Einzeltreffer — DigiBib

Detecting changes in the dynamics of secreted proteins in serum has been a challenge for proteomics. Enter secreted protein database (SEPDB), an integrated secretory proteomics database offering human, mouse and rat secretory proteomics datasets collected from serum, exosomes and cell culture media. SEPDB compiles secreted protein information from secreted protein database, UniProt and Human Protein Atlas databases to annotate secreted proteomics data based on protein subcellular localization and disease markers. SEPDB integrates the latest predictive modeling techniques to measure deviations in the distribution of signal peptide structures of secreted proteins, extends signal peptide sequence prediction by excluding transmembrane structural domain proteins and updates the validation analysis pipeline for secreted proteins. To establish tissue-specific profiles, we have also created secreted proteomics datasets associated with different human tissues. In addition, we provide information on heterogeneous receptor network organizational relationships, reflective of the complex functional information inherent in the molecular structures of secreted proteins that serve as ligands. Users can take advantage of the Refreshed Search, Analyze, Browse and Download functions of SEPDB, which is available online at https://sysomics.com/SEPDB/. Database URL: https://sysomics.com/SEPDB/.

Titel:	SEPDB: a database of secreted proteins.
Autor/in / Beteiligte Person:	Wang, R ; Ren, C ; Gao, T ; Li, H ; Bo, X ; Zhu, D ; Zhang, D ; Chen, H ; Zhang, Y
Link:	Full Text Finder (Volltext)
Zeitschrift:	Database : the journal of biological databases and curation, Jg. 2024 (2024-02-12)
Veröffentlichung:	Oxford : Oxford Journals, 2009-, 2024
Medientyp:	academicJournal
ISSN:	1758-0463 (electronic)
DOI:	10.1093/database/baae007
Schlagwort:	Animals Mice Rats Humans Databases, Protein Protein Sorting Signals Proteins chemistry Proteomics methods
Sonstiges:	Nachgewiesen in: MEDLINE Sprachen: English Publication Type: Journal Article Language: English [Database (Oxford)] 2024 Feb 12; Vol. 2024. MeSH Terms: Proteins* / chemistry ; Proteomics* / methods ; Animals ; Mice ; Rats ; Humans ; Databases, Protein ; Protein Sorting Signals Comments: Erratum in: Database (Oxford). 2024 Mar 26;2024:. (PMID: 38531600) References: Hepatology. 2017 Oct;66(4):1125-1143. (PMID: 28555885) ; Cell Rep. 2021 Mar 16;34(11):108860. (PMID: 33730589) ; Bioinformatics. 2021 Jul 27;37(13):1900-1908. (PMID: 33483739) ; Sci China Life Sci. 2023 May;66(5):893-1066. (PMID: 37076725) ; Int J Mol Sci. 2021 Sep 29;22(19):. (PMID: 34638903) ; Nucleic Acids Res. 2005 Jan 1;33(Database issue):D169-73. (PMID: 15608170) ; Cell Metab. 2019 Jul 2;30(1):92-110.e4. (PMID: 31006592) ; PLoS Biol. 2020 Jan 16;18(1):e3000599. (PMID: 31945054) ; Aging Cell. 2021 Apr;20(4):e13325. (PMID: 33730416) ; Cell Metab. 2019 Jul 2;30(1):78-91.e4. (PMID: 31006590) ; PLoS One. 2014 May 19;9(5):e98027. (PMID: 24842778) ; Nucleic Acids Res. 2007 Jul;35(Web Server issue):W429-32. (PMID: 17483518) ; Gut. 2009 Jun;58(6):825-32. (PMID: 18403495) ; J Proteome Res. 2015 Aug 7;14(8):3229-38. (PMID: 26189946) ; Cell Syst. 2016 Mar 23;2(3):185-95. (PMID: 27135364) ; Cell Metab. 2023 Jul 11;35(7):1261-1279.e11. (PMID: 37141889) ; Nat Biotechnol. 2012 Oct;30(10):984-90. (PMID: 23000932) ; Invest Ophthalmol Vis Sci. 2010 Jul;51(7):3379-86. (PMID: 20207970) ; Nat Methods. 2022 Aug;19(8):909-910. (PMID: 35864166) ; J Proteomics. 2011 Apr 1;74(4):528-38. (PMID: 21262398) ; Nat Med. 2019 Dec;25(12):1843-1850. (PMID: 31806903) ; Nucleic Acids Res. 2023 Jan 6;51(D1):D517-D522. (PMID: 36318239) ; Appl Microbiol Biotechnol. 2017 Jan;101(2):475-491. (PMID: 28013407) ; J Exp Clin Cancer Res. 2022 Jun 14;41(1):203. (PMID: 35701840) ; Ageing Res Rev. 2020 Dec;64:101156. (PMID: 32949770) ; Nat Biotechnol. 2022 Jul;40(7):1023-1025. (PMID: 34980915) ; Biochem Pharmacol. 2012 Jun 1;83(11):1484-94. (PMID: 22230477) ; Proteomics. 2010 Apr;10(8):1592-608. (PMID: 20162557) ; Cell. 2020 May 28;181(5):1112-1130.e16. (PMID: 32470399) ; Cell Chem Biol. 2017 Jun 22;24(6):767-771.e3. (PMID: 28602759) ; Nat Commun. 2021 Feb 17;12(1):1088. (PMID: 33597522) ; Nat Rev Genet. 2021 Feb;22(2):71-88. (PMID: 33168968) ; Nat Rev Endocrinol. 2022 May;18(5):273-289. (PMID: 35304603) ; Int J Radiat Biol. 2020 Jun;96(6):748-758. (PMID: 32149567) ; Nat Protoc. 2020 Apr;15(4):1484-1506. (PMID: 32103204) ; Proc Natl Acad Sci U S A. 2021 Jan 19;118(3):. (PMID: 33431665) ; Bioinformatics. 2008 Dec 15;24(24):2928-9. (PMID: 18945683) ; Nat Methods. 2016 Nov 29;13(12):966-967. (PMID: 27898060) Grant Information: 2022YFA0806002 Key R&D Program of China; JCJQ-ZD-264 Basic Research Projects of the Basic Strengthening Program; 31971080 National Natural Science Foundation of China; 2022YFA0806002 Key R&D Program of China; JCJQ-ZD-264 Basic Research Projects of the Basic Strengthening Program; 31971080 National Natural Science Foundation of China Substance Nomenclature: 0 (Proteins) ; 0 (Protein Sorting Signals) Entry Date(s): Date Created: 20240212 Date Completed: 20240214 Latest Revision: 20240328 Update Code: 20240329 PubMed Central ID: PMC10878045

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SEPDB: a database of secreted proteins.