title: Identification of conserved protein complexes based on a model of protein network evolution.
abstract: BACKGROUND
  Data on protein-protein interactions (PPIs) are increasing exponentially. To date, large-scale protein interaction networks are available for human and most model species. The arising challenge is to organize these networks into models of cellular machinery. As in other biological domains, a comparative approach provides a powerful basis for addressing this challenge.
  
  
  RESULTS
  We develop a probabilistic model for protein complexes that are conserved across two species. The model describes the evolution of conserved protein complexes from an ancestral species by protein interaction attachment and detachment and gene duplication events. We apply our model to search for conserved protein complexes within the PPI networks of yeast and fly, which are the largest networks in public databases. We detect 150 conserved complexes that match well-known complexes in yeast and are coherent in their functional annotations both in yeast and in fly. In comparison with two previous approaches, our model yields higher specificity and sensitivity levels in protein complex detection.
  
  
  BACKGROUND
  The program is available upon request.
authors.affiliation: School of Computer Science, Tel Aviv University, Tel Aviv 69978, Israel.
authors.forename: Eitan
authors.initials: E
authors.lastname: Hirsh
authors.forename: Roded
authors.initials: R
authors.lastname: Sharan
mesh_terms: D000465:Algorithms; D003198:Computer Simulation; D017124:Conserved Sequence / Q000235:genetics; D004252:DNA Mutational Analysis / Q000379:methods*; D019143:Evolution, Molecular; D008957:Models, Genetic; D010802:Phylogeny; D025941:Protein Interaction Mapping / Q000379:methods*; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D017422:Sequence Analysis, DNA / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics*
chemical_list: D011506:Proteins