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Systematic detection of physical interactions between yeast transporters

Systematic detection of physical interactions between yeast transporters Sylvain Brohée +1 , Mohamed El-Bakkouri +2 , Corinna Capellaro +3 , Ekhard Boles 3 , Jacques van Helden 1 and Bruno André 2,*

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Systematic detection of physical interactions between yeast transporters

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  1. Systematic detection of physical interactions between yeast transporters Sylvain Brohée+1, Mohamed El-Bakkouri+2, Corinna Capellaro+3, Ekhard Boles3, Jacques van Helden1 and Bruno André2,* (1) Service de Conformation des Macromolécules Biologiques et de Bioinformatique. Université Libre de Bruxelles, CP 263, Campus Plaine, Bvd du Triomphe, B-1050 Bruxelles. (2) Laboratoire de Physiologie Cellulaire, Université Libre de Bruxelles, Institut de Biologie et de Médecine Moléculaires, CP 300, Rue des Prof. Jeener et Brachet 12, 6041 Charleroi, Belgium.Belgium. (3) Institut für Mikrobiologie, Universität Frankfurt, Marie-Curie-Strasse 9, 60439 Frankfurt, Germany +These authors contributed equally to this work *Email : bran@ulb.ac.be Background Protein-protein interactions are crucial for correct functioning of any cellular process. These last years, several large-scale experimentalapproaches were undertaken to explore cellular interactomes on a systematic basis using the classical two-hybrid assay [1,2] or co-immunoprecipitation coupled to mass spectrometry analyses [3,4]. However, because of the insoluble nature of membrane-associated proteins, associomics data for this category of proteins thus far remain largely fragmentary. As part of the E.U.-funded Associoport project, we have applied the split-ubiquitin two-hybrid method [5-7] for systematic determination of the physical interactions between all inventoried yeast membrane transporters (279 proteins, see YTPdb, http://rsat.scmbb.ulb.ac.be/ytpdb/) [8]. This method, based on the ability of the amino and carboxy-terminal parts of ubiquitin to re-form a functional protein when brought together, presents the advantage that the tested interaction takes place on its natural site and not in the nucleus as for the classical two-hybrid method. After statistical standardization and filtering steps, we built a network containing 1905 significant interactions. As a first step to decipher the biological meaning of this network, we have applied graph clustering methods to detect highly connected groups of proteins. This allowed us to identify several clusters containing a significant number of highly similar transporters Data analysis Experiments any membrane in the cell TRP1 LEU2 CubPLV PLV NUbG CUb cytosol x x x x No growth on ade-his- medium No interaction ADH1 PCRx 279 Met25 NubG PCRx 279 TRP1 LEU2 cytosol PLV NUbG CUb protease nucleus PLV Trp- Leu+MATa Trp+ Leu-MAT + NubG-X Y-CubPLV lexA-lacZlexA-HIS3lexA-ADE2 Prot A-lexA-VP16 Growth on ade-his- medium Interaction In case X and Y interact, diploid strains grow on medium lacking histidine and adenine. • 279 different Nub clones on three 96-wells plates are crossed with one X-Cub-PLV gene on three different media • Selective medium (YNB) • ¨Partially repressive medium (YNB + methionine) • Permissive medium (YNB + adenine and histidine) A single X-Cub clone in 93 wells (+ 3 controls) After standardization and filtering steps, we built an interaction network considering the 1,905 remaining statically significant interactions. We used the RNSC clustering algorithm [9] to discover highly connected groups of proteins. crossing A B C 279 NubG-X clones (+ controls) growth tests Clustering results. (A) All highly connected clusters. Clusters containing a significatively high number of (B) amino acid transporters (C) proteins similar to human lysosomal CTNS cystine transporter. Conclusion EU funded Associoport project allowed us to systematically determine the physical interactions between all yeast transporters. First bioinformatical treatments already delivered very encouraging results. In the following, we will apply supervised learning procedures (discriminant analysis, support vector machine, …) to increase our confidence in the predicted interactions. References • Uetz P, Giot L, et al : A comprehensive analysis of protein-protein interactions in Saccharomyces cerevisiae. Nature 2000, 403(6770):623–7. • Ito T, Chiba T, Ozawa R, Yoshida M, Hattori M, Sakaki Y. A comprehensive two-hybrid analysis to explore the yeast protein interactome. PNAS 2001, 98(8):4569-74. • Ho Y, Gruhler A, Heilbut A, et al: Systematic identification of protein complexes in Saccharomyces cerevisiae by mass spectrometry. Nature 2002, 415(6868):180–3. • Gavin AC, Bosche M, et al: Functional organization of the yeast proteome by systematic analysis of protein complexes. Nature 2002, 415(6868):141–7. • Johnsson N and A Varshavsky. Split ubiquitin as a sensor of protein interactions in vivo.PNAS 1994, 91(22):10340-10344. • Stagljar I, Korostensky C, Johnsson N, te Heesen S. A genetic system based on split-ubiquitin for the analysis of interactions between membrane proteins in vivo. PNAS. 1998, 95(9):5187-5192. • Obrdlik P, El-Bakkoury M, Hamacher T, Cappellaro C, Vilarino C, Fleischer C, Ellerbrok H, Kamuzinzi R, Ledent V, Blaudez D, Sanders D, Revuelta JL, Boles E, André B, Frommer WB. K+ channel interactions detected by a genetic system optimized for systematic studies of membrane protein interactions. PNAS. 2004, 101(33):12242-12247. • Mewes HW, Amid C, Arnold R, Frishman D, Guldener U, Mannhaupt G, Munsterkotter M, Pagel P, Strack N, Stumpflen V, Warfsmann J, Ruepp A: MIPS: analysis and annotation of proteins from whole genomes. Nucleic Acids Res 2004, 32(Database issue):D41–4. • King AD, Przulj N, Jurisica I: Protein complex prediction via cost-based clustering. Bioinformatics. 2004, 20(17):3013–20. Aknowledgments Associoport project is sponsored by the European Commission within the framework of RTD project. Sylvain Brohée is supported by theBelgian Fonds pour la Recherche dans l’Industrie et l’Agriculture (FRIA).

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