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652 results for “amyloid”
III PhasAGE International Conference - Design of novel functional amyloid assemblies - Lecture
<p>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/. </p>
Data from: Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space
<p>This data accompanies the paper entitled <strong>Fluid flow and amyloid transport and aggregation in the Brain Interstitial Space.</strong></p> <p> </p> <p>The zip archive contains the results of Lattice Boltzmann Molecular Dynamics simulations of the systems investigated and presented in the manuscript. Computational Fluid Dynamics data are in VTK format. Molecular Dynamics trajectories are in XYZ format.</p>
Data For Scalco et al. Clinicopathological correlates of quantitative Amyloid-B Pathology in the Temporal Cortex: Machine learning analysis of 131 cases from an ADRC
<p>Dataset containing 131 de-identified whole slide images (WSIs) with a respective data dictionary. </p> <p><strong>Paper</strong>: Scalco, R., Oliveira, L.C., Lai, Z. et al. Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases. acta neuropathol commun 12, 134 (2024). https://doi.org/10.1186/s40478-024-01827-7</p> <p><strong>Details</strong>: A total of 131 .svs. WSIs, de-identified using svs-deidentifier v 0.9.1-beta (https://github.com/pearcetm/svs-deidentifier/releases). Dataset is uploaded in batches due to Zenodo data upload limitations.</p> <p><strong>Slide curation/preparation</strong>: All samples were retrieved from archives of the University of California, Davis Alzheimer’s Disease Center Brain Bank (<a href="https://www.ucdmc.ucdavis.edu/alzheimers/">https://www.ucdmc.ucdavis.edu/alzheimers/</a>). Archival samples analyzed in this study were 5 μm formalin fixed, paraffin embedded sections of the superior and middle temporal gyrus from human brain. The tissue had been previously stained with an amyloid-β antibody (4G8, recognizing residues 17-24, BioLegend, formerly Covance) that were first pretreated with formic acid to rid samples of endogenous protein. All slides were digitized using an Aperio AT2 between 20x and 40x magnification.</p> <p><strong>Code:</strong> Please refer to <a href="https://github.com/ucdrubinet/BrainSec">https://github.com/ucdrubinet/BrainSec</a> and <a href="https://github.com/keiserlab/plaquebox-paper">https://github.com/keiserlab/plaquebox-paper</a></p>
Supplementary data files for exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins
<p>This dataset includes 20 supplementary data files for manuscript <em>Exploring a diverse world of effector domains and amyloid signaling motifs in fungal NLR proteins </em>by Jakub W. Wojciechowski, Emirhan Tekoglu, Marlena Gąsior-Głogowska, Virginie Coustou, Natalia Szulc, Monika Szefczyk, Marta Kopaczyńska, Sven J. Saupe, and Witold Dyrka (under revision). </p> <ul> <li>SF2. Profile HMMs of NLR effector domains. The file includes previously unpublished models.</li> <li>SF3. Multiple sequence alignments of N-termini clusters. </li> <li>SF4. Tabularized results of N-termini annotation.</li> <li>SF5. Structure prediction of HeLo-/Goodbye-/MLKL-like domains. Full AlphaFold2/ColabFold outputs.</li> <li>SF6. Structure prediction of previously unannotated domains. Full AlphaFold2/ColabFold outputs.</li> <li>SF7. PCFGs for BASS. The file includes previously unpublished grammars and a sample scanning configuration.</li> <li>SF8. Candidate short NLR N-termini with ASMs. The FASTA file includes sequences from clusters with high content of ASM-like sequences, according to the BASS PCFGs (SF7).</li> <li>SF9. Profile HMMs of ASMs found in short NLR N-termini.</li> <li>SF10. Profile HMM of HeLo-related HRAMs.</li> <li>SF11. Genomic neighbors of candidate short N-termini NLRs with ASMs The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF8).</li> <li>SF12. Short C-termini of 200–400 aa long proteins genomically neighboring candidate short NLR N-termini with ASMs. The FASTA file concerns target proteins listed in SF11.</li> <li>SF13. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically neighboring proteins. The table is based on SF8–9 and SF11–12. </li> <li>SF14. Lists of HMMER domain hits of effector domain profiles. The lists were obtained through iterative searches in NCBI “nr” starting from Pfam profiles of known NLR effector domains.</li> <li>SF15. Short C-termini of effector proteins. The FASTA file concerns target proteins listed in SF14.</li> <li>SF16. Short N-termini of Pfam NACHT and NB-ARC proteins. The FASTA file concerns proteins from NCBI “nr” associated with the two families in the Pfam database.</li> <li>SF17. Profile HMMs of ASMs found both in effector C-termini and NLR N-termini of genomically neighboring proteins.</li> <li>SF18. Genomic neighbors of candidate short N-termini Pfam NACHT and NB-ARC proteins. The list includes accessions of proteins encoded by genes within the neighborhood of 20kbp of genes encoding the query proteins (SF16).</li> <li>SF19. Pairwise hits of the same ASMs in N-termini of NACHT/NB-ARC NLRs and C-termini of genomically neighboring effector proteins. The table is based on SF15–18. </li> <li>SF20. Pairwise hits of the same ASMs in N-termini of NLRs and C-termini of genomically co-occurring effector proteins. The table is based on SF8–9 and SF15. </li> <li>SF21. BaMLKL homologs identified with hmmsearch in Basidiomycota. A FASTA file.</li> </ul>
Single amyloid PET scan on the Siemens Biograph mMR
<p>In version 1.0, this zipped dataset consists of a full dynamic list-mode PET data of 60 minutes of acquisition, using an amyloid tracer, florbetapir, provided by Eli Lilly. Also, the dataset includes normalisation files and the UTE mu-map, all needed for an independent image reconstruction using NiftyPET.</p> <p>In version 2.0, the dataset is enriched by MR T1w and N4 bias corrected image, the parcellated T1w image into 140+ brain regions, the pseudo-CT image (based on T1w image) for more accurate attenuation correction, and the <testing_reference> folder with all images used for testing the installation or a new version of NiftyPET. </p> <p>https://github.com/pjmark/NiftyPET</p> <p>https://link.springer.com/article/10.1007/s12021-017-9352-y</p>
Research data supporting "Sequence-Dependent Self-Assembly and Structural Diversity of Islet Amyloid Polypeptide-Derived β-Sheet Fibrils"
<p>Research data supporting the publication:</p> <p>Wang, S.-T. et al., 2017, Sequence-Dependent Self-Assembly and Structural Diversity of Islet Amyloid Polypeptide-Derived β-Sheet Fibrils, ACS Nano, http://dx.doi.org/10.1021/acsnano.7b02325</p>
Amyloid-motif-dependent tau self-assembly is modulated by isoform sequence context
<p><span>The microtubule-associated protein tau is implicated in neurodegenerative diseases characterized by amyloid formation. Mutations associated with frontotemporal dementia increase tau aggregation propensity and disrupt its endogenous microtubule-binding activity. However, the structural relationship between aggregation propensity and biological activity remains unclear. We employed a multi-disciplinary approach, including computational modeling, NMR, cross-linking mass spectrometry, and cell models to engineer tau sequences that modulate its structural ensemble. Our findings show that substitutions near the conserved 'PGGG' </span><span>β</span><span>-turn motif informed by tau isoform context reduce tau aggregation in vitro and cells and can even counteract aggregation from disease-associated proline-to-serine mutations. Engineered tau sequences maintain microtubule binding and explain why 3R isoforms exhibit reduced pathogenesis compared to 4R. We propose a simple mechanism to reduce the formation of pathogenic tau species while preserving biological function, thus offering insights for therapeutic strategies aimed at reducing tau protein misfolding in neurodegenerative diseases.</span></p> <p><strong>Description of Source Data and Supplementary Data</strong>: All MD, NMR (peptide and tauRD), ThT, XL-MS, MT stabilization, MT:tau modeling, and cell-based aggregation data are available in the Source_Data directory as Data S1, Data S2, Data S3, Data S4, Data S5, Data S6, Data S7, and Data S8, respectively. Supplementary Data for raw MD trajectory files, structure files for MSM modeling and validation file, and tau:MT modeling are available as "Supplementary_Data_MD_Trajectories_Structures", "Supplementary_Data_MSM_models_Structures" and "Supplementary_Data_MT-tau_complex_models", respectively."</p>
Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids - Datasets
<p>This repository contains data from work "Aggregating gut: on the link between neurodegeneration and bacterial functional amyloids"<br><br></p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/BFA.fasta/content" target="_blank" rel="noopener noreferrer">BFA.fasta</a> - fasta file containing sequences of bacterial functional amyloids used as a query for identification of novel bacterial functional amyloids in UHGP dataset</p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/UHGPAmyloids.fasta/content" target="_blank" rel="noopener noreferrer">UHGPAmyloids.fasta </a>- fasta file containing sequences of amyloids identified in UHGP dataset</p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/UHGPAmyloids.csv/content" target="_blank" rel="noopener noreferrer">UHGPAmyloids.csv</a> - csv file containing information about amyloids identified in UHGP</p> <p>Columns:<br>query_id - Uniprot id of the protein from BFA<br>query_gene_name - gene name of the protein from BFA<br>target_id - UHGP id of the found homolog<br>ProbabilityAMYPred-FRL - score obtained for the target_id sequence according to AMYPred-FRL<br>Archcandy - Prediction of beta arch motif for identified amyloid<br>Genome - UHGP genome id of the target_id<br>Localization - predicted subcellular localization with BUSCA for target_id sequence<br>Lineage - full taxonomy of the target_id sequence (which bacteria produced this specific sequence) </p> <p><a href="https://zenodo.org/api/records/14016809/draft/files/PPIPositivePredictionsBetween_UHGPAmyloids_And_HPAIntestine_filtered.csv/content" target="_blank" rel="noopener noreferrer">PPIPositivePredictionsBetween_UHGPAmyloids_And_HPAIntestine_filtered.csv</a> - csv file contining inforamtions about predicted protein-protein interactions between UHGPAmyloids and human proteins expressed in guts</p> <p>Columns:<br>UHGPAmyloids_id - UHGPAmyloids id (same as target_id in UHGPAmyloids.csv)<br>hp_uniprot_name - Uniprot name of a human protein<br>negative and score - scores returned by ProteinPrompt softwawre for prediction of PPI<br>hp_uniprot_id - Uniprot id of a human protein<br>BFA_sp_uniprot_id - Uniprot id of a BFA source protein<br>BFA_sp_uniprot_name - gene name of a BFA source protein<br>UHGPAmyloids_localization - predicted subcellular localization with BUSCA for UHGPAmyloids_id sequence<br>UHGPAmyloids_lineage - full taxonomy of the UHGPAmyloids_id sequence (which bacteria produced this specific sequence) </p>
Data of curvature model for the study of nanoparticle size effects on amyloid fibril stability and molecular dynamics simulations data
<p>The data provided refer to our published article:</p> <p>T. John, J. Adler, C. Elsner, J. Petzold, M. Krueger, L.L. Martin, D. Huster, H.J. Risselada, B. Abel, Mechanistic insights into the size-dependent effects of nanoparticles on inhibiting and accelerating amyloid fibril formation, J. Colloid Interface Sci. 622 (2022), 804–818. <a href="https://doi.org/10.1016/j.jcis.2022.04.134">https://doi.org/10.1016/j.jcis.2022.04.134</a></p> <p>This article is accompanied by a 'Data in Brief' article that explains in more detail the use of the curvature model and our molecular dynamics (MD) simulations:</p> <p>T. John, L.L. Martin, H.J. Risselada, B. Abel, Curvature model for nanoparticle size effects on peptide fibril stability and molecular dynamics simulation data, Data Brief 45 (2022), 108598. <a href="https://doi.org/10.1016/j.dib.2022.108598">https://doi.org/10.1016/j.dib.2022.108598</a></p>
Characterisation of the conformations of amyloid beta42 in solution that may mediate its initial hydrophobic aggregation
<p>General: This is a data repository comprising all the bias-exchange metadynamics simulation files performed to characterise the structural ensemble of Amyloid-ß 42 peptide in solution.<br> The simulation was performed as an explicit model however the solvent was removed while uploading the trajectory to reduce file size.</p> <p>Files:<br> 1. XTCs : Cat19r0tp.xtc, Cat19r1tp.xtc, Cat19r2tp.xtc<br> 2. TPR : 19-mdrun0.tpr, 19-mdrun1.tpr,19-mdrun2.tpr<br> 3. HILLS and COLVAR files <br> 4. Others: Input.gro, PLumed input file, and topol.top</p> <p>for any further information<br> 1. Krushna Sonar : k.sonar@postgrad.curtin.edu.au<br> 2. Ricardo L. Mancera: <a href="mailto:R.Mancera@curtin.edu.au">R.Mancera@curtin.edu.a</a>u</p>
The effects of PaPE-1 on amyloid beta-induced neurotoxicity
<p>Data related to the article Posttreatment with PaPE-1 Protects from Aβ-Induced Neurodegeneration Through Inhibiting the Expression of Alzheimer's Disease-Related Genes and Apoptosis Process That Involves Enhanced DNA Methylation of Specific Genes. These are biochemical, molecular and microscopic data. We aimed to verify the neuroprotective capacity of PaPE-1 against amyloid beta (Aβ)-induced toxicity. Fig 1 – the presence of amyloid beta in cellular cultures; Fig 2 – the effects of apoptosis inhibitors on amyloid beta-induced caspase-3 activity; Fig 3 – the effects of amyloid beta and PaPE-1 on caspase -3, -8, and -9 activities; Fig 4 – effects of amyloid beta and PaPE-1 on Calcein AM and Hoechst 33342 staining; Fig 5 – effects of amyloid beta and PaPE-1 on expression of apoptosis related factors; Fig 6 – effects of amyloid beta and PaPE-1 on methylation of specific genes; Fig 7 – effects of amyloid beta and PaPE-1 on the degree of neurodegeneration; Fig 8 - effects of amyloid beta and PaPE-1 on the membrane stain; Fig 9 - effects of amyloid beta and PaPE-1 on the expression of Alzheimer’s disease-related genes.</p>
Amyloid Fibril in a Thermophoretic Trap
<p>Small dataset of a amyloid fibril in a thermophoretic trap imaged by the fluorescence of thioflavin T.</p>
Altered T-cell Reactivity to β-amyloid-related Antigens in Early Alzheimer's Disease
<p>The code generated during this study is available at this online repository with free access. Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Dr. Christoph Gericke (christoph.gericke@irem.uzh.ch).</p>
Network of hotspot interactions cluster tau amyloid folds
<p>Datafiles associated with <strong>Network of hotspot interactions cluster tau amyloid folds.</strong></p> <p> </p> <p>The alascan files contain the averaged and raw outputs of the <em>in silico </em>alanine scan conducted on tau fibrils. The combo aggregation file contains ThT fluorescence data from the VQIVYK/VEVKSE alanine mutants and coaggregation experiments. The incorporation_vs_insilico file contains the results of the alanine scan top and bottom hits compared to the results of the incorporation experiment of alanine mutants on that position.</p> <p> </p> <p>Version 2: Files added providing data for how edge and center chains contribute to the total energetics, as well as a per-layer energy contribution to total deltaREU in the alanine scan of the PHF fibril.</p>
Supplemental data for: A bibliometric assessment of the incidence of amyloid-Eszett (Aß), a false positive of amyloid-beta (Aβ), in the neurodegenerative disease literature
<p>One claimed reason for the development of Alzheimer’s disease (AD), a prominent neurodegenerative disease, is the extracellular aggregation of amyloid-beta (Aβ). A linguistic or formatting error has resulted in the misrepresentation of the Greek letter β with the German letter Eszett (ß), resulting in the formation of a non-existent compound, amyloid-Eszett (Aß). These datasets offer a quantified appreciation of the AD-related literature, carrying a mention of this false positive in the title, abstract and keywords of papers indexed in the Web of Science Core Collection and Scopus. Also, as a curiosity given the popularity of this large language model, we asked the questions to ChatGPT. This AI chatbot developed by OpenAI was able to recognize Eszett as a linguistic or typographic error, within this context, recognizing Aß and Aβ as equals. This erroneous substitution of a Greek letter (in Aβ) by a German one (Aß), despite giving a non-existent compound, will likely not change the underlying scientific conclusions of the affected papers, although errata might be useful to enlighten others, including metadata managers and journal copyeditors, so as not to repeat the same mistake.</p>
Dataset for the Amyloid-beta precursor protein antibody screening study
<p>This project contains the following underlying data included in a study aiming at characterizing antibodies for the Amyloid-beta precursor protein. The study is available on Zenodo (https://doi.org/10.5281/zenodo.7971926).</p>
Computational data on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR)
<p>This repository contains the data and files for the computational study on binding of a pyrene-based fluorescent amyloid ligand (Py1SA) to transthyretin (TTR). <br> The repository is organized into different folders as described below:</p> <p>====================================================================================================<br> 1_Starting_structure<br> This folder contains the starting structures of the four binding modes obtained from the crystallographic study. These structures serve as the initial configurations for the Molecular Dynamics (MD) simulations.</p> <p>====================================================================================================<br> 2_mdp_files<br> This folder contains two subfolders:</p> <p>1_MD<br> The "1_MD" subfolder includes the MD simulation files in the mdp format. These files define the parameters and settings for running the MD simulations with Gromacs version 2019.3.</p> <p>2_US<br> The "2_US" subfolder includes the files required for performing Umbrella Sampling (US) simulations for each of the binding modes using Gromacs version 2021.3. Within each mode folder, you will find the following files:</p> <p>constraint: Position restraints files used in the Umbrella Sampling simulations.<br> pull: mdp files containing the settings for pulling in the US simulations.<br> us: mdp files used for the US simulations.</p> <p>====================================================================================================<br> 3_MD_results<br> This folder contains the results of the MD simulations. It includes the structure files (.gro) and trajectory files (.xtc) for each simulation. Due to the large size of the files, the solvent has been excluded, and the results are provided at every 1 nanosecond (ns) interval.</p> <p>====================================================================================================<br> 4_US_results<br> The "4_US_results" folder includes the trajectories of the US simulations for each of the binding modes. For each mode, two trajectories are provided.</p> <p>====================================================================================================<br> 5_pdb<br> This folder includes the pdb files of the simulated structures of the two binding modes after the equilibration step.</p> <p>====================================================================================================</p> <p>We acknowledge funding by the German Research Foundation (DFG) through the Emmy Noether Young Group Leader Programme (CK, project KO 5423/1-1), the Swedish e-Science Research Centre (SeRC, ML, PN), the Swedish Research Council (PN, Grant No. 2018-4343). Computing resources were provided by the Swedish National Infrastructure for Computing (SNIC).</p>
Thermal evaporation as sample preparation for silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues
<p><strong>Thermal evaporation as sample preparation </strong><strong>for </strong><strong>silver‐assisted laser desorption/ionization mass spectrometry imaging of cholesterol in amyloid tissues</strong></p> <p>MSI datasets in SCiLS Lab SL File (*.sl) or as flexImaging sequence (*.mis)</p>
Data from: Longitudinal three-photon imaging for tracking amyloid plaques and vascular degeneration in a mouse model of Alzheimer’s disease
Open the record for dataset details and reuse information.
Transcranial detection of amyloid-beta at single plaque resolution in vivo with large-field multifocal illumination fluorescence microscopy
<p>The abnormal deposition of beta-amyloid proteins in the brain is one of the major histopathological hallmarks of Alzheimer’s disease. Currently available intravital microscopy techniques can visualize plaques with high resolution, but are limited to a small field-of-view and with depth limitation. Here, we report the transcranial detection of amyloid-beta deposits at the whole brain scale with 20 mm resolution in APP/PS1 and arcAb mouse models of Alzheimer’s disease amyloidosis using a novel large-field multifocal illumination (LMI) fluorescence microscopy technique. High sensitive and specific detection of amyloid-beta deposits at single plaque level in APP/PS1 and arcAb mice was facilitated using luminescent conjugated oligothiophene HS-169. Immunohistochemical staining with HS-169, anti-Ab antibody 6E10, conformation antibodies OC (fibrillar) on brain tissue sections further showed that HS-169 resolved compact parenchymal and vessel-associated amyloid deposits. In conclusion, we demonstrate a new <em>in vivo </em>imaging platform for detection of amyloid-beta deposits at single plaque resolution in murine models of Alzheimer’s disease amyloidosis.</p>
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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.