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393 results for “Molecular dynamics simulations”
FORECASTING MOLECULAR DYNAMICS SIMULATIONS OF POLYMER-LIPIDS IN SOLUTION WITH RNNs
<p>Files and scripts pertaining to our work: </p> <ul> <li>GROMACS files for the topology (DSPE+PEG.top) and the initial structure of the aggregate (DSPE+PEG_EA_NPT.gro)</li> <li>GROMACS topology file for the ethyl acetate molecule: EA_SI.top</li> <li>Scripts to submit the <em>GROMACS</em> utilities for calculation of the interaction energies are described in README.txt (Subset_energy.sh , Interaction_energies.sh)</li> <li>Scripts pertaining to <em>PyTorch</em> use and access of methods are described in README.txt (Multiple-run.sh. Job.sh, Pytorch_train-model.py)</li> <li>Scripts pertaining to <em>scikit learn </em>access for the Expectation Maximization clustering are described in the README.txt (Job_EM.sh, EM_Clustering.py)</li> <li>Files with the time series of the potential energy (PE) and interaction energy (IE) of the DSPE-PEG aggregate with the ethyl acetate solvent. Series contain 500,000 snapshots taken every 10 fs along the NVT Molecular Dynamics trajectory at 300 K and 906.3 kg/m<sup>3</sup> density. The molecular solution is in a cubic box of edge length 13.76 nm, containing 16,000 ethyl acetate molecules and one aggregate of 4 DSPE-PEG-amide macromolecules (224,000 atoms): Data_Andrews_etal_DSPE-PEG_2022.zip</li> <li>ArXiv preprint: https://doi.org/10.48550/arXiv.2203.00151 (JAndrews_etal_arXiv-doi.pdf)</li> </ul>
Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup
<p>Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including <sup>15</sup>N spin relaxation, without the need for reweighting. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function.</p>
Molecular dynamics simulations of an Ago2-RNA complex in different force fields
<p>This set of simulations contains 2us of Ago2-RNA complex in Amber ff14SB + OL3, ff19SB + OL3 and Desmond OPLS4 force fields. The polarizable force field AMOEBA has two simulation sets of 10*10ns and 2*100ns. The trajectories have been wrapped in the periodic box, centered around the protein atoms and the water molecules have been stripped out to conserve space using cpptraj. The trajectories are presented in Gromacs xtc-format which can be opened with the corresponding pdb file in multiple software tools such as VMD, PyMol or CaverAnalyst. The simulations are based on the crystal structure PDB ID 4W5O, where the missing loops were modeled using the Schrödinger Suite and missing nucleotides added manually. </p>
All-atom molecular dynamics simulations of phenylalanine-4-hydroxylase (PAH) tetramer to investigate the impact of two novel heterozygous mutations, p.Y198N and p.Y204F, observed in a classical phenylketonuria patient
<p>Phenylalanine-4-hydroxylase (PAH) tetramer system (Robetta modelling to complete the structure with template PDB ID: 6hyc) with parametrised BH<sub>4</sub> ligand (parameters are available in the dataset) and Fe(II) metal ions in a TIP3P water box ionised with 0.15 M KCl were presented as wild-type and carrying two novel mutations as Y198N on dimeric chains A and B, and Y204F on dimeric chains C and D. In addition, E353 and E422 are protonated as predicted by PROPKA. BH<sub>4</sub> molecule parametrization was performed by using GAFF, Antechamber and “amb2chm_par.py” program of Amber2018.</p> <p>5,000-step minimization and 1 ns equilibration were performed by fixing the protein to relax the system. Then, another 5,000-step minimization and 1 ns equilibration were performed without any constraints, except the SHAKE algorithm on water molecules, to relax the protein and system. The production simulations were performed along 100 ns trajectory at 310 K collected under NpT ensemble.</p> <p>All system preparation and simulation details for this dataset is available with the related background, results and conclusions in the following article:</p> <p>Tolga Aslan, Aslı Yenenler-Kutlu, Umut Gerlevik, Ayşe Çiğdem Aktuğlu Zeybek, Ertuğrul Kıykım, Osman Uğur Sezerman & Necla Birgul Iyison (2021) Identifying and elucidating the roles of Y198N and Y204F mutations in the PAH enzyme through molecular dynamic simulations, Journal of Biomolecular Structure and Dynamics, DOI: <a href="https://doi.org/10.1080/07391102.2021.1921619">10.1080/07391102.2021.1921619</a></p>
Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants
<p>This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics). The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.<br> </p>
Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica: dataset
<p><strong>Abstract</strong>:<br> (from [1])</p> <blockquote> <p>The addition of fillers can significantly improve the mechanical behavior of polymers. The responsible mechanisms at the molecular level can be well assessed<br> by particle-based simulation techniques, such as molecular dynamics. However, the high computational cost of these simulations prevents the study of macroscopic<br> samples. Continuum-based approaches, particularly micromechanics, offer a more efficient alternative but require precise constitutive models for all<br> constituents, which are usually unavailable at these small length scales. In this contribution, we derive a molecular-dynamics-informed constitutive law by<br> employing a characterization strategy introduced in a previous publication. We choose silicon dioxide (silica) as an exemplary filler material used in polymer<br> composites and perform uniaxial and shear deformation tests with molecular dynamics. The material exhibits elastoplastic behavior with a pronounced anisotropy.<br> Based on the pseudo-experimental data, we calibrate an anisotropic elastic constitutive law and reproduce the material response for small strains accurately. <br> The study validates the characterization strategy that facilitates the calibration of constitutive laws from molecular dynamics simulations. Furthermore, the<br> obtained material model for coarse-grained silica forms the basis for future continuum-based investigations of polymer nanocomposites. In general, the presented<br> transition from a fine-scale particle model to a coarse and computationally efficient continuum description adds to the body of knowledge of molecular science<br> as well as the engineering community.<br> </p> </blockquote> <p><br> <strong>Contact</strong>:<br> Maximilian Ries<br> Institute of Applied Mechanics<br> Friedrich-Alexander-Universität Erlangen-Nürnberg<br> Egerlandstr. 5<br> 91058 Erlangen</p> <p><br> <strong>Software</strong>:<br> All simulations were performed with LAMMPS [3], version: 29 Oct 2020 / 20201029<br> 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<br> Active compile time flags:<br> -DLAMMPS_GZIP<br> -DLAMMPS_SMALLBIG</p> <p><strong>Installed packages:</strong><br> CLASS2, KSPACE, MANYBODY, MC, MOLECULE, MPIIO, OPT, VORONOI, USER-INTEL, USER-MISC, USER-MOLFILE, USER-NETCD</p> <p><br> <strong>License:</strong><br> Creative Commons Attribution 4.0 International<br> <br> <strong>Context</strong>:<br> Data set supplementing journal paper:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.</p> <p><br> This dataset contains the results presented in [1] and the necessary data to obtain those.</p> <p><br> <strong>Content</strong>:<br> The files to reproduce our simulations and their results are structured as follows:</p> <ul> <li>01_potentials<br> tabulated potentials calibrated via iterative Boltzmann inversion in [2] kindly provided by the Müller-Plathe group at Technische Universität Darmstadt <ul> <li>Angle_table<br> angular interactions</li> <li>Bond_table<br> bond interactions</li> <li>Nonbond_table<br> pair interactions</li> </ul> </li> <li>02_sample<br> Lammps data file (molecular style) of the investigated silica sample</li> <li>03_simulations<br> The condensed simulation directories with the naming convention given below are organized in the following subfolders: <ul> <li>01_time-proportional<br> time-proportional simulation data</li> <li>02_time-periodic<br> time-periodic simulation data</li> </ul> </li> </ul> <p>Each simulation directory contains:</p> <ul> <li>lammps input file (*.in) of the specific simulation</li> <li>input.prm: input parameters of the specific simulation (read by the input file)</li> <li>meta.info: meta data of the specific simulation run</li> <li>LAMMPS_out:<br> simulation results (lammps thermo_out) in tabulated form, an overview of columns is given below <ul> <li>thermo_out.Dat: raw output</li> <li>thermo_out_SG.Dat: smoothed output (Savitzky-Golay filter)</li> <li>thermo_out_STD.Dat: standard deviation of raw output</li> </ul> </li> </ul> <p><br> <strong>Naming convention</strong>:<br> Silica-[deformation]-[direction]_[deformation function]-[deformation magnitude]_[deformation rate]<br> ● [deformation]: uniaxial tension (UT), simple shear (SS)<br> ● [direction]: deformation carried out in X/Y/Z (UT) or XY/XZ/YZ (SS)<br> ● [deformation function]: time-proportional (strain), time-periodic (strain_ampl)<br> ● [deformation magnitude]: maximum strain (time-proportional), strain amplitude (time-periodic); unitless<br> ● [deformation rate]: rate-[strain rate] (only time-proportional): 0.001/ns-0.1/ns</p> <p><br> <strong>Output quantities</strong> (columns of *.Dat files):<br> ● Step: time step<br> ● Time: time in fs<br> ● TotEng: total energy in kcal/mol<br> ● PotEng: potential energy in kcal/mol<br> ● KinEng: kinetic energy in kcal/mol<br> ● E_pair: pair energy in kcal/mol<br> ● E_bond: bond energy in kcal/mol<br> ● E_angle: angle energy in kcal/mol<br> ● E_dihed: dihedral energy in kcal/mol<br> ● Temp: temperature in K<br> ● Press: hydrostatic pressure in atm<br> ● Pxx: xx component of pressure tensor in atm<br> ● Pyy: yy component of pressure tensor in atm<br> ● Pzz: zz component of pressure tensor in atm<br> ● Pxy: xy component of pressure tensor in atm<br> ● Pxz: xz component of pressure tensor in atm<br> ● Pyz: yz component of pressure tensor in atm<br> ● Volume: volume of simulation box in (Angstroms)^3<br> ● Lx: box length in x direction in Angstroms<br> ● Ly: box length in y direction in Angstroms<br> ● Lz: box length in z direction in Angstroms<br> ● Density: density in g/(cm^3)<br> ● c_RG: radius of gyration in Angstroms<br> ● c_RG[1]: squared radius of gyration tensor (xx component) in (Angstroms)^2<br> ● c_RG[2]: squared radius of gyration tensor (yy component) in (Angstroms)^2<br> ● c_RG[3]: squared radius of gyration tensor (zz component) in (Angstroms)^2<br> ● c_RG[4]: squared radius of gyration tensor (xy component) in (Angstroms)^2<br> ● c_RG[5]: squared radius of gyration tensor (xz component) in (Angstroms)^2<br> ● c_RG[6]: squared radius of gyration tensor (yz component) in (Angstroms)^2<br> ● c_bondave[1]: bond energy averaged over all atoms in kcal/mol<br> ● c_bondave[2]: bond distance averaged over all atoms in Angstroms<br> ● c_bondave[3]: squared bond distance averaged over all atoms in (Angstroms)^2<br> ● c_angleave[1]: angle energy averaged over all atoms in kcal/mol<br> ● c_angleave[2]: angle averaged over all atoms degree<br> ● c_angleave[3]: cosine of angle (unitless)<br> ● c_angleave[4]: squared cosine of angle (unitless)<br> ● c_MSD[1]: mean squared displacement x-direction in (Angstroms)^2<br> ● c_MSD[2]: mean squared displacement y-direction in (Angstroms)^2<br> ● c_MSD[3]: mean squared displacement z-direction in (Angstroms)^2<br> ● c_MSD[4]: total mean squared displacement in (Angstroms)^2<br> ● c_COM[1]: x coordinate of center of mass in Angstroms<br> ● c_COM[2]: y coordinate of center of mass in Angstroms<br> ● c_COM[3]: z coordinate of center of mass in Angstroms<br> ● v_strain_xx: xx component of engineering strain tensor (unitless) <br> ● v_strain_yy: yy component of engineering strain tensor (unitless) <br> ● v_strain_zz: zz component of engineering strain tensor (unitless) <br> ● v_vMisesequivstress: von Mises equivalent stress in MPa<br> ● v_Cauchy_xx: xx component of stress tensor in MPa <br> ● v_Cauchy_yy: yy component of stress tensor in MPa<br> ● v_Cauchy_zz: zz component of stress tensor in MPa<br> ● v_Cauchy_xy: xy component of stress tensor in MPa<br> ● v_Cauchy_xz: xz component of stress tensor in MPa<br> ● v_Cauchy_yz: yz component of stress tensor in MPa<br> ● v_strain_xy: xy component of engineering strain tensor (unitless) <br> ● v_strain_xz: xz component of engineering strain tensor (unitless) <br> ● v_strain_yz: yz component of engineering strain tensor (unitless) </p> <p><strong>References</strong>:<br> [1] Ries, M.; Bauer, C.; Weber, F.; Steinmann, P. & Pfaller, S., "Characterization of the material behavior and identification of effective elastic moduli based on molecular dynamics simulations of coarse-grained silica", Mathematics and Mechanics of Solids, 2022, 108128652211080.<br> [2] Ghanbari, A.; Ndoro, T. V. M.; Leroy, F.; Rahimi, M.; Böhm, M. C. & Müller-Plathe, F., “Interphase Structure in Silica-Polystyrene<br> Nanocomposites: A Coarse-Grained Molecular Dynamics Study”, Macromolecules, 2012, 45, 572-584.<br> [3] Plimpton, S., “Fast parallel algorithms for short-range molecular dynamics,” Journal of computational physics, 1995, 117, 1-19.</p> <p> </p>
Outputs of molecular dynamics simulations of two NS1 ZIKV variants in the membrane presence
<p>Files corresponding to outputs obtained through Molecular Dynamics (MD) simulations of two Non-structural (NS) proteins 1 of the Zika virus from Uganda (ZIKV-UG) and Brazil (ZIKV-BR). Simulations were performed using GROMACS 5.1.5 or later versions. Systems were built based on atomistic models (https://zenodo.org/record/5608521#.YvDNZTlBzJw) and converted to a coarse-grained representation employing MARTINI 2.2p ElNeDyn. It was assumed to be NS1 systems in <em>apo</em> and <em>holo</em> forms (<em>i.e.</em>, in the absence and presence of a lipid bilayer, respectively). The membrane model tries to reproduce a lipid concentration of an endoplasmic reticulum lipid bilayer. <em>Holo</em> and <em>apo</em> systems were simulated until they reached 20 and 10 µs, respectively. Trajectories do not include water molecules. For the specific case of <em>holo</em> systems, frames were skipped every 5 frames, which means that processed trajectories are equivalent to simulations when it is recorded every 1000 ps. More details can be found at <a href="https://doi.org/10.1021/acs.jcim.2c01461">https://doi.org/10.1021/acs.jcim.2c01461</a></p> <p>Note: Some topology and index files important for MD analysis are also present.</p>
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>
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>
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>
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>
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>
ScienceDex guides
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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.