title: Cluster conservation as a novel tool for studying protein-protein interactions evolution.
abstract: Protein-protein interactions networks has come to be a buzzword associated with nets containing edges that represent a pair of interacting proteins (e.g. hormone-receptor, enzyme-inhibitor, antigen-antibody, and a subset of multichain biological machines). Yet, each such interaction composes its own unique network, in which vertices represent amino acid residues, and edges represent atomic contacts. Recent studies have shown that analyses of the data encapsulated in these detailed networks may impact predictions of structure-function correlation. Here, we study homologous families of protein-protein interfaces, which share the same fold but vary in sequence. In this context, we address what properties of the network are shared among relatives with different sequences (and hence different atomic interactions) and which are not. Herein, we develop the general mathematical framework needed to compare the modularity of homologous networks. We then apply this analysis to the structural data of a few interface families, including hemoglobin alpha-beta, growth hormone-receptor, and Serine protease-inhibitor. Our results suggest that interface modularity is an evolutionarily conserved property. Hence, protein-protein interfaces can be clustered down to a few modules, with the boundaries being evolutionarily conserved along homologous complexes. This suggests that protein engineering of protein-protein binding sites may be simplified by varying each module, but retaining the overall modularity of the interface.
authors.affiliation: Department of Biological Chemistry, The Weizmann Institute of Science, Rehovot 76100, Israel.
authors.forename: Ofer
authors.initials: O
authors.lastname: Rahat
authors.forename: Assif
authors.initials: A
authors.lastname: Yitzhaky
authors.forename: Gideon
authors.initials: G
authors.lastname: Schreiber
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D019281:Dimerization; D004285:Dogs; D004921:Erythropoietin / Q000737:chemistry; D019143:Evolution, Molecular; D006454:Hemoglobins / Q000737:chemistry*; D019382:Human Growth Hormone / Q000737:chemistry; D006801:Humans; D008958:Models, Molecular; D011228:Prealbumin / Q000737:chemistry; D025941:Protein Interaction Mapping / Q000379:methods*; D017467:Receptors, Erythropoietin / Q000737:chemistry; D011986:Receptors, Somatotropin / Q000737:chemistry; D016415:Sequence Alignment; D012697:Serine Endopeptidases / Q000737:chemistry; D015842:Serine Proteinase Inhibitors / Q000737:chemistry; D012754:Sharks
chemical_list: D006454:Hemoglobins; D011228:Prealbumin; D017467:Receptors, Erythropoietin; D011986:Receptors, Somatotropin; D015842:Serine Proteinase Inhibitors; D004921:Erythropoietin; D019382:Human Growth Hormone; D012697:Serine Endopeptidases