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393 results for “Molecular dynamics simulations”
Molecular dynamics simulation of tricaproin in gas phase using machine-learning potential ANI2x
<p>Tricaproin (Glycerol trihexanoate) is an example of a triglyceride molecule with very short alkyl tails attached to the glycerol moiety, and this deposit contains a 10 ns long simulation of tricaproin in a gas phase.</p> <p>The model chemistry (a.k.a. interaction potential or force field) is the machine-learning potential ANI2x implemented in python package torchANI, which has a close-to-DFT accuracy, yet low cost compared to DFT or other electronic structure theories.</p> <p>Molecular dynamics were run using ASE with Langevin integrator at a constant temperature of 310 K.</p> <p>The resulting trajectory was written every 1 ps ("traj.h5", can be viewed in ASEgui), and gathered every 10 ps in a XTC format ("traj.xtc", open in MDAnalysis, VMD, UnityMol, GROMACS tools ...).</p> <p> </p> <p>Detailed simulation settings are in the python script.</p> <p> </p> <p>This simulation was performed for the purpose of building a coarse grained Martini 3 model of this molecule.</p> <p> </p> <p>ANI2x https://doi.org/10.26434/chemrxiv.11819268.v1</p> <p>torchANI https://aiqm.github.io/torchani/index.html</p> <p>ASE https://wiki.fysik.dtu.dk/ase/tutorials/md/md.html#constant-temperature-md</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>
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>
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>
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>
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>
Molecular dynamics simulation data 3: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes: Production run gromacs trajectories for the Cx43 gap junction channel (500 mV)</p>
Molecular dynamics simulation data 2: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes:</p> <p>1. The starting coordinates, topology, MD inputs</p> <p>2. Production run gromacs trajectories for the Cx43 hemichannel</p>
Input files for the MD simulations and free energy calculations for the article "Water Dissolved in a Variety of Polymers Studied by Molecular Dynamics Simulation and a Theory of Solutions"
<p>Article:<em> </em><a href="https://pubs.acs.org/doi/10.1021/acs.jpcb.1c04818">J. Phys. Chem. B. 125, 9357–9371 (2021) [DOI: 10.1021/acs.jpcb.1c04818]</a></p> <p>The structures of the homopolymers and copolymers simulated are shown in Figures 1 and S1 and Tables 2 and 3. All-atom MD simulation was carried out using GROMACS, and this repository provides the input files with the GAFF/RESP force and initial coordinate files. The free energy of water dissolution was obtained with <a href="https://sourceforge.net/projects/ermod/">ERmod</a>, and the input files for the free-energy calculations are also contained. See the README files for details.</p>
Eliminating Finite-size Effects on the Calculation of X-ray Scattering from Molecular Dynamics Simulations
<p>Data repository for the work with the same name. <a href="https://gitlab.com/asod/grsq_examples/-/tree/final_resubmission?ref_type=tags">Gitlab version</a></p> <p>All plots for the figures in the work can be generated via <code>plots_resubmission.ipynb</code></p> <p><code>This version is accompanying the final resubmission to JCP.</code></p> <p> </p> <p><code>Uses the pypi package <a href="https://pypi.org/project/grsq/">grsq</a></code></p>
Zika virus prM protein contains cholesterol binding motifs required for virus entry and assembly - Molecular Dynamics Simulation Dataset
<p>The molecular dynamics (MD) simulation dataset. The contents:</p> <ul> <li><strong>5ire_BIOMT_expanded.pdb</strong>: The complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE) </li> <li><strong>5ire_Mprotein_BIOMT_expanded.pdb</strong>: The M proteins extracted from the complete biological assembly of the cryo-EM structure of Zika Virus (PDB ID:5IRE). The biological assembly shows the dimeric organization of M proteins.</li> <li><strong>0chol.zip, 10chol.zip, 20chol.zip, and 30chol.zip</strong> contain simulation input and output files for the simulated membrane compositions: 0:100, 10:90, 20:80, 30:70 (mol%:mol%) Cholesterol:POPC, respectively. <ul> <li>In each zip file, there are 5 directories: <strong>wt, R253L+F257A, R253L+F257S, K275L+Y278A, K275L+Y278S</strong> corresponding to each simulated M protein dimer variant: wild type, CARC 2-A, CARC 2-S, CARC 3-A, and CARC 3-S. In each directory, there are the following files: <ul> <li><strong>toppar</strong>: This directory contains all force field topologies and parameters</li> <li><strong>topol.top</strong>: GROMACS topology (top) file</li> <li><strong>index.ndx</strong>: GROMACS index (ndx) file</li> <li><strong>prod.mdp</strong>: GROMACS MD parameters (mdp) file</li> <li><strong>0, 1, 2, 3, 4, 5, 6, 7, 8, 9</strong>: These directories contain the simulation inputs and outputs for each simulation repeat. In each of these directories, there are the following files: <ul> <li><strong>t0.pdb</strong>: The pdb file of the starting coordinates</li> <li><strong>prod0.tpr</strong>: GROMACS binary run input (tpr) file </li> <li><strong>prod0.edr</strong>: GROMACS energy (edr) file</li> <li><strong>prod0.gro</strong>: GROMACS output coordinates and velocities after 1 microsecond of simulation</li> <li><strong>prod0.cpt</strong>: GROMACS checkpoint file after 1 microsecond of simulation</li> <li><strong>noW.pdb</strong>: The pdb file of the starting coordinates with all water molecules removed</li> <li><strong>noW.xtc</strong>: GROMACS compressed trajectory (xtc) file with all water molecules removed</li> </ul> </li> </ul> </li> </ul> </li> </ul>
Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data (Simulation input files)
<p>Simulation input files for gromacs to reproduce the data from the manuscript "Scrutinizing the protein hydration shell from molecular dynamics simulations against consensus small-angle scattering data" (submitted to Comm. Chem.)</p>
Data related to the publication "Efficient molecular dynamics simulations of deep eutectic solvents with first-principles accuracy using machine learning interatomic potentials"
<p>The training data sets, the trained machine learning models, and input scripts for the training and molecular dynamics simulations.</p>
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