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36 results for “intrinsically disordered proteins”

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zenodo44/100

Supplementary Data Files for the paper "Intrinsically disordered compositional bias in proteins: Sequence traits, region clustering, and generation of hypothetical functional associations"

<div> <div> <div> <div> Supplementary data files relating to <a href="https://doi.org/10.1177/11779322241287485">https://doi.org/10.1177/11779322241287485. </a> Suppl. File 1: Protein Family Clusters. Suppl. File 2: Cluster GO enrichments/depletions. Suppl. File 3: The raw ID-CBR data with annotations. Suppl. File 4: ID-CBR Cluster membership. Each file has an explanatory header.    </div> </div> </div> </div>

opencc-by-4.0Oct 2024View details →

zenodo44/100

Intrinsic disorder, phase separation and fibrillation by the Henipavirus V and W proteins | Talk- I PhasAGE International Conference

The I PhasAGE international conference brought together members of the PhasAGE consortium as well as outstanding international speakers showcasing high impact achievements in the field of liquid-liquid phase separation in aging and late-onset diseases. For details on conference program please see: https://phasage.eu/phasage-conference-1/

opencc-by-4.0Oct 2021View details →

zenodo44/100

PhasAGE Expert Seminar- Intrinsic Protein Disorder and Conditional Folding in AlphaFoldDB

The PhasAGE Expert Seminars consist of a series of talks with speakers from PhasAGE partner’s institutions to promote a successful transfer of knowledge about PhasAGE topics – biomolecular phase separation, aging and age-related diseases.

opencc-by-4.0Oct 2022View details →

zenodo44/100

PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-DisProt - PRACTICAL

The Training School 1 “Computational Methods to Study Protein Phase Separation” is the first edition of a series of PhasAGE training activities. The goal of this course is to provide participants with the basic knowledge to understand the phenomenon of Phase Separation, its role in biological processes and diseases. In addition, the course will provide an overview of the available computational resources to navigate this knowledge. Participants will have hands-on training 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.

opencc-by-4.0Jun 2021View details →

zenodo44/100

PhasAGE Training School 1 - Computational prediction of intrinsic disorder in proteins-MobiDB - PRACTICAL

opencc-by-4.0Jun 2021View details →

zenodo44/100

Extreme dynamics of a small molecule in its bound state with an intrinsically disordered protein

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.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:    tar -xzvf Data.tar.gzThe directory should be saved with the name 'Data' placed in the same directory as the GitHub README.md file.This dataset contains:  Nuclear Magnetic Resonance (NMR) spectroscopy data files (.ft2 format) including: * 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/* 1H pseudo-2D Diffusion Ordered SpectroscopY (DOSY) data of 5-fluoroindole (50 uM) with and without NS5A-D2D3 (75 uM) in 1H_DOSY_data/* 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/* 19F 1D ligand-detected chemical shift titration of 5-fluoroindole (50 uM) with increasing concentrations of NS5A-D2D3 in 19F_1D_ft2_data/* 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/* 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/Circular Dichroism (CD) data files (.txt format) including: * CD measurements of NS5A-D2D3 at increasing concentrations in CD_data/no_molecule/* CD measurements of NS5A-D2D3 with and without the small molecule, 5-fluoroindole CD_data/with_molecule/Metadata * 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/

opencc-by-4.0May 2023View details →

zenodo40/100

III PhasAGE International Conference - PED in 2024: improving the community deposition of structural ensembles for intrinsically disordered proteins - Lecture

The III PhasAGE International Conference "Multiscale understanding of protein aggregation and biomolecular condensates in aging and disease" brought together members of the PhasAGE consortium as well as outstanding international speakers from multidisciplinary fields dedicated to unraveling the intricacies of protein aggregation and biomolecular condensates in the context of aging and disease. For details on the conference program please see https://phasage.eu/iii-phasage-international-conference/.

opencc-by-4.0Nov 2023View details →

zenodo40/100

Borg tandem repeats undergo rapid evolution and are under strong selection to create new intrinsically disordered regions in proteins

This repository contains files that accompany the Schoelmerich et al. (2022) bioRxiv preprint. These files include - all Borg proteins used for protein family clustering (<a href="https://zenodo.org/api/files/f75689a0-d40c-44a6-b154-b74e3594fc04/all_Borg_proteins.fasta">all_Borg_proteins.fasta</a>) - 37 additional aaTR-proteins from manually curated Borg contigs (<a href="https://zenodo.org/api/files/f75689a0-d40c-44a6-b154-b74e3594fc04/37_Borg_aaTR-proteins.fasta?versionId=bfdfe7b6-e3b5-48b8-b7e6-a20304097e7d">37_Borg_aaTR-proteins.fasta</a>) - IQ-TREE of Borg DNA polymerases and reference sequences from doi: 10.1093/nar/gkaa760 (<a href="https://zenodo.org/api/files/f75689a0-d40c-44a6-b154-b74e3594fc04/DNAPolB_iqtree.treefile?versionId=c588a98c-822d-4a2f-a92d-7dcb3b1675f0">DNAPolB_iqtree.treefile</a>) - Borg Sm ribonucleoprotein sequences (<a href="https://zenodo.org/api/files/f75689a0-d40c-44a6-b154-b74e3594fc04/21_Borg_Sm_ribonucleoproteins.fasta">21_Borg_Sm_ribonucleoproteins.fasta</a>) - Borg MHC sequences (<a href="https://zenodo.org/api/files/f75689a0-d40c-44a6-b154-b74e3594fc04/14_Borg_MHC_proteins.fasta">14_Borg_MHC_proteins.fasta</a>) - AlphaFold2 predicted structural models of Borg Sm ribonucleoproteins and MHCs with aaTRs

opencc-by-4.0May 2022View details →

dryad40/100

Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup

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 15N 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.

opencc-zeroMay 2022View details →

zenodo40/100

Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface

<h3>Data for: Intrinsically Disordered Proteins form Condensates with Gradually Collapsing Conformations at the Interface</h3> We ran simulations for four different systems: <ul> <li>WT: A1-LCD WT (N=137), wild-type (WT) sequence of the low-complexity domain (LCD) of the heterogeneous nuclear ribonucleoprotein A1 (hnRNPA1), with electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T260: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=260K</li> <li>WT_noEL_T290: A1-LCD WT (N=137), without electrostatic interactions, at temperature T=290K</li> <li>HP: homopolymer consisting of prolines (N=137), at temperature T=550</li> </ul> For every system, we ran five independent simulations over 5µs (1000 frames) and used the last 900 frames (4.5µs) for our analysis. This data repository consists of    (1) folders containing the data for every seperate run (*_i, i=1,2,3,4,5) in simulation units    (2) folders containing the averaged data of all five runs (*_AVG), converted to SI units    (3) a droplet folder, containing the data (square radius of gyration and asphericity) for the whole droplet (for all four systems, all five runs) Units are also clarified in each file's header. The simulation units can be converted to SI units via: <ul> <li>Distance: D = 0.45nm</li> <li>Mass: M = 57.05amu</li> <li>Energy: epsilon = 0.2 kcal/mol</li> </ul>   Details for (1) and (2): Each folder (*_i, i=1,2,3,4,5, and *_AVG) contains the following subfolders and files: Ree: <ul> <li>distribCos2_all.dat: distribution of cos^2(θ_{ee}) of the whole chains, where θ_{ee} is the angle between the polymer's center r_c and the chain’s end-to-end vector Ree [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_{ee,s}) of segment seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_{ee,s}) of all segments seg_i, where θ_{ee,s} is the angle between the segment's center r_{c,s} and the segment’s end-to-end vector R_{ee,s} [Fig. S4d, Fig. S7d, Fig. S10d, Fig. S13d]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos.dat: distribution of the polymer end positions (whole chains) [Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymerEndPos_segment_i.dat: distribution of the polymer segment end positions of seg_i</li> <li>distribPolymerEndPos_segment_all.dat: distribution of the polymer segment end positions of all segments</li> <li>distribPolymerRee2_all.dat: distribution of Ree^2 (whole chains), binned via polymer center position r_c</li> <li>distribPolymerRee_segment_i.dat: distribution of Ree^2 of segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerRee_segments_all.dat: distribution of Ree^2 of all segments, binned via segment center position r_{c,s}</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all polymer segments, binned via segment center position r_{c,s}</li> </ul> Rg: <ul> <li>distribCos2_all.dat: distribution of cos^2(θ) of the whole chains, where θ is the angle between the polymer's center r_c and the eigenvector belonging to the largest eigenvalue of the chain’s gyration tensor [Fig. S3b, Fig. S6b, Fig. S9b, Fig. S12b]</li> <li>distribCos2_segment_i.dat: distribution of cos^2(θ_s) of segment seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribCos2_segments_all.dat: distribution of cos^2(θ_s) of all segments seg_i, where θ_s is the angle between r_{c,s} and the eigenvector belonging to the largest eigenvalue of the segment’s gyration tensor [Fig. S4b, Fig. S7b, Fig. S10b, Fig. S13b]</li> <li>distribMonomer_all.dat: distribution of the monomers [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribPolymer_all.dat: distribution of the polymers (whole chains, binned via polymer center position) [Fig. 1, Fig. S4c, Fig. S7c, Fig. S10c, Fig. S13c]</li> <li>distribMonomerRg_all.dat: distribution of monomer weighted Rg^2 (whole chains), referred to as R_{g,mono}^2 (following Farag et. al) [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_all.dat: distribution of Rg^2 (whole chains), binned via polymer center position r_c [Fig. 2, Fig. S3a, Fig. S6a, Fig. S9a, Fig. S12a]</li> <li>distribPolymerRg_segment_i.dat: distribution of Rg^2 of segment seg_i, referred to as R_{g,s}^2, binned via segment center position r_{c,s} [Fig. S4a, Fig. S7a, Fig. S10a, Fig. S13a]</li> <li>distribPolymerSegment_i.dat: distribution of polymer segment seg_i, binned via segment center position r_{c,s}</li> <li>distribPolymerSegments_all: distribution of all segments, binned via segment center position r_{c,s}</li> </ul> resDist: <ul> <li>distribPolymerRee2_base_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRee2_center_resDistance_s.dat: distribution of Ree2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> <li>distribPolymerRg2_base_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment base position r_i [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]</li> </ul> distribPolymerRg2_center_resDistance_s.dat: distribution of Rg2 of all chain segments of length s=|j-i|, binned according to the segment center position r_{c,s} [Fig. 3, Fig. S5, Fig. S8, Fig. S11, Fig. S14]     Details for (3): The folder 'droplet' contains four system folders (HP, WT, WT_noEL_T260, WT_noEL_T290). Each of those folders contains the following files: <ul> <li>runX_cluster_Rg2_Rg2Normal_kappa2.dat: for every run X, one finds the time evolution (in simulation units, with 1e8 timesteps = 1µs) of the square radius of gyration Rg2 of the full droplet, its x-, y- and z-components, its three eigenvalues and the droplet asphericity A (referred to as kappa2 in the header) [Fig.S1c, Fig.S1d]</li> <li>AVG_cluster_Rg2_Rg2Normal_kappa2.dat: average of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> <li>STD_cluster_Rg2_Rg2Normal_kappa2.dat: standard deviation of the parameters from the runX_cluster_Rg2_Rg2Normal_kappa2.dat files, over all five runs, using the last 900 snapshots (4.5µs) of every run [Fig. S1a, Fig. S1b]</li> </ul>

opencc-by-4.0Jun 2024View details →

dryad40/100

Molecular dynamics simulations of intrinsically disordered proteins p53TAD and Pup

Open the record for dataset details and reuse information.

publicMay 2022View details →

zenodo36/100

alfa-synuclein spectra for the paper "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins"

The experimental data for the paper  "Linear discriminant analysis reveals hidden patterns in NMR chemical shifts of intrinsically disordered proteins" - four spectra of an intrinsically disordered protein alfa-synuclein: 3D HNCO 4D HabCab(CO)NH 4D (H)N(CA)CONH 4D HNCACO All spectra were acquired using non-uniform sampling (schedules included as 'schedule.txt' files in a format of 'Kozminski' method in VnmrJ). 3D spectrum (HNCO_nuFT.ucsf) was processed using multidimensional Fourier transform (http://nmr.cent3.uw.edu.pl/software, program 'toastd').  Additionally, we provide a spectrum cleaned of NUS artifacts (HNCO_artifacts_cleaned.ucsf) using program handy (http://nmr.cent3.uw.edu.pl/software, program 'handy'). 4D spectra were processed using sparse multidimensional Fourier transform (http://nmr.cent3.uw.edu.pl/software, program 'reduced'), based on a HNCO peak list ('peak.list' file). Files with processing parameters are included as 'parameters.txt' files. The resulting spectra are included as Sparky .ucsf files. The files for 4D spectra are the collections of 2D cross-sections with the cross-section number corresponding to the numbering of HNCO peaks in the 'peak.list' file. The folder 'Application2_assignment_transfer' contains Sparky 'ucsf' and 'save' files of the two-dimensional NH projection of the HNCO spectrum with the experimental (HSQC_exp.list) and BMRB (HSQC_bmrb.list) peak lists read into the spectrum. The experimental peaks are marked with arbitrary numbers and BMRB peaks - with names of the preceding aa-residues. The correct assignment is shown in the file 'assignment.txt'.

opencc-by-4.0May 2022View details →

zenodo36/100

PhasAGE Training School 1 - Phase separation and emergent functions of Intrinsically Disordered Proteins- Lecture

opencc-by-4.0Jun 2021View details →

zenodo36/100

Intrinsically Disordered Regions Promote Protein Refoldability and Facilitate Retrieval from Biomolecular Condensates–Peptide Quantifications

Many eukaryotic proteins contain intrinsically disordered regions (IDRs) that intersperse globular folded domains, in contrast with bacterial proteins which are typically highly globular. Recent years have seen great progress in identifying biological functions associated with these elusive protein sequence: in specific cases, they mediate liquid- liquid phase separation, perform molecular recognition, or act as sensors to changes in the environment. Nevertheless, only a small number of IDRs have annotated functions despite their presence in 64% of yeast proteins, stimulating some to question what ‘general purpose’ they may serve. Here, by interrogating the refoldability of two fungal proteomes (Saccharomyces cerevisiae and Neurosporra crassa), we show that IDRs render their host proteins more refoldable from the denatured state, allowing them to cohere more closely to Anfinsen’s thermodynamic hypothesis. The data provide an exceptionally clear picture of which biophysical and topological characteristics enable refoldability. Moreover, we find that almost all yeast proteins that partition into stress granules during heat shock are refoldable, a finding that holds for other condensates such as P-bodies and the nucleolus. Finally, we find that the Hsp104 unfoldase is the principal actor in mediating disassembly of heat stress granules and that the efficiency with which condensed proteins are returned to the soluble phase is also well explained by refoldability. Hence, these studies establish spontaneous refoldability as an adaptive trait that endows proteins with the capacity to reform their native soluble structures following their extraction from condensates. Altogether, our results provide an intuitive model for the function of IDRs in many multidomain proteins and clarifies their relationship to the phenomenon of biomolecular condensation. This dataset provides peptide quantifications (and their respective P-values) from three separate types of experiments used to support the claims in this study. 1. Peptide quantifications from global refolding reactions, assessed with limited-proteolysis mass spectrometry (LiP-MS), carried out on two fungal organisms (S. cerevisiae [yeast] & N. crassa), at three refolding times, repeated on three separate iterations (for yeast). 2. Peptide quantifications from LiP-MS experiments conducted on yeast extracts during heat shock or recovery from heat shock 3. Annotations for peptides in #1 that are associated with linker regions between folded domains.

opencc-by-4.0Jun 2023View details →

dryad36/100

Glassy dynamics and memory effects in an intrinsically disordered protein construct

Open the record for dataset details and reuse information.

publicJun 2021View details →

zenodo32/100

Sequence/simulation data for Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence

This is a DOI-linked deposition of sequence/biophysical properties pairs used in the associated paper by Lotthammer et al:Lotthammer, J. M.*, Ginell, G. M.*, Griffith, D.*, Emenecker, R. J. & Holehouse, A. S.  Direct Prediction of Intrinsically Disordered Protein Conformational Properties From Sequence. Nature Methods (in press), (2023).

opencc-by-4.0Nov 2023View details →

zenodo32/100

Conformational ensembles used in "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022"

Ensemble of intrinsically disordered proteins used in: "Assessment of forward models for the hydrodynamic radius of intrinsically disordered proteins. Pesce et al. 2022". Ensembles are produced with Flexible-meccano and Langevin simulations with CALVADOS for: <ul> <li>Hst5</li> <li>RS</li> <li>DSS1</li> <li>Sic1</li> <li>ProTa</li> <li>NHE6cmdd</li> <li>A1</li> <li>aSyn</li> <li>ANAC046</li> <li>GHR-ICD</li> <li>Tau</li> </ul>

opencc-by-4.0Jun 2022View details →

zenodo32/100

Salt Induced Transitions in Structural Ensemble of Intrinsically Disordered Proteins

Simulation data and the corrosponding analysis script for the work "Salt Induced Transitions in Conformational Ensemble of Intrinsically Disordered Proteins "  by Hiranmay Maity, Lipika Baidya and Govardhan reddy are deposited here.  Analysis Scripts: The scripts for analysing the simulation data are in analysis_script.zip. The folder contains: <ul> <li> autocorrelation.c : code for calculating end_to_end distance autocorrelation function with time in C. </li> <li> average_property.cpp: code for calculating average property such as radius of gyration (Rg) from trajectory files in C++. </li> <li> calculate_saxs_kratky.c: code for calculating  scattering profile (SAXS and Kratky) from simulation data in C. </li> <li> compute_contact_map.cpp: code for calculating contact map in C. </li> <li> probablity_distribution.c:  code for calculating probablity distribution of Rg in C. </li> <li> structure_factor.c: code for calculating structure factor in C. </li> </ul>

opencc-by-4.0Feb 2022View details →

zenodo32/100

Strategy of selection and optimization of single domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein

Dataset pertaining to Strategy of selection and optimization of single domain antibodies targeting the PHF6 linear peptide within the Tau intrinsically disordered protein

opencc-by-4.0Jun 2023View details →

zenodo28/100

Assessing SIRAH's Capability to Simulate Intrinsically Disordered Proteins and Peptides

This dataset contains the structures, topologies, and trajectory files of coarse-grained molecular dynamics simulations of five Intrinsically Disordered Proteins (IDPs). We explored the dynamics of α-synuclein (randomly generated conformers), p31-43(PDB is: 6QAX), PaaA2 antitoxin (PDB id:3ZBE), Amyloid-beta 1-40 (PDB id: 2FLM), and Insulin C-peptide (PDB id: 1T0C) using the SIRAH force field running with the Gromacs 18.4 under the conditions reported in <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00006">Machado et al. JCTC 2019</a>, adding 150 mM NaCl according to <a href="https://pubs.acs.org/doi/10.1021/acs.jctc.9b00953">Machado & Pantano JCTC 2020</a>. Briefly, simulations were performed in triplicate for 5 μs at 300 K and 1 atm in the NPT ensemble. We used a time-step of 20 fs, a direct cutoff for non-bonded interactions of 1.2 nm, and Particle Mesh Ewald summation for long-range electrostatics. For α-synuclein and p31-43, initial conformations were obtained from models built on arbitrary conformations and heated up to 340 K. For PaaA2 antitoxin, Amyloid-beta 1-40, and Proinsulin C-peptide, three different NMR conformers were arbitrarily selected.  The information is divided into five tar files containing each system's data (only protein coordinates are reported). Additionally, the Sirah Tools'  tcl script with macros for selections and analyses is also included. Please visit http://www.sirahff.com for step-by-step tutorials on running and analyzing CG simulations with SIRAH.  To take a quick look at the trajectories: 1- Untar the tar file of interest  2- Open the trajectory on VMD using the command line: vmd "your_protein".psf  "your_protein".xtc -e sirah_vmdtk.tcl Note that using the tcl script you can use normal VMD drawing methods as vdw, licorice, etc., and coloring by restype, element, name, etc. This dataset contains simulations associated to a manuscript with the same title and by the same authors. For questions, kindly contact Florencia Klein (fklein@pasteur.edu.uy), Exequiel Barrera (ebarrera@pasteur.edu.uy), or Sergio Pantano (spantano@pasteur.edu.uy).

opencc-by-4.0Nov 2020View details →

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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

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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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record