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46 results for “protein aggregation”
Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation
<p>This dataset contains the raw and processed microscopy data that form the basis of our research article titled <em><a href="https://www.cell.com/heliyon/fulltext/S2405-8440(24)14817-7" target="_blank" rel="noopener">Conserved mechanism of Xrn1 regulation by glycolytic flux and protein aggregation</a></em> (DOI: <a href="https://kwnsfk27.r.eu-west-1.awstrack.me/L0/https:%2F%2Fdoi.org%2F10.1016%2Fj.heliyon.2024.e38786/1/010201924b2cde3a-4e9b4379-d805-432d-b94e-11d8b32354dc-000000/-WTssLbV_1JADo7ZwuFgWEmy9i0=394" target="_blank" rel="noopener noreferrer">doi.org/10.1016/j.heliyon.2024.e38786</a>) publlished in the <a href="https://www.cell.com/">CellPress</a> journal <a href="https://www.cell.com/heliyon/home">Heliyon</a> (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/heliyon/vol/10/issue/19"><span><span>Volume 10, Issue 19</span></span></a>, 15 October 2024, e38786).<em> </em>The paper describes the mechanism underlying the binding of <a href="https://www.yeastgenome.org/locus/S000003141">yeast Xrn1</a> to the plasma membrane microdomain stabiliser <a href="https://doi.org/10.1016/j.cub.2017.11.073">eisosome</a> in a glucose-dependent manner. The images stored in the dataset were acquired with a <a href="https://www.iem.cas.cz/en/devices/zeiss-lsm-880-airyscan-en/">Zeiss LSM 880 confocal microscope</a> performed at the <a href="https://www.iem.cas.cz/en/department/microscopy-unit/">Microscopy Service Centre</a> of the <a href="https://www.iem.cas.cz/en/home-en/">Institute of Experimental Medicine CAS</a> supported by the MEYS CR (LM2023050 <a href="https://www.czech-bioimaging.cz/">Czech-Bioimaging</a>). Detailed step-by-step instructions for live microscopy sample preparation that we follow can be found at protocols.io: <a href="https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b">https://www.protocols.io/view/live-cell-microscopy-sample-preparation-yeast-cult-8epv5r23dg1b</a>. The quantification of microscopy data was performed using our custom-developed Fiji and R scripts that can be found at <a href="https://github.com/jakubzahumensky/microscopy_analysis">https://github.com/jakubzahumensky/microscopy_analysis</a>. Their use is described in detail in the <a href="https://doi.org/10.1101/2024.03.28.587214">https://doi.org/10.1101/2024.03.28.587214</a>. For further information, please refer to the README file attached to the dataset.</p> <p>Note: This final version of datasets supplements the previous datasets of version 1 (DOI: <a href="https://doi.org/10.5281/zenodo.12748899">10.5281/zenodo.12748899</a>) and version 2 (DOI: <a href="https://doi.org/10.5281/zenodo.13772845">10.5281/zenodo.13772845</a>).</p>
PhasAGE Training School 1-Protein aggregation prediction-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>
A versatile "Synthesis Tag" (SynTag) for the chemical synthesis of aggregating peptides and proteins
<p>Raw data for the project "A versatile "Synthesis Tag" (SynTag) for the chemical synthesis of aggregating peptides and proteins".</p><p>Manuscript and supporting information available on ChemRxiv: https://doi.org/10.26434/chemrxiv-2023-7mz2c-v2.</p>
Dataset 2 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution
<p><strong>Dataset</strong></p> <p>Molecular dynamics simulation trajectories of TTR peptide aggregation kinetics:</p> <ul> <li>multi-eGO-XXmM-Y: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration replicate Y.</li> </ul>
uncropped western blots for analysis of RPN13 ubiquitylation and NRF1 activation by protein aggregates, as well as source data for qPCR plots and flow cytometry gating and FCS files for agDD-GFP in HeLa or HEK cells
<p>This entry contains uncropped blots for Fig 4D and Fig S4C, Fig. 5B, Fig S5 and Fig S6, and the raw FCS files for Flow Cytometry data in doi.org/10.1101/2024.08.30.610524.</p>
The prefoldin complex stabilizes the von Hippel-Lindau protein against aggregation and degradation
<p>This is the dataset corresponding to our submitted article "The prefoldin complex stabilizes the von Hippel-Lindau protein against aggregation and degradation" by Chesnel et al.</p>
Condensate targeting as a strategy to prevent irreversible protein aggregation: implications for ALS and FTD | Talk - I PhasAGE International Conference
<p>The <strong>I PhasAGE international conference</strong> 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.</p> <p>For details on conference program please see: https://phasage.eu/phasage-conference-1/ </p>
Data from: The deubiquitinase USP5 prevents accumulation of protein aggregates in cardiomyocytes
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Data for: Investigating the aggregation and prionogenic properties of human cancer-related proteins
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Loss of intracellular ATP affects axoplasmic viscosity and pathological protein aggregation in mammalian neurons
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Validation of huntingtin domain protein samples mass spectrometry and aggregation assays 20170130
<p>Open lab notebook huntingtin structure function project.<br> </p>
Label-free Identification of Protein Aggregates Using Deep Learning
<p>This is the training and test datasets for 'Label-free Identification of Protein Aggregates Using Deep Learning'.</p>
Dataset 1 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution
<p><strong>Dataset</strong></p> <p>Molecular dynamics simulation trajectories of TTR peptide monomers and aggregation kinetics:</p> <ul> <li>Amber99sb_disp: TTR monomer in explicit solvent using amber99disp force field.</li> <li>multi-GO-monomer: TTR monomer simulation using the multi-GO force field.</li> <li>multi-eGO-monomer: TTR monomer simulation using the multi-eGO ensemble force field.</li> <li>multi-eGO-XXmM-Y: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration replicate Y.</li> <li>.ipynb files: analysis script employed for the aggregation kinetics simulations.</li> <li>TTR aggregation kinetics movies.</li> </ul>
Dataset 3 for: Multi-eGO: an in-silico lens to look into protein aggregation kinetics at atomic resolution
<p><strong>Dataset</strong></p> <p>Multi-GO Molecular dynamics simulation trajectories of TTR peptide and aggregation kinetics; Multi-<em>e</em>GO oligomer structures and trajectories:</p> <ul> <li>multi-GO-XXmM: aggregation kinetics simulations of TTR using the multi-eGO force field at XXmM concentration.</li> <li>TTR structures and trajectories of oligomers from dimers to decamers.</li> </ul>
Protein aggregation and calcium dysregulation are the earliest hallmarks of familial Parkinson's disease in human midbrain dopaminergic neurons
<p>Mutations in the <em>SNCA</em> gene cause autosomal dominant Parkinson’s disease (PD), with loss of dopaminergic neurons in the substantia nigra, and aggregation of α-synuclein. The sequence of molecular events that proceed from an <em>SNCA</em> mutation during development, to end stage pathology is unknown. Utilising human induced pluripotent stem cells (hiPSCs), we resolved the temporal sequence of SNCA induced pathophysiological events in order to discover early, and likely causative, events. Our small molecule-based protocol generates highly enriched midbrain dopaminergic (mDA) neurons: molecular identity was confirmed using single-cell RNA sequencing and proteomics, and functional identity through dopamine synthesis, and measures of electrophysiological activity. At the earliest stage of differentiation, prior to maturation to mDA neurons, we demonstrate the initial formation of small β-sheet rich oligomeric aggregates, in <em>SNCA</em>-mutant cultures. Aggregation persists and progresses, ultimately resulting in the accumulation of phosphorylated aggregates. Impaired intracellular calcium signalling, increased basal calcium, and impairments in mitochondrial calcium handling occurred early at day 34-41 post differentiation. Once midbrain identity fully developed, at day 48-62 post differentiation, <em>SNCA</em>-mutant neurons exhibited mitochondrial dysfunction, oxidative stress, lysosomal swelling and increased autophagy. Ultimately these multiple cellular stresses lead to abnormal excitability, altered neuronal activity, and cell death. Our differentiation paradigm generates an efficient model for studying disease mechanisms in PD, and highlights that protein misfolding to generate intraneuronal oligomers is one of the earliest critical events driving disease in human neurons, rather than a late-stage hallmark of the disease.</p>
A3D database: structure-based predictions of protein aggregation for the human proteome
<p>A3D database: structure-based predictions of protein aggregation for the human proteome</p>
Composition and liquid-to-solid maturation of protein aggregates contribute to bacterial dormancy development and recovery
<p>Source data accompanying scientific publication.</p>
Constitutive nuclear accumulation of endogenous alpha-synuclein in mice causes motor impairment and cortical dysfunction, independent of protein aggregation
<p>Dataset for all quantification performed in the affiliated manuscript.</p>
Data from: Revisiting protein aggregation as pathogenic in sporadic Parkinson’s and Alzheimer’s diseases
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Data from: Reversible, specific, active aggregates of endogenous proteins assemble upon heat stress
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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.
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.