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219 results for “protein dynamics”
Data for manuscript: Functional Protein Dynamics in a Crystal
<p>The data is provided as a part of the manuscript "<strong>Functional Protein Dynamics in a Crystal</strong>". This repository includes an archive with folders:<br> <br> <strong>md_data</strong></p> <ul> <li>contains various simulation systems (crystal supercell, apo and ligand-bound solution) built from the crystal structure of the PDZ domain (PDB ID: 5E11) and carried out using three force fields: Amber ff14SB, CHARMM36m, Amber ff94. <em>The details of the simulations are provided in the Methods and Supplementary methods sections of the manuscript. </em></li> </ul> <p><strong>fig_data</strong></p> <ul> <li>contains the data sets underlying Figures 1-5 of the manuscript's main text. </li> </ul> <p> </p>
Dynamics of SARS-CoV-2 spike protein in open and closed states and identification of key structural perturbations upon mutations
<p>The SARS-Cov-2 spike protein resides on the exterior surface of the coronavirus, and therefore, acts as the first point of contact that mediates cell attachment and fusion. During this process, it undergoes dramatic conformational changes upon host receptor binding. We are leveraging high-performance computing to identify these structural perturbations in wildtype and mutant spike protein models. The files contain structures from molecular dynamics simulations of closed SARS-Cov-2 spike protein embedded in POPC membrane.</p>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
A molecular dynamics study of adenylyl cyclase: the impact of ATP and G-protein binding
<p>Adenylyl cyclases (ACs) catalyze the biosynthesis of cyclic adenosine monophosphate (cAMP) from adenosine triphosphate (ATP) and play an important role in many signal transduction pathways. The enzymatic activity of ACs is carefully controlled by a variety of molecules, including G-protein subunits that can both stimulate and inhibit cAMP production. Using homology models developed from existing structural data, we have carried out all-atom, microsecond-scale molecular dynamics simulations on the AC5 isoform of adenylyl cyclase and on its complexes with ATP and with the stimulatory G-protein subunit Gsα. The results show that both ATP and Gsα binding have significant effects on the structure and flexibility of adenylyl cyclase. New data on ATP bound to AC5 in the absence of Gsα notably help to explain how Gsα binding enhances enzyme activity and could aid product release. Simulations also suggest a possible coupling between ATP binding and interactions with the inhibitory G-protein subunit Gαi.</p> <p>All-atom molecular dynamics simulations were performed with the GROMACS 5 package. The simulations were carried out in an NTP ensemble at a temperature of 310 K and a pressure of 1 bar using a Bussi velocity-rescaling thermostat (t<sub>T</sub> = 1 ps) and a Parrinello-Rahman barostat (t<sub>P</sub> = 1 ps). We provide the atomistic trajectories of the following 6 systems after 400 ns of equilibration:</p> <ul> <li>AC5</li> <li>AC5+ATP</li> <li>AC5+Gsα</li> <li>AC5+ATP+Gsα</li> <li>AC5+FOK</li> <li>AC5+ATP+FOK</li> </ul> <p>In each trajectory, the frames are saved each 20 ps.</p>
Evaluating Changes in the Local Protein Physicochemical En-vironment induced by Molecular Dynamics Simulation
<p><span>Mutation of a single amino acid residue may significantly affect the structure and function of an entire protein. The effect of single amino acid substitutions can be assessed by examining the physicochemical environment surrounding the amino acid of interest, an emerging form of quantification of which is multidimensional tensors. However, the effect with respect to a protein variant’s inherent dynamics in tensor space is rarely assessed despite the potential importance of this form of analysis in revealing local physicochemical properties of the protein and response to mutation. Using the wild-type and 936 mutant structures of the protein domain 1pga, the present research evaluated the effects of local protein context and single amino acid substitutions on molecular dynamics simulation-derived structural distributions via the use of tensors capturing a range of biochemical properties. It was observed that the extent of simulated </span><span>physicochemical</span><span> variation local to a substituted amino acid is positively associated with local mechanical stiffness, loss of protein thermostability and decreased local hydrophobicity. In addition, it was observed that the largest tensor variation occurs in densely-packed, hydrophobic core-associated regions of protein structures. In summary, the pattern of tensor change aligns with prior knowledge about protein stability and physicochemical properties.</span></p>
Molecular Dynamics Simulation of SARS-CoV-2 Spike Protein
<p>Trajectory data corresponding to the manuscript, tentatively titled "Distant Residues Modulate the Conformational Opening in SARS-CoV-2 Spike Protein"</p> <p>Authors: Dhiman Ray, Ly Le, Ioan Andricioaei</p> <p>Affiliation: University of California Irvine, USA</p> <p>Description: Multiple unbiased simulations of 40 ns were performed for the SARS-CoV-2 spike protein. Frames are saved at 50 ps interval. The initial structures were generated from umbrella sampling simulation starting from PDB ID: 6VSB and 6VXX. The index at the end of filename stands for the umbrella sampling window from which the trajectory was initiated. The indices are not continuous as not all the umbrella sampling windows were used to start trajectories. Additionally 3 trajectories, each of length 80 ns, are included for the closed, partially open and fully open state. The topology is provided as a PDB file ("spike_dry.pdb").</p> <p>The trajectories are for the spike head only structure obtained from the CHARMM-GUI Covid-19 archive. No solvent or ions are included in the trajectory or the topology.</p> <p>Update: Additional trajectories and PDB files for D614G mutant added. Each trajectory is 40 ns long. The PDB files are named 6VXX_mutant_dry.pdb and 6VSB_mutant_dry.pdb for the closed and partially open state.</p> <p>Pre-print available: https://doi.org/10.1101/2020.12.07.415596</p>
Coarse-grained molecular dynamics simulations of SARS-CoV-2 envelope protein E in the pentameric form
<p>The trajectories of coarse-grained (CG) molecular dynamics (MD) simulations of<br> 1) unmodified (FeigLab_NMR; FeigLab_PentamerNoPTM_POPC_Martini3b: 5 μs; 5 μs); <br> 2) palmitoylated (FeigLab_PentamerCYSP43; PentamerCYSP44_POPC_Martini3b: 5 μs; 5 μs); <br> SARS-CoV-2 E protein pentamer in a POPC bilayer.</p> <p>The trajectory of CG MD of system containing 2 pentamers in the membrane buckled in a single direction (BuckledMembrane_FeigLab_2xPentamerNoPTM_POPC_Martini3b: 1 μs).</p> <p>FeigLab_Pentamer: https://github.com/feiglab/sars-cov-2-proteins/blob/master/Membrane/E_protein.pdb<br> FeigLab_NMR_Pentamer is assembled based on the transmembrane domain determined by NMR (PDB ID: 7K3G) and FeigLab model for the rest.</p>
Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein
<p>These data support the manuscript entitled "Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein" by Heller, Shukla, Figueiredo, and Hansen.</p><p>This data should be used with the code provided on GitHub at https://github.com/hansenlab-ucl/R2_IDP_small_mol. Once downloaded, this directory should be extracted using the following command:</p><p> tar -xzvf Data.tar.gz</p><p>The directory should be saved with the name 'Data' placed in the same directory as the GitHub README.md file.</p><p><strong>This dataset contains: </strong><br><i>Nuclear Magnetic Resonance (NMR) spectroscopy data files (.ft2 format) including: </i></p><p>* 1H 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of the protein, non-structural protein 5A, domains 2 and 3 (NS5A-D2D3), in 1H_1D_ft2_data/</p><p>* 1H pseudo-2D Diffusion Ordered SpectroscopY (DOSY) data of 5-fluoroindole (50 uM) with and without NS5A-D2D3 (75 uM) in 1H_DOSY_data/</p><p>* 1H-15N Heteronuclear Single Quantum Coherence (HSQC) measurements of NS5A-D2D3 (40 uM) in the absence and presence of 5-fluoroindole (160 and 320 uM) in 1H_15N_HSQC_ft2_and_metadata/</p><p>* 19F 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_1D_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-lattice, R1,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R1eff_ft2_data/</p><p>* 19F pseudo-2D ligand-detected longitudinal (spin-spin, R2,eff) relaxation titration data of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_R2eff_ft2_data/</p><p><i>Circular Dichroism (CD) data files (.txt format) including: </i></p><p>* CD measurements of NS5A-D2D3 at increasing concentrations in CD_data/no_molecule/</p><p>* CD measurements of NS5A-D2D3 with and without the small molecule, 5-fluoroindole CD_data/with_molecule/</p><p><i>Metadata </i></p><p>* Metadata from the Biological Magnetic Resonance Data Bank (https://bmrb.io/) used to determine scaling factors for the calculation of chemical shift perturbations in 1H_15N_HSQC_ft2_and_metadata/</p>
An information theory-based machine learning approach to detecting functionally conserved and coordinated protein dynamics
<p>The application of machine learning classification to the molecular dynamics of the functional states of protein allows for application of an information theoretic framework familiar to traditional bioinformatics. The functional states of proteins involving binding interactions with partners comprised of protein, DNA or small molecules can first be defined in a binary fashion (i.e. bound vs unbound), subsequently simulated in molecular dynamics software, and then employed as a comparative training set for a binary machine learning classifier capable of discerning the complex dynamical consequences of binding interaction. This learner can subsequently be deployed on new simulations of the functionally bound state to validate its ability to recognize the molecular motions that are supporting binding function. Regions of proteins with functionally conserved dynamics will induce significant local correlations in learning performance across independent validation runs. Through case studies of Rbp subunit 4/7 interaction in RNA Pol II and DNA-protein interactions of TATA binding protein, we demonstrate this method of detecting functionally conserved protein dynamics. We also demonstrate how Shannon information, relative entropy and mutual information can be applied to these binary classification states of dynamic simulations in order to compare dynamics and identify concerted motions involved in dynamic interactions across sites.</p>
Plasmonic Nanosensors for the Label-Free Imaging of Dynamic Protein Patterns
<p>Additional data to support our work on "Plasmonic Nanosensors for the Label-Free Imaging of Dynamic Protein Patterns" published in the Journal of Physical Chemistry Letters (DOI: 10.1021/acs.jpclett.0c01400)</p> <p>Movies:<br> - S1: MinVideo_EColi.mp4<br> - S2: MinVideo_DOPC_DOPG_CL.mp4<br> - S3: MinVideo_DOPC_DOPG.mp4<br> Audio Files:<br> - S1: MinSound_EColi.mp4<br> - S2: MinSound_DOPC_DOPG_CL.mp4<br> - S3: MinSound_DOPC_DOPG.mp4</p>
Small molecules targeting the structural dynamics of AR-V7 partially disordered protein using deep learning and physics based models.
<p>Partially disordered proteins can contain both stable and unstable secondary structure segments and are involved in various (mis)functions in the cell. The extensive conformational dynamics of partially disordered proteins scaling with extent of disorder and length of the protein hampers the efficiency of traditional experimental and in-silico structure-based drug discovery approaches. Therefore new efficient paradigms in drug discovery taking into account conformational ensembles of proteins need to emerge. In this study, using as a test case the AR-V7 transcription factor splicing variant related to prostate cancer, we present an automated methodology that can accelerate the screening of small molecule binders targeting partially disordered proteins. By swiftly identifying the conformational ensemble of AR-V7, and reducing the dimension of binding-sites by a factor of 90 by applying appropriate physicochemical filters, we combine physics based molecular docking and multi-objective classification machine learning models that speed up the screening of thousands of compounds targeting AR-V7 multiple binding sites. Our method not only identifies previously known binding sites of AR-V7, but also discovers new ones, as well as increases the multi-binding site hit-rate of small molecules by a factor of 17 compared to naive physics-based molecular docking. </p>
Supplementary data frames, AlphaFold models, Normal Mode Analysis (NMA) Data, and NMA of Corresponding NMR Ensembles in the S2RCI, MD, and S2 Datasets for "Gradations in protein dynamics captured by experimental NMR are not well represented by AlphaFold2 models and other computational metrics"
<h1><strong>Changes applied to V2</strong></h1> <p>In addition to the supplementary dataframes and AlphaFold models from each dataset in V1, V2 includes the additional data outlined below.</p> <p>The <strong>S2RCI</strong> and <strong>MD</strong> datasets include comprehensive analyses of AlphaFold2 models (both before and after truncation). These datasets feature: </p> <ul> <li><strong>AlphaFold2 Models</strong>: Both original and truncated structures. </li> <li><strong>WEBnma Modes</strong>: `modes.txt` files generated from WEBnma analysis, available for both non-truncated and truncated AF2 models. </li> <li><strong>Root-Mean-Square-Fluctuations (RMSF)</strong>: Profiles calculated before and after truncation of AF2 models. </li> <li><strong>NMR Data: Normal Mode Analysis (NMA)</strong>: Performed on corresponding NMR ensembles (see below). </li> </ul> <p> </p> <p>The <strong>NMR Data</strong> of NMA in these datasets includes: </p> <ul> <li>NMR ensembles </li> <li>Individual NMR models extracted from each ensemble </li> <li>STRIDE secondary structure calculations per-individual NMR models</li> <li>RMSF profiles per-individual NMR models</li> </ul> <p>For detailed information, please refer to the `Readme.txt` file within each corresponding folder. </p> <p>The <strong>S2 dataset</strong> includes all the features listed above, except for the NMR analysis.</p>
All-atom Molecular Dynamics Simulations of Meiosis 1-associated protein (M1AP) to Investagate the Impact of Known Missense Mutations Associated with Male Infertility through Non-obstructive Azoospermia
<p>Protein structure of meiosis 1-associated protein (M1AP) was modelled by using GalaxyWeb (from Seok Lab). We used this model to investigate the impact of variants (i.e., S50P, R266Q, P389L, G317R, and L430P) in M1AP which were recently associated with non-obstructive azoospermia (NOA). NOA is a male infertility-related condition causing absence of sperm in the seminal fluid due to meiosis failure. We aimed to elucidate the pathogenicity mechanisms of these five missense NOA-related mutations on M1AP by performing molecular modeling and molecular dynamics (MD) simulations. This dataset includes the results of 1000 ns MD simulations (two repeats, each 500 ns) for each of the mutant and wild-type systems.</p> <p>Systems were prepared in Visual Molecular Dynamics (VMD 1.9.3) by placing them in a TIP3P water box with approximately 20 Å thickness from the protein surface and neutralizing the system charge with 0.15 M KCl. Of note, only protein parts were kept for the submission to reduce the size of files. Nanoscale Molecular Dynamics (NAMD 2.13-CUDA) was used to perform MD simulations with CHARMM36m force field. For pressure and temperature controls, Nosé-Hoover Langevin barostat and Langevin thermostat were used. ShakeH algorithm of NAMD was applied for water molecule constraints. 12 Å cut-off distance was used for van der Waals interactions. Switching function starts at 10 Å and reaches zero at 14 Å. Integration time-step was 2 fs. To compute the long-range Coulomb interactions, the particle-mash Ewald method was used. NPT ensemble was applied for whole simulations. Two step minimization & equilibration procedure was performed: (1) 5,000-step minimization and 1 ns equilibrium with constraints on the protein; (2) 5,000-step minimization and 1 ns equilibrium without the constraints on the protein. All related configuration files for wild-type system were also included to the dataset. Production simulations were run twice along 500 ns by using different random seeds to assign the velocities from Boltzmann distribution (total simulation time for each system was 1000 ns, which are given as 500 ns repeat 1, and 500 ns repeat 2). The production simulations were supplied in the dataset. "out" and "log" files were used for energy analysis.</p> <p>For all analysis scripts, see https://github.com/ugerlevik/M1AP_analysis.</p>
Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations
<p>MD simulations used in the studies of the publication "<strong>Neural relational inference to learn long-range allosteric interactions in proteins from molecular dynamics simulations</strong>"</p>
Underlying data for IsoAligner: dynamic mapping of amino acidpositions across protein isoforms
<p>The human isoform library (list_of_gene_objects_25th_july_final.txt) for the IsoAligner webtool is generated from these resources.</p>
Screening routine for integrative dynamic structural biology using SAXS and intramolecular FRET and DEER-EPR on hGBP1 (human guanalyte binding protein 1)
<p>Initial and selected ensemble for major and minor species of the human guanalyte binding protein 1 with scripts for the reading routine to combine and analyse jointly SAXS, EPR and FRET data.</p>
Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup
<p>Intrinsically disordered proteins (IDPs) are highly dynamic systems that play an important role in cell signaling processes and their misfunction often causes human disease. Proper understanding of IDP function not only requires the realistic characterization of their three-dimensional conformational ensembles at atomic-level resolution but also of the time scales of interconversion between their conformational substates. Large sets of experimental data are often used in combination with molecular modeling to restrain or bias models to improve agreement with experiment. It is shown here for the N-terminal transactivation domain of p53 (p53TAD) and Pup how the latest advancements in molecular dynamics (MD) simulations methodology produces native conformational ensembles by combining replica exchange with series of microsecond MD simulations. They closely reproduce experimental data at the global conformational ensemble level, in terms of the distribution properties of the radius of gyration tensor, and at the local level, in terms of NMR properties including <sup>15</sup>N spin relaxation, without the need for reweighting. The IDP ensembles were analyzed by graph theory to identify dominant inter-residue contact clusters and characteristic amino-acid contact propensities. These findings indicate that modern MD force fields with residue-specific backbone potentials can produce highly realistic IDP ensembles sampling a hierarchy of nano- and picosecond time scales providing new insights into their biological function.</p>
Molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants
<p>This data set accompanies the publication by S. Conti, E. Lau, and V. Ovchinnikov entitled "On the rapid calculation of binding affinities for antigen and antibody design and affinity maturation simulations", to be published in the MDPI journal Antibodies. It contains molecular dynamics simulation trajectories of HIV protein gp120 in complex with antibody VRC01 and 30 of its Ala mutants, as described in the paper. The format of the trajectory files is CHARMM-compatible dcd. The files can be visualized with the program Visual Molecular Dynamics (VMD) (see paper by Humphrey et al. 1996, J. Molec. Graphics). The accompanying file "view" is a tcl-based script for VMD that can be executed in the Linux environment using: "vmd -e view", which will display the trajectory of the mutant specified by editing the first noncomment line of the script.<br> </p>
A dynamical view of protein-protein complexes: studies by molecular dynamics simulations
<p>All-atom MD simulations generated for the manuscript "A dynamical view of protein-protein complexes: studies by molecular dynamics simulations". Eight binary protein-protein complexes from the Docking benchmark and the Affinity benchmark are studied in this work: 2OOB (an ubiquitin/ubiquitin ligase complex), 1AY7 (a ribonuclease Sa/barstar complex), 1BRS (a barnase/barstar complex), 3SGB (a proteinase B/inhibitor), 1EMV (a colicin/immunity protein complex), 1PVH (complex between Interleukine 6 receptor and leukemia inhibitory factor), 1GCQ (Vav/GRB2 SH3 domains complex) and 1AK4 (cyclophilin/HIV capsid complex)</p> <p>Each folder for a binary complex is organised as followed:</p> <p>- in <strong>A</strong> and <strong>B</strong> there are the dry MD simulations for the unbound proteins</p> <p>- in <strong>complex</strong> there are two folders (<strong>without_water</strong> and <strong>water</strong>) where the dry simulation and the simulation with water molecules are provided</p>
X-ray diffraction images recorded for Aumonier et al., (2022) Slow protein dynamics probed by time-resolved oscillation crystallography at room temperature, IUCrJ
<p>The present repository contains diffraction images corresponding to 27 distinct datasets collected at room temperature on the ESRF beamline ID30A-3 using an Eiger X 4M detector.</p> <p>Datasets have been uploaded with their original names to maintain the metadata integrity. The two following tables match the original names with those attributed in the supplementary table S1 of Aumonier et al., IUCrJ (2022) (https://doi.org/10.1107/S2052252522009150).</p> <table> <tbody> <tr> <td> <p>Data set name on Zenodo</p> </td> <td> <p>X06_01</p> </td> <td> <p>X12_05</p> </td> <td> <p>X07_02_</p> </td> <td> <p>X06_08</p> </td> <td> <p>X14_06</p> </td> <td> <p>X13_03</p> </td> <td> <p>X08_06</p> </td> <td> <p>X11_05</p> </td> <td> <p>X13_05</p> </td> <td> <p>X06_02</p> </td> <td> <p>X11_01</p> </td> <td> <p>X08_01</p> </td> <td> <p>X14_01</p> </td> <td> <p>X13_01</p> </td> <td> <p>X06_03</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>Dark</p> </td> <td> <p>PS2</p> </td> <td> <p>PS2</p> </td> <td> <p>PS3</p> </td> <td> <p>PS4</p> </td> <td> <p>PS5</p> </td> <td> <p>PS6</p> </td> <td> <p>PS7</p> </td> <td> <p>R<sub>2”</sub></p> </td> <td> <p>R<sub>3”</sub></p> </td> <td> <p>R<sub>7”</sub></p> </td> <td> <p>R<sub>10”</sub></p> </td> <td> <p>R<sub>13”</sub></p> </td> <td> <p>R<sub>21”</sub></p> </td> <td> <p>R<sub>35”</sub></p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p>Data set on Zenodo</p> </td> <td> <p>X08_02</p> </td> <td> <p>X11_02</p> </td> <td> <p>X12_02</p> </td> <td> <p>X14_02</p> </td> <td> <p>X13_04</p> </td> <td> <p>X13_02</p> </td> <td> <p>X12_06</p> </td> <td> <p>X06_09</p> </td> <td> <p>X09_04</p> </td> <td> <p>X12_04</p> </td> <td> <p>X06_07</p> </td> <td> <p>X13_07</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>R<sub>51”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>67”</sub></p> </td> <td> <p>R<sub>72”</sub></p> </td> <td> <p>R<sub>80”</sub></p> </td> <td> <p>R<sub>90”</sub></p> </td> <td> <p>R<sub>130”</sub></p> </td> <td> <p>R<sub>166”</sub></p> </td> <td> <p>R<sub>258”</sub></p> </td> <td> <p>R<sub>630”</sub></p> </td> <td> <p>R<sub>1620”</sub></p> </td> </tr> </tbody> </table> <p>One dataset consists of a master file, four data files and two metadata files.</p>
ScienceDex guides
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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.