1. Proteome survey reveals modularity of the yeast cell machinery
- Author
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Paola Grandi, Georg Casari, Roland Krause, Marie-Anne Heurtier, Lars Juhl Jensen, Anne-Marie Michon, Gerard Drewes, Martina Marzioch, Angela Edelmann, Andreas Bauer, Christian Hoefert, Christina Rau, Verena Hoffman, Tatjana Rudi, Marita Remor, Giulio Superti-Furga, Karin Klein, Bernhard Kuster, Tewis Bouwmeester, Patrick Aloy, Robert B. Russell, Manuela Hudak, Sonja Bastuck, Birgit Dümpelfeld, Jens Rick, Markus Schirle, Markus Boesche, Peer Bork, Sean D. Hooper, Malgorzata Schelder, Gitte Neubauer, and Anne-Claude Gavin
- Subjects
Proteomics ,Biological data ,Saccharomyces cerevisiae Proteins ,Multidisciplinary ,Proteome ,Systems biology ,Saccharomyces cerevisiae ,Computational biology ,Biology ,biology.organism_classification ,Cell biology ,Open Reading Frames ,Phenotype ,Protein structure ,Structural biology ,Multiprotein Complexes ,ddc:610 ,Genome, Fungal ,Functional genomics - Abstract
Protein complexes are key molecular entities that integrate multiple gene products to perform cellular functions. Here we report the first genome-wide screen for complexes in an organism, budding yeast, using affinity purification and mass spectrometry. Through systematic tagging of open reading frames (ORFs), the majority of complexes were purified several times, suggesting screen saturation. The richness of the data set enabled a de novo characterization of the composition and organization of the cellular machinery. The ensemble of cellular proteins partitions into 491 complexes, of which 257 are novel, that differentially combine with additional attachment proteins or protein modules to enable a diversification of potential functions. Support for this modular organization of the proteome comes from integration with available data on expression, localization, function, evolutionary conservation, protein structure and binary interactions. This study provides the largest collection of physically determined eukaryotic cellular machines so far and a platform for biological data integration and modelling.
- Published
- 2006