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      Inferring protein domain interactions from databases of interacting proteins

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      Author(s): 1 , 2 , 1 , 2 , 3 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

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          Abstract

          A new method for inferring domain interactions from databases of interacting proteins was used to deduce 3,005 high-confidence domain interactions from over 177,000 potential interactions.

          Abstract

          We describe domain pair exclusion analysis (DPEA), a method for inferring domain interactions from databases of interacting proteins. DPEA features a log odds score, E ij , reflecting confidence that domains i and j interact. We analyzed 177,233 potential domain interactions underlying 26,032 protein interactions. In total, 3,005 high-confidence domain interactions were inferred, and were evaluated using known domain interactions in the Protein Data Bank. DPEA may prove useful in guiding experiment-based discovery of previously unrecognized domain interactions.

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          Basic Local Alignment Search Tool

          S Altschul (1990)
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            The Pfam protein families database.

            A. Bateman, Lachlan Coin (2004)
            Pfam is a large collection of protein families and domains. Over the past 2 years the number of families in Pfam has doubled and now stands at 6190 (version 10.0). Methodology improvements for searching the Pfam collection locally as well as via the web are described. Other recent innovations include modelling of discontinuous domains allowing Pfam domain definitions to be closer to those found in structure databases. Pfam is available on the web in the UK (http://www.sanger.ac.uk/Software/Pfam/), the USA (http://pfam.wustl.edu/), France (http://pfam.jouy.inra.fr/) and Sweden (http://Pfam.cgb.ki.se/).
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              A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae.

              Peter Uetz, Loic Giot, Gerard Cagney … (2000)
              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.
              Article 0 comments Cited 895 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2005
                Publication date (Electronic): 19 September 2005
                Volume: 6
                Issue: 10
                Page: R89
                Affiliations
                [1 ]Department of Human Genetics, David Geffen School of Medicine at UCLA, University of California Los Angeles, Los Angeles, CA 90095, USA
                [2 ]Institute for Genomics and Proteomics, University of California Los Angeles, Los Angeles, CA 90095, USA
                [3 ]Howard Hughes Medical Institute, University of California Los Angeles, Los Angeles, CA 90095-1570, USA
                Article
                Publisher ID: gb-2005-6-10-r89
                DOI: 10.1186/gb-2005-6-10-r89
                PMC ID: 1257472
                PubMed ID: 16207360
                SO-VID: e6b26473-8635-4887-8dba-43d09bb55249
                Copyright © Copyright © 2005 Riley et al.; licensee BioMed Central Ltd.
                License:

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                Date received : 15 April 2005
                Date revision received : 18 July 2005
                Date accepted : 17 August 2005
                Categories
                Subject: Method

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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