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691 results for “Molecular dynamics”
Research data supporting: "Machine learning of microscopic structure-dynamics relationships in complex molecular systems"
<p>This repository contains the set of data and the code to reproduce the results shown in "Machine learning of microscopic structure-dynamics relationships in complex molecular systems" published on Machine Learning: Science and Technology (DOI: 10.1088/2632-2153/ad0fa5).</p>
Molecular dynamics simulation of human ρ1 GABAA receptor with neurosteriod pregnenolone sulfate
<p>Molecular dynamics simulation trajectory, parameter files for the systems of human ρ1 GABAA receptor with neurosteriod pregnenolone sulfate. Pregnenolone sulfate was tested with two different poses, either sulfate group "up" or "down".</p> <p> </p>
Molecular Insights into the Effects of F16L and F19L Substitutions on the Conformation and Aggregation Dynamics of Human Calcitonin
<p><a name="_Hlk145518059"></a><span>Human calcitonin (hCT) regulates calcium-phosphorus metabolism, but its amyloid aggregation disrupts physiological activity, increases thyroid carcinoma risk, and hampers its clinical use for bone-related diseases like osteoporosis and Paget’s disease. Improving hCT with targeted modifications to mitigate amyloid formation while maintaining function holds promise as a strategy. Understanding how each residue in hCT's amyloidogenic core affects its structure and aggregation dynamics is crucial for designing effective analogs. Mutants F16L-hCT and F19L-hCT, where Phe residues in the core are replaced with Leu as in non-amyloidogenic salmon calcitonin, showed different aggregation kinetics. However, the molecular effects of these substitutions in hCT are still unclear. Here, </span><a name="OLE_LINK4"></a><span><span>we systematically investigated the folding and self-assembly conformational dynamics of hCT, F16L-hCT, and F19L-hCT through multiple long-timescale independent atomistic discrete molecular dynamics (DMD) simulations. </span></span><span><span>Our results indicated that the hCT monomer primarily assumed unstructured conformations with dynamic helices around residues 4-12 and 14-21. During self-assembly, the amyloidogenic core of hCT<sub>14-21</sub> converted from dynamic helices to β-sheets. However, substituting F16L did not induce significant conformational changes, as F16L-hCT exhibited similar characteristics to wild-type hCT in both monomeric and oligomeric states. In contrast, F19L-hCT exhibited substantially more helices and fewer β-sheets than hCT, irrespective of their monomers or oligomers. The substitution of F19L significantly enhanced the stability of the helical conformation for hCT<sub>14-21</sub>, thereby suppressing the helix-to-β-sheet conformational conversion. Overall, our findings elucidate the molecular mechanisms underlying hCT aggregation and the effects of F16L and F19L substitutions on the conformational dynamics of hCT, highlighting the critical role of F19 as an important target in the design of amyloid-resistant hCT analogs for future clinical applications.</span></span></p>
How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study
<p>The data necessary to independently reproduce the MD simulations and the first part of the simulation trajectories reported in the paper "<strong>How Binding Site Flexibility Promotes RNA Scanning in TbRGG2 RRM: A Molecular Dynamics Simulation Study</strong>", by Lemmens et al.</p> <p>Due to Zenodo data deposition limits, every 10th frame of the MD simulation trajectories is included. Due to Zenodo deposition limits, MD trajectory files for this paper are also available at 10.5281/zenodo.14260246.</p>
Solvent sites detection from molecular dynamics in mixed solvents
<p>In this zip file, you will find 3 molecular dynamics (MDs) that were run using the AMBER molecular dynamics package (https://ambermd.org/), and the necessary scripts to obtain the solvent sites.</p> <p>The MDs correspond to the kinase and rubredoxin domains of protein kinase G from Mycobacterium tuberculosis. </p> <p> </p> <p> </p>
An estimate for thermal diffusivity in highly irradiated tungsten using Molecular Dynamics simulation
<p>The changing thermal conductivity of an irradiated material is among the principal design considerations for any nuclear reactor, but at present few models are capable of predicting these changes starting from an arbitrary atomistic model. Here we present a simple model for computing the thermal diffusivity of tungsten, based on the conductivity of the perfect crystal and resistivity per Frenkel pair, and dividing a simulation into perfect and athermal regions statistically. This is applied to highly irradiated microstructures simulated with Molecular Dynamics. A comparison to experiment shows that simulations closely track observed thermal diffusivity over a range of doses from the dilute limit of a few Frenkel pairs to the high dose saturation limit at 3 displacements per atom (dpa).<br> </p>
Alchemical Free Energy Estimators and Molecular Dynamics Engines: Accuracy, Precision and Reproducibility - Dataset
<p>This zip contains all input structures for paper the: Alchemical Free<br> Energy Estimators and Molecular Dynamics<br> Engines: Accuracy, Precision and Reproducibility</p> <p>Authors: Alexander D. Wade, Agastya P. Bhati, Shunzhou Wan, Peter V.Coveney</p> <p>The structures of the folders are protein/ligand_transformation/alchemical_leg/input/files</p> <p>The ligand transformation are derived from previous work by wang et al. (https://pubs.acs.org/doi/10.1021/ja512751q)</p> <p>There are two files for the solvent alchemical leg: complex.pdb and complex.prmtop</p> <p>complex.pdb is structure file that also denotes the alchemical atoms in the pdb beta column. complex.prmtop is an AMBER parameter/topology file</p> <p>For the complex alchemical leg there is an additional file constraints.pdb that contains the constraint information in the pdb beta column.</p> <p>These files can be used with TIES_MD (https://ucl-ccs.github.io/TIES_MD/) or other molecular dynamics engiens that take AMBER input.</p>
All-atom Molecular Dynamics Simulations of Meiosis 1-associated protein (M1AP) to Investagate the Impact of Known Missense Mutations Associated with Male Infertility through Non-obstructive Azoospermia
<p>Protein structure of meiosis 1-associated protein (M1AP) was modelled by using GalaxyWeb (from Seok Lab). We used this model to investigate the impact of variants (i.e., S50P, R266Q, P389L, G317R, and L430P) in M1AP which were recently associated with non-obstructive azoospermia (NOA). NOA is a male infertility-related condition causing absence of sperm in the seminal fluid due to meiosis failure. We aimed to elucidate the pathogenicity mechanisms of these five missense NOA-related mutations on M1AP by performing molecular modeling and molecular dynamics (MD) simulations. This dataset includes the results of 1000 ns MD simulations (two repeats, each 500 ns) for each of the mutant and wild-type systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system charge with 0.15 M KCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. NPT ensemble was applied for whole simulations. Two step minimization & equilibration procedure was performed: (1) 5,000-step minimization and 1 ns equilibrium with constraints on the protein; (2) 5,000-step minimization and 1 ns equilibrium without the constraints on the protein. All related configuration files for wild-type system were also included to the dataset. Production simulations were run twice along 500 ns by using different random seeds to assign the velocities from Boltzmann distribution (total simulation time for each system was 1000 ns, which are given as 500 ns repeat 1, and 500 ns repeat 2). The production simulations were supplied in the dataset. "out" and "log" files were used for energy analysis.</p> <p>For all analysis scripts, see https://github.com/ugerlevik/M1AP_analysis.</p>
Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations
<p>MD simulations used in the studies of the publication "<strong>Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations</strong>"</p>
Data of curvature model for the study of nanoparticle size effects on amyloid fibril stability and molecular dynamics simulations data
<p>The data provided refer to our published article:</p> <p>T. John, J. Adler, C. Elsner, J. Petzold, M. Krueger, L.L. Martin, D. Huster, H.J. Risselada, B. Abel, Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation, J. Colloid Interface Sci. 622 (2022), 804–818. <a href="https://doi.org/10.1016/j.jcis.2022.04.134">https://doi.org/10.1016/j.jcis.2022.04.134</a></p> <p>This article is accompanied by a 'Data in Brief' article that explains in more detail the use of the curvature model and our molecular dynamics (MD) simulations:</p> <p>T. John, L.L. Martin, H.J. Risselada, B. Abel, Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data, Data Brief 45 (2022), 108598. <a href="https://doi.org/10.1016/j.dib.2022.108598">https://doi.org/10.1016/j.dib.2022.108598</a></p>
Molecular dynamics trajectories of C3 H8 O molecule and its structural isomers
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p>------------------------------------------------</p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
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>
Isomorph Invariant Dynamic Mechanical Analysis: A Molecular Dynamics Study
<p>This data set contains the data required to reproduce most of the figures in our paper, [arXiv:2204.06962] which will soon be submitted to a journal. Abstract of the paper:</p> <p>We simulate dynamic mechanical analysis experiments for the Kob-Andersen binary Lennard-Jones system. For this, the SLLOD algorithm with time-dependent strain rates is applied to give a sinusoidally varying strain at different densities and temperatures. The starting point is a temperature scan at a fixed reference density. Isomorph theory predicts that for other densities corresponding temperatures can be identified at which the mechanical properties are unchanged when scaled appropriately. We determine the isomorphically equivalent temperatures by analysing how<br> particle forces change upon scaling configurations to the new density. Loss moduli expressed in suitable reduced units are compared for isomorphic state points. When plotted against the unscaled temperatures, these reduced loss curves are observed to collapse indicating the validity of isomorph theory for dynamic mechanical analysis experiments. Two different methods to determine isomorphic temperatures are considered. While one of them breaks down for the largest density rescalings considered in this study, the other one is still applicable in this region. The decorrelation of force vectors upon rescaling is investigated as a possible origin of this effect. Our results demonstrate that the simplification of the phase diagram entailed by isomorph theory for a wide class of system<br> is relevant also for the mechanical properties of glasses.</p> <p> </p> <p> </p>
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>
Molecular Dynamics of Jelly Candies by Means of Nuclear Magnetic Resonance Relaxometry
<p><sup>1</sup>H spin-lattice Nuclear Magnetic Resonance relaxation studies have been performed for different kinds of Haribo jelly and Vidal jelly in a very broad frequency range from about 10 kHz to 10 MHz to obtain insight into the dynamic and structural properties of jelly candies on the molecular level. This extensive data set has been thoroughly analyzed revealing three dynamic processes, referred to as slow, intermediate and fast dynamics occurring on the timescale of 10<sup>−6</sup> s, 10<sup>−7</sup> s and 10<sup>−8</sup> s, respectively. The parameters have been compared for different kinds of jelly for the purpose of revealing their characteristic dynamic and structural properties as well as to enquire into how increasing temperature affects these properties. It has been shown that dynamic processes in different kinds of Haribo jelly are similar (this can be treated as a sign of their quality and authenticity) and that the fraction of confined water molecules is reduced with increasing temperature. Two groups of Vidal jelly have been identified. For the first one, the parameters (dipolar relaxation constants and correlation times) match those for Haribo jelly. For the second group including cherry jelly, considerable differences in the parameters characterizing their dynamic properties have been revealed.</p>
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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.