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      Hsp multichaperone complex buffers pathologically modified Tau

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          Abstract

          Alzheimer’s disease is a neurodegenerative disorder in which misfolding and aggregation of pathologically modified Tau is critical for neuronal dysfunction and degeneration. The two central chaperones Hsp70 and Hsp90 coordinate protein homeostasis, but the nature of the interaction of Tau with the Hsp70/Hsp90 machinery has remained enigmatic. Here we show that Tau is a high-affinity substrate of the human Hsp70/Hsp90 machinery. Complex formation involves extensive intermolecular contacts, blocks Tau aggregation and depends on Tau’s aggregation-prone repeat region. The Hsp90 co-chaperone p23 directly binds Tau and stabilizes the multichaperone/substrate complex, whereas the E3 ubiquitin-protein ligase CHIP efficiently disassembles the machinery targeting Tau to proteasomal degradation. Because phosphorylated Tau binds the Hsp70/Hsp90 machinery but is not recognized by Hsp90 alone, the data establish the Hsp70/Hsp90 multichaperone complex as a critical regulator of Tau in neurodegenerative diseases.

          Abstract

          Alzheimer’s disease is characterized by the accumulation of aggregated tau protein. Here the authors find that Hsp chaperones, which normally protect cell homeostasis, can assemble with co-chaperones in a “multichaperone machinery” to target tau aggregation.

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          UCSF Chimera--a visualization system for exploratory research and analysis.

          The design, implementation, and capabilities of an extensible visualization system, UCSF Chimera, are discussed. Chimera is segmented into a core that provides basic services and visualization, and extensions that provide most higher level functionality. This architecture ensures that the extension mechanism satisfies the demands of outside developers who wish to incorporate new features. Two unusual extensions are presented: Multiscale, which adds the ability to visualize large-scale molecular assemblies such as viral coats, and Collaboratory, which allows researchers to share a Chimera session interactively despite being at separate locales. Other extensions include Multalign Viewer, for showing multiple sequence alignments and associated structures; ViewDock, for screening docked ligand orientations; Movie, for replaying molecular dynamics trajectories; and Volume Viewer, for display and analysis of volumetric data. A discussion of the usage of Chimera in real-world situations is given, along with anticipated future directions. Chimera includes full user documentation, is free to academic and nonprofit users, and is available for Microsoft Windows, Linux, Apple Mac OS X, SGI IRIX, and HP Tru64 Unix from http://www.cgl.ucsf.edu/chimera/. Copyright 2004 Wiley Periodicals, Inc.
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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              The PRIDE database and related tools and resources in 2019: improving support for quantification data

              Abstract The PRoteomics IDEntifications (PRIDE) database (https://www.ebi.ac.uk/pride/) is the world’s largest data repository of mass spectrometry-based proteomics data, and is one of the founding members of the global ProteomeXchange (PX) consortium. In this manuscript, we summarize the developments in PRIDE resources and related tools since the previous update manuscript was published in Nucleic Acids Research in 2016. In the last 3 years, public data sharing through PRIDE (as part of PX) has definitely become the norm in the field. In parallel, data re-use of public proteomics data has increased enormously, with multiple applications. We first describe the new architecture of PRIDE Archive, the archival component of PRIDE. PRIDE Archive and the related data submission framework have been further developed to support the increase in submitted data volumes and additional data types. A new scalable and fault tolerant storage backend, Application Programming Interface and web interface have been implemented, as a part of an ongoing process. Additionally, we emphasize the improved support for quantitative proteomics data through the mzTab format. At last, we outline key statistics on the current data contents and volume of downloads, and how PRIDE data are starting to be disseminated to added-value resources including Ensembl, UniProt and Expression Atlas.
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                Author and article information

                Contributors
                Markus.Zweckstetter@dzne.de
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                27 June 2022
                27 June 2022
                2022
                : 13
                : 3668
                Affiliations
                [1 ]GRID grid.424247.3, ISNI 0000 0004 0438 0426, German Center for Neurodegenerative Diseases (DZNE), ; Von-Siebold-Str. 3a, 37075 Göttingen, Germany
                [2 ]GRID grid.4372.2, ISNI 0000 0001 2105 1091, Department for NMR-based Structural Biology, , Max Planck Institute for Multidisciplinary Sciences, ; Am Fassberg 11, 37077 Göttingen, Germany
                [3 ]Max Planck Institute for Multidisciplinary Sciences, Bioanalytical Mass Spectrometry Group, Am Fassberg 11, 37077 Göttingen, Germany
                [4 ]GRID grid.411984.1, ISNI 0000 0001 0482 5331, University Medical Center Goettingen, Institute of Clinical Chemistry, Bioanalytics, ; Robert-Koch-Strasse 40, 37075 Göttingen, Germany
                Author information
                http://orcid.org/0000-0002-0808-7003
                http://orcid.org/0000-0003-1837-5233
                http://orcid.org/0000-0002-2536-6581
                Article
                31396
                10.1038/s41467-022-31396-z
                9237115
                35760815
                0f703e48-c612-4a95-9aed-7e425d530dc8
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 24 September 2021
                : 16 June 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001659, Deutsche Forschungsgemeinschaft (German Research Foundation);
                Award ID: SFB860/3
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/100010663, EC | EU Framework Programme for Research and Innovation H2020 | H2020 Priority Excellent Science | H2020 European Research Council (H2020 Excellent Science - European Research Council);
                Award ID: 787679
                Award Recipient :
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                © The Author(s) 2022

                Uncategorized
                solution-state nmr,alzheimer's disease
                Uncategorized
                solution-state nmr, alzheimer's disease

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