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44 results for “cryo-EM”
Supplementary Information: Effects of cryo-EM cooling on structural ensembles
<p>This data set contains a pdb file with the ribosome-EF-Tu complex atoms used for analysis. The trajectories (xtc files) contain the ensembles of structures before cooling and after cooling with various cooling time spans.</p> <p>model3_training.zip contains the code to train and and analyse kinetic model3 as well as the rmsf quantiles obtained from MD simulations, and the temperature drop estimates used for the model.</p>
Substrate recognition and cryo-EM structure of the ribosome-bound TAC toxin of Mycobacterium tuberculosis
<p>Datasets for the Figures 2 and S2 of the manuscript "Substrate recognition and cryo-EM structure of the ribosome-bound TAC toxin of Mycobacterium tuberculosis".</p> <p> </p> <p>The HTML files describe the analysis and the raw counts after nEMOTE-conv treatment.</p> <p>There are 5 files for each MMEMOTExx dataset:</p> <p>EmoteBarcodesReport.csv = summary<br> UnambNegTable.csv = counts of unique cuts on the reverse strand<br> UnambPosTable.csv = counts of unique cuts on the forward strand<br> AmbPosTable.csv = counts of all cuts on the forward strand<br> AmbNegTable.csv = counts of all cuts on the reverse strand</p> <p> </p>
Automated systematic evaluation of cryo-EM specimens with SmartScope - Training data for hole detector
<p>Training data for hole detector<br> =======================</p> <p>This dataset includes 36 images.<br> Circles are annotated in COCO format.</p> <p>The following pre-processing was applied to each image:<br> * Auto-orientation of pixel data (with EXIF-orientation stripping)</p> <p>The following augmentation was applied to create 1 versions of each source image:<br> * 50% probability of horizontal flip</p>
Automated systematic evaluation of cryo-EM specimens with SmartScope - Training data for square detector
<p>Training data for square detector<br> =========================</p> <p>This dataset includes 26 images.<br> Squares are annotated in COCO format.</p> <p>The following pre-processing was applied to each image:<br> * Auto-orientation of pixel data (with EXIF-orientation stripping)</p> <p>The following augmentation was applied to create 1 versions of each source image:<br> * 50% probability of horizontal flip<br> * Equal probability of one of the following 90-degree rotations: none, clockwise, counter-clockwise</p>
Micrographs of empty cryo-EM grids that are used in training of crYOLO
<p>Micrographs of empty cryo-EM grids that are used in training of <a href="http://sphire.mpg.de/wiki/doku.php?id=downloads:cryolo_1&redirect=1">crYOLO</a>. The micrographs do not contain any protein, only ice and contamination. Datasets were recorded with different cameras (Falcon 3 / K2) and grid types.</p>
Data for cryo-EM structure refinement with density-guided simulations
<p>Data accompanying prepared manuscript to describe novel density-guided simulation algorithms.</p>
A large expert-curated cryo-EM image dataset for machine learning protein particle picking
<p>Cryo-electron microscopy (cryo-EM) is a powerful technique for determining the structures of biological macromolecular complexes. Picking single-protein particles from cryo-EM micrographs is a crucial step in reconstructing protein structures. However, the widely used template-based particle picking process is labor-intensive and time-consuming. Though machine learning and artificial intelligence (AI) based particle picking can potentially automate the process, its development is hindered by lack of large, high-quality labelled training data. To address this bottleneck, we present CryoPPP, a large, diverse, expert-curated cryo-EM image dataset for protein particle picking and analysis. It consists of labelled cryo-EM micrographs (images) of 34 representative protein datasets selected from the Electron Microscopy Public Image Archive (EMPIAR). The dataset is 2.6 terabytes and includes 9,893 high-resolution micrographs with labelled protein particle coordinates. The labelling process was rigorously validated through 2D particle class validation and 3D density map validation with the gold standard. The dataset is expected to greatly facilitate the development of both AI and classical methods for automated cryo-EM protein particle picking.</p>
ISOLDE model and validation statistics to support: Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies
<p>These files provide the pdb atom coordinates and structural validation of the ISOLDE structural model (Fig. 6) contained in the manuscript "Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies" </p>
Cryo-EM tomographic tilt series of helical supramolecule consisting of cobalt phthalocyanine
<p>Cryo-EM tomographic tilt series of a helical supramolecule consisting of cobalt phthalocyanine. Collected using a JEOL CRYO ARM 300 electron microscope operated at an accelerating voltage of 300kV and recorded on a K3 detector at RIKEN, SPring-8 Center. The datasets provided represent a portion of the entire datasets due to the limitation on zenodo. Please let us know if you need access to all the datasets.</p>
Data for "CryoDRGN: Reconstruction of heterogeneous cryo-EM structures using neural networks"
<p>Trained models and reconstructed density maps for:</p> <ul> <li>EMPIAR-10028: "Cryo-EM structure of a <em>Plasmodium falciparum</em> 80S ribosome bound to the anti-protozoan drug emetine" from Wong et al. (2014)</li> <li>EMPIAR-10049: "Molecular Mechanism of V(D)J Recombination from Synaptic RAG1-RAG2 Complex Structures" from Ru et al. (2015)</li> <li>EMPIAR-10076: "Modular assembly of the large bacterial ribosome" from Davis et al. (2016)</li> <li>EMPIAR-10180: "Structure of a pre-catalytic spliceosome" from Plaschka et al. (2017)</li> </ul> <p>Synthetic datasets with simulated heterogeneity and their ground truth density maps, poses, and labels:</p> <ul> <li>Uniform: 50k particle images (128x128, 6A/pix) uniformly sampled from 50 models along a 1-dimensional reaction coordinate</li> <li>Cooperative: 50k particle images (128x128, 6A/pix) sampled along the above reaction coordinate according to a 3-component Gaussian mixture model with overlapping components </li> <li>Noncontiguous: 50k particle images (128x128, 6A/pix) sampled along the above reaction coordinate according to a 3-component Gaussian mixture model without overlapping components</li> <li>Ribosomes: 50k particle images (128x128, 3A/pix) containing a mixture of 30S, 50S, 70S ribosomes simulating compositional heterogeneity</li> </ul>
Cryo-EM HTT Q23 (+/- DNA) sample generation (2017/06/05)
<p>Huntingtin structure-function open lab notebook project</p>
Cryo-EM HTT Q23 (+/- DNA) sample generation (2017/08/01)
<p>Huntingtin structure-function open lab notebook project</p>
Cryo-EM structure of gas vesicles - supplementary data
<p>This deposition contains supplementary data from our preprint:</p><p>Huber, S. T., Terwiel, D., Evers, W. H., Maresca, D., & Jakobi, A. J. (2022). Cryo-EM structure of gas vesicles for buoyancy-controlled motility. <i>bioRxiv</i>, 2022-05.</p><p>(Update 26.04.2023), Now in:</p><p>Huber, S. T., Terwiel, D., Evers, W. H., Maresca, D., & Jakobi, A. J. (2023). Cryo-EM structure of gas vesicles for buoyancy-controlled motility. <i>Cell</i>, <i>186</i>(5), 975-986.</p><p> </p><p><strong>2Dclasses_AnaMega_SeamsEdgesWallTips.zip</strong></p><p>This folder contains 2D class averages of different features of <i>A.flos-aquae</i> and <i>B.megaterium</i> gas vesicles (GVs) such as the seams between both GV halves, GV wall edges, collapsed walls and tips. We use this data to inform our pseudo-atomic model of an entire gas vesicle, and derive a model for biogenesis and growth. Some 2D classes provide high-resolution views on the GV wall in projection with a resolution that show the α-helical pitch and large side-chain densities. We use those views to confirm the evolutionarily conserved fold of the gas vesicle wall protein.</p><p>The box size and pixel size of the 2D classes is contained in the file names. A selected and sharpened 2D class for display in the manuscript is included in .png format.</p><p><strong>AlphaFold2_Models_5merRib_DifferentOrganisms.zip</strong></p><p>This folder contains five AlphaFold2 predictions of a single rib of the gas vesicle wall for organisms that are evolutionarily only little related (GvpA1 and GvpA2 from <i>B.megaterium</i>, GvpA from <i>A.flos-aquae</i> and GvpA1 and GvpA2 from <i>H.salinarum</i>). AF2 predicts very similar structure for these organisms, further supporting the high conservation of the gas vesicle wall.</p><p><strong>AnaGvpA_GvpC_ComputationalDocking_HADDOCK.zip</strong></p><p>In the manuscript, we propose a binding mode of the secondary protein GvpC to <i>A.flos-aquae</i> gas vesicles using computational docking with HADDOCK. We present two possible solutions with opposite orientation. The cif-files of the docking solution is contained in this folder.</p><p><strong>GasPoreAnalysis.zip</strong></p><p>We analysed gas pores between α-helix 1 of adjacent GvpA monomers using MOLE2.5. The three resulting tunnels are in this folder. The respective pdb files contain the constriction in Ångstrom in each line. A Chimera 1.13.1 session is included for visualisation.</p>
EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling
<p>EMRNA: Accurate RNA structure determination from cryo-EM maps by deep learning and integrated modeling.</p><p>Here stores the input files and output structures of EMRNA and the reproduction result of auto-DRRAFTER.</p>
Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin
<p>Data underlying the figures in the publication “Cryo-EM structure and dynamics of the green-light absorbing proteorhodopsin”, published in <em>Nat Commun,</em> <strong>2021</strong>, 12, 4107.</p> <p> <a href="https://doi.org/10.1038/s41467-021-24429-6">https://doi.org/10.1038/s41467-021-24429-6</a></p> <p>Table of contents:</p> <p><strong>1. Dataset</strong>; Excel file containing the numerical data for <em>Figures S1A</em> and <em>4C.</em></p> <p><strong>2. Methods</strong>; Word file containing the description of the methods.</p> <p>The cryo-EM map was deposited in the <strong>Electron Microscopy Data Bank (EMDB)</strong> under accession code <a href="https://www.ebi.ac.uk/pdbe/entry/emdb/EMD-11955">EMD-11955</a> and the protein coordinates were deposited in the <strong>Protein Data Bank (PDB)</strong> with the accession code <a href="https://doi.org/10.2210/pdb7B03/pdb">7B03</a>.</p> <p>EMDB: <a href="https://www.emdataresource.org/EMD-11955">https://www.emdataresource.org/EMD-11955</a></p> <p>PDB: <a href="https://www.rcsb.org/structure/7B03">https://www.rcsb.org/structure/7B03</a> (DOI: <a href="http://doi.org/10.2210/pdb7B03/pdb">10.2210/pdb7B03/pdb</a>)</p>
Case studies from doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models
<p>Case studies from "doubleHelix: nucleic acid sequence identification, assignment and validation tool for cryo-EM and crystal structure models"</p>
Archive of biochemical data sets contained in: Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies
<p>This archive contains all the biochemical data for the study "Guanine-containing ssDNA and RNA induce dimeric and tetrameric SAMHD1 in cryo-EM and binding studies". Further data supporting the structural data in this study is found in pdb and EMD accession numbers PDB ID 8TDV and EMD-41174 (RNA complex SAMHD1-T*<sub>cl</sub>) and PDB ID 8TDW and EMD-41175 (RNA complex SAMHD1-T*<sub>op</sub>). Further proteomics data is found in the Protein Exchange Database (PXD043587). </p>
Cryo-EM Structure of Actin Filaments from Zea mays Pollen
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Dataset for "MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning"
<div></div> <div><a href="https://zenodo.org/api/records/17093439/draft/files/micrographCleaner_dataset.tar.gz/content" target="_blank" rel="noopener noreferrer">micrographCleaner_dataset.tar.gz</a>: </div> <p>The dataset used for the Publicaton "MicrographCleaner: A python package for cryo-EM micrograph cleaning using deep learning" <a href="https://doi.org/10.1016/j.jsb.2020.107498">https://doi.org/10.1016/j.jsb.2020.107498</a></p> <div> </div> <div></div> <div><a href="https://zenodo.org/api/records/17093439/draft/files/deepMicrographCleaner.tgz/content" target="_blank" rel="noopener noreferrer">deepMicrographCleaner.tgz</a>: the tensorflow-2-compatible model checkpoint</div> <p> </p>
Automated systematic evaluation of cryo-EM specimens with SmartScope - Trained models for square and hole detectors
<p>Trained models used by SmartScope for square and hole detection.</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.