title: What do we learn from high-throughput protein interaction data?
abstract: The biological significance of protein interactions, their method of generation and reliability is briefly reviewed. Protein interaction networks adopt a scale-free topology that explains their error tolerance or vulnerability, depending on whether hubs or peripheral proteins are attacked. Networks also allow the prediction of protein function from their interaction partners and therefore, the formulation of analytical hypotheses. Comparative network analysis predicts interactions for distantly related species based on conserved interactions, even if sequences are only weakly conserved. Finally, the medical relevance of protein interaction analysis is discussed and the necessity for data integration is emphasized.
authors.affiliation: Institut fur Genetik, Forschungszentrum Karlsruhe, Box 3640, D-76021 Karlsruhe, Germany.
authors.forename: Björn
authors.initials: B
authors.lastname: Titz
authors.forename: Matthias
authors.initials: M
authors.lastname: Schlesner
authors.forename: Peter
authors.initials: P
authors.lastname: Uetz
mesh_terms: D000818:Animals; D015195:Drug Design; D006801:Humans; D010802:Phylogeny; D011485:Protein Binding; D025941:Protein Interaction Mapping; D011506:Proteins / Q000737:chemistry* / Q000145:classification / Q000235:genetics / Q000378:metabolism*; D015203:Reproducibility of Results
chemical_list: D011506:Proteins