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299 results for “MD simulation”
A2aR Oligomeric assemblies identified from MD simulations using in-vivo mimetic biomembranes
<p>GPCR oligomerisation is known to play an important role in the receptor signalling. However, due to the technical challenges, the structural information of GPCR oligomerisation is still very limited, which hinders our understanding of GPCR signalling in a fuller picture. In this deposit, we provide the structural coordinates of various oligomeric assemblies of Adenosine A2a receptor that were sampled from unbiased MD simulations.</p> <p>For more information regarding the MD simulation setup and definitions of the various calculated values, please check out our paper on <a href="https://www.biorxiv.org/content/10.1101/2020.06.24.168260v2">BioRxiv</a> (doi: https://doi.org/10.1101/2020.06.24.168260)</p> <p>** Simulation setup **<br> 9 copies of A2aR were randomly inserted into an <em>in-vivo </em>mimetic biomembrane (of size of 45nm x 45nm) to build the initial configuration of the simulations. 10 such systems were set up for A2aR in the inactive state, 10 for the active state and 10 for the active in complex with the mini Gs state. These systems were represented by MARTINI 2 coarse-grained models and were simulated for 50 micro-seconds. The use of MARTINI coarse-grained force field would freeze the receptor conformation in the initial configuration, thus decoupled the oligomerization from such process as ligand-induced conformational change. The more efficient sampling of coarse-grained force field therefore allowed us to explore fully the protein-protein associations in the oligomerisation process. Protein-protein associations were identified when any atoms from two protomers were getting closer than 0.75 nm. The oligomerisation process was monitored and the sampled various oligomeric assemblies were identified for calculation of oligomer residence time. </p> <p><br> ** Coordinate file explained **<br> These pdb files contain the A2aR oligomer structures in atomistic models. The coarse-grained oligomeric structures were converted back to atomistic models using CHARMM 36 force field. The identified oligomeric structures from each oligomeric order were clustered. 10 structures were randomly taken from each cluster and stored as individual models in the pdb files with a naming format <strong><em>{Conf. State}_OS{Oligomeric Order}_cl{Cluster id}.pdb</em></strong>. The pdb files can be viewed by such visualization tools as PyMol, Chimera or JMol etc.</p> <p><br> ** Spreadsheet file explained **<br> The calculated properties, including residence time and geometry, of each identified oligomer were stored in the Excel shreadsheet (Oligomeric_Assembly_Distribution.xlsx). Each oligomeric order, i.e. oligomer order = 2,3,4,5, opens an individual spreadsheet page where the calculated data were grouped by oligomers' conformational states (i.e. Inactive, Active and Act + mini Gs) and then ranked by oligomers' residence time. The measurements for describing the oligomer geometry were shown in columns after "Cluster ID" and before "Count". For definitions of these measurements, please refer to our paper. Pictures of the oligomers viewed from the extracellular side and intracellular side were also provided in the the spreadsheet to assist visualisation.</p>
MD simulation data: An Entropic Safety Catch Controls Hepatitis C Virus Entry and Antibody Resistance
<p><strong>Background</strong></p> <p>Equilibration, relaxation and production runs were performed on GPUs using the CUDA version of PMEMD in AMBER 16 and AMBER ff14SB force field. Minimisation steps were performed on a CPU using PMEMD in AMBER 16 and the AMBER ff14SB force field. All software is available from http://ambermd.org/. </p> <p><strong>Contents</strong></p> <p>There are three tarball (<strong>.tar.gz</strong>) files containing the <strong>core simulation data</strong>: one for wild type (WT), the second for the I438V A524T mutant and the third for the S449P mutant. Each contains:</p> <p>1. a source PDB (<strong>.pdb</strong>) file</p> <p>2. Five AMBER trajectory (<strong>.nc</strong>) files for five independent MD simulations, numbered 1 to 5. <strong>Note: </strong>each of these files is over 2GB.</p> <p>There is an additional tarball containing the <strong>control files</strong> <strong>and scripts</strong> used for running the MD simulations:</p> <p>1. Multiple control (<strong>.ctl</strong>) files numbered 1 to 10 that are used to minimize (<strong>min</strong> prefix), relax (<strong>rel</strong> prefix) and equilibrate (<strong>equ</strong> prefix) the model</p> <p>2. Executable <strong>do_md</strong> that performed all the minimisation, relaxation and equilibration steps</p> <p>3. control file <strong>prod.ctl</strong> used for the production run </p> <p>4. Executable <strong>run_prod</strong> that was used to perform the production run</p> <p>5. Two control files (<strong>prod_short.ctl </strong>and <strong>prod_short_2.ctl</strong>) for the short runs used to de-correlate the simulation for the independent runs</p> <p>6. Executable <strong>run_short</strong> and <strong>run_short_2</strong> used to carry out the de-correlated production runs.</p>
MD simulations of phosphorylated peptides (GGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of six repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXGGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of phosphorylated peptides (GGXGGGXGG)
<p>This repository contains MD simulations and associated analyses of a peptide series of short peptides including phosphorylated residues. It is one of five repositories that are associated to the following research article:</p> <blockquote> <p>Bickel,D., and Vranken,W. (2024) Effects of Phosphorylation on Protein Backbone Dynamics and Conformational Preferences. <em>J. Chem. Theory Comput</em>. https://doi.org/10.1021/acs.jctc.4c00206.</p> </blockquote> <p>The full list of the related repositories is given here:</p> <ol> <li>Pentapeptide simulations: <code>10.5281/zenodo.10517328</code></li> <li>Hexapeptides simulations: <code>10.5281/zenodo.10518872</code></li> <li>Heptapeptides simulations: <code>10.5281/zenodo.10518971</code></li> <li>Octapeptides simulations: <code>10.5281/zenodo.10518993</code></li> <li>Nonapeptides simulations: <code>10.5281/zenodo.10519033</code></li> </ol>
MD simulations of SARS-CoV-2 Spike Protein under static electric fields
<p>This dataset contains trajectories corresponding to all-atom MD simulations of segments of the SARS-CoV-2 Spike Protein, and in-silico mutations, under the influence of moderate external electric fields. The final structures of some of the simulations were used to perform in-silico docking with ACE2 receptor to evaluate the effect of comformational changes (docking was perform with PyDOCK).</p> <p>The file trajectories_6vsb_dt1ns.zip contains trajectories of simulations that were performed on a segment of the Protein Data Bank ID 6VSB comprising RBD, SD1 and SD2. The file trajectories_6m0j_dt1ns.zip correspond to the RBD in Protein Data Bank ID 6M0J. The file trajectories_in-silico_mutations_dt1ns.zip correspond to simulations performed on in-silico generated mutations following the mutations corresponding to WHO Variants of Concern UK, South Africa and Brazil. In all cases, simulations were performed at different electric field intensities ranging between 10<sup>4</sup> V/m and 10<sup>7</sup> V/m, with an extra short simulation under very high intensity (10<sup>9</sup> V/m). The file docked_structures_6m0j.zip contains the 100 best scored docked structures for each case as the output of PyDOCK.</p> <p>Trajectories are stored in GROMACS compressed trajectory file format (.xtc), downsampled to a 1ns timestep. Individual trajectories length are between 300 nanoseconds and 1 microsecond. In-silico docked structures are in PDB format. See linked preprint for more details.</p>
An integrated approach including docking, MD simulations, and network analysis highlights the action mechanism of the cardiac hERG activator RPR260243
<p>500 ns MD trajectories of the hERG bound states (protein and ligand) used for the analyses discussed in the paper. There are three replica for each system. The trajectories can be visualized using molecular visualization programs such as Pymol or VMD uploading the PDB and the DCD file.</p> <p>The PDB and the topology files of the hERG bound state (protein, membrane, ions and ligand) are also included.</p> <p>An example of input file used for the production step of the dynamics has been provided (production_1.conf). </p>
Supplementary Material: Conformational Ensemble of the Poliovirus 3CD Precursor Observed by MD Simulations and Confirmed by SAXS: A Strategy to Expand the Viral Proteome?
<p>Supplementary video for <em>Viruses</em> <strong>2015</strong>, <em>7</em>(11), 5962-5986; doi:10.3390/v7112919; http://www.mdpi.com/1999-4915/7/11/2919.</p> <p><strong>Movie S1.</strong> Dynamic interface between 3C and 3D domains revealed by accelerated MD. The 3C and 3D domains are colored cyan and blue, respectively. The active-site residues of the protease (His-40, Glu-71, Cys-147) and the polymerase (Asp-416, Asp-511, Asp-512) domains are represented by red spheres to help identifying the relative orientations of two domains.</p>
Simulation files for POPC lipid membrane with Slipids-VIS force field for Gromacs MD simulation engine
<p>The tar.gz archive contains simulation input files that were used in the publication Transmembrane potential modeling: Comparison between methods of constant electric field and ion imbalance.</p> <p>http://pubs.acs.org/doi/abs/10.1021/acs.jctc.5b01202</p> <p>The files are meant to be used with <strong>Gromacs</strong> simulation package (gromacs.org).</p> <p>A modified Slipids force field, <strong>Slipids-VIS</strong>, is introduced. It uses Virtual Interaction sites in order to speed up simulation. The technique is described in the aforementioned work. The archive contains working topology for <strong>POPC</strong> lipid molecules and 6fs timestep without any significant loss of accuracy.</p>
Water will find its way: transport through narrow tunnels in hydrolases (Hal protein with different MD simulation settings)
<h1>Hal_2fs.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_2fs protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_2fs.dat</em>: Parsed database of transport events for Hal system. Command used: <br><em>python3 01_build_database.py -c 04_TransportTools/Hal_2fs.ini -o Hal_2fs.dat</em></p> <p> </p> <h1>Hal_300K.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (Hal_300K protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.<br> 2. <em>Hal_300K.dat:</em> Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_300K.ini -o Hal_300K.dat</em></p> <p> </p> <h1>Hal_TIP3P.zip</h1> <h2><em>"Water will find a way: transport through narrow tunnels in hydrolases (TIP3P protein variant)"</em></h2> <p>The input files and results used for the paper <em>"Water will find a way: transport through narrow tunnels in hydrolases"</em> are separated in the different folders depending the stage they belong to.</p> <h3>Folders</h3> <p>1. <em>01_Simulations.tar.gz</em>: All the files used to get the data employing Molecular Dynamics simulations. <br>2. <em>02_Caver.tar.gz</em>: Caver config used together with the <em>"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"</em> method (https://doi.org/10.1016/j.mex.2022.101968), and after re-clustering as described in the methods section of the paper.<br> 3. <em>03_Aquaduct.tar.gz</em>: Aquaduct results for all the MD trajectories.<br> 4. <em>04_TransportTools.tar.gz</em>: TransportTools results (https://doi.org/10.1093/bioinformatics/btab872).<br> 5. <em>05_WaterAnalysis.tar.gz</em>: The results from the exact matching analysis were parsed to perform H-bond analysis. Here are the PDBs where the <em>minimum sphere event</em> is present. Also the txt files with the results from the H-bond analysis are here.</p> <h3>Files</h3> <p> 1. <em>01_build_database.py</em>: Python3 script to parse the results from the exact matching analysis from TransportTools into a dictionary of transport events. For more detailed information read the script documentation.</p> <p> 2. <em>Hal_TIP3P.dat</em>: Parsed database of transport events for Hal system. Command used:<br><em>python3 01_build_database.py -c 04_TransportTools/Hal_TIP3P.ini -o Hal_TIP3P.dat</em></p>
MD Simulation of AtALMT9 TMD Using Martini3 and charmm36 Force Field
<p>This dataset contains the MD simulation data associated with the article:</p> <p>"Structural basis for malate-driven, pore lipid-regulated activation of the Arabidopsis vacuolar anion channel ALMT9"</p> <p><em>(Not published yet)</em></p> <p> </p> <p>Folder</p> <p>AA : All-atom simulation files.</p> <p>CG : Coarse-grained simulation files.</p> <p>toppar : parameter files.</p> <p> </p> <p>File Description</p> <p>conf.pdb : Initial structure of the simulation.</p> <p>all.fit.10ns.now.zen.xtc : trajectory file without water. </p> <p>now.pdb : coordinate file of corresponding trajectory.</p> <p>topol.top : GROMACS topology file.</p> <p> </p> <p> </p>
Ion permeation through a narrow cavity constriction in KCNQ1 channels, scours files of MD simulations and analysis of electrophysiological experiments.
<p>Source files of Molecular Dynamic (MD) simulations and analysis files of electrophysiology data in Igor pro software format. KCNQ1 channel pore region (G245-K354) was embedded in a lipid bilayer consisting of phosphatidylcholine phospholipids (POPC) and ion permission mechanism was analized by MD simulations using the computational electrophysiology (compEL) method implemented in GROMACS v2022.4. Ion imbalance between compartments of double-membrane system created a membrane potential of abour 300 mV which drives ion movment.</p>
MD simulations on MnmE
<p>Unbiased and biased MD simulations of MnmE that were generated in the context of the research presented in the following publication:</p> <blockquote> <p>INSERT PUBLICATION</p> </blockquote>
MD simulation of the crystal unit cell of the second PDZ domain of LNX2 protein
<p>This molecular dynamics simulation data is provided as part of the manuscript "<strong>LAWS: Local Alignment for Water Sites - a method to analyze crystallographic water in simulations</strong>". The code for the algorithm is provided: <a href="https://github.com/rauscher-lab/LAWS">on github</a><br> <br> The system contains one unit cell of the crystal (PDB ID: 5E11) with 4 symmetrically related protein chains. The total simulation length is 1 microsecond. </p> <p><strong>Force field + water model</strong>: CHARMM36m + CHARMM-modified TIP3P<br> <strong>Number of atoms</strong>: 9650<br> <strong>Number of time frames:</strong> 100,000 with 10-ps stride<br> <em>The details of the simulations are provided in the manuscript.</em></p>
Results of MD simulations of collision cascades in hcp Zr at 600 K.
<p>This is a database consisting of pre- and post-processing scripts, input files and results from molecular dynamics (MD) simulations of collision cascades in LAMMPS. The database is associated with the paper: <a href="https://arxiv.org/abs/2405.03332">Molecular dynamics simulations of neutron induced collision cascades in Zr</a>. The material studied was hcp Zr at the temperature of 600 K. A detailed description of the record can be found in the <code>pdf</code> file, as well as in Emacs <code>org</code> and Jupyter notebooks (<code>ipynb</code>). Files also include Python code describing how to access and process the data.</p>
A structural model of the human serotonin transporter in an outward-occluded state: MD simulation data
<p>The uploads contain relevant data to supplement the study https://www.biorxiv.org/content/10.1101/637009v1, where the details of the methods are described.</p> <p>charmm_energy_minimization.inp is the input file that was used to run an energy minimization on structural models</p> <p>The two archives contain relevant MD simulation data in coordinate, parameter and trajectory files:</p> <p>hSERT_Ce.tar.gz outward-open X-ray structure PDB 5I71</p> <p>hSERT_Ceo.tar.gz outward-occluded structural model</p>
MD SIMULATION DATA for cytochromeP450 with SCC and AFB1.
<p>MD simulation data submission.</p>
MD simulations of bOG:DMPC in CHARMM36 force field
<p>CHARMM-GUI based series of simulations of beta-octyl-D-glucopyranoside (b-OG) mixed with DMPC at different dilutions. bOG:DMPC mol ratios are 1:1, 1:2, and 2:3. The only change in the force field was changing atom names from 2H2 to H2 in BOG residue to allow **gmx grompp** to recognize protons as protons when setting up constraints for bonds with hydrogens.</p> <p>Simulations are performed in highly hydrated state. I can name it "more than 50 water per two acyl chains"; "water per lipid" measure doesn't work here because bOG has just a single hydrocarbon tail.</p> <p>Trajectory length: 500 ns (20 ps step). T = 303 K. </p> <p>In this version we add *znd files where atom names are unique (changed in a new version of BOG.itp).</p>
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Allen Brain Atlas
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