systems. </p>
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>
Data supporting: "Interaction of MRI Contrast Agent [Gd(DOTA)]− with Lipid Membranes: A Molecular Dynamics Study"
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Nanoscale dipole dynamics of protein membranes studied by broadband dielectric microscopy
<p>Original data in support of our publication: Nanoscale dipole dynamics of protein membranes studied by broadband dielectric microscopy</p>
Data for publication: Pixelated High-Q Metasurfaces for in Situ Biospectroscopy and Artificial Intelligence-Enabled Classification of Lipid Membrane Photoswitching Dynamics
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Molecular dynamics simulations of CD59 and CD59-inhibited Membrane Attack Complex
<p>Coarse-grain (CG) trajectories of CD59-C5b8 (last 1,500 ns):</p> <ul> <li>cd59-c5b8_1500ns_rep1.xtc</li> <li>cd59-c5b8_1500ns_rep2.xtc</li> <li>cd59-c5b8_1500ns_rep3.xtc</li> </ul> <p>PyLipID analysis results from CG CD59-C5b8 simulations:</p> <ul> <li>Interactions_CHOL.csv</li> <li>Interactions_DOPC.csv</li> </ul> <p>Atomistic CD59 simulations in DOPC membrane:</p> <ul> <li>cd59_at_rep1_light.trr</li> <li>cd59_at_rep2_light.trr</li> <li>cd59_at_rep3_light.trr</li> </ul> <p>CD59 Euler angles relative to the membrane: </p> <ul> <li>rep1.csv</li> <li>rep2.csv</li> <li>rep3.csv</li> </ul> <p>All xtc and trr files were down-sampled (frames removed) to decrease file size.</p>
Molecular mechanism underlying SNARE-mediated membrane fusion enlightened by all-atom molecular dynamics simulations
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Data from: Conformational dynamics and asymmetry in multimodal inhibition of membrane-bound pyrophosphatases
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Data from: Elucidating the impact of red blood cell membrane components on melittin-induced pore formation with molecular dynamics simulations
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Molecular Dynamics Simulation Dataset for "Hydrophobic Mismatch Drives Self-Organization of Designer Proteins into Synthetic Membranes"
<p>This repository contains molecular dynamics (MD) simulation data from the study on the self-organization of designer proteins in synthetic membranes. The data includes simulations for different single lipid compositions (DOPC, DPPC, DYPC) denoted as [lipid]-PL* where PL stands for the different TMD constructs. Multi component simulation are named accordingly. The repository provides initial (eqi.gro) and final (prod.gro) coordinates for each simulation. The 'cmd' file in each directory outlines the assembly process of each simulation, and the 'mdp' folder contains all input files for the simulations. </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.