Global organization of protein complexome in the yeast Saccharomyces cerevisiae
2011 (English)In: BMC Systems Biology, ISSN 1752-0509, Vol. 5, no 126, 15- p.Article in journal (Refereed) Published
Background: Proteins in organisms, rather than act alone, usually form protein complexes to perform cellular functions. We analyze the topological network structure of protein complexes and their component proteins in the budding yeast in terms of the bipartite network and its projections, where the complexes and proteins are its two distinct components. Compared to conventional protein-protein interaction networks, the networks from the protein complexes show more homogeneous structures than those of the binary protein interactions, implying the formation of complexes that cause a relatively more uniform number of interaction partners. In addition, we suggest a new optimization method to determine the abundance and function of protein complexes, based on the information of their global organization. Estimating abundance and biological functions is of great importance for many researches, by providing a quantitative description of cell behaviors, instead of just a "catalogues" of the lists of protein interactions.
Results: With our new optimization method, we present genome-wide assignments of abundance and biological functions for complexes, as well as previously unknown abundance and functions of proteins, which can provide significant information for further investigations in proteomics. It is strongly supported by a number of biologically relevant examples, such as the relationship between the cytoskeleton proteins and signal transduction and the metabolic enzyme Eno2's involvement in the cell division process.
Conclusions: We believe that our methods and findings are applicable not only to the specific area of proteomics, but also to much broader areas of systems biology with the concept of optimization principle.
Place, publisher, year, edition, pages
BioMed Central , 2011. Vol. 5, no 126, 15- p.
Bioinformatics (Computational Biology)
IdentifiersURN: urn:nbn:se:umu:diva-45963DOI: 10.1186/1752-0509-5-126OAI: oai:DiVA.org:umu-45963DiVA: diva2:436593