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ComPPI:用于蛋白质-蛋白质相互作用网络分析的细胞区室特异性数据库。

ComPPI: a cellular compartment-specific database for protein-protein interaction network analysis.

作者信息

Veres Daniel V, Gyurkó Dávid M, Thaler Benedek, Szalay Kristóf Z, Fazekas Dávid, Korcsmáros Tamás, Csermely Peter

机构信息

Department of Medical Chemistry, Semmelweis University, Budapest, Hungary.

Department of Medical Chemistry, Semmelweis University, Budapest, Hungary Faculty of Electrical Engineering and Informatics, Budapest University of Technology and Economics, Budapest, Hungary.

出版信息

Nucleic Acids Res. 2015 Jan;43(Database issue):D485-93. doi: 10.1093/nar/gku1007. Epub 2014 Oct 27.

Abstract

Here we present ComPPI, a cellular compartment-specific database of proteins and their interactions enabling an extensive, compartmentalized protein-protein interaction network analysis (URL: http://ComPPI.LinkGroup.hu). ComPPI enables the user to filter biologically unlikely interactions, where the two interacting proteins have no common subcellular localizations and to predict novel properties, such as compartment-specific biological functions. ComPPI is an integrated database covering four species (S. cerevisiae, C. elegans, D. melanogaster and H. sapiens). The compilation of nine protein-protein interaction and eight subcellular localization data sets had four curation steps including a manually built, comprehensive hierarchical structure of >1600 subcellular localizations. ComPPI provides confidence scores for protein subcellular localizations and protein-protein interactions. ComPPI has user-friendly search options for individual proteins giving their subcellular localization, their interactions and the likelihood of their interactions considering the subcellular localization of their interacting partners. Download options of search results, whole-proteomes, organelle-specific interactomes and subcellular localization data are available on its website. Due to its novel features, ComPPI is useful for the analysis of experimental results in biochemistry and molecular biology, as well as for proteome-wide studies in bioinformatics and network science helping cellular biology, medicine and drug design.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e859/4383876/5988cc266ac6/gku1007fig1.jpg

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