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691 results for “Molecular dynamics”
A dynamical view of protein-protein complexes: studies by molecular dynamics simulations
<p>All-atom MD simulations generated for the manuscript "A dynamical view of protein-protein complexes: studies by molecular dynamics simulations". Eight binary protein-protein complexes from the Docking benchmark and the Affinity benchmark are studied in this work: 2OOB (an ubiquitin/ubiquitin ligase complex), 1AY7 (a ribonuclease Sa/barstar complex), 1BRS (a barnase/barstar complex), 3SGB (a proteinase B/inhibitor), 1EMV (a colicin/immunity protein complex), 1PVH (complex between Interleukine 6 receptor and leukemia inhibitory factor), 1GCQ (Vav/GRB2 SH3 domains complex) and 1AK4 (cyclophilin/HIV capsid complex)</p> <p>Each folder for a binary complex is organised as followed:</p> <p>- in <strong>A</strong> and <strong>B</strong> there are the dry MD simulations for the unbound proteins</p> <p>- in <strong>complex</strong> there are two folders (<strong>without_water</strong> and <strong>water</strong>) where the dry simulation and the simulation with water molecules are provided</p>
Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites: supplementary information and dataset
<p><strong>Abstract:</strong><br> (from [1])</p> <blockquote> <p>The addition of nano-sized filler particles enhances the mechanical performance of polymers. The resulting properties of the polymer nanocomposite depend on a complex interplay of influence factors such as material pairing, filler size, and content as well as filler-matrix adhesion. As a complement to experimental studies, numerical methods, such as molecular dynamics (MD), facilitate an isolated examination of the individual factors in order to understand their interaction better. However, particle-based simulations are, in general, computationally very expensive, rendering a thorough investigation of nanocomposites’ mechanical behavior both expensive and time-consuming. Therefore, this paper presents a fast coarse-grained MD model for a generic nanoparticle-reinforced thermoplastic. First, we examine the matrix and filler phase individually, which exhibit isotropic elasto-viscoplastic and anisotropic elastic behavior, respectively. Based on this, we demonstrate that the effect of filler size, filler content, and filler-matrix adhesion on the stiffness and strength of the nanocomposite corresponds very well with experimental findings in the literature. Consequently, the presented computationally efficient MD model enables the analysis of a generic polymer nanocomposite. In addition to the obtained insights into the mechanical behavior, the material characterization provides the basis for a future continuum mechanical description, which bridges the gap to the engineering scale. </p> </blockquote> <p> </p> <p><strong>Contact:</strong></p> <p>Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><strong>Software:</strong></p> <p>All MD simulations were performed with LAMMPS [2], version: 29 Oct 2020 / 20201029</p> <p>Compiled with<br> Compiler: GNU C++ 4.8.5 20150623 (Red Hat 4.8.5-39) with OpenMP not enabled<br> C++ standard: C++11</p> <p>Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p>Installed packages<strong>:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p>Polymer and polymer composite samples generated with self-avoiding random-walk algorithm [3]</p> <p>Post-processing Matlab R2019b</p> <p>Evaluation of polymer entanglements with Z1-Algorithm [4]</p> <p> </p> <p><strong>License:</strong></p> <p>Creative Commons Attribution 4.0 International</p> <p> </p> <p><strong>Context:</strong></p> <p>Data set supplementing journal paper:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, Express Polymer Letters, <strong>2022</strong>, 16.</p> <p>This dataset contains the results presented in [1] and the necessary data to obtain those as well as supplementary information.</p> <p><strong>Content:</strong></p> <p>supplementary material:</p> <p>supplementary_information.pdf</p> <p>data:<br> folder names vary depending on the context, explained in the following:</p> <p> </p> <p>01_matrix</p> <ul> <li> <p>01_equilibration<br> sample equilibration to different temperatures<br> nomenclature: equil_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>[-<batch_ID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> <li> <p>batch_ID: 2-5 </p> </li> </ul> </li> <li> <p>02_temperature_dependence<br> uniaxial tension simulations to identify temperature dependence<br> nomenclature: 01_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.1-1.0</p> </li> </ul> </li> <li> <p>03_directional_dependence<br> uniaxial tension simulations to prove isotropy in Y and Z direction; X direction in 04_rate_dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-5</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>04_rate_dependence<br> uniaxial tension simulations to identify strain rate dependence<br> nomenclature: 03_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>[-<batchID>]</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>05_cyclic_loading<br> sinusoidal uniaxial deformation<br> nomenclature: 05_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>06_relaxation<br> relaxation subsequent to time-proportional deformation<br> nomenclature: 07_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperature>-rate_<strain_rate>-sin_<strain_amplitude>_relax</p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>strain_rate: 5E-4</p> </li> <li> <p>strain_amplitude: 0.01, 0.05, 0.15, 0.2</p> </li> </ul> </li> <li> <p>07_simple_shear<br> time-proportional simple shear deformation with different strain rates<br> nomenclature: SS_P2VPSi-rate_<strain_rate>-<batchID></p> <ul> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>08_large_deformation<br> uniaxial deformation up to 100% strain<br> nomenclature: 02_UT_<chains>-<chain_atoms>-box_<initial_box_length>-min_<SARW_distance>-angle_<SARW_angle>-T_<final_temperatur>-strain_<max_strain></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>initial_box_length: 100</p> </li> <li> <p>SARW_distance: 0.9</p> </li> <li> <p>SARW_angle: 50</p> </li> <li> <p>final_temperature: 0.3</p> </li> <li> <p>max_strain: 1</p> </li> </ul> </li> </ul> <p>02_filler</p> <ul> <li> <p>01_Silica_equilibration<br> sample equilibration</p> </li> <li> <p>02_time_proportional<br> time-proportional uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>-strain_<max_strain>-rate_<strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>max_strain: 0.1</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_rate: 5E-4, 5E-5, 5E-6</p> </li> </ul> </li> <li> <p>03_time_periodic<br> time-periodic uniaxial and simple shear tests<br> nomenclature: Silica_BV-<loadcase>_<direction>_sin-ampl_<strain_amplitude>-rate_<max_strain_rate></p> <ul> <li> <p>loadcase: uniaxial tension (UT), simple shear (SS)</p> </li> <li> <p>direction: X, Y, Z (UT); XY, XZ, YZ (SS)</p> </li> <li> <p>strain_amplitude: 0.025</p> </li> </ul> </li> </ul> <p>03_composite</p> <ul> <li> <p>01_equilibration<br> sample equilibration<br> nomenclature: equil_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>02_uniaxial-tension<br> uniaxial tension simulations<br> nomenclature: UT_P2VPSi-rNP_<filler_radius>-nNP_<filler_number>-<batchID></p> <ul> <li> <p>filler_radius: 2.5-10.0</p> </li> <li> <p>filler_number: 1-160 (depending on filler_radius)</p> </li> <li> <p>batchID: 1-5</p> </li> </ul> </li> <li> <p>03_filler-maxtrix-adhesion<br> equilibration and uniaxial deformation of samples with mid and weak filler-matrix adhesion (for strong adhesion see 01_equilibration and 02_uniaxial-tension<br> nomenclature: see above</p> </li> <li> <p>04_IP_equilibration<br> equilibration of samples to evaluate the microstructure for neat polymer and composites with filler radius 2.5-7.5<br> nomenclature: P2VPSi-<chains>x<chain_atoms>_rNP_<filler_radius>-nNP_<filler_number>_pos_<filler_pos>-<batchID></p> <ul> <li> <p>chains: 200</p> </li> <li> <p>chain_atoms: 200</p> </li> <li> <p>filler_radius: 0 (neat), 2.5, 5.0, 7.5</p> </li> <li> <p>filler_number: 0 (neat), 1</p> </li> <li> <p>batchID: 1-20</p> </li> </ul> </li> </ul> <p> </p> <p> </p> <p>Each simulation directory contains:</p> <ul> <li> <p>lammps input file (*.in) of the specific simulation</p> </li> <li> <p>data file (*.data) containing the initial sample configuration</p> </li> <li> <p>input.prm: input parameters of the specific simulation (read by the input file)</p> </li> <li> <p>meta.info: meta data of the specific simulation run</p> </li> <li> <p>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below</p> <ul> <li> <p>thermo_out.Dat: raw output </p> </li> <li> <p>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</p> </li> <li> <p>thermo_out_STD.Dat: standard deviation of raw output</p> </li> </ul> </li> </ul> <p> </p> <p>Output quantities (columns of *.Dat files):<br> Please note that the normalized Lennard-Jones unit set is used, so all quantities are normalized to fundamental mass, length, energy, time and the Boltzmann constant. Thus all entries are unitless [1].</p> <ul> <li> <p>Step: time step </p> </li> <li> <p>Time: time </p> </li> <li> <p>TotEng: total energy </p> </li> <li> <p>PotEng: potential energy</p> </li> <li> <p>KinEng: kinetic energy </p> </li> <li> <p>E_pair: pair energy </p> </li> <li> <p>E_bond: bond energy </p> </li> <li> <p>E_angle: angle energy </p> </li> <li> <p>E_dihed: dihedral energy </p> </li> <li> <p>Temp: temperature</p> </li> <li> <p>Press: hydrostatic pressure</p> </li> <li> <p>Pxx: xx component of pressure tensor </p> </li> <li> <p>Pyy: yy component of pressure tensor </p> </li> <li> <p>Pzz: zz component of pressure tensor </p> </li> <li> <p>Pxy: xy component of pressure tensor</p> </li> <li> <p>Pxz: xz component of pressure tensor</p> </li> <li> <p>Pyz: yz component of pressure tensor</p> </li> <li> <p>Volume: volume of simulation box </p> </li> <li> <p>Lx: box length in x direction </p> </li> <li> <p>Ly: box length in y direction </p> </li> <li> <p>Lz: box length in z direction </p> </li> <li> <p>Density: density </p> </li> <li> <p>c_RG: radius of gyration scalar </p> </li> <li> <p>c_RG[1]: squared radius of gyration tensor (xx component) </p> </li> <li> <p>c_RG[2]: squared radius of gyration tensor (yy component) </p> </li> <li> <p>c_RG[3]: squared radius of gyration tensor (zz component) </p> </li> <li> <p>c_RG[4]: squared radius of gyration tensor (xy component) </p> </li> <li> <p>c_RG[5]: squared radius of gyration tensor (xz component) </p> </li> <li> <p>c_RG[6]: squared radius of gyration tensor (yz component) </p> </li> <li> <p>c_bondave[1]: bond energy averaged over all atoms </p> </li> <li> <p>c_bondave[2]: bond distance averaged over all atoms </p> </li> <li> <p>c_bondave[3]: squared bond distance averaged over all atoms </p> </li> <li> <p>c_angleave[1]: angle energy averaged over all atoms </p> </li> <li> <p>c_angleave[2]: angle averaged over all atoms degree</p> </li> <li> <p>c_angleave[3]: cosine of angle </p> </li> <li> <p>c_angleave[4]: squared cosine of angle </p> </li> <li> <p>c_MSD[1]: mean squared displacement x-direction </p> </li> <li> <p>c_MSD[2]: mean squared displacement y-direction </p> </li> <li> <p>c_MSD[3]: mean squared displacement z-direction </p> </li> <li> <p>c_MSD[4]: total mean squared displacement </p> </li> <li> <p>c_COM[1]: x coordinate of center of mass </p> </li> <li> <p>c_COM[2]: y coordinate of center of mass </p> </li> <li> <p>c_COM[3]: z coordinate of center of mass </p> </li> <li> <p>v_strain_xx: xx component of engineering strain tensor </p> </li> <li> <p>v_strain_yy: yy component of engineering strain tensor </p> </li> <li> <p>v_strain_zz: zz component of engineering strain tensor </p> </li> <li> <p>v_vMisesequivstress: von Mises equivalent stress </p> </li> <li> <p>v_Cauchy_xx: xx component of stress tensor </p> </li> <li> <p>v_Cauchy_yy: yy component of stress tensor</p> </li> <li> <p>v_Cauchy_zz: zz component of stress tensor</p> </li> <li> <p>v_Cauchy_xy: xy component of stress tensor </p> </li> <li> <p>v_Cauchy_xz: xz component of stress tensor </p> </li> <li> <p>v_Cauchy_yz: yz component of stress tensor </p> </li> <li> <p>v_strain_xy: xy component of engineering strain tensor </p> </li> <li> <p>v_strain_xz: xz component of engineering strain tensor </p> </li> <li> <p>v_strain_yz: yz component of engineering strain tensor </p> </li> </ul> <p><br> </p> <p><strong>References</strong>:</p> <p>[1] M. Ries, J. Seibert, P. Steinmann, S. Pfaller. “Applying a generic and fast coarse-grained molecular dynamics model to extensively study the mechanical behavior of polymer nanocomposites”, <em>Express Polymer Letters</em>, <strong>2022</strong>, 16.</p> <p>[2] S. Plimpton, “Fast parallel algorithms for short-range molecular dynamics,” <em>Journal of computational physics</em>, <strong>1995</strong>, 117, 1-19.</p> <p>[3] A. P. Thompson et al., “LAMMPS - a flexible simulation tool for particle-based materials modeling at the atomic, meso, and continuum scales,” <em>Computer Physics Communications</em>, vol. 271, p. 108171, <strong>2022</strong>.</p> <p>[4] M. Ries, V. Dötschel, J. Seibert, S. Pfaller. “A self-avoiding random walk algorithm (SARW) for generic thermoplastic polymers and nanocomposites”, <em>Zenodo</em>, 2022. <a href="https://doi.org/10.5281/zenodo.6245699">https://doi.org/10.5281/zenodo.6245699</a></p>
Molecular dynamics trajectories obtained from simulations of mechanically-controlled break-junctions and associated zero-bias conductance.
<p>This data set contains structural information and the associated zero-bias conductance of mechanically-controlled break-junction experiments. It contains:</p> <ul> <li>Six (multi) xyz files (trajectory_0X.xyz), which contain different trajectories produced by molecular dynamic simulations (using <a href="https://www.lammps.org/">LAMMPS</a> and <a href="https://docs.lammps.org/Packages_details.html#pkg-reaxff">reactive force fields</a>) of a mechanically-controlled break-junction. These simulations start from a gold wire with attached molecules. One side of the wire is slowly pulled away, until the gold wire is broken apart and a molecular junction is formed. The outermost six layers of the goldwire are frozen in the simulation. The temperature of the simulation was set to 300K.</li> <li>Six files (transmission_0X.dat) with the calculated zero-bias conductance (G/G<sub>0</sub>). Each entry corresponds to the zero-bias conductance of the corresponding structure from the xyz files. The zero-bias conductance was calculated using non-scc DFTB+, as, e.g., described <a href="https://dftbplus-recipes.readthedocs.io/en/latest/transport/carbon2d-trans.html">here</a>.</li> </ul> <p>For more information see dx.doi.org/XXXXXXX.</p>
Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations
<p># *"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"*</p> <p>The input files and data used for the paper *"Divide-and-conquer approach to study protein tunnels in long molecular dynamics simulations"* are separated in the different folders depending stage they belong to.</p> <p>## Folders</p> <p> 1. **01_inputs:** The MD trajectory of DhaA used (only protein atoms present)<br> 2. **02_sliced_trajectory:** The CAVER3 results for the sliced trajectory (eight parts)<br> 3. **03_sliced_filtered:** Filtered CAVER3 results and results from the divide-and-conquer approach<br> 4. **04_full_trajectory:** The CAVER3 results for the full trajectory analysis<br> 5. **05_guided_example:** Guided example for the divide-and-conquer approach<br> </p>
Reinforcing Tunnel Network Exploration in Proteins using Gaussian Accelerated Molecular Dynamics (inputs, outputs, analysis)
<ul> <li>00_LinB-Wt.tar.gz - LinB-Wt: contains raw data that are used for analysis, also conatin folder for GaMD testing.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. GaMD-testing :</p> <p> 1. Input file of GaMD used to run testing and output gamd.log files for multiple run of σOP 1.2 - 1.4 and σOD 2.5.</p> <p> 4. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>01_LinB-Open.tar.gz - LinB Open mutant: contains raw data that are used for analysis.</li> </ul> <p> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>02_LinB-Closed.tar.gz - LinB Closed mutant: contains raw data that are used for analysis.</li> </ul> <p><br> 1. cMD(Classical MD simulation) analysis files :<br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.<br> <br> 2. GaMD(Gaussian Accelerated MD simulation) analysis files : <br> <br> 1. Analysis of catalytic residue’s RMSD, whole protein RMSD and RMSF along with whole protein’s Rg and sasa.<br> 2. Inputs and output files of caver calculations. <br> 3. H-bond raw distance files from all simulations named run1-run5. <br> 4. Distance files used to calculate PCA and cluster analysis.<br> 5. Input files and input structure used to run simulations along with output restart files from each stage of production.</p> <p> 3. Initial 200ns cMD simulation files used for cluster analysis :</p> <p> 1. Force field parameters and input coordinates *.inpcrd, parameters *.parm7 and 200ns stripped water and ions simulation in Amber *.nc format<br> 2. Restart files for each stage of the minimization, equilibration and production runs in Amber *.rst format in rst folder.<br> 3. Ouput files from Simulation for each stage of the minimization, equilibration and production runs in Amber *.out format in out folder.</p> <ul> <li>03_TT_analysis.tar.gz - TransportTools: contains config file and all the raw data from all set and subset of reclustered (using in-house python script) caver calculations used for running TT.</li> </ul> <p> 1. Caver input data for comparison between 500ns, 1 us, 2.5 us and 5us between LinB-Wt and it’s mutants.<br> 2. TransportTools log file.<br> 3. Main statistics result of comparative analysis.</p> <ul> <li>04_reweighting.tar.gz: directory contains reweighted .csv files after running in-house reweighting protocol.<br> <br> 1. GaMD log files from each simulation of LinB-Wt and it’s mutants.<br> 2. CSV files from TT result folder.<br> 3. Result *.csv file contained reweighted tunnel properties in folder reweighted_filtered_new.</li> <li>05_caverdock.tar.gz: contains raw data for caverdock calculations uisng 100 best tunnels with four ligands 2-bromoethanol (be), 1,2-dibromoethane (dbe), Bromide ion (br-) and water (h2o).</li> </ul> <p> 1. Top 100 tunnels present in tunnel folder for all three tunnels ST, p1b and p3 with subdirectory containing three variants and four ligand, whichare used for running caverdock.<br> 2. Ligand *.pdbqt file and receptor *.pdbqt are present in each 100 tunnel folder of respective caverdock calculation.<br> 3. Inside each variant and each ligand, there is respective result of migration analysis with energy barrier calculation of respective tunnels *energy_barriers-new.log* and further simplied *.csv files that was used for preparing figure in manuscript.</p> <p> </p>
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (in POPE/POPG mixture)
<p>Simulation in 2:1 POPE:POPG mixture.</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 bilayer using CHARMM-GUI and simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p>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>
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>
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 4)
<p>Replicate simulation 4/4</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 simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p>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>
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 3)
<p>Replicate simulation 3/4</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 simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p>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>
Molecular dynamics simulation of SpoIVFB:Pro-SigmaK complex (replicate 2)
<p>Replicate simulation 2/4</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 simulated using OpenMM. The README file is a C-shell script that will run equilibration and 250ns of unrestrained simulation. </p> <p>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>
Molecular Dynamics Simulations of Hydrophilic (QTY) Potassium Ion Channels in Water
<p>You can find here the molecular dynamics (MD) trajectories of QTY proteins in water performed for the "Computational engineering of water-soluble potassium ion channels through QTY transformation" manuscript. Please cite our paper and the previous Zenodo dataset when referring to or using this data. If you have any questions, please contact me (Eva Smorodina) at ribes.ev@gmail.com. Thank you!<br><br>Smorodina, E. (2024). Molecular Dynamics Simulations of Hydrophobic (cryo-EM and Native) and Hydrophilic (QTY) Potassium Ion Channels [Data set]. Zenodo. https://doi.org/10.5281/zenodo.10592842</p>
Data For New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics
<p>Data necessary to reproduce results in the dissertation : Thomas, Stephen, "New Methods for Understanding and Controlling the Self-Assembly of Reacting Systems Using Coarse-Grained Molecular Dynamics" (2018). <em>Boise State University Theses and Dissertations</em>. 1448.</p> <p>10.18122/td/1448/boisestate</p>
Supplementary data for "Molecular-scale thermally activated fractures in methane hydrates: A molecular dynamics study"
<p>In this dataset you can find</p> <p>- a data sample that can be used to confirm the plots in the paper. This can be found in the folder "data_sample". Each folder inside "data_sample" is one simulation. It contains the thermodynamic output from the simulation (log.lammps), and the input script (mw_hydrate_pennycrack.in) and input data (s1_unit_cell_mw.data and water_methane_hydrate.sw) that enables rerunning the simulation using LAMMPS.</p> <p>- A custom LAMMPS region, region_ellipsoid. This has to be compiled into LAMMPS in order to create the systems that we simulate.</p> <p>- A python script, plot_data.py, that shows how to extract the relevant data from the lammps log files, which enables the partial reproduction of figure 3 in the paper. See instructions below for requirements to use this script.</p> <p> </p> <p>"region_ellipsoid" and "data_sample" are in zip containers. In order to use them, please unzip them and leave the resulting folders in the same directory as this README file.</p> <p> </p> <p>Installation instructions to make "plot_data.py" work (assuming you already have numpy and matplotlib):</p> <p>> pip3 install git+https://github.com/henriasv/regex-file-collector.git</p> <p>> pip3 install git+https://github.com/henriasv/lammps-logfile.git</p> <p> </p> <p>If this does not work, please contact Henrik Andersen Sveinsson, henriasv@fys.uio.no</p>
A Refined Open State of the Glycine Receptor Obtained Via Molecular Dynamics Simulations
<p>Representative simulation trajectory and coordinate files (starting coordinates a representative structure) from the work which first appeared on bioRxiv:</p> <p>A Refined Open State of the Glycine Receptor Obtained Via Molecular Dynamics Simulations</p> <p>Marc A. Dämgen, Philip C. Biggin</p> <p>bioRxiv 668830; doi: https://doi.org/10.1101/668830</p>
Neural-network-based molecular dynamics simulations reveal that proton transport in water is doubly gated by sequential hydrogen-bond exchange: Neural network potentials training data
<h1>Neural network potentials of an excess proton in bulk water, training data</h1> <p>This dataset contains 2188 configurations labeled at two hybrid DFT levels (revPBE0-D3 and B3LYP-D3).</p> <p>The configurations are given as a single XYZ file: configurations.xyz</p> <p>The box dimensions are written in box.txt</p> <p>The energies for all configurations at a given level of theory are written in energies_LEVEL.txt (one configuration per line)</p> <p>The atomic forces for each configuration at a given level of theory are gathered in a XYZ file: forces_LEVEL.xyz</p> <p>The relative displacements of the Wannier centroids, with respect to the closest oxygen atom, for each configuration at a given level of theory, are in the following XYZ file: wannier-centroids-displacements_LEVEL.xyz</p>
Molecular Dynamics Trajectories Exploring the Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution
<h3>Primary data for the publication "Impact of Phosphorylation on the Physiological Form of Human alpha-Synuclein in Aqueous Solution" by de Bruyn, Dorn, Rossetti, Fernandez, Outeiro, Schulz and Carloni. Submitted to the Journal of Chemical Information and Modeling.</h3> <p>Included are all GROMACS input files, parameterised topologies, starting and final configurations, and trajectories for the lowest temperature replica (at 300 K, lowest of 32 replicas between 300-500 K exchanging according to the REST2 algorithm (Wang et al. 2011)). The data is split into three archives:</p> <ol> <li><strong>all_atom_trajectories.zip</strong> <ul> <li>contains all input files and all atom trajectories including solvent</li> <li>trajectories written at 100 ps intervals</li> </ul> </li> <li><strong>protein+ion_trajectories.zip</strong> <ul> <li>contains configuration/non-parameterised topologies and trajectories excluding solvent, but including ions</li> <li>trajectories written at 10 ps intervals</li> </ul> </li> <li><strong>additional_simulations.zip</strong> <ul> <li>contains the all atom trajectories and input files, and</li> <li>solvent-free trajectories above,</li> <li>for the additional simulations in the Supplemental Information of the article: <ul> <li>includes the DES-Amber-based simulations with 64 replicas between 300-600 K, and</li> <li>a99SB-<em>disp</em>-based simulations</li> </ul> </li> </ul> </li> </ol> <p> </p> <p>Folders are named according to the following top level scheme:</p> <ul> <li><strong>DES-Amber simulations/</strong> Simulations created using the DES-Amber force field (Tucker et al. 2022)</li> <li><strong>a99SB-<em>disp</em> simulations/</strong> SImulations created using the a99SB-<em>disp</em> force field for Intrinsically Disordered Proteins (IDPs) (Robustelli et al. 2018)</li> </ul> <p>Sub-folders follow the following scheme:</p> <ul> <li><strong>AS/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein <ul> <li>unphosphorylated</li> </ul> </li> <li><strong>pAS/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein <ul> <li>phosphorylated at S129</li> <li>with double negative charge</li> </ul> </li> <li><strong>pASH/</strong> Simulations of the physiological form of <em>wild-type </em>Human α-Synuclein (a99SB-<em>disp</em> simulations only) <ul> <li>phosphorylated at S129</li> <li>with a single negative charge (monoprotonated)</li> </ul> </li> </ul> <p> </p>
Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes Confined Between Electrodes
<p>Metadata record collecting related data sets that contain molecular dynamics simulations of PEO-LiTFSI polymer electrolytes confined between model electrodes.</p> <p>Related data sets:</p> <ul> <li>Uncharged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13164944">https://doi.org/10.5281/zenodo.13164944</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165450">https://doi.org/10.5281/zenodo.13165450</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13165725">https://doi.org/10.5281/zenodo.13165725</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> <li><a href="https://doi.org/10.5281/zenodo.13166024">https://doi.org/10.5281/zenodo.13166024</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Uncharged Electrodes</li> </ul> </li> <li>Charged electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13166152">https://doi.org/10.5281/zenodo.13166152</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167128">https://doi.org/10.5281/zenodo.13167128</a>:<br>Molecular Dynamics Simulations of Monoglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167338">https://doi.org/10.5281/zenodo.13167338</a>:<br>Molecular Dynamics Simulations of Tetraglyme-LiTFSI Liquid Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167551">https://doi.org/10.5281/zenodo.13167551</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Salt Concentrations Confined Between Charged Electrodes (+/- 1.00 e/nm²)</li> <li><a href="https://doi.org/10.5281/zenodo.13167614">https://doi.org/10.5281/zenodo.13167614</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths Confined Between Charged Electrodes With Various Surface Charges</li> </ul> </li> <li>Plots: <ul> <li><a href="https://doi.org/10.5281/zenodo.13168242">https://doi.org/10.5281/zenodo.13168242</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations Confined Between Charged Electrodes With Various Surface Charges: Plots</li> </ul> </li> </ul>
Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes in the Bulk and Confined Between Electrodes
<p>Metadata record collecting related data sets that contain molecular dynamics simulations of PEO-LiTFSI polymer electrolytes in the bulk and confined between model electrodes.</p> <p>Related data sets:</p> <ul> <li>In the Bulk: <ul> <li><a href="https://doi.org/10.5281/zenodo.13144737">https://doi.org/10.5281/zenodo.13144737</a>:<br>Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes With Various Chain Lengths and Salt Concentrations in the Bulk</li> </ul> </li> <li>Confined Between Electrodes: <ul> <li><a href="https://doi.org/10.5281/zenodo.13169120">https://doi.org/10.5281/zenodo.13169120</a>:<br>Collection: Molecular Dynamics Simulations of PEO-LiTFSI Polymer Electrolytes Confined Between Electrodes</li> </ul> </li> </ul>
Initial Structures of PKM1/M2 proteins for AMOEBA Molecular Dynamics studies (xyz Tinker format)
<p>Here are presented our initial structures of PKM1/M2 (solvated and neutralized) for the different states to initiate molecular dynamics in AMOEBA force field.</p> <p>Those are represented in xyz Tinker format and come from their respectives PDB crystal structure after extraction of the unwanted ligands :</p> <p>3SRF for PKM1,</p> <p>1ZJH for monomer PKM2,</p> <p>6B6U for dimer PKM2,</p> <p>3SRH for free-tetramer PKM2,</p> <p>3SRD for tetramer PKM2 bound to FBP,</p> <p>3U2Z for tetramer PKM2 bound to TEPP-46.</p>
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>
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.