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      AtPID: Arabidopsis thaliana protein interactome database—an integrative platform for plant systems biology

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          Abstract

          Arabidopsis thaliana Protein Interactome Database (AtPID) is an object database that integrates data from several bioinformatics prediction methods and manually collected information from the literature. It contains data relevant to protein–protein interaction, protein subcellular location, ortholog maps, domain attributes and gene regulation. The predicted protein interaction data were obtained from ortholog interactome, microarray profiles, GO annotation, and conserved domain and genome contexts. This database holds 28 062 protein–protein interaction pairs with 23 396 pairs generated from prediction methods. Among the rest 4666 pairs, 3866 pairs of them involving 1875 proteins were manually curated from the literature and 800 pairs were from enzyme complexes in KEGG. In addition, subcellular location information of 5562 proteins is available. AtPID was built via an intuitive query interface that provides easy access to the important features of proteins. Through the incorporation of both experimental and computational methods, AtPID is a rich source of information for system-level understanding of gene function and biological processes in A. thaliana. Public access to the AtPID database is available at http://atpid.biosino.org/.

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          Most cited references57

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          A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

          Two large-scale yeast two-hybrid screens were undertaken to identify protein-protein interactions between full-length open reading frames predicted from the Saccharomyces cerevisiae genome sequence. In one approach, we constructed a protein array of about 6,000 yeast transformants, with each transformant expressing one of the open reading frames as a fusion to an activation domain. This array was screened by a simple and automated procedure for 192 yeast proteins, with positive responses identified by their positions in the array. In a second approach, we pooled cells expressing one of about 6,000 activation domain fusions to generate a library. We used a high-throughput screening procedure to screen nearly all of the 6,000 predicted yeast proteins, expressed as Gal4 DNA-binding domain fusion proteins, against the library, and characterized positives by sequence analysis. These approaches resulted in the detection of 957 putative interactions involving 1,004 S. cerevisiae proteins. These data reveal interactions that place functionally unclassified proteins in a biological context, interactions between proteins involved in the same biological function, and interactions that link biological functions together into larger cellular processes. The results of these screens are shown here.
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            A comprehensive two-hybrid analysis to explore the yeast protein interactome.

            Protein-protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the approximately 6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623-627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.
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              A map of the interactome network of the metazoan C. elegans.

              To initiate studies on how protein-protein interaction (or "interactome") networks relate to multicellular functions, we have mapped a large fraction of the Caenorhabditis elegans interactome network. Starting with a subset of metazoan-specific proteins, more than 4000 interactions were identified from high-throughput, yeast two-hybrid (HT=Y2H) screens. Independent coaffinity purification assays experimentally validated the overall quality of this Y2H data set. Together with already described Y2H interactions and interologs predicted in silico, the current version of the Worm Interactome (WI5) map contains approximately 5500 interactions. Topological and biological features of this interactome network, as well as its integration with phenome and transcriptome data sets, lead to numerous biological hypotheses.
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                Author and article information

                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                January 2008
                25 October 2007
                25 October 2007
                : 36
                : Database issue , Database issue
                : D999-D1008
                Affiliations
                1College of Life Sciences, the Northeast Forestry University, Harbin, Heilongjiang 150040, 2Shanghai Information Center for Life Sciences, Chinese Academy of Sciences, Shanghai 200031, 3Bioinformatics Center, Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, 4Daqing Institute of Biotechnology, Northeast Forestry University, Daqing, Heilongjiang 163316, 5College of Life and Environmental Sciences, Shanghai Normal University, Shanghai 200234 and 6Bioinformatics Center, Shanghai University, Shanghai 200444, China
                Author notes
                * To whom correspondence should be addressed.+86 21 54922980+86 21 5492 0143 tlshi@ 123456sibs.ac.cn
                Correspondence may also be addressed to Yuhua Li.+86 451 8219 1737 +86 451 8219 1733; E-mail: lyhshen@ 123456mail.hl.com
                Correspondence may also be addressed to Yixue Li.+86 21 54920089 +86 21 5492 0143; E-mail: yxli@ 123456sibs.ac.cn

                The authors wish it to be known that, in their opinion, the first five authors should be regarded as joint First Authors

                Article
                10.1093/nar/gkm844
                2238993
                17962307
                cb8b49c4-0ecf-4439-b710-a5d939f26d04
                © 2007 The Author(s)

                This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( http://creativecommons.org/licenses/by-nc/2.0/uk/) which permits unrestricted non-com

                History
                : 15 August 2007
                : 25 September 2007
                : 25 September 2007
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                Genetics
                Genetics

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