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289 results for “Molecular structure”
Rapid structure determination of microcrystalline molecular compounds using electron diffraction (nanoArgovia Project A3EDPI)
<p>The are the data linked to the publication "Rapid structure determination of microcrystalline molecular compounds using electron diffraction", <a href="https://doi.org/10.1002/anie.201811318">10.1002/anie.201811318</a>. Electron Diffraction data collected with an EIGER X 1M detector (DECTRIS Ltd.).</p> <p>Each tar file contains the raw files in HDF5 format, together with the XDS.INP file used for data integration. Images of the respective crystals have '_img_' in their file names. The log files for recording the stage alpha angle are included with the same name and suffix .txt. See publication for details.</p> <p>NB: The meta-data in the HDF5 files have no meaning, please refer to the respective XDS.INP file for respective information.</p> <p>The crystallographic data (CIF-files) have been uploaded to the ICSD (High--throughput Structural Chemistry with Electron Diffraction) and CSD (https://www.ccdc.cam.ac.uk/) respectively:</p> <p>Paracetamol from Grippostad CCDC 1856579<br> electron structure of MBBF4 CCDC 1856580</p> <p>ZSM-5 x227 CSD 1856581</p> <p>ZSM-5 x331 CSD 1856582</p> <p>ZSM-5 x79 CSD 1856583<br> ZSM-5 x811 CSD 1856584</p> <p> </p>
Representative Structures from Molecular Dynamics Simulations of the Inward Facing and Outward Facing States of LaINDY
<p>This upload is a supplementary data set for the following publication: <a href="https://doi.org/10.7554/eLife.61350">D. B. Sauer, N. Trebesch, J. J. Marden, N. Cocco, J. Song, A. Koide, S. Koide, E. Tajkhorshid, and D.-N. Wang. "Structural basis for the reaction cycle of DASS dicarboxylate transporters." <em>eLife</em>. <strong>9</strong>, e61350. DOI: 10.7554/eLife.61350</a>. Please see the main publication for the methods, analysis, and discussion associated with this data set.</p>
Molecular Details Underlying Dynamic Structures and Regulation of the Human 26S Proteasome
<p>The 26S proteasome is the macromolecular machine responsible for ATP/ubiquitin dependent degradation. As aberration in proteasomal degradation has been implicated in many human diseases, structural analysis of the human 26S proteasome complex is essential to advance our understanding of its action and regulation mechanisms. In recent years, cross-linking mass spectrometry (XL-MS) has emerged as a powerful tool for elucidating structural topologies of large protein assemblies, with its unique capability of studying protein complexes in cells. To facilitate the identification of cross-linked peptides, we have previously developed a robust amine reactive sulfoxide-containing MS-cleavable cross-linker, disuccinimidyl sulfoxide (DSSO). To better understand the structure and regulation of the human 26S proteasome, we have established new DSSO-based in vivo and in vitro XL-MS workflows by coupling with HB-tag based affinity purification to comprehensively examine protein-protein interactions within the 26S proteasome. In total, we have identified 447 unique lysine-to-lysine linkages delineating 67 inter-protein and 26 intra-protein interactions, representing the largest cross-link dataset for proteasome complexes. In combination with EM maps and computational modeling, the architecture of the 26S proteasome was determined to infer its structural dynamics. In particular, three proteasome subunits Rpn1, Rpn6 and Rpt6 displayed multiple conformations that have not been previously reported. Additionally, cross-links between proteasome subunits and 15 proteasome interacting proteins including 9 known and 6 novel ones have been determined to demonstrate their physical interactions at the amino-acid level. Our results have provided new insights on the dynamics of the 26S human proteasome and the methodologies presented here can be applied to study other protein complexes.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/26S-PIPs or the README file.</p>
Data for "A Quantum Definition of Molecular Structure"
<p>Supplemental data for our article "A Quantum Definition of Molecular Structure".</p><p>Version 1.1.0 contains data for additional k-medoids runs performed on different subsets of the complete sample.</p>
Unstable Crystallographic & Molecular Structures for Machine Learning of System Energies
<div> <div> <div> <p>Extended QM9 (E-QM9) includes diverse sizes (i.e. number of atoms) and compositions of OoE molecules, through extending a subset of QM9 with OoE versions of 10k of its molecules.</p> <p>Periodic crystals (PC) allows learning regular bonding patterns that arise in periodic structures by repeating the base crystal lattice. We use the Face-Centred Cubic (fcc) Bravais lattice for aluminium (Al) and copper (Cu) crystals.</p> <p>Crystal Growth (CG) contains growing crystals of increasing size and complexity. Starting from a basic fcc crystal seed of 14 atoms, new systems are generated by iteratively placing atoms at a random location on the surface of the growing crystal following its lattice pattern, with sizes ranging from 15 to 114 atoms. We use 20 random seeds for each atom type, thus creating 40 varied Al and Cu crystal growths and 4,000 stable systems. As a result, for a given crystal size and composition (atom type), there are 20 samples with differently located atoms. CG enables experi- menting with large scale atomic interactions in non-regular sys- tems, and enables evaluation of an ML method’s ability to learn how each atom contributes to the final potential energy.</p> <p>In all datasets, OoE systems are obtained by compressing/dilating all interatomic distances (i.e. isometrically) at regular intervals within 90-150% of stable geometry, which we refer to as ‘scaling’. In other words, scaling is applied to the coordinates of all atoms within the system. At each geometry, the ground-truth potential energy is calculated using CP2K7’s DFT.</p> </div> </div> </div>
Molecular dynamics simulation data 1: 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 gap junction channel</p>
Structural and Molecular Analysis of Adult Mouse Astrocytes and Vascular Connectivity in the Cortex and Hippocampus
<p>After image acquisition (0-RAW_CL230331_E2_serie1) and deconvolution (1-Deconvolved_CL230331_E2_serie1) using confocal microscopy and the SVI Huygens software,respectively, the image processing was conducted using Imaris, Fiji, and Matlab software. This process involved a sequence of manual operations (2-Imaris_surfaces_CL230331_E2_serie1) and custom Groovy scripts (5-Groovy scripts).</p> <p>The dataset analysis (3-Imaris_final_CL230331_E2_serie1_ims) allowed for a deeper investigation of morphological and molecular properties of adult mouse astrocytes (4-Image analysis_CL230331_E2_serie1) in two brain regions, the Isocortex and the Hippocampus, known to be interconnected to support multiple cognitive functions.</p>
Molecular dynamics trajectories for "Structure and chemistry of graphene oxide in liquid water from first principles"
<p>This dataset contains molecular dynamics (MD) trajectories from the paper <a href="https://doi.org/10.1038/s41467-020-15381-y">“Structure and chemistry of graphene oxide in liquid water from first principles”, F. Mouhat, F.-X. Coudert and M.-L. Bocquet, <em>Nature Commun.</em>, <strong>2020</strong>, <em>11</em>, 1566, 10.1038/s41467-020-15381-y</a></p> <p> </p>
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized. in A new species of freshwater Chaetonotidae (Gastrotricha, Chaetonotida) from Obodska Cave (Montenegro) based on morphological and molecular characters
Appendix Morphometric parameters of Chaetonotus (Chaetonotus) antrumus Kolicka sp. nov. Abbreviations: N = number of specimens or structures analysed; Range = the smallest and the largest structure measurement found among all specimens measured; SD = standard deviation. All measurements are given in micrometers (μm); all indicators are given as a percentage (%) and italicized.
Input Data for "Molecular Lignin Solubility and Structure in Organic Solvents"
<p>Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf ligninsolvationstudy.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*log" --exclude="*[0-9].out" --exclude="*npz" --exclude="*pkl" --exclude="*npy" --exclude="*png" --exclude="*bmim*" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*txt" --exclude="*state_prev.cpt" LigninSolvation`, which intentionally excludes large files. The full dataset is available upon request.</p> <p><strong>Directory Descriptions</strong></p> <p><strong>BuildSolventBoxes</strong> contains the scripts and inputs needed to make the solvent boxes suitable for use with the VMD solvate plugin.<br> <strong>BuildSystems</strong> assembles the lignin polymers and solvates them into a complete simulation system. Depends on the outputs from [LigninBuilder](https://github.com/jvermaas/LigninBuilder).<br> <strong>Equilibrium</strong> has all the equilibrium trajectories and the scripts needed to set them up.<br> <strong>FEP</strong> has the free energy perturbation calculation key outputs (the fepout files) and the scripts needed to set up the calculation and analyze them.</p> <p>The scripts are <em>mostly</em> python scripts, but some are also in tcl, and have the appropriate file endings. GROMACS run input files (.tpr) and namd configuration files (.namd) may also be of general interest.</p>
Classification of Matching Molecular Series on the Basis of SAR Phenotypes and Structural Relationships
<p>A database comprising a total of 13,236 pairs of MMS with different SAR characteristics is provided. For each pair the corresponding MMS-cores are provided as SMILES. In addition, for each MMS-core the number of compounds and the SAR phenotype are given. ChEMBL target IDs (CHEMBLID_Target) designate target sets from which the MMS pairs originate. </p>
Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"
<p>Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe. </p>
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 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>
Structural Homology of Epitope Pair Candidates for Molecular Mimicry Trigger of Type 1 Diabetes Mellitus
<p><strong><em><span>Background:</span></em></strong><span> </span><span>Molecular mimicry, where foreign and self-peptides contain similar epitopes, can induce autoimmune responses. Identifying potential molecular mimics and studying their properties is key to understanding the onset of autoimmune diseases such as type 1 diabetes mellitus (T1DM). Previous work identified pairs of infectious epitopes (E<sub>INF</sub>) and T1DM epitopes (E<sub>T1D</sub>) that demonstrated sequence homology; however, structural homology was not considered. Correlating sequence homology with structural properties is important for streamlining translational investigation of potential molecular mimics. Therefore, the purpose of this work is to compare sequence homology with structural homology by calculating the structures and electrostatic potential surfaces of the epitope pairs identified in previous work from our laboratory. </span></p> <p><strong><span> </span></strong><strong><em><span>Results:</span></em></strong><span> </span><span> For each epitope pair the root mean square deviation (RMSD) was calculated between their predicted structures and their electrostatic potentials were compared. Structures were predicted using the AlphaFold software program. </span><span>Of the 52 epitope pairs considered here only 10 do not exhibit any matching (i.e. less than 3 residues overlap). When considering all residues the RMSD ranges from 0.33 Å to 11.66 Å with an average of 2.68 Å. Twenty-two pairs (42%) have RMSD of less than 1.5 Å and 30 (58%) less than 3 Å. Even some of the matching pairs show some electrostatic similarities that need to be considered. In general there is good agreement between the folding predicted for the isolated </span><span>E<sub>INF</sub></span><span> and E<sub>T1D</sub> epitopes and the folding of the corresponding amino acid sequence in the parent antigen, but in some cases there are deviation that need to be considered, even when the RMDS is small.</span></p> <p><span> </span><strong><em><span>Conclusions:</span></em></strong><span> </span><span>Despite differences, most of the E<sub>INF</sub><span>/</span>E<sub>T1D </sub>pairs selected by sequence homology show similar structural and electrostatic distributions, indicating that the E<sub>INF</sub> may bind to the same protein targets, the major histocompatibility complex molecules, for T1DM, leading to molecular mimicry onset of the disease. These findings suggest that searching for epitope pairs using sequence homology, a much less computationally demanding approach, leads to strong candidates for molecular mimicry that should be considered for further study. Still structure and full docking calculations will be necessary to advance the in-silico molecular mimicry predictions. </span> Here we presnt the following files:</p> <p><span><span>·<span> </span></span></span>Fasta files of all epitopes studied.</p> <p><span><span>·<span> </span></span></span>Alphafold calculated Structures of all epitopes.</p> <p><span><span>·<span> </span></span></span>Antigen structures.</p> <p><span><span>·<span> </span></span></span>Epitope pair structure comparison and their electrostatics.</p> <p> </p> <p> </p>
Dataset of molecular structures of PNAS article " Ca2+ permeation through C-terminal cleaved, but not full-length human Pannexin1 hemichannels, mediates cell death"
<p>The PMFWT_91_80.tar.gz file contains the WT molecular system described in the cited article. Briefly, the package contains the structure and topology files for AMBER software, along with configuration files to run Umbrella Sampling method and calculation of PMF of a Ca+2 ion traslocating the human pannexin channel (WT). </p> <p>The TRUCWT_91_80.tar.gz file contains the truncated molecular system described in the cited article. Briefly, the package contains the structure and topology files for AMBER software, along with configuration files to run Umbrella Sampling method and calculation of PMF of a Ca+2 ion traslocating the truncated human pannexin channel as described in the article.</p> <p>Two NetCDF trajectories (*.nc) of a single PMF window are provided for each system.</p>
Figs 15–17 in Microsporidia in a Woodland Pool I. Lanatospora costata sp. n. (Opisthosporidia, Microsporidia), Parasite of Megacyclops viridis (Crustacea, Copepoda): Fine Structure and Molecular Phylogeny
Figs 15–17. Lanatospora costata, parasite of Megacyclops viridis, structure of spores as seen in SEM and TEM. 15 – Spore surface ornamentation as seen by SEM. Note that the exospore ribs form a complex armour on the spore surface. Scale bar: 1 µm. 16 – Detail of the polaroplast lamellae (pl) in the apical part of the spore, pf – polar filament. Scale bar: 200 nm. 17 – Details of the polar filament coils (pf) in cross section. Scale bar: 500 nm.
Fig. 19 in Microsporidia in a Woodland Pool I. Lanatospora costata sp. n. (Opisthosporidia, Microsporidia), Parasite of Megacyclops viridis (Crustacea, Copepoda): Fine Structure and Molecular Phylogeny
Fig. 19. The woodland pool near Přerov nad Labem, Central Bohemia Region, Czech Republic (50°167′N, 14°810′E), the type habitat of Lanatospora costata sp. n.
◂Fig. 13 Crystallographic structure on the columellar lamellae of Dinaride Zospeum and Iberozospeum shells; (a) Zospeum spelaeum, (AJC 847), Betalov Spodmol jama, Slovenia (45.7922 14.1877), pattern of low, non-overlapping, wedges of crystallographic structure on the lamella; (b) Zospeum spelaeum, (MCBI CSR SASA 37049a), Velika Pasica, Slovenia (N45.9189 E14.4934), non-overlapping wedges of crystallographic structure on lamella in old shell; (c) Iberozospeum sp., (RMNH.MOL. 234,120), Cueva Refugio, Trucios, overview of dense, overlapping, scale-like wedges of localized, crystallographic structure on upper part of the lower lamella; (d) ibid., closeup view of c; (e) Iberozospeum sp., (RMNH.MOL. 234,104), Cueva del Comediante, Santander, upper part of the lamella of chemically treated shell showing dense, overlapping wedges of localized, crystallographic structure; (f) Iberozospeum sp., (RMNH. MOL. 234,141), Cueva a Sul, Oviedo, localized, overlapping wedges of crystallographic structure on lamella of chemically treated shell; (g) Iberozospeum vasconicum, (AJC 1849), Cueva Arrikrutz, overview of dense, localized, crystallographic structure on lower part of the lamella; h, ibid., closeup view of g. — Magnification varies for each perspective, see scale bars; Figs. a–b, g–h) imaged by M. Ruppel, (ret.) Goethe University Frankfurt am Main; Figs. c–f imaged by Dirk Vendermarel, Naturalis Biodiversity Center in Molecular investigation and description of Iberozospeum n. gen., including the description of one new species (Eupulmonata, Ellobioidea, Carychiidae)
◂Fig. 13 Crystallographic structure on the columellar lamellae of Dinaride Zospeum and Iberozospeum shells; (a) Zospeum spelaeum, (AJC 847), Betalov Spodmol jama, Slovenia (45.7922 14.1877), pattern of low, non-overlapping, wedges of crystallographic structure on the lamella; (b) Zospeum spelaeum, (MCBI CSR SASA 37049a), Velika Pasica, Slovenia (N45.9189 E14.4934), non-overlapping wedges of crystallographic structure on lamella in old shell; (c) Iberozospeum sp., (RMNH.MOL. 234,120), Cueva Refugio, Trucios, overview of dense, overlapping, scale-like wedges of localized, crystallographic structure on upper part of the lower lamella; (d) ibid., closeup view of c; (e) Iberozospeum sp., (RMNH.MOL. 234,104), Cueva del Comediante, Santander, upper part of the lamella of chemically treated shell showing dense, overlapping wedges of localized, crystallographic structure; (f) Iberozospeum sp., (RMNH. MOL. 234,141), Cueva a Sul, Oviedo, localized, overlapping wedges of crystallographic structure on lamella of chemically treated shell; (g) Iberozospeum vasconicum, (AJC 1849), Cueva Arrikrutz, overview of dense, localized, crystallographic structure on lower part of the lamella; h, ibid., closeup view of g. — Magnification varies for each perspective, see scale bars; Figs. a–b, g–h) imaged by M. Ruppel, (ret.) Goethe University Frankfurt am Main; Figs. c–f imaged by Dirk Vendermarel, Naturalis Biodiversity Center
Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.
<p>Data and code for behavioral analysis of: Structural and Molecular Properties of Insect Type II Motor Axon Terminals.</p> <p>v1.2: typos corrected and all files available in a single .zip file for download</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.