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691 results for “Molecular dynamics”
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
Data associated to the article "Effects of fluoride salt addition to the physico-chemical properties of the MgCl2-NaCl-KCl heat transfer fluid : a molecular dynamics study"
<p>Contains input file and data used to generate the figures of the article:</p> <p>Effects of fluoride salt addition to the physico-chemical properties of the MgCl<sub>2</sub>-NaCl-KCl heat transfer fluid : a molecular dynamics study</p> <p>Weiguang Zhou, Yanping Zhang, Mathieu Salanne</p> <p>https://chemrxiv.org/engage/chemrxiv/article-details/618e903a2bf8a950c7d98e5d</p> <p>The files <em>data.inpt</em> and <em>runtime.inpt </em>are used to simulate the system using the software MetalWalls</p> <p>The files <em>MgNaKCl.txt, MgNaKClF01.txt, MgNaKClF05.txt, MgNaKClF10.txt, MgNaKClF20.txt</em> contain the computed densities, viscosities and thermal conductivities at various temperatures for several compositions (provided in the header of the files)</p>
Choline acetyltransferase (ChAT) - Amyloid beta peptides complex Molecular dynamics Trajectories
<p>In silico molecular dynamics study was performed for the choline acetyltransferase (ChAT) - Abeta peptides complex. The molecular docking of Aβ<sub>40 </sub>and Aβ<sub>42</sub> on ChAT suggested three most probable binding clusters for both the Aβ peptides. Thus generating ChAT-Aβ<sub>40</sub> Cluster-0, ChAT-Aβ<sub>40</sub> Cluster-1, ChAT-Aβ<sub>40</sub> Cluster-2 for Aβ<sub>40</sub> peptide on ChAT, likewise ChAT-Aβ<sub>42</sub> Cluster-0, ChAT-Aβ<sub>42</sub> Cluster-1, ChAT-Aβ<sub>42</sub> Cluster-2 were generated for Aβ<sub>42</sub> peptide on ChAT.</p> <p>Each of the folders contains the topology file with a ‘.gro’ extension and a trajectory file with ‘.xtc’ extension generated from the 100 ns molecular dynamics performed for each of the clusters mentioned above that were generated from the molecular docking. The folders are named as follows:</p> <ol> <li>ChAT_AB40_Cluster_0: Containing the topology file (ab40_0.gro) and the trajectory file (ab40_0.xtc)</li> <li>ChAT_AB40_Cluster_1: Containing the topology file (ab40_1.gro) and the trajectory file (ab40_1.xtc)</li> <li>ChAT_AB40_Cluster_2: Containing the topology file (ab40_2.gro) and the trajectory file (ab40_2.xtc)</li> <li>ChAT_AB42_Cluster_0: Containing the topology file (ab42_0.gro) and the trajectory file (ab42_0.xtc)</li> <li>ChAT_AB42_Cluster_1: Containing the topology file (ab42_1.gro) and the trajectory file (ab42_1.xtc)</li> <li>ChAT_AB42_Cluster_2: Containing the topology file (ab42_2.gro) and the trajectory file (ab42_2.xtc</li> </ol>
Molecular Dynamics of Omicron-RBD and hACE2 performed with NAMD at 37 degrees Celsius
<p>Molecular Dynamics of Omicron-RBD and hACE2 performed with NAMD at 37 degrees Celsius. The PDB used is 7T9K for an MD of 9 nanoseconds. Amino acids side chains are in yellow which are the 11 amino acids specific to the Omicron variant mapped to the interface with hACE2 receptor. The mutations in Omicron make this variant to bind better to the hACE2 receptor and to evade most previous immunity including vaccines. Visualization in UCSF Chimera.</p>
Molecular Dynamics of hACE2 Receptor and SARS-CoV-2 Omicron-RBD (Receptor Binding Domain) in Electrostatics View
<p>Molecular Dynamics of hACE2 Receptor and SARS-CoV-2 Omicron-RBD (Receptor Binding Domain) in Electrostatics View.</p> <p>Molecular Dynamics performed with NAMD in Frontera supercomputer for 8 nanoseconds at 37 degrees Celsius. Electrostatics is visualized with ChimeraX (red is negative and blue is positive). By Victor Padilla-Sanchez, PhD; Texas Advanced Computing Center.</p>
High-Throughput Screening of Tribological Properties of Monolayer Films using Molecular Dynamics and Machine Learning: Supplemental Repository
<p>Supplemental repository for the "High-Throughput Screening of Tribological Properties of Monolayer Films using Molecular Dynamics and Machine Learning" article. Contains calculated tribological properties of dual-monolayer systems from Molecular Dynamics (MD) simulation and Machine Learning (ML).</p>
Molecular dynamics trajectories of protein folding
<p>Molecular dynamics trajectories of protein folding are deposited for educational purposes.</p> <p>Currently, the following trajectories are available:</p> <ul> <li>Chignolin (five independent NVT simulations up to 1.5 micro-sec): <ul> <li>`movie.pse` is a PyMOL session file of MD trajectories. </li> <li>`movie.mp4` is a movie file that shows you how a protein folds during simulation.</li> <li>xtc files (Gromacs compressed format) and corresponding tpr files.</li> <li>gro files for movie making</li> </ul> </li> </ul> <p> </p> <p>Computational setting </p> <ul> <li>Amber ff99SB-ILDN for protein (Lindorff-Larsen, K. <em>et al.</em> Improved side-chain torsion potentials for the Amber ff99SB protein force field. <em>Proteins</em> <strong>78</strong>, 1950–1958 (2010))</li> <li>TIP3P water model</li> <li>0.1 M salt concentration </li> <li>NVT ensemble at 300 K with V-rescale thermostat (Bussi, G., Donadio, D. & Parrinello, M. Canonical sampling through velocity rescaling. <em>J. Chem. Phys.</em> <strong>126</strong>, 014101 (2007))</li> <li>Time step : 2 fs</li> <li>gromacs-2020.6</li> </ul>
All-atom molecular dynamics simulations of synaptic vesicle fusion I: a glimpse at the primed Synaptotagmin-SNARE-complexin complex
<p>Synaptic vesicles are primed into a state that is ready for fast neurotransmitter release upon Ca<sup>2+</sup>-binding to Syt1. This state likely includes trans-SNARE complexes between the vesicle and plasma membranes that are bound to Syt1 and complexins. However, the nature of this state and the steps leading to membrane fusion are unclear, in part because of the difficulty of studying this dynamic process experimentally. To shed light into these questions, we performed all-atom molecular dynamics simulations of systems containing trans-SNARE complexes between two flat bilayers or a vesicle and a flat bilayer with or without fragments of Syt1 and/or complexin-1. Our results need to be interpreted with caution because of the limited simulation times and the absence of key components, but suggest mechanistic features that may control release and help visualize potential states of the primed Syt1-SNARE-complexin-1 complex. In particular, the simulations suggest that SNAREs alone induce formation of extended membrane-membrane contact interfaces that may fuse slowly, and that the primed state contains macromolecular assemblies of trans-SNARE complexes bound to the Syt1 C<sub>2</sub>B domain and complexin-1 in a spring-loaded configuration that prevents premature membrane merger and formation of extended interfaces but keeps the system ready for fast fusion upon Ca<sup>2+</sup> influx.</p>
Reaction Mechanism of the PET Degrading Enzyme PETase Studied with DFT/MM Molecular Dynamics Simulations
<p>Raw simulations of the acylation step by PETase on a PET dimer model substrate, ran with CP2K 6.1 software at the PBE:AMBER level. Details can be found in the original manuscript (<a href="https://doi.org/10.1021/acscatal.1c03700">https://doi.org/10.1021/acscatal.1c03700</a>): Molecular topology in AMBER Parameter Topology format and Trajectories in CHARMM binary coordinate format DCD.</p> <p>RESIDUE LIST:<br> GLY57<br> TYR58<br> SER131<br> MET132<br> TRP156<br> ASP177<br> SER178<br> ILE179<br> ALA180<br> HID208<br> MOL262</p> <p>VMD selection:<br> (name CA C O HA2 HA3 and resname GLY and resid 57) or (name N CA CB H HA HB2 HB3 and resname TYR and resid 58) or (name CA C O OG CB HA HB2 HB3 HG and resname SER and resid 131) or (name N CA SD CE CB CG H HA HB2 HB3 HG2 HG3 HE1 HE2 HE3 and resname MET and resid 132) or (name CB CG CD1 CD2 CE2 CE3 NE1 CZ2 CZ3 CH2 HB2 HB3 HD1 HE1 HE3 HZ2 HZ3 HH2 and resname TRP and resid 156) or (name CG OD1 OD2 CB HB2 HB3 and resname ASP and resid 177) or (name C O and resname SER and resid 178) or (name N CA C O CG2 CD1 CB CG1 H HA HB HG12 HG13 HG21 HG22 HG23 HD11 HD12 HD13 and resname ILE and resid 179) or (name N CA H HA and resname ALA and resid 180) or (name CB CG CD2 ND1 CE1 NE2 HB2 HB3 HD1 HD2 HE1 and resname HID and resid 208) or (name C1 C10 C11 C12 C13 C14 C15 C16 C17 C18 C19 C2 C20 C3 C4 C5 C6 C7 C8 C9 H1 H10 H11 H12 H13 H14 H15 H16 H17 H2 H3 H4 H5 H6 H7 H8 H9 O1 O2 O3 O4 O5 O6 O7 O8 O9 and resname MOL and resid 262)</p> <p>PYMOL selection:<br> (name CA+C+O+HA2+HA3 & resn GLY & resi 57) | (name N+CA+CB+H+HA+HB2+HB3 & resn TYR & resi 58) | (name CA+C+O+OG+CB+HA+HB2+HB3+HG & resn SER & resi 131) | (name N+CA+SD+CE+CB+CG+H+HA+HB2+HB3+HG2+HG3+HE1+HE2+HE3 & resn MET & resi 132) | (name CB+CG+CD1+CD2+CE2+CE3+NE1+CZ2+CZ3+CH2+HB2+HB3+HD1+HE1+HE3+HZ2+HZ3+HH2 & resn TRP & resi 156) | (name CG+OD1+OD2+CB+HB2+HB3 & resn ASP & resi 177) | (name C+O & resn SER & resi 178) | (name N+CA+C+O+CG2+CD1+CB+CG1+H+HA+HB+HG12+HG13+HG21+HG22+HG23+HD11+HD12+HD13 & resn ILE & resi 179) | (name N+CA+H+HA & resn ALA & resi 180) | (name CB+CG+CD2+ND1+CE1+NE2+HB2+HB3+HD1+HD2+HE1 & resn HID & resi 208) | (name C1+C10+C11+C12+C13+C14+C15+C16+C17+C18+C19+C2+C20+C3+C4+C5+C6+C7+C8+C9+H1+H10+H11+H12+H13+H14+H15+H16+H17+H2+H3+H4+H5+H6+H7+H8+H9+O1+O2+O3+O4+O5+O6+O7+O8+O9 & resn MOL & resi 262)</p>
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Sintering of alumina nanoparticles: comparison of interatomic potentials, molecular dynamics simulations, and data analysis
<p>This is the dataset for the publication in MSMSE 2022 containing all plot scripts and data for reproducing all figures. The dataset is a snapshot of the repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022_MSMSE_Roy_et_al_MD-sintering (SHA 7ad2f421deb055f3384c00ba29f2fb1acd0e78ea) that might contain additional/newer data and scripts.</p>
Molecular dynamics dataset of Synaptotagmin-1
<p>This dataset contains all-atom molecular dynamics trajectories of synaptotagmin-1 C2A (calcium free). Each trajectory is 2 µs long and the total cumulated simulation time is 184 µs. Details, including the molecular dynamics setup, are given in Hempel, T.; Plattner, N.; Noé, F. Coupling of Conformational Switches in Calcium Sensor Unraveled with Local Markov Models and Transfer Entropy. <em>J. Chem. Theory Comput.</em> <strong>2020</strong>, <em>16</em> (4), 2584–2593. <a href="https://doi.org/10.1021/acs.jctc.0c00043">https://doi.org/10.1021/acs.jctc.0c00043</a>.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with hBN sheets
<p>Ab initio molecular dynamics trajectories of water confined between hBN sheets for different confinement widths and system sizes.</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with graphene sheets
<p>Ab initio molecular dynamics trajectories of water in contact with graphene sheets for different confinement widths and system sizes</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Ab initio molecular dynamics trajectories of liquid water at the interface with MoS2 sheets
<p>Ab initio molecular dynamics trajectories of water confined between MoS2 sheets for different confinement widths and system sizes.</p> <p>This repository contains data supporting the findings of the paper: </p> <p>G. Tocci, M. Bilichenko, L. Joly, M. Iannuzzi, <em>Ab initio</em> nanofluidics: disentangling the role of the energy landscape and of density correlations on liquid/solid friction, Nanoscale, 12, 10994-11000 (2020). DOI: 10.1039/D0NR02511A.</p>
Molecular Dynamics Simulation of Solar Wind Implantation in the Permanently Shadowed Regions on the Lunar Surface
<p>Supporting data for "Molecular Dynamics Simulation of Solar Wind Implantation in the Permanently Shadowed Regions on the Lunar Surface"</p>
BioExcel Use Case 1: collection of output data from molecular dynamics simulation
<p>The Use Case aims to address all the challenges related to antibody design through an integrative approach combining the core BioExcel software comprising of GROMACS, HADDOCK and PMX.</p> <p>The folder contains the GROMACS output files (xtc and pdb file). Molecular Dynamics simulations have been performed with GROMACS version 2020 and CHARMM36 force field. The input files and scripts of the final protocol are publicly available on BioExcel GitHub https://github.com/bioexcel/BioExcel-UseCase1.</p> <p>The Use Case 1 protocol was presented at the BioExcel Summer School on Biomolecular Simulation in 2021 (see <a href="https://doi.org/10.5281/zenodo.7009238">https://doi.org/10.5281/zenodo.7009238</a> or <a href="https://youtu.be/_TDKfKX4kwM">https://youtu.be/_TDKfKX4kwM</a>)</p> <p> </p>
Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
Thermal conductivity of hydrous wadsleyite determined by non-equilibrium molecular dynamics based on machine learning
<p>This repository contains data used in "Thermal conductivity of hydrous wadsleyite determined by non-equilibrium molecular dynamics based on machine learning" submitted by Dong Wang, Zhongqing Wu and Xin Deng.</p> <p>Figure S4 : "MLP test-Energy" in <strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a></strong>、<strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/MLP%20test-force.txt?versionId=acfeccd1-ecbf-42c8-a6a3-0d6daf72b078">MLP test-force.txt</a></strong></p> <p>Figure 1 : "MLP test-NEMD" in <strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a></strong></p> <p>Figure 3 : "Modeing" in <strong><a href="https://zenodo.org/api/files/353c5b70-3e53-4192-92af-7bf7f12328fd/Data%20for%20Figures.xlsx?versionId=96ac296c-13f9-4871-976a-d7ad087e0b62">Data for Figures.xlsx</a></strong></p>
Molecular dynamics simulations with grand-canonical reweighting suggest cooperativity effects in RNA structure probing experiments
<p>Molecular dynamics simulations of an RNA GAAA tetraloop interacting with SHAPE reagent 1-Methyl-7-nitroisatoic anhydride (1m7) in different numer of copies (1 to 19). See also https://arxiv.org/abs/2209.12640 and https://github.com/bussilab/paper-shapemd.</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.