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Genome-Scale Modeling of the Protein Secretory Machinery in Yeast

Overview of attention for article published in PLOS ONE, May 2013
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Title
Genome-Scale Modeling of the Protein Secretory Machinery in Yeast
Published in
PLOS ONE, May 2013
DOI 10.1371/journal.pone.0063284
Pubmed ID
Authors

Amir Feizi, Tobias Österlund, Dina Petranovic, Sergio Bordel, Jens Nielsen

Abstract

The protein secretory machinery in Eukarya is involved in post-translational modification (PTMs) and sorting of the secretory and many transmembrane proteins. While the secretory machinery has been well-studied using classic reductionist approaches, a holistic view of its complex nature is lacking. Here, we present the first genome-scale model for the yeast secretory machinery which captures the knowledge generated through more than 50 years of research. The model is based on the concept of a Protein Specific Information Matrix (PSIM: characterized by seven PTMs features). An algorithm was developed which mimics secretory machinery and assigns each secretory protein to a particular secretory class that determines the set of PTMs and transport steps specific to each protein. Protein abundances were integrated with the model in order to gain system level estimation of the metabolic demands associated with the processing of each specific protein as well as a quantitative estimation of the activity of each component of the secretory machinery.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 165 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Sweden 3 2%
United States 3 2%
Portugal 1 <1%
Austria 1 <1%
Germany 1 <1%
Canada 1 <1%
France 1 <1%
Unknown 154 93%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 45 27%
Researcher 35 21%
Student > Master 20 12%
Student > Doctoral Student 9 5%
Professor > Associate Professor 7 4%
Other 28 17%
Unknown 21 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 66 40%
Biochemistry, Genetics and Molecular Biology 37 22%
Engineering 18 11%
Computer Science 5 3%
Unspecified 4 2%
Other 12 7%
Unknown 23 14%