Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
691
datasets available to search
ShareScore release 0.7.1
Dataset results
691 results for “Molecular dynamics”
Data for 'Ranking Single Fluorescent Protein Based Calcium Biosensor Performance by Molecular Dynamics Simulations'
<h2>Melike Berksoz, Canan Atilgan* </h2> <h3>Faculty of Engineering and Natural Sciences, Sabanci University </h3> <p><strong>*Correspondance:</strong> Canan Atilgan, Faculty of Natural Sciences and Engineering, Sabancı University, Tuzla 34956 Istanbul, Türkiye, E-mail: canan@sabanciuniv.edu</p> <p>Genetically Encoded Fluorescent Biosensors (GEFBs) have become indispensable tools for visualizing biological processes <em>in</em> <em>vivo.</em> A typical GEFB is composed of a sensory domain (SD) which undergoes a conformational change upon ligand binding and a genetically fused fluorescent protein (FP). Ligand binding in the SD allosterically modulates the chromophore environment and changes its spectral properties. Single fluorescent (FP)-based biosensors, a subclass of GEFBs, offer a simple experimental setup; they are easy to produce in living cells, structurally stable and simple due to their single-wavelength operation. However, they pose a significant challenge for structure optimization, especially concerning the length and residue content of linkers between the FP and SD which effect how well the chromophore responds to conformational change in the SD. In this work, we use classical all-atom molecular dynamics simulations to analyze the dynamic properties of a series of calmodulin-based calcium biosensors, all with different FP-SD interaction interfaces and varying degrees of calcium binding dependent fluorescence change. Our results indicate that biosensor performance can be predicted based on distribution of water molecules around the chromophore and shifts in hydrogen bond occupancies between the ligand-bound and ligand-free sensor structures.</p> <p>Hydrogen bond occupancies were calculated with merging_bonds.py script. Double counted hydrogen bonds where a residue acts both as acceptor and donor are merged into a single entry with merge_files.py. To run sasa.tcl, you need VMD software. Trajectories were created with NAMD2 with a dcdfrequency of 5000 timesteps (every 10 ps) and strided in a 1:100 ratio (every 1 ns=1 frame in dcd). </p>
Vancomycin Molecular Dynamics
<p>Vancomycin Molecular Dynamics. Solvated in water for 10 nanoseconds in UCSF Chimera software. By Victor Padilla Sanchez, PhD Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com</p>
Ciclosporin Molecular Dynamics
<p>Ciclosporin Molecular Dynamics. By Victor Padilla Sanchez, PhD. President, Washington Metropolitan University. Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com </p>
Antibody Molecular Dynamics
<p>Antibody Molecular Dynamics. By Victor Padilla Sanchez, PhD Email: drvictorpadilla@aol.com Website: https://www.drvictorpadillasanchez.com</p>
Free Energy of Membrane Pore Formation and Stability from Molecular Dynamics Simulations
Open the record for dataset details and reuse information.
Perpendicular Crossing Chains Enable High Mobility in a Non-Crystalline Conjugated Polymer: Molecular Dynamics Forcefields and Structures
<p>This repository contains molecular dynamics forcefields and structures used to produce results described in the research article: "<em>Perpendicular Crossing Chains Enable High Mobility in a Non-Crystalline Conjugated Polymer</em>" published in <em>Proceedings of the National Academy of Sciences</em> (DOI:10.1073/pnas.2403879121)</p> <p>The following data is available:</p> <ul> <li>Coarse-grained forcefields of the conjugated polymer C16-IDTBT (12mer and 24mer).</li> <li>Coarse-grained structures of single chains of C16-IDTBT (12mer and 24mer).</li> <li>Atomistic forcefields of the conjugated polymer C16-IDTBT (12mer and 24mer).</li> <li>Atomistic structures of single chains of C16-IDTBT (12mer and 24mer).</li> <li>Bonded and non-bonded parameter files for the atomistic C16-IDTBT forcefields.</li> <li>Coarse-grained structures of thin film models (1 x C16-IDTBT 24mers, 3 x C16-IDTBT 12mers, 3 x P3HT 48mers, 3 x PffBT4T-2OD 12mers).</li> <li>Backmapped (atomistic) structures of thin film models (1 x C16-IDTBT 24mers, 3 x C16-IDTBT 12mers, 3 x P3HT 48mers, 3 x PffBT4T-2OD 12mers).</li> </ul> <p>Coarse-grained models are based on the Martini 3 forcefield: Souza, P. et al., <em>Nature Methods</em>, 2021, (https://doi.org/10.1038/s41592-021-01098-3). The bonded and nonbonded parameters may be sourced from the Martini website: https://cgmartini.nl/</p> <p>Atomistic models are based on the OPLS-AA forcefield: Kaminski, G. A. et al.,<em> J. Phys. Chem. B</em>, 2001, (https://doi.org/10.1021/jp003919d). The required bonded and nonbonded parameters have been included in this repository for convenience.</p> <p>Please note that the C16-IDTBT atomistic forcefields and single chain structures have previously been published in another repository (https://doi.org/10.11583/DTU.c.5254236.v1). They are included here for completeness.</p> <p>For any further data related to this research article, please contact the authors.</p>
Molecular dynamics of LYVE-1 and CD44 in complex with hyaluronan
<p>Input and output data for MD of mouse/human LYVE-1 and mouse CD44, both, as apo proteins (PDB codes 8ORX, 8OS2, 2JCP, respectively) and in complex with hyaluronan hexasaccharide (HA6; PDB codes 8OX3, 8OXD, 2JCR, respectively). AMBER parm7 topology (.top), restart (.rst), minimisation and MD inputs (.tin, .in), binary NETCDF trajectories (.netcdf) and volumetric maps (.dx) are shared.</p> <p>LYVE-1/CD44 input structures in their apo forms or with HA6 bound were immersed in an octahedral box of TIP3P water molecules and 150 mM NaCl was added. Hydrogen mass repartitioning to 3Da enabled us to use a time step of 4 fs. A stepwise relaxation protocol using sander.MPI of AMBER20 was followed by 1 µs MD production run using pmemd.cuda of AMBER20. Trajectories were first analysed for structural stability using RMSD metrics by use of cpptraj of AMBER20. Due to the high flexibility of the systems, we analysed only portions of 500 ns length of residues 29 to 138, 24 to 133 and 25 to 134 for mLYVE-1/hLYVE-1/CD44, respectively. The following hydrogen-bonding criteria were used: 3.6 Å cutoff for acceptor‧‧‧donor distance and 120-180º range for acceptor‧‧‧H-donor angle. Bridging water molecule occupancies were calculated by summing up binary, ternary and quaternary interactions (the cutoff for each was set to a minimum of 10 %).</p>
Molecular Dynamics Study on the Shock Induced Spallation of Polyethylene
<p>This repository contains the LAMMPS models (data files) we used in our recent work. More information and the required LAMMPS files to run shock simulations are available here: https://github.com/nuwan-d/shock_response_pe</p> <p> </p>
Molecular Dynamics of HLA-A 02:01 with neoantigen and wild-type peptides from AKP6 and ASTN1 genes.
<p>Molecular Dynamics simulations of 4 complexes :</p> <p>1. Protein HLA-A*02:01 with the wild-type peptide WLIDMESLV from AKP6 gene.</p> <p>2. Protein HLA-A*02:01 with the neoantigen peptide WLIDMKSLV from AKP6 gene.</p> <p>3. Protein HLA-A*02:01 with the neoantigen peptide KLYGLDWAEL from ASTN1 gene.</p> <p>4. Protein HLA-A*02:01 with the wild-type peptide KPYGLDWAEL from ASTN1 gene.</p>
Simulating a Chemically-Fueled Molecular Motor with Nonequilibrium Molecular Dynamics
<p>This .zip file contains the code, simulation scripts and settings, and figure generation scripts for our manuscript "Simulating a Chemically-Fueled Molecular Motor with Nonequilibrium Molecular Dynamics." More detailed descriptions are found in the 0README.txt files within the .zip folder.</p> <p> </p> <p>Manuscript link:</p> <p>https://arxiv.org/abs/2102.06298</p> <p> </p> <p>Manuscript abstract:</p> <p>Most computer simulations of molecular dynamics take place under equilibrium conditions—in a closed, isolated system, or perhaps one held at constant temperature or pressure. Sometimes, extra tensions, shears, or temperature gradients are introduced to those simulations to probe one type of nonequilibrium response to external forces. Catalysts and molecular motors, however, function based on the nonequilibrium dynamics induced by a chemical reaction's thermodynamic driving force. In this scenario, simulations require chemostats capable of preserving the chemical concentrations of the nonequilibrium steady state. We develop such a dynamic scheme and use it to observe cycles of a new particle-based classical model of a catenane-like molecular motor. Molecular motors are frequently modeled with detailed-balance-breaking Markov models, and we explicitly construct such a picture by coarse graining the microscopic dynamics of our simulations in order to extract rates. This work identifies inter-particle interactions that tune those rates to create a functional motor, thereby yielding a computational playground to investigate the interplay between directional bias, current generation, and coupling strength in molecular information ratchets.</p>
Molecular dynamics simulations reveal the selectivity mechanism of structurally similar agonists to TLR7 and TLR8
<p>Trajectory, topology and index files for TLR7 (apo), TLR7-R, TLR7-H, TLR7-G, TLR8 (apo), TLR8-R, TLR8-H, TLR8-G systems. </p>
Uncertainty-aware molecular dynamics from Bayesian active learning: Phase Transformations and Thermal Transport in SiC
<p>Machine learning interatomic force fields are promising for combining high computational efficiency and accuracy in modeling quantum interactions and simulating atomic level processes. Active learning methods have been recently developed to train force fields efficiently and automatically. Among them, Bayesian active learning utilizes principled uncertainty quantification to make data acquisition decisions. In this work, we present an efficient Bayesian active learning workflow, where the force field is constructed from a sparse Gaussian process regression model based on atomic cluster expansion descriptors. To circumvent the high computational cost of the sparse Gaussian process uncertainty calculation, we formulate a high-performance approximate mapping of the uncertainty and demonstrate a speedup of several orders of magnitude. As an application, we train a model for silicon carbide (SiC), a wide-gap semiconductor with complex polymorphic structure and diverse technological applications in power electronics, nuclear physics and astronomy. We show that the high pressure phase transformation is accurately captured by the autonomous active learning workflow. The trained force field shows excellent agreement with both \textit{ab initio} calculations and experimental measurements, and outperforms existing empirical models on vibrational and thermal properties. The active learning workflow is readily generalized to a wide range of systems, accelerates computational understanding and design.</p>
Figures S1–S10 from: Shoman ME, Abd El-Hafeez AA, Khobrani M, Assiri AA, Al Thagfan SS, Othman EM, Ibrahim ARN (2022) Molecular docking and dynamic simulations study for repurposing of multitarget coumarins against SARS-CoV-2 main protease, papain-like protease and RNA-dependent RNA polymerase. Pharmacia 69(1): 211-226. https://doi.org/10.3897/pharmacia.69.e77021
Molecular docking and Dynamic simulations study for repurposing of multitarget Coumarins against SARS-CoV-2 main protease, papain like protease and RNA-Dependent RNA polymerase.
Molecular Dynamics trajectories of the human PDZ2 domain
<p>Molecular Dynamics trajectories of the human PDZ2 domain. This dataset contains simulations of two systems (PDZ2 apo and PDZ2 bound to the RA-GEF-2 peptide) started from either a crystal or NMR structure.</p>
Molecular Dynamics simulation dataset for : Hypoxia increases the methylated histones to prevent histone clipping and redistribution of heterochromatin during Raf-induced senescence.
<p>Files are Initial structures of Cathepsin L(CTSL) with histone H3 peptides (naive and K23me3) and their Molecular Dynamics simulation trajectories. AutoDock Vina and Charmm were used in H3 peptide docking. </p> <p>Wild type CTSL was modeled with apo-Cathepsin L C25A mutant(PDB ID : 3K24) using Charmm-GUI. Trimethylated histone H3 peptide was generated with PyMol. H3 peptide Molecular Dynamics simulations were performed with GPU-accelerated OpenMM.</p>
Molecular Dynamics snapshots of the Glycine receptor MD-open state
<p>Molecular Dynamics snapshots (10 files .pdb) of the Glycine receptor MD-open state full-length, used in computational electrophysiology experiments to explore conductance and permeation pathways.</p> <p> </p> <p>Associated with:</p> <p>“Lateral fenestrations in the extracellular domain of the glycine receptor contribute to the<br> main chloride permeation pathway”</p> <p>Adrien H. Cerdan, Laurie Peverini, Jean-Pierre Changeux, Pierre-Jean Corringer, Marco Cecchini</p>
Research Data for "Molecular Dynamics Study of Structure and Reactions at the Hydroxylated Mg(0001)/Bulk Water Interface"
<p>This folder contains data used in the paper "Molecular Dynamics Study of Structure and Reactions at the Hydroxylated Mg(0001)/Bulk Water Interface" (Title subject to change). Some details on the structure are as follows:</p> <p>The folder "figures" contains data to create various figures in the main paper. Each file contains a json object, where keys "0", "1", etc. refer to the 1st/2nd subplot for a figure. The values are then another json object with the data used to create the relevant plot; the most useful of which is the "data" key which contains the plot data.</p> <p>The folder "md_traj" contains the molecular dynamics trajectory. The file "traj.exyz" is a standard format which can be opened with various software. "traj.json" contains the same information in an in-house format used by the author. The "thermo_data.json" contains various thermodynamics properties over the simulation, such as temperatures and kinetic energies. Units are femtoseconds, Angstrom, electron-volts and Kelvin.</p> <p>The folder "react_traj" contains trajectories in the same formats as "md_traj", but each are limited to time-windows where reactions occurred (so each trajectory will only be hundreds of femtoseconds long at most). The subfolder "full_traj" contains the trajectories with all atoms present, whilst "trimmed_traj" contains trajectories with most atoms removed (the atoms included are those nearest the reaction).We include these "trimmed_traj" files as it is difficult to find the reacting molecules when visualising all atoms in the simulation.</p> <p>The folder "opt_geoms" contains geometries for various structures used in the paper (in *.exyz format). These also contain *.json files; these contain information on how the calculations were carried out in a format used by the author (they are small files primarily included for the benefit of the author).</p> <p> </p> <p> </p>
All-atom accelerated molecular dynamics (aMD) simulations of Filamin-A (FLNa) actin-binding Domain, immunoglobulin-like Domains 3, 4, 5, 21 and 24 to investagate the impact of known missense mutations associated with periventricular nodular heterotopia in the liveborn males
<p>Data includes all of the wild-type and mutant trajectories of accelerated all-atom molecular dynamics (aMD) simulations of Filamin-A (FLNa, the product of <em>FLNA</em> gene located on chromosome X). Wild-type proteins are from the PDB structures with IDs: 4M9P, 3HOP, 3CNK. The mutations, including R484Q that we discovered in a Turkish family, were formerly found in the liveborn males with <em>FLNA</em>-associated periventricular nodular heterotopia (PNH), who survived with the only copy of mutated <em>FLNA</em>. To understand how these mutations lead to the PNH and simultaneously allow their survival, we performed these MD simulations for the wild-type and mutant 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 by adding counter ions in the form of NaCl. 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. After a 10000-step minimization with conjugate gradient algorithm and an equilibration for 1 ns at 298 K under NVT ensemble, production simulations were run along 100 ns. Only the production simulations were supplied in this dataset. Further details are available in the regarding configuration files.</p> <p>Resulting analysis files and scripts are included with the carbon alpha-containing dcd files of the simulations.</p> <p>This dataset is not used directly for any study, but they are preliminary results for the usage of aMD to understand rare disease mechanisms.</p> <p>Related publications:</p> <pre>Zenodo repo of classical MD for these variants: https://doi.org/10.5281/zenodo.4483108</pre> <p>Journal article based on classical MD:</p> <p>Gerlevik U, Saygı C, Cangül H, Kutlu A, Çaralan EF, Topçu Y, et al. (2022) Computational analysis of missense filamin-A variants, including the novel p.Arg484Gln variant of two brothers with periventricular nodular heterotopia. PLoS ONE 17(5): e0265400. https://doi.org/10.1371/journal.pone.0265400</p>
Spatio-temporal dynamics of genetic variation at the quantitative and molecular levels within a natural Arabidopsis thaliana population
<p><span>Evolutionary change begins at the population scale. Therefore, understanding adaptive variation requires the identification of the factors maintaining and shaping standing genetic variation at the within-population level. Spatial and temporal environmental heterogeneity represent ecological drivers of within-population genetic variation, determining the evolutionary trajectory of populations along with random processes. Here, we focused on the effects of </span><span>spatio-temporal heterogeneity on quantitative and molecular variation in a natural population of the annual plant <em>Arabidopsis thaliana</em>.</span></p> <p><span>We sampled 1,093 individuals from a Spanish <em>A. thaliana </em>population across an area of 7.4 ha for 10 years (2012-2021). Based on a sample of 279 maternal lines, we estimated spatio-temporal variation in life-history traits and fitness from a common garden experiment. We genotyped 884 individuals with nuclear microsatellites to estimate spatio-temporal variation in genetic diversity. We assessed spatial patterns by estimating spatial autocorrelation of traits and fine-scale genetic structure. We analyzed the relationships between phenotypic variation, geographic location and genetic relatedness, as well as the effects of environmental suitability and genetic rarity on phenotypic variation. </span></p> <p><span>The common garden experiment indicated that there was more temporal than spatial variation in life-history traits and fitness. Despite the differences among years, genetic distance in ecologically relevant traits (e.g. flowering time) tended to be positively correlated to genetic distance among maternal lines, whilst isolation by distance was less important. Genetic diversity exhibited significant spatial structure at short distances, which were consistent among years. Finally, genetic rarity, and not environmental suitability, accounted for genetic variation in life-history traits.</span></p> <p><span>Synthesis. Our study highlighted the importance of repeated sampling to detect the large amount of genetic diversity at the quantitative and molecular levels that a single <em>A. thaliana</em> population can harbor. Overall, population genetic attributes estimated from our long-term monitoring scheme (genetic relatedness and genetic rarity), rather than biological (dispersal) or ecological (vegetation types and environmental suitability) factors, emerged as the most important drivers of within-population structure of phenotypic variation in <em>A. thaliana.</em></span></p>
CG molecular dynamics simulations. Supporting data for "Improving Martini 3 for Disordered and Multidomain Proteins".
<pre>Coarse-grained molecular dynamics simulations with Martini 3 with varying rescaling of protein-water interactions. Supporting data for "Improving Martini 3 for Disordered and Multidomain Proteins".</pre>
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.