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      Ulysses - an application for the projection of molecular interactions across species

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          Abstract

          Ulysses, a new software for the parallel analysis and display of protein interactions detected in various species, is described.

          Abstract

          We developed Ulysses as a user-oriented system that uses a process called Interolog Analysis for the parallel analysis and display of protein interactions detected in various species. Ulysses was designed to perform such Interolog Analysis by the projection of model organism interaction data onto homologous human proteins, and thus serves as an accelerator for the analysis of uncharacterized human proteins. The relevance of projections was assessed and validated against published reference collections. All source code is freely available, and the Ulysses system can be accessed via a web interface http://www.cisreg.ca/ulysses.

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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.
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            Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry.

            Yuen Ho, Albrecht Gruhler, Adrian Heilbut … (2002)
            The recent abundance of genome sequence data has brought an urgent need for systematic proteomics to decipher the encoded protein networks that dictate cellular function. To date, generation of large-scale protein-protein interaction maps has relied on the yeast two-hybrid system, which detects binary interactions through activation of reporter gene expression. With the advent of ultrasensitive mass spectrometric protein identification methods, it is feasible to identify directly protein complexes on a proteome-wide scale. Here we report, using the budding yeast Saccharomyces cerevisiae as a test case, an example of this approach, which we term high-throughput mass spectrometric protein complex identification (HMS-PCI). Beginning with 10% of predicted yeast proteins as baits, we detected 3,617 associated proteins covering 25% of the yeast proteome. Numerous protein complexes were identified, including many new interactions in various signalling pathways and in the DNA damage response. Comparison of the HMS-PCI data set with interactions reported in the literature revealed an average threefold higher success rate in detection of known complexes compared with large-scale two-hybrid studies. Given the high degree of connectivity observed in this study, even partial HMS-PCI coverage of complex proteomes, including that of humans, should allow comprehensive identification of cellular networks.
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              The Ka/Ks ratio: diagnosing the form of sequence evolution

              Laurence D. Hurst (2002)
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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): 2 December 2005
                Volume: 6
                Issue: 12
                Page: R106
                Affiliations
                [1 ]Center for Genomics and Bioinformatics, Karolinska Institutet, 171 77 Stockholm, Sweden
                [2 ]Centre for Molecular Medicine and Therapeutics, University of British Columbia, Vancouver V5Z 4H4, BC, Canada
                [3 ]UBC Bioinformatics Centre, University of British Columbia, Vancouver V6T 1Z4, BC, Canada
                [4 ]Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
                [5 ]Michael Smith Laboratories, University of British Columbia, Vancouver V6T 1Z4, BC, Canada
                [6 ]Department of Computer Science, University of British Columbia, Vancouver V6T 1Z4, BC, Canada
                Article
                Publisher ID: gb-2005-6-12-r106
                DOI: 10.1186/gb-2005-6-12-r106
                PMC ID: 1414088
                PubMed ID: 16356269
                SO-VID: bdb63642-088c-4a7d-bb42-701cc429ab07
                Copyright © Copyright © 2005 Kemmer 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 : 23 February 2005
                Date revision received : 3 August 2005
                Date accepted : 8 November 2005
                Categories
                Subject: Software

                ScienceOpen disciplines: Genetics
                Data availability:
                ScienceOpen disciplines: Genetics

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