Skip to main content
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

99

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

99 results for “lipid membrane”

Learn how ShareScore rates datasets ↗
zenodo32/100

Simulation Dataset: The antimicrobial fibupeptide lugdunin forms water-filled channel structures in lipid membranes

<p>The Zenodo repository contains data to the molecular dynamics simulations in the following manuscript:&nbsp;</p> <p><strong>The antimicrobial fibupeptide lugdunin forms water-filled channel structures in lipid membranes&nbsp;</strong></p> <p>by</p> <p>Dominik Ruppelt, Marius F. W. Trollmann, Taulant Dema, Sebastian N. Wirtz, Hendrik <br>Flegel, Sophia M&ouml;nnikes, Stephanie Grond, Rainer A. B&ouml;ckmann*, Claudia Steinem*</p> <p>0) Lugdunin-parameterization</p> <p>1) Lugdunin-stack-stability</p> <p>&nbsp; &nbsp; - trans_configuration.gro: Artifical lugdunin stack in trans configuration (see SFig. 23 a).</p> <p>&nbsp; &nbsp; - gauche_configuration.gro: Artifical lugdunin stack in gauche configuration (see SFig. 23 b)</p> <p>&nbsp; &nbsp; - DMPC</p> <p>&nbsp; &nbsp; - DOPC</p> <p>&nbsp; &nbsp; - POPC</p> <p>&nbsp; &nbsp; - POPC_CHOL</p> <p>2) Partitioning-of-Lugdunin-in-Membranes</p> <p>&nbsp; &nbsp; - POPC</p> <p>&nbsp; &nbsp; - POPC_CHOL</p> <p>&nbsp; &nbsp; - GRAMPOS</p> <p>3) PMF-calculations</p> <p>&nbsp; &nbsp; - GRAMPOS_WT</p> <p>&nbsp; &nbsp; - POPC_CHOL_MUT</p> <p>&nbsp; &nbsp; - POPC_CHOL_WT</p> <p>&nbsp; &nbsp; - POPC_MUT</p> <p>&nbsp; &nbsp; - POPC_WT</p>

opencc-by-4.0Mar 2024View details →
zenodo32/100

Computational dataset, scripts and models for 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide'

<p>Analysis scripts and computational models used in the manuscript 'Lipid shape as a membrane activity modulator of a model antimicrobial peptide', by Marcin Makowski, Oct&aacute;vio L. Franco, Nuno C. Santos and Manuel N. Melo.</p>

opencc-by-4.0Nov 2024View details →
zenodo32/100

Data for "Electrically Controlling and Optically Observing the Membrane Potential of Supported Lipid Bilayers"

<p>Raw data of all EIS, imaging and time-resolved fluorescence measurements presented in &quot;Electrically Controlling and Optically Observing the Membrane Potential of Supported Lipid Bilayers&quot;.</p>

opencc-by-4.0Dec 2021View details →
zenodo32/100

Stiffness of Fluid and Gel Phase Lipid Nanovesicles: Weighting the Contributions of Membrane Bending Modulus and Luminal Pressurization

<p>In the manuscript, Atomic Force Microscopy (AFM) is employed for characterizing the mechanical response of nanosized lipid vesicles presenting membranes with different physical state. Results suggest that the mechanical response of lipid vesicles can be ascribed to two main contributions, i) the luminal pressure and ii) the intrinsic membrane rigidity. By developing a spring-based model, authors were able to rationalize the apparent disagrement between the two most commonly employed models for describing vesicle mechanics.</p>

opencc-by-4.0Oct 2021View details →
zenodo32/100

Simulations of membrane proteins in a mixture of lipids with varying levels of chain unsaturation

<p>Four structurally distinct membrane proteins were simulated in a mixture of phospholipids. The membranes contained one copy of one of the protein types with equimolar concentrations of DPPC, DOPC, DLiPC, SDPC, and cholesterol. The CHARMM36 force field [1] was employed, and the systems were built using the CHARMM-GUI web portal [2]. The 4-microsecond long simulations were performed using GROMACS [3]. Further details on the setup of the systems as well as on the simulation protocol can be found in the related paper at DOI: 10.1371/journal.pcbi.1007033.</p> <p>The files are named based on the PDB codes of the proteins (1AFO, 2M0B, 3EML, and 3EMN). For each system, the original simulation run input file (tpr) and the corresponding outputs (xtc, edr) are provided. Additionally, the simulations can be extended using the continue point (cpt) file. For each system the files required to generate the run input file are also provided: the topology file (top), index file (ndx), and a common run parameter file (mdp). The CHARMM36 force field can be downloaded from http://mackerell.umaryland.edu/charmm_ff.shtml</p> <p>Notably, the protein definition files (itp) were re-generated. After performing these simulations, the atom ordering of cholesterol in the CHARMM36 force field was changed. The provided gro files, as well as the lipid definition files (itp) correspond to this new atom order. While they can be used to generate a run input file, yet this run input file will not be identical to the one provided. Therefore: 1) for analysis, the provided xtc+tpr files are fine; 2) for extending the simulation, the tpr+cpt files are fine; 3) for new simulations, the tpr file can be generated based on the provided files, yet it cannot be used together with the provided xtc file.</p> <p>[1] <strong>DOI: </strong>10.1021/jp101759q</p> <p>[2] <strong>DOI: </strong>10.1021/acs.jctc.5b00935</p> <p>[3] <strong>DOI:&nbsp;</strong>10.1016/j.softx.2015.06.001</p>

opencc-by-4.0Apr 2019View details →
zenodo32/100

Coarse-grained simulation of a dilute lipid membranes with 7 protein types

<pre>A simulation of a DPPC lipid membrane with one copy of seven protein types. This is an extension to the dataset [1] with an even more dilute membrane. Topologies and mdp files can be obtained from [1], and all the simulation parameters are equal to those described in [1] and in the related publication [2]. The trajectory does not contain the solvent and is stored every 10 ns. [1] https://doi.org/10.5281/zenodo.846428</pre> <p>[2] M. Javanainen,&nbsp;H.&nbsp;Martinez-Seara,&nbsp;R. Metzler, and&nbsp;I. Vattulainen;&nbsp;Diffusion of Integral Membrane Proteins in Protein-Rich Membranes.&nbsp;J. Phys. Chem. Lett.,&nbsp;2017,&nbsp;8&nbsp;(17), pp 4308&ndash;4313, DOI: 10.1021/acs.jpclett.7b01758<br> &nbsp;</p>

opencc-by-4.0Sep 2019View details →
zenodo32/100

Molecular Probes for Tracking Lipid Droplet Membrane Dynamics

<p><strong><span>Abstract</span></strong></p> <p><span>Lipid droplets (LDs) and their membrane proteins play crucial roles in lipid metabolism, signaling, and information transport within cells. LDs feature a unique monolayer lipid membrane that has not been extensively studied due to the lack of suitable molecular probes that are able to distinguish this membrane from the LD lipid core. In this work, we present a three-pronged molecular probe design strategy that combines lipophilicity-based organelle targeting with microenvironment-dependent activation. As a proof-of-concept, we designed an <u>LD</u> <u>m</u>embrane labeling pro-probe called<strong> LDM</strong>, which selectively localizes around LD membranes. Upon activation by the HClO/ClO</span><sup><span>&minus;</span></sup><span> microenvironment that surrounds LDs, <strong>LDM</strong> pro-probe undergoes a color change and releases<strong> LDM-OH</strong> probe that binds to LD membrane proteins. This localizes the probe to the LD-as</span><span>sociated protein space which is restricted to the membrane thus enabling visualization of the ring-like LD membrane. By utilizing<strong> LDM</strong>, we identified the dynamic mechanism of LD membrane contacts and their protein accumulation parameters. Furthermore, using <strong>LDM</strong> in liver cancer cells allowed us to examine the changes in LD/mitochondrial protein accumulation caused by the state of starvation these cells encounter. This led to the discovery that liver cancer cells respond to energy stress during hunger by enhancing LD-mitochondria interactions. Taken together, <strong>LDM</strong> represents the first molecular probe for imaging LD membranes in live cells, and represents an attractive tool for further investigations into the specific regulatory mechanisms and drug discovery associa</span><span>ted with LD related metabolism diseases.</span></p> <p><span>&nbsp;</span></p> <p><em><span>Keywords:</span></em><span> Molecular Imaging, Cancer, Lipid droplets, Super-resolution Imaging</span></p>

opencc-by-4.0Aug 2024View details →
zenodo32/100

Supplementary Dataset for Membrane lipid homeostasis dually regulates conformational transition of phosphoethanolamine transferase EptA

Open the record for dataset details and reuse information.

opencc-by-4.0Oct 2024View details →
dryad32/100

Data from: The cutaneous lipid composition of bat wing and tail membranes: a case of convergent evolution with birds

The water vapour permeability barrier of mammals and birds resides in the stratum corneum (SC), the outermost layer of the epidermis. The molar ratio and molecular arrangement of lipid classes in the SC determine the integrity of this barrier. Increased chain length and polarity of ceramides, the most abundant lipid class in mammalian SC, contribute to tighter packing and thus to reduced cutaneous evaporative water loss (CEWL). However, tighter lipid packing also causes low SC hydration, making it brittle, whereas high hydration softens the skin at the cost of increasing CEWL. Cerebrosides are not present in the mammalian SC; their pathological accumulation occurs in Gaucher's disease, which leads to a dramatic increase in CEWL. However, cerebrosides occur normally in the SC of birds. We tested the hypothesis that cerebrosides are also present in the SC of bats, because they are probably necessary to confer pliability to the skin, a quality needed for flight. We examined the SC lipid composition of four sympatric bat species and found that, as in birds, their SC has substantial cerebroside contents, not associated with a pathological state, indicating convergent evolution between bats and birds.

opencc-zeroDec 2015View details →
zenodo32/100

Long chain lipids facilitate insertion of large nanoparticles into membranes of small unilamellar vesicles

<p>DLS data and Cryo Images of SUVs-QDs</p>

opencc-by-4.0Jul 2021View details →
dryad32/100

Membrane lipid metabolism, heat shock response, and energy costs mediate the interaction between acclimatization and heat hardening response

<p>Thermal plasticity on different timescales, including acclimation/acclimatization and heat hardening response – a rapid adjustment for thermal tolerance after a nonlethal thermal stress, can interact on organisms to improve the resilience to thermal stress. However, little is known about the physiological mechanisms mediating this interaction. To investigate underpinnings of heat hardening responses after acclimatization in warm season, we measured thermal tolerance plasticity, compared transcriptomic and metabolomic changes after heat hardening at 33 or 37<sup>o</sup>C followed by recovery of 3 h or 24 h in an intertidal bivalve <i>Sinonovacula constricta</i>. The clams showed explicit heat hardening responses after acclimatization in warm season. The higher inducing temperature (37<sup>o</sup>C) caused a less effective heat hardening effect than the inducing temperature that was closer to seasonal maximum temperature (33<sup>o</sup>C). Metabolomic analysis highlighted the elevated contents of membrane glyceropholipids in all heat hardened clams, which may help to maintain structure and function of membrane. Heat shock proteins (HSPs) tended to be up-regulated after heat hardening at 37<sup>o</sup>C but not at 33<sup>o</sup>C, indicating that there was no complete dependency of heat hardening effects on up-regulated HSPs. Enhanced energy metabolism and decreased energy reserves were observed after heat hardening at 37<sup>o</sup>C, suggesting more energy costs during exposure to higher inducing temperature which may restrict heat hardening effects. These results highlighted the mediating role of membrane lipid metabolism, heat shock responses and energy costs in the interaction of heat hardening response and seasonal acclimatization, and benefit the mechanistic understanding of evolutionary change and thermal plasticity during global climate change.</p>

opencc-zeroJul 2021View details →
zenodo32/100

A novel mechanism that maintains outer membrane lipid asymmetry in Pseudomonas aeruginosa

<p>Newick file the PA2800 phylogenetic tree in&nbsp;&quot;A novel mechanism that maintains outer membrane lipid asymmetry in Pseudomonas aeruginosa&quot;</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Repository for 'Surface-induced phase separation of reconstituted nascent integrin clusters on lipid membranes'

<p>Surface-induced phase separation of reconstituted nascent integrin clusters on lipid membranes (PNAS)</p> <p>The structure of this repository is as follows:</p> <p>1. FRAP_curves.zip -- contains FRAP curves&nbsp;</p> <ul> <li>FRAP_curves_for_fig2F_figS5</li> <li>FRAP_curves_for_fig2G</li> <li>FRAP_curves_for_fig2H</li> <li>FRAP_curves_for_fig2I</li> <li>FRAP_curves_for_fig2J</li> <li>FRAP_curves_for_fig2K</li> <li>FRAP_curves_for_figS8&amp;E</li> </ul> <p>2. Images.zip --&nbsp;contains confocal images&nbsp;epifluorescence images</p> <ul> <li>Images_for_fig1G_fig1H</li> <li>Images_for_fig3D</li> <li>Images_for_figS1</li> <li>Images_for_figS2C</li> <li>Images_for_figS3C</li> <li>Images_for_figS6</li> </ul> <p>3. Source_data.xlsx -- contains the data for plotting the Figures</p>

opencc-by-4.0Feb 2023View details →
ClinicalTrials.gov32/100

Membrane Lipid Replacement in Fibromyalgia

ClinicalTrials.gov study NCT03288389. IPD Sharing: NO. Countries: 1. Publications: 3.

closedIPD-NOFeb 2026View details →
dryad32/100

Data from: The cutaneous lipid composition of bat wing and tail membranes: a case of convergent evolution with birds

Open the record for dataset details and reuse information.

publicMay 2016View details →
dryad32/100

Membrane lipid metabolism, heat shock response, and energy costs mediate the interaction between acclimatization and heat hardening response

Open the record for dataset details and reuse information.

publicAug 2021View details →
zenodo28/100

Partitioning of Catechol Derivatives in Lipid Membranes: Implications for Substrate Specificity to Catechol-O-methyltransferase

<p>The data used for publication &quot;Partitioning of Catechol Derivatives in Lipid Membranes: Implications for Substrate Specificity to Catechol-<em>O</em>-methyltransferase&quot; in ACS Chemical Neuroscience (2020), 11(6), 969-978.</p>

opencc-by-4.0Mar 2020View details →
zenodo28/100

On calculating the bending modulus of lipid bilayer membranes from buckling simulations

<p>Molecular dynamics simulations files that correspond to the buckling simulations method to calculate the bending modulus and the box area fluctuations method to calculate the area compressibility modulus. We performed all molecular dynamics (MD) simulations using the GROMACS software (v. 2016.4)&nbsp;and the MARTINI coarse-grained (CG) force field (v. 2.2),&nbsp;using standard simulation parameters. Each file contains GRO,&nbsp;TOP, ITPs, and MDP files that correspond to the equilibration and the production&nbsp;runs. The name of each&nbsp;folder indicates the types of lipids. We simulate&nbsp;single-component lipid bilayers includes:&nbsp;DLPC, (14:0-14:0), DPPC&nbsp;(16:0-16:0), POPC (16:0-18:1),&nbsp; DOPC&nbsp;(18:1-18:1),&nbsp; PUPC&nbsp;(16:0-22:6),&nbsp; DLiPC&nbsp;(18:2-18:2), DNPC&nbsp;(24:6-24:6), POPG (16:0-18:1), POPS&nbsp;(16:0-18:1), POPE&nbsp;(16:0-18:1), and DPSM&nbsp;(16:0-16:0), and lipid mixtures includes :&nbsp;DOPC:CHOL, DPPC:CHOL, POPC:POPE, DPPC:DLPC, POPC:PUPC, DNPC:DLPC, and DPPC:DLiPC:CHOL, with different molar ratios specified in the folders names.&nbsp;&nbsp;</p>

opencc-by-4.0Jun 2020View details →
zenodo28/100

Annealing simulations of a lipid membrane with different amounts of transmembrane proteins

<p>soon...</p>

opencc-by-4.0Oct 2020View details →
zenodo28/100

Annealing simulations of a lipid membrane with different transmembrane proteins

<p>soon...</p>

opencc-by-4.0Oct 2020View details →

ScienceDex guides

Understand access before you commit

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

Compare curated 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.

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