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      The catalytic activity of TCPTP is auto-regulated by its intrinsically disordered tail and activated by Integrin alpha-1

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          Abstract

          T-Cell Protein Tyrosine Phosphatase (TCPTP, PTPN2) is a non-receptor type protein tyrosine phosphatase that is ubiquitously expressed in human cells. TCPTP is a critical component of a variety of key signaling pathways that are directly associated with the formation of cancer and inflammation. Thus, understanding the molecular mechanism of TCPTP activation and regulation is essential for the development of TCPTP therapeutics. Under basal conditions, TCPTP is largely inactive, although how this is achieved is poorly understood. By combining biomolecular nuclear magnetic resonance spectroscopy, small-angle X-ray scattering, and chemical cross-linking coupled with mass spectrometry, we show that the C-terminal intrinsically disordered tail of TCPTP functions as an intramolecular autoinhibitory element that controls the TCPTP catalytic activity. Activation of TCPTP is achieved by cellular competition, i.e., the intrinsically disordered cytosolic tail of Integrin-α1 displaces the TCPTP autoinhibitory tail, allowing for the full activation of TCPTP. This work not only defines the mechanism by which TCPTP is regulated but also reveals that the intrinsically disordered tails of two of the most closely related PTPs (PTP1B and TCPTP) autoregulate the activity of their cognate PTPs via completely different mechanisms.

          Abstract

          TCPTP is a non-receptor type protein tyrosine phosphatase involved in various signalling pathways. Here, the authors provide structural insights into TCPTP activation, showing that TCPTP is inhibited by its C-terminal tail, which can be displaced by the cytosolic tail of integrin-α1, leading to activation.

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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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            The Phyre2 web portal for protein modeling, prediction and analysis.

            Phyre2 is a suite of tools available on the web to predict and analyze protein structure, function and mutations. The focus of Phyre2 is to provide biologists with a simple and intuitive interface to state-of-the-art protein bioinformatics tools. Phyre2 replaces Phyre, the original version of the server for which we previously published a paper in Nature Protocols. In this updated protocol, we describe Phyre2, which uses advanced remote homology detection methods to build 3D models, predict ligand binding sites and analyze the effect of amino acid variants (e.g., nonsynonymous SNPs (nsSNPs)) for a user's protein sequence. Users are guided through results by a simple interface at a level of detail they determine. This protocol will guide users from submitting a protein sequence to interpreting the secondary and tertiary structure of their models, their domain composition and model quality. A range of additional available tools is described to find a protein structure in a genome, to submit large number of sequences at once and to automatically run weekly searches for proteins that are difficult to model. The server is available at http://www.sbg.bio.ic.ac.uk/phyre2. A typical structure prediction will be returned between 30 min and 2 h after submission.
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              NMRPipe: a multidimensional spectral processing system based on UNIX pipes.

              The NMRPipe system is a UNIX software environment of processing, graphics, and analysis tools designed to meet current routine and research-oriented multidimensional processing requirements, and to anticipate and accommodate future demands and developments. The system is based on UNIX pipes, which allow programs running simultaneously to exchange streams of data under user control. In an NMRPipe processing scheme, a stream of spectral data flows through a pipeline of processing programs, each of which performs one component of the overall scheme, such as Fourier transformation or linear prediction. Complete multidimensional processing schemes are constructed as simple UNIX shell scripts. The processing modules themselves maintain and exploit accurate records of data sizes, detection modes, and calibration information in all dimensions, so that schemes can be constructed without the need to explicitly define or anticipate data sizes or storage details of real and imaginary channels during processing. The asynchronous pipeline scheme provides other substantial advantages, including high flexibility, favorable processing speeds, choice of both all-in-memory and disk-bound processing, easy adaptation to different data formats, simpler software development and maintenance, and the ability to distribute processing tasks on multi-CPU computers and computer networks.
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                Author and article information

                Contributors
                peti@uchc.edu
                tcmeng@gate.sinica.edu.tw
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                10 January 2022
                10 January 2022
                2022
                : 13
                : 94
                Affiliations
                [1 ]GRID grid.506934.d, ISNI 0000 0004 0633 7878, Institute of Biological Chemistry, Academia Sinica, ; 128 Academia Road Sec. 2, Nankang, Taipei 115 Taiwan
                [2 ]GRID grid.28665.3f, ISNI 0000 0001 2287 1366, Chemical Biology and Molecular Biophysics, Taiwan International Graduate Program, , Academia Sinica, ; 128 Academia Road Sec. 2, Nankang, Taipei 115 Taiwan
                [3 ]GRID grid.38348.34, ISNI 0000 0004 0532 0580, Department of Chemistry, , National Tsing-Hua University, ; 101 Kuang-Fu Road Sec. 2, Hsinchu, 300 Taiwan
                [4 ]GRID grid.208078.5, ISNI 0000000419370394, Department of Molecular Biology and Biophysics, , University of Connecticut Health Center, ; Farmington, CT 06030 USA
                [5 ]GRID grid.28665.3f, ISNI 0000 0001 2287 1366, Academia Sinica Common Mass Spectrometry Facilities for Proteomics and Protein Modification Analysis, ; 128 Academia Road Sec. 2, Nankang, Taipei 115 Taiwan
                [6 ]GRID grid.19188.39, ISNI 0000 0004 0546 0241, Institute of Biochemical Sciences, , National Taiwan University, ; 1 Roosevelt Road Sec. 4, Taipei, 106 Taiwan
                [7 ]GRID grid.208078.5, ISNI 0000000419370394, Department of Cell Biology, , University of Connecticut Health Center, ; Farmington, CT 06030 USA
                Author information
                http://orcid.org/0000-0003-3601-1126
                http://orcid.org/0000-0002-6070-7687
                http://orcid.org/0000-0002-7231-0185
                http://orcid.org/0000-0002-8830-6594
                http://orcid.org/0000-0001-7693-2021
                Article
                27633
                10.1038/s41467-021-27633-6
                8748766
                35013194
                1a38a3b2-c893-49e2-87a5-ff8047b790fc
                © 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
                : 29 July 2021
                : 29 November 2021
                Funding
                Funded by: This work is supported by the Taiwan Protein Project (Grant AS-KPQ-105-TPP) and the Academia Sinica Next-generation Pathway of Taiwan Cancer Precision Medicine Program (Grant AS-KPQ-107-TCPMP) to TCM, the American Diabetes Association Pathway to Stop Diabetes Grant 1-14-ACN-31 and grant R01NS091336 from the National Institute of Neurological Disorders and Stroke to WP, grant R01GM098482 from the National Institute of General Medicine to RP.
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                © The Author(s) 2022

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                hydrolases,intrinsically disordered proteins,phosphorylation,solution-state nmr,enzyme mechanisms

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