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691 results for “Molecular dynamics”
Amorphous Niobium Oxide Structures Calculated from First Principles using Density Functional Theory and Molecular Dynamics
<p>The dataset contains fifteen different amorphous niobium oxide structures. Nine of the structures have the same stoichiometry as Nb2O5. The other six are defect structures containing 1 or 2 oxygen vacancies, or 1 or 2 interstitial oxygens, or 1 Nb vacancy. Each of the structure files is in the VASP POSCAR file format. Each structure was created using ab-initio molecular dynamics at 5000~K to liquidate the structure, then snapshots of the structure were taken every 2 ps, and geometry optimizations were performed on each individual snapshot. The naming convention is relatively simple: 'conf_x_POSCAR' is a stoichiometric POSCAR, and 'conf_x_oadd1_POSCAR' is a defect structure originating from structure 'x' with a single oxygen interstitial. The defect labels correspond to 1 oxygen interstitial (oadd1), 2 oxygen interstitials (oadd2), 1 oxygen vacancy (ovac1), 2 separated oxygen vacancies (ovac2), 2 nearest neighbor oxygen vacancies (ovac2nn), and 1 Nb vacancy (nbvac).</p>
Molecular dynamics simulations of Liquid crystalline elastomer
<p>This dataset includes the input files for Molecular Dynamics (MD) simulations of liquid crystalline elastomers (LCE) in LAMMPS. The results are incorporated in the journal publication, "Nematic liquid crystalline elastomers are aeolotropic materials" in the Proceedings of the Royal Society A, 2021, authored by L. Angela Mihai, Haoran Wang, Johann Guilleminot, and Alain Goriely. </p> <p>The files of in.LCE_quench_for_phase_transition and restart.LCE_isotropic_500K are for the MD simulations of quenching isotropic LCE from 500K to 450K under an external field, during which the isotropic-nematic phase transition will happen. The file of restart.LCE_isotropic_500K includes the molecular topologies for a crosslinked LCE with 64 chains. </p> <p>The files of in.LCE_shear and restart.LCE_nematic_300K are for the MD simulations of shearing the nematic LCEs at 300K. restart.LCE_nematic_300K includes the molecular topologies for nematic LCE with 64 chains. </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>
Exploring the interaction of a curcumin azobioisostere with Abeta42 dimers using replica exchange molecular dynamics simulations
<p>Structural data and parameters relative to the evaluation of the interaction of an anti aggregating azobioisostere compound with the full-length Aβ42 peptide by replica-exchange molecular dynamics (REMD) simulations. Two different force fields (Amber and CHARMM) were used to simulate the azobioisostere-Abeta42 (AZ-Ab42) complex in a monomeric and dimeric assembly.</p> <p>A brief description of the shared output data is reported below:</p> <p><strong>1. Amber and CHARMM-adapted parameters for the simulated azobioisostere (AZ) compound.</strong></p> <p><strong>2. Modified version of the CHARMM36m FF - (CHARMM36mW)</strong></p> <p><strong>3. Starting (equilibrated) structures (first 5 T-replicas) for REMD on each of the following three systems (PDB): </strong></p> <ul> <li>Amber: AZ-Ab42 (monomeric ensemble): <strong>Repl.0 </strong>(315.0 K), <strong>Repl.1</strong> (316.7 K), <strong>Repl.2</strong> (318.4 K), <strong>Repl.3</strong> (320.1 K), <strong>Repl.4</strong> (321.8 K) </li> <li>Amber: AZ-Ab42 (dimeric ensemble): <strong>Repl.0</strong> (315.0 K), <strong>Repl.1</strong> (316.0 K), <strong>Repl.2</strong> (317.0 K), <strong>Repl.3</strong> (318.0 K), <strong>Repl.4</strong> (319.1 K) </li> <li>CHARMM: AZ-Ab42 (dimeric ensemble): <strong>Repl.0</strong> (315.0 K), <strong>Repl.1</strong> (316.0 K), <strong>Repl.2</strong> (317.0 K), <strong>Repl.3 </strong>(318.0 K), <strong>Repl.4</strong> (319.1 K)</li> </ul> <p><strong>4. Most populated clusters for the three simulated systems (PDB):</strong></p> <ul> <li>Amber AZ-Ab42 (monomeric ensemble): <strong>7</strong> clusters (<strong>Cl.0</strong>: 12.4%, <strong>Cl.1</strong>: 10.1%, <strong>Cl.2</strong>: 9.8%, <strong>Cl.3</strong>: 5.4%, <strong>Cl.4</strong>: 3.7%,<strong> Cl.5</strong>: 3.6%, <strong>Cl.6</strong>: 2.0%) </li> <li>Amber AZ-Ab42 (dimeric ensemble): <strong>5</strong> clusters (<strong>Cl.0</strong>: 7.1%, <strong>Cl.1</strong>: 4.3%, <strong>Cl.2</strong>: 3.5%,<strong> Cl.3</strong>: 3.2%, <strong>Cl.4</strong>: 2.4%) </li> <li>CHARMM: AZ-Ab42 (dimeric ensemble): <strong>3</strong> clusters (<strong>Cl.0</strong>: 4.7%, <strong>Cl.1</strong>: 4.6%, <strong>Cl.2</strong>: 2.5%) </li> </ul>
Molecular dynamics simulation of chitin nanocrystal-water interfaces
<p>This is the data repository for the paper "­Probing the structural details of chitin nanocrystal-water interfaces by three-dimensional atomic force microscopy" by Ayhan Yurtsever, Pei-Xi Wang, Fabio Priante, Ygor Morais Jaques, Kazuki Miyata, Mark J. MacLachlan, Adam S. Foster, and Takeshi Fukuma.</p> <p>It contains:</p> <p>- The system's starting geometry (water-chitin.pdb)</p> <p>- The production trajectory, in .dcd format (nvt_prod_chitin.tar.xz, uncompressed size 1.9 GB)</p> <p>- The resulting water density, in .cube format, computed on each of the chitin surfaces (chitin_cube_densities_vmd.tar.xz, uncompressed size 3.1 GB)</p>
dataset for bioRxiv preprint titled 'Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease'
<p>This data supports the Figures in the preprint titled</p> <p><strong>Evolution of drug resistance drives progressive destabilizations in functionally conserved molecular dynamics of the flap region of the HIV-1 protease</strong></p> <p><strong>working abstract</strong></p> <p>The HIV-1 protease is one of several common key targets of combination drug therapies for human immunodeficiency virus infection and acquired immunodeficiency syndrome (HIV/AIDS). During the progression of the disease, some individual patients acquire -drug resistance due to mutational hotspots on the viral proteins targeted by combination drug therapies. It has recently been discovered that drug-resistant mutations accumulate on the ‘flap region’ of the HIV-1 protease, which is a critical dynamic region involved in non-specific polypeptide binding during invasion and infection of the host cell. In this study, we utilize machine learning assisted comparative molecular dynamics, conducted at single amino acid site resolution, to investigate the dynamic changes that occur during functional dimerization and polypeptide binding of the main protease. We use a multi-agent machine learning model to identify conserved dynamics of the HIV-1 main protease that are preserved across simian and feline protease orthologs (SIV and FIV). We also investigate changes in dynamics due to common drug-resistant mutations in many patients. We find that a key functional site in the flap region, a solvent-exposed isoleucine (ILE50) and surrounding sites that control flap dynamics is often targeted by drug-resistance mutations, likely leading to malfunctional molecular dynamics affecting the overall flexibility of the flap region. We conclude that better long term patient outcomes may be achieved by designing drugs that target protease regions which are less dependent upon single sites with large functional binding effects.</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases
<p>The files included here are a set of Galaxy workflows, starting structure files (PDB, mol2, and frcmod), and specialized force field files (ZAFF) for the simulation of coronavirus helicases in the apo and drug-bound state. The inhibitor molecules include those from virtual screening (FCID1 and thioguanine), as well as experimentally validated candidates (Lumacaftor and SSYA10-001).</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Flavivirus Helicases
<p>The files included here are a set of Galaxy workflows and starting structure files (PDB, mol2, and frcmod) for the simulation of flavivirus helicases in the apo and drug-bound state. The inhibitors include the 4th highest ranking compound from a virtual screening of more than 12.7 million drug-like molecules.</p>
X-ray scattering datasets associated with the publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation"
<p>X-ray scattering datasets for samples described in the 2020 publication "Molecular Dynamics of Janus Polynorbornenes: Glass Transitions and Nanophase Separation". This dataset includes both raw and processed X-ray scattering data for samples PTCHSiO-Pr, Bu, Hx, Oc and De, alongside background measurements files (BKG).</p>
Silica in Silico: a Molecular Dynamics Characterization of the Early Stages of Protein Embedding for Atom Probe Tomography
<p>The .zip archive contains the trajectories of all the simulations performed and analysed within the manuscript. The water molecules were removed for control systems.</p>
The 400 ns molecular dynamic (MD) trajectories for the wild type and mutant forms of the S. tuberosum eIF4E1 and eIF4E2
<p>Truncated from the N termini models of the wild type and mutant forms of the S. tuberosum eIF4E1 and eIF4E2. <br> The molecular dynamic (MD) trajectories with 400-ns length for each wild type and mutant forms of the eIF4E in the water environment according to the standard MD procedure.</p>
Dataset of the Article "Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation"
<p>This dataset contains concatenated trajectory files of the SuMD simulation of the unbinding pathways of the new inhibitors for SARS-CoV-2 papain-like protease. This data will be published in an article titled: "<strong>Reconstruction of the unbinding pathways of new inhibitors of the SARS-CoV-2 Papain-like protease using molecular dynamics simulation".</strong></p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases -- Output Files
<p>These are the output files generated using the input files and Galaxy workflows for coronavirus helicase simulations, from: </p> <pre>https://doi.org/10.5281/zenodo.7492987</pre>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
10 ns Molecular Dynamics simulations of mAMCase at pH 2.0 and 6.5 in complex with GlcNAc6.
<p>This directory contains all files required to analyze the 10 ns MD simulations of mouse AMCase at pH 2.0 and 6.5 presented in <strong>Figure 5</strong> in the manuscript <a href="https://www.biorxiv.org/content/10.1101/2023.06.03.542675">Díaz et al.<em> </em>(2023)</a>.</p> <p>All simulations were performed using Molecular Operating Environment (Chemical Computing Group) and simulation data was analyzed using Graphpad Prism. Structure models were analyzed using PyMOL. Figures were compiled using Adobe Illustrator.</p> <p> </p> <p>Files included in this directory:</p> <p><strong>Figures</strong></p> <p>- contains PDFs of Asp138 X1 angle distribution, Asp138 X1 angle timecourse, PNGs of representative structure models from pH 2.0 <em>active</em> conformation simulation and pH 6.5 <em>inactive</em> conformation simulation with and without distances labeled.</p> <p><strong>MOE</strong></p> <p>- README.txt defines what each variable in "<strong>Production_phX_Conformation.xlsx</strong>" represents</p> <p><strong>/MOE/pHX_Conformation</strong></p> <p>- contains the starting structure for each simulation, a video of the 10 ns simulation, different variables measured during the simulation as an Excel (.xlsx) and Moe databasse (.mdb) files.</p> <p><strong>PyMOL</strong></p> <p>- contains all structure models, 2mFo-DFc maps, mFo-DFc maps, PyMOL script, and PyMOL session used to generate <strong>Figure 5</strong>.</p> <p> </p> <p><strong>MD.pzfx</strong></p> <p>- contains raw data from 10 ns simulations at pH 2.0 and pH 6.5 with Asp138 starting conformation in the <em>active </em>or <em>inactive </em>conformation.</p> <p> </p> <p>Contact:<br> Roberto Efraín Díaz, robertoefrain.diaz@ucsf.edu</p> <p>James Fraser, jfraser@fraserlab.com</p>
Interactive Molecular Dynamics Simulation Movie using MDsrv - BioGem
<p>The protein structure and molecular dynamics simulation trajectories used to make interactive movie using MDsrv and NGL Viewer.</p> <p>https://www.youtube.com/watch?v=m62lg6ZInAI</p>
U-helix:drug Complex Molecular Dynamics Trajectories
<p>Molecular dynamics trajectories for U-helix RNA-ligand complexes. Described in the following paper:</p> <p><em>Targeting RNA Structure to Inhibit Editing in Trypanosomes </em>in the International Journal of Molecular Sciences, 2023, volume 24.</p> <p> </p>
Shear viscosity coefficient of acqueous glycerol from non-equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of non-equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using the periodic perturbation technique [1]. </p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains three sets of simulations, with different perturbation force parameters ("Em*"), where configuration files necessary to reproduce molecular simulations simulations are provided. Maps of density and velocity field are in "Em*"->"Flow".</p> <p>A small self-contained Python script to fit the velocity fields to a periodic cosine perturbation is provided (fit-periodic.py). Alternatively, viscosity can be obtained from energy outputs by running:</p> <pre><code>gmx energy -f ener.edr</code></pre> <p>and selecting "1/Viscosity". Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p>
Shear viscosity coefficient of acqueous glycerol from equilibrium Molecular Dynamics simulations
<p>This dataset contains the results of equilibrium atomistic Molecular Dynamics simulations of water-glycerol liquid mixtures, at various relative concentrations. The goal of the simulations is to quantify the shear viscosity coefficient of said mixtures using linear response theory (Einstein relations) [1]. The post-processing of simulation results is inspired by the method of Zhang et al. [2].</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol, being "000" pure water (0%) and "100" pure glycerol (100%). Zip folders contain</p> <ul> <li>Expected value and integral of the square of the off-diagonal components of the pressure gradient ("EnergyOutputs")</li> <li>Initial configurations used to start the ensemble of replicas from which viscosity is computed ("InitConfReplicas")</li> <li>Output, state and topology of replica simulations ("MdrunOutputs")</li> </ul> <p>A self-contained Python script (compute-visco.py) for the computation of viscosity from the output of an ensemble of simulations is provided. Integrals used to quantify viscosity via Einstein's relation can be obtained from energy output files (.edr) by running:</p> <pre><code>gmx energy -f ener.edr -evisco -eviscoi -vis</code></pre> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p> <p>References:</p> <p>[1] B. Hess, Determining the shear viscosity of model liquids from molecular dynamics simulations, J. Chem. Phys. 116, 209–217 (2002) <a href="https://doi.org/10.1063/1.1421362">https://doi.org/10.1063/1.1421362</a></p> <p>[2] Y. Zhang et al., Reliable Viscosity Calculation from Equilibrium Molecular Dynamics Simulations: A Time Decomposition Method, J. Chem. Theory Comput. 2015, 11, 3537−3546, <a href="https://doi.org/10.1021/acs.jctc.5b00351">https://doi.org/10.1021/acs.jctc.5b00351</a></p>
Equilibrium contact angle of acqueous glycerol on a silica-like surface from Molecular Dynamics
<p>This dataset contains the results of Molecular Dynamics simulations of quasi-2D water-glycerol liquid droplets, spreading on silica-like surfaces. The goal of the simulations is to quantify the equilibrium contact angle of said droplets.</p> <p>The pattern "Glycerol***" refers to the mass fraction of glycerol ("000": pure water, "100": pure glycerol). Each folder contains configuration files and compressed output molecular trajectories. The contact angle is computed from density maps binned on-the-fly using a customized Gromacs version (<a href="https://github.com/pjohansson/gromacs-flow-field">https://github.com/pjohansson/gromacs-flow-field</a>); frames are placed in a tarball ("flow-***p.tar.gz").</p> <p>The zipped folder 'scripts.zip' contains a self-contained library of functions to read density maps and a Jupyter notebook with an example of density reading and plotting.</p> <p>Simulations are performed with Gromacs. We refer to the code documentation for further information (<a href="https://manual.gromacs.org/">https://manual.gromacs.org/</a>).</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.