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Subcellularlocalization of theyeast proteome Anuj KumarSeema AgarwalJohn A. Heyman2002 год

Subcellular localization of the yeast proteome
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Полный текстСтраница публикацииПубликация в OpenAlex
Аннотация: Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome-wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass ∼5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability—a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu .
Год издания: 2002
Авторы: Anuj Kumar, Seema Agarwal, John A. Heyman, Sandra Matson, Matthew Heidtman, Stacy Piccirillo, Lara Umansky, Amar Drawid, Ronald Jansen, Yang Liu, Kei-Hoi Cheung, Perry L. Miller, Mark Gerstein, G. Shirleen Roeder, M Snyder
Издательство: Cold Spring Harbor Laboratory Press
Источник: Genes & Development
Ключевые слова: Machine Learning in Bioinformatics, RNA and protein synthesis mechanisms, Genomics and Phylogenetic Studies
Другие ссылки: Genes & Development (PDF)
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Europe PMC (PubMed Central) (PDF)
Europe PMC (PubMed Central) (HTML)
PubMed Central (HTML)
CiteSeer X (The Pennsylvania State University) (PDF)
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PubMed (HTML)