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44 results for “cryo-EM”
Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images
<p>Synthetic dataset used for validating MDSPACE method for analyzing continuous conformational variability of biomolecules in cryo-EM single particle images. A README file with the contents of the dataset is included. </p>
Simulated water models with Apoferritin for use in cryo-EM image simulations with amorphous ice
<p>This dataset contains the atomic coordinates of several water models produced using the NAMD molecular dynamics software. The contents of each file is listed below.</p> <ul> <li><strong><em>water_81_coords.pdb</em></strong> - water only in a cubic box with side length 81A</li> <li><strong><em>water_243_coords.pdb</em></strong> - water only in a cubic box with side length 243A</li> <li><strong><em>water_486_coords.pdb</em></strong> - water only in a cubic box with side length 486A</li> <li><strong>water_567_coords.pdb</strong> - water only in a cubic box with side length 567A</li> <li><strong><em>water_645_coords.pdb</em></strong> - water only in a cubic box with side length 645A</li> <li><strong><em>water_735_coords.pdb</em></strong> - water only in a cubic box with side length 735A</li> <li><strong><em>apo_water_723_coords.pdb</em></strong> - water and apoferritin in a cubic box with side length 723A where apoferritin atoms are constrained</li> </ul>
LocScale-EMmerNet deep learning models for contrast optimisation of cryo-EM maps
<p>EMmerNet deep learning models for local optimisation of cryo-EM map contrast using <a href="https://gitlab.tudelft.nl/aj-lab/locscale">LocScale</a>.</p>
Code outputs and figures from "Efficient high-resolution refinement in cryo-EM with stochastic gradient descent"
<p>Code outputs and figures for the numerical experiments on preconditioned SGD for cryo-EM reconstruction for reproducing the results in the article:</p> <blockquote> <p><a title="doi" href="https://doi.org/10.1107/S205979832500511X" target="_blank" rel="noopener"><code><em>Efficient high-resolution refinement in cryo-EM with stochastic gradient descent</em>.</code></a></p> <p><em>Bogdan Toader, Marcus A. Brubaker, Roy R. Lederman</em></p> <pre>Acta Crystallographica Section D, 2025</pre> </blockquote> <p>The outputs are obtained by running the Jupyter notebooks in the <em>notebooks/preconditioned_sgd</em> directory in the GitHub repository (release v0.2):</p> <blockquote> <p><a title="github link" href="https://github.com/bogdantoader/simplecryoem">https://github.com/bogdantoader/simplecryoem</a></p> </blockquote> <div>The particle images used for these experiments can be downloaded from <a title="empiar-10076 link" href="https://www.ebi.ac.uk/empiar/EMPIAR-10076">EMPIAR-10076</a> and require inverting the contrast. The file containing the pose variables and CTF parameters is the <em>particles_file/my_particles_8.star </em>file in the attached archive.</div>
Cryo-4D-STEM datasets on cells and cellular organelles for demonstrating a dose-Efficient cryo-EM technique: tilt-Corrected Scanning Transmission Electron Microscopy
<p>This upload contains three 4D-STEM datasets in .raw format for demonstrating a dose-efficient cryo-EM technique for thick samples: tilt-corrected Scanning Transmission Electron Microscopy (tcBF-STEM). The dataset dimension is 128130256*256. Data were acquired on vitrified intact E.coli cells and isolated human cell organelles. This upload also contains the EFTEM images in .mrc acqired in the same ROI as the 4D-STEM dataset. </p> <p>It also contains analysis of the manuscript's Fig 3 and Ext. data fig 8. </p>
CryoVirusDB: An Expert Labelled Cryo-EM Image Dataset for AI-Driven Virus Particle recognition and Extraction
<p><span>With the advancements in instrumentation, image processing algorithms, and computational capabilities, single-particle electron cryo-microscopy (cryo-EM) has achieved nearly atomic resolutions in the 3D reconstruction of viruses. These detailed structures play a crucial role in comprehending the biological functions and advancing the development of more precise vaccines and antiviral treatments. Despite the effectiveness of deep learning in analyzing microscopic images, its potential in identifying and extracting virus particles from cryo-EM micrographs has been hindered by the limited availability of diverse and high-quality datasets. In this study, we introduce 'CryoVirusDB,' a labeled dataset containing coordinates of accurately selected virus particles in cryo-EM micrographs. CryoVirusDB comprises 9,941 micrographs featuring 9 different viruses along with the coordinates of 0.2 million virus particles in total. We anticipate that CryoVirusDB will enhance the capabilities of deep learning in accurately identifying virus particles in cryo-EM micrographs, thereby facilitating the subsequent 2D-3D reconstruction process.</span></p> <p><span>Instructions to download and use dataset: https://github.com/BioinfoMachineLearning/CryoVirusDB</span></p>
Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN | Software, datasets, and results
<p>Software, datasets, and results referenced in "Uncovering structural ensembles from single particle cryo-EM data using cryoDRGN"</p>
Integration of an event-driven Timepix3 hybrid pixel detector into a cryo-EM workflow
<p><strong>Abstract</strong></p> <p>The development of direct electron detectors has played a key role in low-dose electron microscopy imaging applications. Monolithic active-pixel sensor (MAPS) detectors are currently widely applied for cryogenic electron microscopy (cryo-EM); however, they have best performance at 300~kV, have relatively low read-out speed and only work in imaging mode. Hybrid pixel detectors (HPDs) can operate at any energy, have a higher DQE at lower voltage, have unprecedented high time resolution, and can operate in both imaging and diffraction modes. This could make them well-suited for novel low-dose life-science applications, such as cryo-ptychography, iDPC, and liquid cell imaging. Timepix3 is not frame-based, but truly event-based, and can record individual hits with 1.56~ns time resolution. Here, we present the integration of such a detector into a cryo-EM workflow and demonstrate that it can be used for automated data collection on biological specimens. The performance of the detector in terms of MTF and DQE has been investigated at 200~kV and we studied the effect of deterministic blur. We describe a single-particle analysis structure of \SI{3}{\angstrom} resolution and compare it with Falcon3 data collected under the same microscope. These studies could pave the way toward more efficient low-dose single-particle techniques.</p> <p><strong>Data description</strong></p> <p>Data has been split up in several different directories. In general: each directory contains individual READMEs</p> <p><strong>Flat fields</strong></p> <p>Collected on both TImepix3 and Falcon3 at 200 kV using a Tecnai Arctica microscope. These data have been used for calculating NPS, ToT correction and gain correction. </p> <p><strong>Knife edge</strong></p> <p>Collected on both TImepix3 and Falcon3 at 200 kV using a Tecnai Arctica microscope. These data have been used for calculating MTF.</p> <p><strong>ToT correction calibration file</strong></p> <p>This calibration file has been used to correct all raw Timepix3 data. Including micrographs deposited in EMPIAR.</p> <p><strong>Gain correction</strong></p> <p>Gain correction files calculated from flat field data for several different image formation methods of the Timepix3. The Python script for calculating the gain has been included.</p> <p><strong>Software</strong></p> <p>The software tpx3HitParser, tpx3EventViewer and the MTF-NPS-DQE scripts have listed as related identifiers to this entry.</p> <p> </p>
Synthetic single particle cryo-EM dataset of the SARS-CoV-2 spike protein
<p>PDBs were generated using molecular dynamics.<br> See DESRES_README.txt for more details on molecular dynamics simulation.<br> PDBs were converted to volumetric data using EMAN2.<br> The image stack contains 100 000 projection images each <br> of the 10 states (see PDBs), at an SNR of 1/10 in the following order:</p> <p>state00 (closed)<br> state01 (closed)<br> state02 (closed)<br> state10 (intermediate)<br> state11 (intermediate)<br> state12 (intermediate)<br> state13 (intermediate)<br> state20 (open)<br> state21 (open)<br> state22 (open)</p> <p>Projections were made using relion_project. <br> White gaussian noise with standard deviation 1.0<br> CTF multiplied signal<br> High signal-to-noise ratio<br> Image size 96x96x96<br> <br> MRC-files used for the projections not included, but can be generated using the PDB files.<br> Final RELION reconstruction resolution is 5.33334 Angstrom (Nyqvist is at 5.33334).</p> <p>Command line for RELION reconstruction:<br> relion_refine_mpi --o refine3d/run --auto_refine --split_random_halves --i rot_trans_ctf_noise/stack.star --ref pdb2mrc/state21.mrc --ini_high 20 --dont_combine_weights_via_disc --preread_images --pool 30 --pad 2 --ctf --particle_diameter 130 --flatten_solvent --zero_mask --oversampling 1 --healpix_order 2 --auto_local_healpix_order 4 --offset_range 5 --offset_step 2 --low_resol_join_halves 40 --norm --scale --j 2 --gpu --fristiter_cc --grad </p> <p>This dataset is generated as a testbed for cryo-EM heterogeneity analysis.</p>
DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model
<p>Here, we store the modeled structures generated by DiffModeler for its 4 benchmark datasets: CryoREAD dataset(0-5A resolution, protein-DNA/RNA complex), ModelAngelo dataset(0-5A resolution, most protein complexes, a few protein-RNA complex), intermediate resolution dataset (5-10A resolution, protein complex), low resolution dataset (10-20A resolution, protein complex). For all protein-DNA/RNA complex, the map will be modeled by CryoREAD+DiffModeler.</p> <p>For each dataset, we keep the modeled structures by DiffModeler, named as [EMD-ID]_DiffModeler.cif; and their corressponding native structures from RCSB are saved as [EMD-ID]_[PDB_ID]_native.cif.</p> <p>For CryoREAD dataset, it includes 61 targets. For ModelAngelo dataset, it includes 28 targets. For intermediate resolution dataset , it includes 71 targets. For low resolution dataset, it incldues 6 targets.</p> <p>For intermediate resolution dataset, many maps were run with inaccurate AF2 predicted single-chain structures. We also benchmarked DiffModeler's performance by using native single-chain structures as input. They are saved under "dataset_5_10A_nativechain" folder.</p> <p>Additionally, we have stored the traced backbone map of the intermediate resolution dataset in the "dataset_5_10A_diffusion_traced_backbone_map" folder. The traced maps are saved as [EMD-ID]_diffusion.mrc. The intermediate reverse diffusion maps of the intermediate resolution dataset are saved in the "dataset_5_10A_reverse_diffusion_maps" folder. Each sub-folder is named according to the corresponding map's [EMD-ID] and contains three intermediate reverse diffusion maps: 20percentile_reverse_diffusion.mrc, 50percentile_reverse_diffusion.mrc, and 80percentile_reverse_diffusion.mrc. A higher percentile indicates a map closer to the end of the reverse diffusion steps.</p> <p>If you used DiffModeler, please cite: "Wang, Xiao, Han Zhu, Genki Terashi, Manav Taluja, and Daisuke Kihara. "DiffModeler: Large Macromolecular Structure Modeling in Low-Resolution Cryo-EM Maps Using Diffusion Model." bioRxiv (2024): 2024-01.".</p> <p>If you used CryoREAD, please cite: "Xiao Wang, Genki Terashi & Daisuke Kihara. De novo structure modeling for nucleic acids in cryo-EM maps using deep learning. Nature Methods, 2023."</p>
cryo-EM density maps used for the analyses in <Assembly of the Bacterial Ribosome with Circularly Permuted rRNA>
<p>Here, we deposit the cryo-EM density maps used for the analyses in "Assembly of the Bacterial Ribosome with Circularly Permuted rRNA." These maps represent the structures of E. coli ribosomal intermediates reconstructed in the iSAT (integrated synthesis, assembly, and translation) reaction, featuring circularly permuted rRNAs.</p> <p>The cryo-EM density maps deposited here have been aligned and rescaled to the reference 4YBB 50S model (included in the file list).</p>
Comp-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>Synthetic cryo-EM datasets with simulated compositional heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume:</p> <ul> <li>Ribosembly: 335,240 particle images (128x128, 6A/pix) containing 16 bacterial ribosome assembly states from "Cryo-em captures early ribosome assembly in action" from Qin et al. (2023)</li> <li>Tomotwin-100: 100k particle images (128x128, 9A/pix) containing a mixture of 100 different biomolecular complexes from "Tomotwin: generalized 3d localization of macromolecules in cryo-electron tomograms with structural data mining" from Rice et al. (2023)</li> </ul>
Conf-het Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>Synthetic cryo-EM datasets with simulated conformational heterogeneity and their ground truth atomic models, density maps, poses, labels, mask, and consensus volume:</p> <ul> <li>IgG-1D: 100k particle images (128x128, 6A/pix) of IgG with conformations uniformly sampled from a simple one-dimensional continuous circular motion</li> <li>IgG-1D-noisier: The IgG-1D dataset with noise increased from SNR 0.01 to 0.005</li> <li>IgG-1D-noisiest: The IgG-1D dataset with noise increased from SNR 0.01 to 0.001</li> <li>IgG-RL: 100k particle images (128x128, 6A/pix) of IgG with conformations of its flexible linker generated by sampling backbone dihedral angles according to the Ramachandran distributions of disordered peptides</li> </ul>
Cryo-EM/Cryo-ET raw images and tilt series for the figures in the paper entitled "Angle Between DNA Linker and Nucleosome Core Particle Regulates Array Compaction by Individual-Particle Cryo-Electron Tomography"
<p>Cryo-EM and cryo-ET raw images and tilt-series for the 3D reconstructions showed in the Figures of the paper entilted "Angle between DNA linker and nucleosome core particle regulates array compaction by individual-particle cryo-electron tomography"</p>
Multi-body cryo-em maps and models of a pentameric KCTD5/Cullin3/Gβγ E3 ubiquitin ligase complex
<p>Heterotrimeric G proteins can be regulated by post-translational modifications, including ubiquitylation. KCTD5, a pentameric substrate receptor protein consisting of an N-terminal BTB domain and a C-terminal domain (CTD), engages CUL3 to form the central scaffold of a cullin-RING E3 ligase complex (CRL3<sup>KCTD5</sup>) that ubiquitylates Gβγ and reduces Gβγ protein levels in cells. The cryo-EM structure of a 5:5:5 KCTD5/CUL3<sup>NTD</sup>/Gβ<sub>1</sub>γ<sub>2</sub> assembly reveals a highly dynamic complex with rotations of over 60° between the KCTD5<sup>BTB</sup>/CUL3<sup>NTD</sup> and KCTD5<sup>CTD</sup>/Gβγ moieties of the structure. CRL3<sup>KCTD5</sup> engages the E3 ligase ARIH1 to ubiquitylate Gβγ in an E3-E3 super-assembly, and extension of the structure to include full-length CUL3<sup> </sup>with RBX1 and an ARIH1~ubiquitin conjugate reveals that some conformational states position the ARIH1~ubiquitin thioester bond to within 10 Å of lysine-23 of Gβ and likely represent priming complexes. Most previously described CRL/substrate structures have consisted of monovalent complexes and have involved flexible peptide substrates. The structure of the KCTD5/CUL3<sup>NTD</sup> Gβγ complex shows that the oligomerization of a substrate receptor can generate a polyvalent E3 ligase complex and that the internal dynamics of the substrate receptor can position a structured target for ubiquitylation in a CRL3 complex.</p>
Coordinate files from LipIDens: Simulation assisted interpretation of lipid densities in cryo-EM structures of membrane proteins.
<p>Coordinate files from the first and last frame of coarse-grained (CG) and atomistic (AT) molecular dynamics (MD) simulations used throughout the LipIDens pipeline.</p><p>CG simulations were run for HHAT, OTOP1, ELIC, MscS, TRPV6, ChRmine, Ste2, Connexin-50, NPC1 and the PAT complex. All CG simulations were run for 10 x 15 μs with the exception of NPC1 which was simulated for 10 x 30 μs.</p><p>AT simulations were run for HHAT (5 x 200 ns) and ELIC (3 x 200 ns) in apo configurations.</p><p><strong>File description:</strong></p><p>Directories for each protein are listed with the suffix CG or AT used to indicate the simulation resolution. </p><p>md_fit_firstframe_<i>X</i>.gro - GROMACS structure file for the first frame of replicate <i>X</i>. </p><p>md_fit_lastframe_<i>X</i>.gro - GROMACS structure file for the last frame of replicate <i>X</i>. </p>
Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM
<p>This respository corresponds to the following paper: Wankowicz et al. Uncovering Protein Ensembles: Automated Multiconformer Model Building for X-ray Crystallography and Cryo-EM (2024). These are the qFit models. MTZ and deposited models cna be downloaded from the PDB. </p>
Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models
<p>Ligand datasets used to train and evaluate the models studied in <em>"Ligand Identification using Deep Learning</em><em>"</em> by Karolczak, J. <em>et al.</em></p> <p>The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.</p>
Spike-MD Data for "CryoBench: Diverse and challenging datasets for the heterogeneity problem in cryo-EM"
<p>A synthetic cryo-EM dataset with heterogeneity from a molecular dynamics simulation and ground truth atomic models, poses, labels, mask, and consensus volume:</p> <ul> <li>Spike-MD: 100k particle images (256x256, 1.5A/pix) of SARS-CoV-2 spike protein with conformations sampled from a molecular dynamics simulation from Wieczór et al. (2023)</li> <li>sampled_pdbs.xtc: atomic models stitched into a trajectory</li> <li>seed_structure.pdb: initial seed structure for .xtc trajectory</li> </ul>
Cryo-EM maps: Mitoribosomal small subunit maturation involves formation of initiation-like complexes
<p><strong>Final cryo-EM maps for the complexes described in the article <em>Mitoribosomal small subunit maturation involves formation of initiation-like complexes</em></strong></p> <p>Abstract: Mitochondrial ribosomes (mitoribosomes) play a central role in synthesizing mitochondrial inner membrane proteins responsible for oxidative phosphorylation. Although mitoribosomes from different organisms exhibit considerable structural variations, recent insights into mitoribosome assembly suggest that mitoribosome maturation follows common principles and involves a number of conserved assembly factors. To investigate the steps involved in the assembly of the mitoribosomal small subunit (mt-SSU) we determined the cryo-electron microscopy structures of middle and late assembly intermediates of the Trypanosoma brucei mitochondrial small subunit (mt-SSU) at 3.6 and 3.7 Å resolution, respectively. We identified five novel assembly factors that together with the mitochondrial initiation factor 2 specifically interact with functionally important regions of the rRNA including the decoding center, thereby preventing premature mRNA or large subunit binding. Structural comparison of assembly intermediates with mature mt-SSU combined with RNAi experiments suggests a new, non-canonical role of mitochondrial initiation factor 2 and a stepwise assembly process, where modular exchange of ribosomal proteins and assembly factors together with mt-IF-2 ensure proper 9S rRNA folding and protein maturation during the final steps of assembly.</p>
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