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140 results for “molecular dynamics data”
Data associated to the article "Effects of fluoride salt addition to the physico-chemical properties of the MgCl2-NaCl-KCl heat transfer fluid : a molecular dynamics study"
<p>Contains input file and data used to generate the figures of the article:</p> <p>Effects of fluoride salt addition to the physico-chemical properties of the MgCl<sub>2</sub>-NaCl-KCl heat transfer fluid : a molecular dynamics study</p> <p>Weiguang Zhou, Yanping Zhang, Mathieu Salanne</p> <p>https://chemrxiv.org/engage/chemrxiv/article-details/618e903a2bf8a950c7d98e5d</p> <p>The files <em>data.inpt</em> and <em>runtime.inpt </em>are used to simulate the system using the software MetalWalls</p> <p>The files <em>MgNaKCl.txt, MgNaKClF01.txt, MgNaKClF05.txt, MgNaKClF10.txt, MgNaKClF20.txt</em> contain the computed densities, viscosities and thermal conductivities at various temperatures for several compositions (provided in the header of the files)</p>
Data for "Quantum-corrected thickness-dependent thermal conductivity in amorphous silicon predicted by machine learning molecular dynamics simulations"
<p>This is the data set for the preprint <a href="https://arxiv.org/abs/2206.07605">arXiv:2206.07605</a> [cond-mat.mtrl-sci], obtained by the GPUMD code.</p> <p>Here are 6 directories.<br> 1). NEMD<br> 2). NEPpotential<br> 3). PDOS<br> 4). kappa-quenchRate<br> 5). kappa-size<br> 6). kappa-temperature<br> <br> 1). NEMD directory contains calculations of ballistic conductance using NEMD method, where 6 independent cycles are run to average.</p> <p>2). NEPpotential directory is the trained NEP potential.</p> <p>3). PDOS directory contains phonon density of states of a-Si samples generated by the quench rate of 10^{11} K/s.</p> <p>4). kappa-quenchRate directory contains HNEMD calculations of a-Si samples which are prepared using melt-quench temperature protocols with the quench rates covering from 10^{11} to 5x10^{12} K/s. In each case, 3 independent cycles are run.</p> <p>5). kappa-size directory contains HNEMD calculations based on different supercells. 6 independent cycles are run.</p> <p>6). kappa-temperature directory contains HNEMD calculations of a-Si samples which are prepared for different targeted temperatures using slow quench rate of 10^{11} K/s.</p> <p> </p>
Sintering of alumina nanoparticles: comparison of interatomic potentials, molecular dynamics simulations, and data analysis
<p>This is the dataset for the publication in MSMSE 2022 containing all plot scripts and data for reproducing all figures. The dataset is a snapshot of the repository https://gitlab.com/computational-materials-science/public/publication-data-and-code/2022_MSMSE_Roy_et_al_MD-sintering (SHA 7ad2f421deb055f3384c00ba29f2fb1acd0e78ea) that might contain additional/newer data and scripts.</p>
BioExcel Use Case 1: collection of output data from molecular dynamics simulation
<p>The Use Case aims to address all the challenges related to antibody design through an integrative approach combining the core BioExcel software comprising of GROMACS, HADDOCK and PMX.</p> <p>The folder contains the GROMACS output files (xtc and pdb file). Molecular Dynamics simulations have been performed with GROMACS version 2020 and CHARMM36 force field. The input files and scripts of the final protocol are publicly available on BioExcel GitHub https://github.com/bioexcel/BioExcel-UseCase1.</p> <p>The Use Case 1 protocol was presented at the BioExcel Summer School on Biomolecular Simulation in 2021 (see <a href="https://doi.org/10.5281/zenodo.7009238">https://doi.org/10.5281/zenodo.7009238</a> or <a href="https://youtu.be/_TDKfKX4kwM">https://youtu.be/_TDKfKX4kwM</a>)</p> <p> </p>
Data samples for Flow-matching -- efficient coarse-graining molecular dynamics without forces
<p>CG samples generated during the training and validation processes in the flow-matching project. Accompanying the preprint "Flow-matching -- efficient coarse-graining molecular dynamics without forces": https://arxiv.org/abs/2203.11167. Detailed descriptions can be found in the preprint as well as the included README.</p>
Molecular dynamics simulations data of Caspase-3 enzyme with pentapeptide ligand DEVDG and its chiral mutant DEVdG having D-Asp at fourth position
<p>Amino acids in proteins are maintained in one specific L chiral form in the body. D-amino acids are not normally incorporated into proteins and their accumulation has been associated with several conditions including schizophrenia, amyotrophic lateral sclerosis, and other age-related disorders. However, the mechanisms by which the accumulation of D-amino-acids in proteins may lead to pathophysiological consequences remain poorly understood. In this work, we studied a model protease system, caspase-3 that specifically hydrolyses the 4’–5’ peptide bond of the pentapeptide substrate DEVDG. Through extensive molecular dynamics simulations, free energy calculations and distance maps, we reveal that caspase-3 naturally rejects the pentapeptide containing D-Asp substrate, DEVdG and prevents catalytic activity by caspase. The importance of this chiral discriminating capacity is evident from chiral-selective in vivo experimental assays to detect caspase-bound D-Asp in Drosophila where altering the chiral balance created impaired caspase activity and impaired apoptosis, increased tumour formation, and premature death. The modelling data reveals the molecular level charge balancing that enforces the chiral recognition necessary to maintain homeostasis across the cell, tissue, and organ level.</p>
Data from "Allostery and evolution: a molecular journey throught the structural and dynamical landscape of an enzyme super family."
<p>This data accompanies the paper entitled Allostery and evolution: a molecular journey throught the structural and dynamical landscape of an enzyme super family.</p> <p>The zip archive contains: </p> <p>1- Starting configurations of the proteins after equilibration in PDB format and trajectories of unrestrained molecular dynamics simulations with the positions of the proteins every 100 ps in XTC gromacs format are provided for all systems. </p> <p>2- The free energy profiles and histograms are provided for all umbrella sampling simulations and the scripts used to run it with gromacs.</p>
Molecular Dynamics (MD) Simulation Data for Dynamics Underlie the Drug Recognition Mechanism by the Efflux Transporter EmrE
<p>MD simulations on the proton bound (PDB 8UWU), deprotonated on E14A (PDB 8UWU), TPP Bound (PDB 8UWU) on our NMR derived structures.</p> <p> </p> <p>MD simulations on the proton bound (7MH6) and deprotonated on E14A (7MH6) on X-ray structures. </p> <p> </p> <p>Total raw simulation data would be too large for uploading to repositories. To reduce size of file, starting structure and tpr files are uploaded. Final structure at 2.5 μs are also uploaded. </p>
Data supporting: "Interaction of MRI Contrast Agent [Gd(DOTA)]− with Lipid Membranes: A Molecular Dynamics Study"
Open the record for dataset details and reuse information.
Speciation data for "Pressure-induced coordination changes in a pyrolitic silicate melt from ab initio molecular dynamics simulations"
<p>With <em>ab initio</em> molecular dynamics simulations on pyrolite melt, we examine the detailed changes in elemental coordination as a function of pressure and temperature. We consider the average coordination as well as the proportion and distribution of coordination environments at pressures and temperatures encompassing the conditions at which molten silicates may exist in present-day Earth and those of the Early Earth's magma ocean. At ambient pressure and 2000 K, we find that the average coordination of cations with respect to oxygen is 4.0 for Si-O, 4.0 for Al-O, 3.7 for Fe-O, 4.6 for Mg-O, 5.9 for Na-O and 6.2 for Ca-O. Although the coordination for iron with respect to oxygen is underestimated, the coordination number for all other cations are consistent with experiments. At the base of the upper mantle (~15 GPa and 2000 K), the average coordination for Si-O remains at 4.0, but increases to 4.1 for Al-O, 4.2 for Fe-O, 4.9 for Mg-O, 8.0 for Na-O and 6.8 for Ca-O. The coordination environment for Na-O remains approximately constant up to core-mantle boundary conditions (135 GPa and 4000 K), but increases to about 6 for Si-O, 6.5 for Al-O, 6.5 for Fe-O, 8 for Mg-O, 9.5 for Ca-O. Our results have implications for melt properties, such as viscosity, transport coefficients, thermal conductivities and electrical conductivities, and will help interpret experimental results on silicate glasses.</p> <p>Detailed speciation statistics for pyrolite melt were determined using <em>a</em><em>b initio</em> molecular dynamics simulations with the Vienna Ab Initio Simulation Package (VASP) (Kresse and Furthmuller, 1996). Simulations were performed with a time step of 0.5-2 femtoseconds for 10-50 picoseconds, depending on the temperature and density. The composition of the Bulk Silicate Earth was modeled with a pyrolite melt with the stoichiometry NaCa<sub>2</sub>Fe<sub>4</sub>Mg<sub>30</sub>Al<sub>3</sub>Si<sub>24</sub>O<sub>89</sub>. Bond distances were determined from the pair distribution functions, which describe the probability of finding an atom type at a given distance from the reference atom. We used the first peak in the pair distribution function to approximate the average bond length; the distance at which the first minimum occurs marks the radius of the first coordination sphere of atoms that are directly bonded to the reference atom. We used this value to define the bond criterion between two atom types. Additional computational details can be found in the manuscript.</p>
Contrasting Views of the Electric Double Layer in Electrochemical CO2 Reduction: Continuum Models vs Molecular Dynamics (data for figures)
<p>This is the data used to create the figures in the article:</p> <h4>Contrasting Views of the Electric Double Layer in Electrochemical CO<sub>2</sub> Reduction: Continuum Models vs Molecular Dynamics</h4> <div>Evan Johnson and Sophia Haussener</div> <div>The Journal of Physical Chemistry C <strong>2024</strong> <em>128</em> (25), 10450-10464</div> <p>DOI: 10.1021/acs.jpcc.4c03469</p> <p>See the file "Naming conventions" for the file names and column/row meanings. </p>
Supplementary Data for "Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein"
<p>Supplementary Data for "Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein", Journal of Biomolecular Structure and Dynamics, 2021, 10.1080/07391102.2021.1942213</p> <p>A random selection of 10 representative structures from each MSM state of STING/CDN complexes is provided in .pdb file format. The selected MSM representatives are aligned and available as PyMOL session files.</p>
Supporting data for "Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics"
<p>Supporting data for "<a href="https://www.nature.com/articles/s41467-023-36666-y">Nuclear quantum effects on zeolite proton hopping kinetics explored with machine learning potentials and path integral molecular dynamics</a>" by M. Bocus, R. Goeminne, A. Lamaire, M. Cools-Ceuppens, T. Verstraelen and V. Van Speybroeck, <em>Nature Communications</em>, <strong>2023</strong>, 14, 1008.</p> <p>This dataset contains examples of input files, submission and analysis scripts to train and use a machine learning potential based on the Schnet architecture for the proton hopping reaction in the H-CHA zeolite. The complete DFT training set, obtained by unbiasing the forces printed by CP2K (with PLUMED coupling), is stored as extended xyz files in the folders DFT/A-B/training_data.xyz where A=1-3 and A<B<5. More details on the folder architecture can be found in the README.md file.</p>
ConforMine Molecular Dynamics Data: Conformational Variability, Secondary Structure Propensities and Molecular Dynamics Simulations
<pre>This dataset contains all the data used to calculate Conformational Variability (ConVa) and Conformational Propensities as well as to train ConforMine. Each directory one level below this document contains another readme for further explanation on the contained data. The following information can be found in this dataset: </pre> <ul> <li>ConforMine_MD_training_sequences.fasta: FASTA file with the amino acid sequences of all used proteins.</li> <li>simulations (directory): Contains all the raw data derived from the MD simulations.</li> <li>ConforMine_training_MD_dihedrals (directory): Contains .xvg files with the dihedral angles of each amino acid at each step of the MD simulation.</li> <li>ConforMine_training_data_conformational_variability (directory): Contains the Conformational Variability values for all amino acids. Each file contains all ConVa values for a whole protein. The data is provided in .csv and .npy format.</li> <li>ConforMine_training_data_conformational_propensities (directory): Contains the Conformational Propensities values for all amino acids. Each file contains all propensities for a whole protein. The data is provided in .csv and .npy format.</li> </ul>
Supporting data for: Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics
<p>This repository contains supporting data and code for the paper titled "Condensed-phase molecular representation to link structure and thermodynamics in molecular dynamics" by Bernadette Mohr, Diego van der Mast, and Tristan Bereau.</p>
Input data for Reversible Unwrapping Algorithm for Constant-Pressure Molecular Dynamics Simulations
<p>As described in the main text, here is the input data used for simulation, as well as analysis directories. The archive was generated in my project folder with "tar --exclude=*trr --exclude=pbctools --exclude=qtwrap --exclude=old* --exclude=*npz --exclude=*pdf --exclude=*png --exclude=*ppm --exclude=*dcd* --exclude=*xtc --exclude=*slurm* --exclude=core* --exclude=*sh --exclude=*xvg --exclude=*out --exclude=*git* --exclude=*edr --exclude=*log --dereference -zcvf kulke-$(date +"%F").tar.gz data figures scripts Simulations". Big data and trajectory files were excluded to keep the archive size small. The archive includes all necessary files to reproduce the simulations, analysis and figures for the publication.</p>
Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"
<p>The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making.</p> <p>All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow.</p> <p>The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a></p> <p>The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a></p> <p>To obtain maximum compression of the data, the tar command used to generate this tarball was:</p> <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre> <p> </p>
Data for: Optimal inference of molecular interaction dynamics in FRET microscopy
<p>Intensity-based time-lapse fluorescence resonance energy transfer (FRET) microscopy has been a major tool for investigating cellular processes, converting otherwise unobservable molecular interactions into fluorescence time series. However, inferring the molecular interaction dynamics from the observables remains a challenging inverse problem, particularly when measurement noise and photobleaching are nonnegligible—a common situation in single-cell analysis. The conventional approach is to process the time-series data algebraically, but such methods inevitably accumulate the measurement noise and reduce the signal-to-noise ratio (SNR), limiting the scope of FRET microscopy. Here, we introduce an alternative probabilistic approach, B-FRET, generally applicable to standard 3-cube FRET-imaging data. Based on filtering theory, B-FRET implements a statistically optimal way to infer molecular interactions and thus drastically improves the SNR. We validate B-FRET using simulated data and then apply it to real data, including the notoriously noisy in vivo FRET time series from individual bacterial cells to reveal signaling dynamics otherwise hidden in the noise.</p>
Molecular Simulation Data Associated with the Manuscript "Function and dynamics of the intrinsically disordered carboxyl terminus of β2 adrenergic receptor"
<p>Molecular Simulation Data Associated with the Manuscript<br> <br> "Function and dynamics of the intrinsically disordered carboxyl terminus of β2 adrenergic receptor"<br> <br> by Jie Heng, Yunfei Hu, Guillermo Pérez-Hernández, Asuka Inoue, Jiawei Zhao, Xiuyan Ma, Xiaoou Sun, Kouki Kawakami, Tatsuya Ikuta, Jienv Ding, Yujie Yang, Lujia Zhang, Sijia Peng, Xiaogang Niu, Hongwei Li, Ramon Guixà-González, Changwen Jin, Peter W. Hildebrand, Chunlai Chen & Brian K. Kobilka</p> <p>Nature Communications 2023, <a href="https://doi.org/10.1038/s41467-023-37233-1">https://doi.org/10.1038/s41467-023-37233-1</a><br> <br> The representative molecular dynamics (MD) trajectories shown in the <strong>Supplementary Fig. 8,<br> Variable contacts of the β2AR CT</strong> can be 3D visualized in the browser in the following link:</p> <ul> <li><a href="https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://base/B2CT/variants.ngl"> https://proteinformatics.uni-leipzig.de/mdsrv.html?load=file://base/B2CT/variants.ngl</a></li> </ul>
EMD data for the paper "Impact of ad-hoc post-processing parameters on the lubricant viscosity calculated with equilibrium molecular dynamics simulations"
<p>This archive contains the post-processing data obtained from EMD simulations of <strong>2,2,4-Trimethylhexane</strong> lubricant molecule under various operating conditions. The EMD simulations were performed using LAMMPS with COMPASS force field. (See manuscript and README for details.)</p>
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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.