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289 results for “Molecular structure”
Initial Structures of PKM1/M2 proteins for AMOEBA Molecular Dynamics studies (xyz Tinker format)
<p>Here are presented our initial structures of PKM1/M2 (solvated and neutralized) for the different states to initiate molecular dynamics in AMOEBA force field.</p> <p>Those are represented in xyz Tinker format and come from their respectives PDB crystal structure after extraction of the unwanted ligands :</p> <p>3SRF for PKM1,</p> <p>1ZJH for monomer PKM2,</p> <p>6B6U for dimer PKM2,</p> <p>3SRH for free-tetramer PKM2,</p> <p>3SRD for tetramer PKM2 bound to FBP,</p> <p>3U2Z for tetramer PKM2 bound to TEPP-46.</p>
Amorphous Niobium Oxide Structures Calculated from First Principles using Density Functional Theory and Molecular Dynamics
<p>The dataset contains fifteen different amorphous niobium oxide structures. Nine of the structures have the same stoichiometry as Nb2O5. The other six are defect structures containing 1 or 2 oxygen vacancies, or 1 or 2 interstitial oxygens, or 1 Nb vacancy. Each of the structure files is in the VASP POSCAR file format. Each structure was created using ab-initio molecular dynamics at 5000~K to liquidate the structure, then snapshots of the structure were taken every 2 ps, and geometry optimizations were performed on each individual snapshot. The naming convention is relatively simple: 'conf_x_POSCAR' is a stoichiometric POSCAR, and 'conf_x_oadd1_POSCAR' is a defect structure originating from structure 'x' with a single oxygen interstitial. The defect labels correspond to 1 oxygen interstitial (oadd1), 2 oxygen interstitials (oadd2), 1 oxygen vacancy (ovac1), 2 separated oxygen vacancies (ovac2), 2 nearest neighbor oxygen vacancies (ovac2nn), and 1 Nb vacancy (nbvac).</p>
Molecular architecture of nucleosome remodeling and deacetylase sub-complexes by integrative structure determination
<p>Drawing on information from SEC-MALLS, DIA-MS, XLMS, negative-stain EM, X-ray crystallography, NMR spectroscopy, secondary structure predictions, and homology models, we applied Bayesian integrative structure determination to investigate the molecular architecture of three NuRD sub-complexes: MTA1-HDAC1-RBBP4 (MHR), MTA1<sup>N</sup>-HDAC1-MBD3<sup>GATAD2CC</sup> (MHM), and MTA1-HDAC1-RBBP4-MBD3-GATAD2A (NuDe). The present dataset pertains to the results of this study.</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases
<p>The files included here are a set of Galaxy workflows, starting structure files (PDB, mol2, and frcmod), and specialized force field files (ZAFF) for the simulation of coronavirus helicases in the apo and drug-bound state. The inhibitor molecules include those from virtual screening (FCID1 and thioguanine), as well as experimentally validated candidates (Lumacaftor and SSYA10-001).</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Flavivirus Helicases
<p>The files included here are a set of Galaxy workflows and starting structure files (PDB, mol2, and frcmod) for the simulation of flavivirus helicases in the apo and drug-bound state. The inhibitors include the 4th highest ranking compound from a virtual screening of more than 12.7 million drug-like molecules.</p>
Dataset for: "Quantification of Geometric Errors Made Simple: Application to Main-Group Molecular Structures"
<p>This dataset contains geometric energy offset (GEO') values for a set of density functional theory (DFT) methods for the B2se set of molecular structures. The data was generated as part of a research project aimed at quantifying geometric errors in main-group molecular structures. The dataset is in XLSX format created with MS Excel (version 16.69), and contains multiple worksheets with GEO' values for different basis sets and DFT methods. The worksheet headings, such as "AVQZ AVTZ AVDZ VQZ VTZ VDZ" represent different basis sets of Dunning theory, and the naming convention "(A)VnZ = aug-cc-pVnZ" is being used to label the worksheets. The data is organized in columns, with the first column providing the molecular ID and the names of the DFT methods specified in the first row of each worksheet. The molecular structures corresponding to each of these IDs can be found in Figure S1 of the supplementary information of the underlying publication [<a href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf">https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf</a>]. The data have been generated from quantum-chemical calculations from the G16 and ORCA 5.0.0 packages, with further computational details, methodology, and data validation strategies (e.g., comparisons with higher-level quantum-chemical calculations) given in the supplementary information of the underlying publication [<em>J. Phys. Chem. A</em> 2022, 126, 7, 1300–1311] and its supporting information [<a href="https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf">https://pubs.acs.org/doi/suppl/10.1021/acs.jpca.1c10688/suppl_file/jp1c10688_si_001.pdf</a>].<br> The dataset is expected to be useful to researchers in the field of computational chemistry and materials science. All values are given in kcal/mol. The data is generated by the authors of the underlying publication and it is shared under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. The data is expected to be re-usable and the quality of the data is assured by the authors. The size of the data is 71 KB.</p>
Protein Structure Files and Galaxy Workflows for Conducting Molecular Dynamics Simulations of Coronavirus Helicases -- Output Files
<p>These are the output files generated using the input files and Galaxy workflows for coronavirus helicase simulations, from: </p> <pre>https://doi.org/10.5281/zenodo.7492987</pre>
Simulation data and code used for the publication in Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes"
<p>Simulation data used in the publication Magn. Reson. "Time-domain proton-detected local-field NMR for molecular structure determination in complex lipid membranes". The simulation data set, and the code developed to generate such data, are included. Details in the published paper </p>
Molecular dynamics simulation data 3: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes: Production run gromacs trajectories for the Cx43 gap junction channel (500 mV)</p>
Molecular dynamics simulation data 2: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes:</p> <p>1. The starting coordinates, topology, MD inputs</p> <p>2. Production run gromacs trajectories for the Cx43 hemichannel</p>
Updated MD trajectories for "Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)"
<p>MD trajectories relative to the preprint "Hidden GPCR structural transitions addressed by multiple walker supervised molecular dynamics (mwSuMD)"</p> <p>For a summary of all the simulations performed and the settings employed see Table S1 of the preprint:</p> <p>https://www.biorxiv.org/content/10.1101/2022.10.26.513870v1</p> <p> </p>
Data from: Chemical structure predicts the effect of plant-derived low-molecular weight compounds on soil microbiome structure and pathogen suppression
<p>1. Plant-derived low molecular weight compounds play a crucial role in shaping soil microbiome functionality. While various compounds have been demonstrated to affect soil microbes, most data are case-specific and do not provide generalizable predictions on their effects. Here we show that the chemical structural affiliation of low molecular weight compounds typically secreted by plant roots – sugars, amino acids, organic acids and phenolic acids – can predictably affect microbiome diversity, composition and functioning in terms of plant disease suppression.</p> <p>2. We amended soil with single or mixtures of representative compounds, mimicking carbon deposition by plants. We then assessed how different classes of compounds, or their combinations, affected microbiome composition and the protection of tomato plants from the soil-borne Ralstonia solanacearum bacterial pathogen.</p> <p>3. We found that chemical class predicted well the changes in microbiome composition and diversity. Organic and amino acids generally decreased the microbiome diversity compared to sugars and phenolic acids. These changes were also linked to disease incidence, with amino acids and nitrogen-containing compound mixtures inducing more severe disease symptoms connected with a reduction in bacterial community diversity.</p> <p>4. Together, our results demonstrate that low molecular weight compounds can predictably steer rhizosphere microbiome functioning providing guidelines to engineer microbiomes based on root exudation patterns by specific plant cultivars or crop regimes.</p>
Dataset for the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems"
<p>This dataset contains additional material related to the article "Dalton Project: A Python platform for molecular- and electronic-structure simulations of complex systems". The article is available at <a href="https://doi.org/10.1063/1.5144298">https://doi.org/10.1063/1.5144298</a> (open access).<br> <br> Note that the current version of the dataset is not complete. The complete dataset will be uploaded as soon as possible.</p>
Experimental and DFT structural data for OPE3-Ph - Au molecular junctions
<p>Experimental crystal structures (CIF), plane wave DFT relaxed structures (Quantum ESPRESSO) and DFTB transport geometries (CIF, GEN) for OPE3-Ph - Au molecular junctions.</p> <p>Data for article: "Electronic Conductance and Thermopower of Single-molecule<br> Junctions of Oligo(phenyleneethynylene) Derivatives" by Hervé Dekkiche, Andrea Gemma, Fatemeh Tabatabatai, Andrei S. Batsanov, Thomas Niehaus, Bernd Gotsmann, and Martin R. Bryce to appear in Nanoscale</p> <p> </p> <p> </p>
Molecular dynamics simulations data for "Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes".
<p>Molecular dynamics simulation data created and used in "Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes".</p> <p> </p>
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
Data from "Compact Disks in a High-resolution ALMA Survey of Dust Structures in the Taurus Molecular Cloud"
<p>Continuum fits images for all disks in our ALMA Cycle 4 Taurus disk survey - see details in Long et al., 2018, ApJ, 869, 17 and Long et al., 2019, ApJ, 882, 49</p>
Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry
<p>This repository contains the data associated with: "Unraveling Molecular Structure: A Multimodal Spectroscopic Dataset for Chemistry" (see here: <a href="https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/">https://rxn4chemistry.github.io/multimodal-spectroscopic-dataset/</a>)</p>
molxspec: Deep learning models for predicting MS2 spectra from molecular structures
<p>This repository contains a pre-processed dataset derived from the <a href="https://gnps.ucsd.edu">GNPS public repository</a> of natural product mass spectra as well as pretrained model weights for four different types of model architectures using pytorch (version 1.9.0). The contents are as follows:</p> <ul> <li>gnps_processed_data.tgz: Contains tab separated files of molecule/MS2 spectra pairs derived from GNPS after filtering for invalid structures, too large molecules (bigger than 2000 M/Z spectra), and structures that yielded valid 3D geometry optimization. The processing steps were done for positive ionization mode (pos_* files), though negative ionization data is also included (neg_* files)</li> <li>models.tgz: Contains pytorch format pretrained models for four different architecutres: MLP (a residual block multilayer perceptron trained on ECFP molecular fingerprints), BERT (the same MLP but trained on pretrained representations from the Zinc V1 pretrained ChemBERTa models on SMILES), GCN (a graph convolution architecture), and EGNN (an equivariant graph neural network). Models were trained on pos_processed_gnps_shuffled_with_3d_train.tsv found in the gnps_processed_data.tgz file described previously.</li> </ul>
Raw NGS Data for "Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins"
<p>This directory contains relevant fastq files used for deep sequencing analysis in the publication “Deep mutational scanning and machine learning reveal structural and molecular rules governing allosteric hotspots in homologous proteins”. </p> <p>Fastq files are provided for presorted, uninduced and induced populations from DMS experiments of four homologs (TtgR, TetR, RolR, and MphR). Three replicates were performed for each sample.</p> <p>Data analysis of this deep sequencing data was performed using custom scripts, which are described in the methods section of the publication.</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
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Annotated Behaviour and Observability Dataset (ABODe)
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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.