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347 results for “structural proteins”
Hairpin protein partitioning from the ER to Lipid Droplets involves major structural rearrangements
<div> <p>The project includes dataset from MD simulations and EPR measurements.</p> <p>Description of the MD simulation dataset:<br>-Data type: MD simulations of UBXD8 peptide at varying depths/conformations in POPC bilayer, POPC/Triolein:Cholesteryl oleate monolayer, and in Bilayer-Lipid droplet setup. <br>-Force fields: All-atom simulations were carried out using Charmm36 force field. The parameters for Triolein and Cholesteryl oleate are derived from Olarte et al., 2020 and were obtained from the corresponding authors of that publication. Coarse-grained simulations were carried out using Martini force field. <br>-Simulation Package: All simulations were carried out using GROMACS 2021 simulation package.<br>-File types: The uploaded files include structure files in PDB format, input parameter files (.mdp), topology (topol.top), and force field files.</p> </div> <div>Description of the EPR dataset:<br>- Data type: Experimental spectroscopic measurements, Easyspin simulation and analysis<br>- Files are with filename extensions: DSC, DAT<br>- Information on origin of the data:<br>- EPR spectroscopic measurements with filename extensions DSC and DTA.<br>- EPR spectroscopic simulation and analyses with filename extension m.<br>- EPR simulations were generated using Easyspin version 5.2.36 and Matlab version 23.2.0.2428915.<br>- X-band CW-EPR spectroscopic measurements were generated by EMX spectrometer equipped with ER4123D cavity produced by Bruker.</div>
Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation - ESI
<p>ESI data for the paper 'Reactions of cold argon plasma with condensed-phase peptides and proteins for mass spectrometry imaging and structural elucidation'.</p>
Uncovering the structure and function of Pseudomonas aeruginosa periplasmic proteins by an in silico approach
<p><strong>Caprari_et_al_models_and_docking: </strong>Predicted three-dimensional structures and docking simulations results described in the manuscript "Uncovering the structure and function of Pseudomonas aeruginosa periplasmic proteins by an in silico approach" by Caprari et al. More informations are given in the "README.txt" file.</p>
Data file with manuscript titled 'A Structurally Validated Sequence Alignment of 497 Human Protein Kinase Domains'
<p>The files used in different analysis reported in the manuscript titled - 'A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains' are shared at two locations. Following is a brief description of these files.</p> <p>Location - https://github.com/DunbrackLab/Kinases<br> 1. HMM profile files - HMM files for each of the nine groups computed separately labeled as Groupname.hmm, like AGC.hmm<br> 2. HMM profile file - HMM file computed from the full alignment including all the sequences - Human-PK.hmm<br> 3. Score files - HMM scores of each kinase sequence against all the groupwise HMMs both for iteration1 (HMM-iter1-scores-tables.txt) and iteration2 (HMM-iter1-scores-tables.txt)<br> 4. Jalview session file - Kinase alignment with sequences colored by secondary structure information from PDB file if the structure is known; or predicted secondary structure if the experimental structure is not known. The file could be opened in Jalview - kinases-PDB-SSPred.jvp</p> <p>Location - https://zenodo.org/record/3445533<br> 1. The file contains list of residue pairs aligned in pairwise structural alignments of 272 human protein kinases which were used as a benchmark in the study. The alignments were created by FATCAT and optimized by SE program.</p>
Simulated and experimental data distributed to the CASP13 participants in protein structure prediction assisted with sparse NMR data
<p>All simulated and experimental data distributed to the CASP participants in protein structure prediction assisted with sparse NMR data in CASP13.</p> <p>Also available at http://predictioncenter.org/casp13/results.cgi?view=targets&model=first&tr_type=others&sub_type=N&groups_id=</p> <p> </p>
Figure 1 in Solution structure of the first RRM domain of human spliceosomal protein SF3b49
Figure 1. – Sampling locality of the present specimen of Lutjanus madras.
Data table 5 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from pull-down experiment using cerebellar cortex lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 2_cortex isogenic mouse IgG control dynabeads.xlsx" </div> <div> </div>
Data table 4 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<div>An Excel table contains a list of proteins identified by MS from a pull-down experiment using cerebellum lysates with Dynabeads coated with anti-ataxin-3 1H9 mouse monoclonal antibodies. The false positive interactor proteins were excluded from this list by subtracting proteins found in "Supplementary_table 3_cerebellum isogenic mouse IgG control dynabeads.xlsx<span><br></span></div>
Data table 2 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table containing a list of proteins identified by MS from samples after a pull-down experiment using cerebral cortex lysates with Dyna beads coated with control isogenic mouse IgG. The proteins from the list were considered false positive interactors of ataxin-3 in the cortex.</p>
Data table 1 from publication "Impaired interactions of ataxin-3 with protein complexes reveals their specific structure and functions in SCA3 Ki150 model" (doi.org/10.3389/fnmol.2023.1122308)
<p>An excel table contains LFQ intensity and other raw MS data for proteins identified in fractions 3,4,5 (i), 11, 12, 13, (ii) 18, 19, and 20 (iii) from ki150 and ki21 model brains. These fractions showed enrichment in ATXN3 protein. The fractions are visualized in Figure 4 (<a href="http://doi.org/10.3389/fnmol.2023.1122308" target="_blank" rel="noopener">doi.org/10.3389/fnmol.2023.1122308</a>)</p>
Computational data for Structure of G protein-coupled receptor GPR1 bound to full-length chemerin adipokine reveals a chemokine-like reverse binding mode
<p>MD simulation data for the research article titled "Structure of G protein-coupled receptor GPR1 bound to full-length chemerin adipokine reveals a chemokine-like reverse binding mode".</p>
A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology
<p>Source Data and Supplementary Data associated with the paper “A multiscale functional map of somatic mutations in cancer integrating protein structure and network topology” (DOI: https://doi.org/10.1101/2023.03.06.531441).</p>
Benchmark Dataset for Structure Refinement Methods of Protein Complex Models
<p>This is the dataset used in our work entitled " Benchmarking of Structure Refinement Methods for Protein Complex Model " by Jacob Verburgt and Daisuke Kihara, which is under review.</p> <p>ZDOCK Derived Benchmark Dataset:</p> <p>The primary benchmark set used in was directly derived from the <a href="https://zlab.umassmed.edu/benchmark/">ZDOCK Benchmark set</a>. The ZDOCK set contains four structures per target: An unbound ligand, an unbound receptor, a bound ligand, and a bound receptor. The benchmark set is available in such a way where the coordinates of the bound subunits are oriented identical to their complex structure, and the unbound subunits are superimposed onto their respective bound subunits. Our dataset creates the optimially oriented "unbound" complexes by combining the superimposed and unbound subunits, along with removal of waters, ligands, and other non-protein atoms. These unbound complexes are saved in the dataset in the form "XXXX_c_u.pdb", where XXXX is the PDB ID.</p> <p>From the complete ZDOCK Benchmark of 230 targets, 18 targets were removed due to containing multiple ligand chains, which is incompatible with the standard ligand to receptor model used within CAPRI. The ZDOCK PDB ID’s of these targets are 1AKJ", "1BJ1", "1DE4", "1EER", "1EXB", "1EZU", "1GP2", "1I9R", "1JMO", "1K74", "1N2C", "1QFW", "2HMI", "3EO1", "3HMX", "4FQI", "4GXU", and "9QFW".</p> <p>There are an additional 8 targets where the superimpostion of the ligand and receptor structures onto the complex led to entanglement of the chains and were subsequently removed from the dataset. The ZDOCK PDB IDs for these targets are "1BGX", "1H1V", "1IRA", "1R8S", "1Y64", "2OT3", "3AAD", "4GAM".</p> <p>Note:</p> <p>In the work, we also used CAPRI scoring model dataset derived from CAPRI rounds 38-45. This dataset is unable to be distributed directly by us due to CAPRI guidelines, but can be derived from "Scoring round" models from the <a href="https://www.ebi.ac.uk/pdbe/complex-pred/capri/">CAPRI Website </a>.</p> <p>The targets considered were T122-T125, T131-T133, and T136, as these were targets which contained globular protein ligands and receptors. Please contact us directly if you have any further questions on this dataset.</p>
PhasAGE Training School 1 - Structure and protein interactions of repeated and low complexity regions - PRACTICAL
<p>The Training School 1 <strong>“Computational Methods to Study Protein Phase Separation”</strong> is the first edition of a series of PhasAGE training activities.</p> <p>The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of <strong>Phase Separation</strong>, its role in biological processes and diseases. In addition, the course will provide <strong>an overview of the available computational resources</strong> to navigate this knowledge. Participants will have <strong>hands-on training</strong> in tools and resources available for life sciences, to collect information from the literature on biomolecular phase transitions, identify features triggering phase transitions, mutations associated with diseases, known or predicted PTMs and molecular interaction sites.</p>
Supplementary Data for "Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein"
<p>Supplementary Data for "Molecular dynamics simulations provide structural insight into binding of cyclic dinucleotides to human STING protein", Journal of Biomolecular Structure and Dynamics, 2021, 10.1080/07391102.2021.1942213</p> <p>A random selection of 10 representative structures from each MSM state of STING/CDN complexes is provided in .pdb file format. The selected MSM representatives are aligned and available as PyMOL session files.</p>
Structures for "Protonation States of Molecular Groups in the Chromophore-Binding Site Modulate Properties of the Reversibly Switchable Fluorescent Protein rsEGFP2"
<p>Files with atomic coordinates of the structures obtained in geometry optimization of molecular systems within the protein and cluster models. Supplementary materials for the paper "Protonation States of Molecular Groups in the Chromophore-Binding Site Modulate Properties of the Reversibly Switchable Fluorescent Protein rsEGFP2"</p>
Initial structures of macrocyclic Gαq protein inhibitors bound to the Gq heterotrimer
<p>Initial PDB structures of the Gq heterotrimer in its apo form or bound to either ym2 or fr9 inhibitors. These structures were used in the molecular dynamics simulations described in the manuscript below: </p> <p><strong>Unraveling binding mechanism and kinetics of macrocyclic Gαq protein inhibitors</strong> (under review)<br> <em>Jan H. Voss<sup>1</sup>, Jessica Nagel<sup>1</sup>, Muhammad Rafehi<sup>1</sup>, Ramon Guixà-González<sup>2,5</sup>, Davide Malfacini<sup>3</sup>, Julian Patt<sup>3</sup>, Stefan Kehraus<sup>3</sup>, Asuka Inoue<sup>4</sup>, Gabriele M. König<sup>3</sup>, Evi Kostenis<sup>3</sup>, Xavier Deupi<sup>2,5</sup>, Vigneshwaran Namasivayam<sup>1</sup>, and Christa E. Müller<sup>1</sup></em></p> <p><em><sup>1</sup>PharmaCenter Bonn, Pharmaceutical Institute, Pharmaceutical & Medicinal Chemistry, University of Bonn, An der Immenburg 4, D-53121 Bonn, Germany<br> <sup>2</sup>Condensed Matter Theory Group, Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland<br> <sup>3</sup>Institute of Pharmaceutical Biology, University of Bonn, Nussallee 6, 53113 Bonn, Germany<br> <sup>4</sup>Tohoku University, Graduate School of Pharmaceutical Sciences, Sendai, Miyagi, 980-8578 Japan<br> <sup>5</sup>Laboratory of Biomolecular Research, Paul Scherrer Institute, Forschungsstrasse 111, 5232 Villigen PSI, Switzerland</em></p>
Dataset of Bnet values of 93,978 PDB-REDO structures analysed in "Quantifying and comparing radiation damage in the Protein Data Bank"
<p>Dataset of Bnet values, plus associated metadata (resolution, Rwork, Rfree, temperature, etc.), for 93,978 PDB-REDO structures analysed in the publication "Quantifying and comparing radiation damage in the Protein Data Bank"</p>
Data deposition for "Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins"
<p>The deposited data for the publication "Reliability and accuracy of single-molecule FRET studies for characterization of structural dynamics and distances in proteins".</p> <p>Data contains folder and sub-folders for the raw data, Main excel sheet named as "MasterTable_FRET-Challenge-Protein-Dynamics_Nat_Meth_Agam et al" has most of the data used in the publication. Another excel sheets "Data List for FIgures for Agam et al_revised" and "Data List for Supplmentary FIgures for Agam et al_revised" have the information regarding the Figure-wise data description and where the respective data locates.</p>
Data for manuscript "Adaptive Ensemble Refinement of Protein Structures in High Resolution Electron Microscopy Density Maps with Radical Augmented Molecular Dynamics Flexible Fitting"
<p>The tar file contains the input files for RADICAL augmented MDFF implementation (R-MDFF) for two protein systems, Adenylate Kinase (ADK) and Carbon Monoxide Dehydrogenase (CODH). These examples demonstrate the implementation of R-MDFF using RADICAL-Cybertools to flexibly fit biomolecules in cryo-EM density maps with on-the-fly decision making.</p> <p>All molecular simulations were performed using CUDA enabled NAMD 2.14 installed on OLCF Summit HPC resource. The CHARMM36 force field parameters were used for the proteins. Synthetic density maps were prepared at 1.8, 3 and 5 Å for ADK and 1.8 and 3 Å for CODH using VMD 1.9.3 software installed on OLCF Summit HPC resource. During the analysis stage, the cross correlation coefficients between density maps and atomic model were computed using VMD 1.9.3 on Summit HPC as part of the R-MDFF workflow.</p> <p>The source code is publicly available on GitHub: <a href="https://github.com/radical-collaboration/MDFF-EnTK">https://github.com/radical-collaboration/MDFF-EnTK </a></p> <p>The preprint of this research is submitted on bioRxiv, doi: <a href="https://doi.org/10.1101/2021.12.07.471672">https://doi.org/10.1101/2021.12.07.471672 </a></p> <p>To obtain maximum compression of the data, the tar command used to generate this tarball was:</p> <pre><code class="language-bash">GZIP=-9 tar --exclude='last.pdb' --exclude='*last_from_prev_iter.pdb' --exclude='*old' --exclude='*log' --exclude='*coor' --exclude='*vel' --exclude='*xsc' --exclude='*dcd' --exclude='lastframepdbs_fix' --exclude='*out' --exclude='*sl' --exclude='*rs' --exclude='*prof' --exclude='*err' --exclude='*dx' --exclude='*grid.pdb' --exclude='*txt' -cvzf rmdffv2.tar.gz rmdff-zenodo/</code></pre> <p> </p>
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.