title: Structural interpretation of protein-protein interaction network.
abstract: BACKGROUND
  Currently a huge amount of protein-protein interaction data is available from high throughput experimental methods. In a large network of protein-protein interactions, groups of proteins can be identified as functional clusters having related functions where a single protein can occur in multiple clusters. However experimental methods are error-prone and thus the interactions in a functional cluster may include false positives or there may be unreported interactions. Therefore correctly identifying a functional cluster of proteins requires the knowledge of whether any two proteins in a cluster interact, whether an interaction can exclude other interactions, or how strong the affinity between two interacting proteins is.
  
  
  METHODS
  In the present work the yeast protein-protein interaction network is clustered using a spectral clustering method proposed by us in 2006 and the individual clusters are investigated for functional relationships among the member proteins. 3D structural models of the proteins in one cluster have been built--the protein structures are retrieved from the Protein Data Bank or predicted using a comparative modeling approach. A rigid body protein docking method (Cluspro) is used to predict the protein-protein interaction complexes. Binding sites of the docked complexes are characterized by their buried surface areas in the docked complexes, as a measure of the strength of an interaction.
  
  
  RESULTS
  The clustering method yields functionally coherent clusters. Some of the interactions in a cluster exclude other interactions because of shared binding sites. New interactions among the interacting proteins are uncovered, and thus higher order protein complexes in the cluster are proposed. Also the relative stability of each of the protein complexes in the cluster is reported.
  
  
  CONCLUSIONS
  Although the methods used are computationally expensive and require human intervention and judgment, they can identify the interactions that could occur together or ones that are mutually exclusive. In addition indirect interactions through another intermediate protein can be identified. These theoretical predictions might be useful for crystallographers to select targets for the X-ray crystallographic determination of protein complexes.
authors.affiliation: L.H. Baker Center for Bioinformatics and Biological Statistics, Iowa State University, Ames, Iowa 50011-0320, USA.
authors.forename: Ataur R
authors.initials: AR
authors.lastname: Katebi
authors.forename: Andrzej
authors.initials: A
authors.lastname: Kloczkowski
authors.forename: Robert L
authors.initials: RL
authors.lastname: Jernigan
mesh_terms: D016000:Cluster Analysis; D008954:Models, Biological; D008958:Models, Molecular; D011485:Protein Binding; D025941:Protein Interaction Mapping / Q000379:methods*; D012441:Saccharomyces cerevisiae / Q000737:chemistry / Q000378:metabolism*; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry / Q000378:metabolism*
chemical_list: D029701:Saccharomyces cerevisiae Proteins