A unified representation of multiprotein complex data for modeling interaction networks.
The protein interaction network presents one perspective for understanding cellular processes. Recent experiments employing high-throughput mass spectrometric characterizations have resulted in large data sets of physiologically relevant multiprotein complexes. We present a unified representation of such data sets based on an underlying bipartite graph model that is an advance over existing models of the network. Our unified representation allows for weighting of connections between proteins shared in more than one complex, as well as addressing the higher level organization that occurs when the network is viewed as consisting of protein complexes that share components. This representation also allows for the application of the rigorous MinMaxCut graph clustering algorithm for the determination of relevant protein modules in the networks. Statistically significant annotations of clusters in the protein-protein and complex-complex networks using terms from the Gene Ontology indicate that this method will be useful for posing hypotheses about uncharacterized components of protein complexes or uncharacterized relationships between protein complexes.
Ding
Chris
C
Computational Research Division, Lawrence Berkeley National Laboratory, Berkeley, California 94720, USA. chqding@lbl.gov
He
Xiaofeng
X
Meraz
Richard F
RF
Holbrook
Stephen R
SR
D000465:Algorithms; D008956:Models, Chemical; D046912:Multiprotein Complexes / Q000737:chemistry*; D025941:Protein Interaction Mapping; D020836:Protein Structure, Quaternary; D029701:Saccharomyces cerevisiae Proteins / Q000737:chemistry; D012984:Software
D046912:Multiprotein Complexes; D029701:Saccharomyces cerevisiae Proteins