Bioinformatics 20(3) © Oxford University Press 2004; all rights reserved.
Functional topology in a network of protein interactions
ulj 1
1 Department of Computer Science, University of Toronto, Toronto, M5S 3G4, Canada, 2 Department of Surgery, University of Toronto, Toronto, M5G 1L5, Canada and 3 Ontario Cancer Institute, Division of Cancer Informatics, Toronto, M5G 2M9, Canada
Received on May 24, 2003
; revised on August 1, 2003
; accepted on August 6, 2003
Motivation: The building blocks of biological networks are individual protein–protein interactions (PPIs). The cumulative PPI data set in Saccharomyces cerevisiae now exceeds 78 000. Studying the network of these interactions will provide valuable insight into the inner workings of cells.
Results: We performed a systematic graph theory-based analysis of this PPI network to construct computational models for describing and predicting the properties of lethal mutations and proteins participating in genetic interactions, functional groups, protein complexes and signaling pathways. Our analysis suggests that lethal mutations are not only highly connected within the network, but they also satisfy an additional property: their removal causes a disruption in network structure. We also provide evidence for the existence of alternate paths that bypass viable proteins in PPI networks, while such paths do not exist for lethal mutations. In addition, we show that distinct functional classes of proteins have differing network properties. We also demonstrate a way to extract and iteratively predict protein complexes and signaling pathways. We evaluate the power of predictions by comparing them with a random model, and assess accuracy of predictions by analyzing their overlap with MIPS database.
Conclusions: Our models provide a means for understanding the complex wiring underlying cellular function, and enable us to predict essentiality, genetic interaction, function, protein complexes and cellular pathways. This analysis uncovers structure–function relationships observable in a large PPI network.
Supplementary information: We are placing the full predicted tables on the web page: http://www.cs.utoronto.ca/~juris/data/b03/SuppDataTables.zip
Contact: juris{at}ai.utoronto.ca
* To whom correspondence should be addressed at Ontario Cancer Institute, Princess Margaret Hospital, University Health Network, Division of Cancer Informatics, 610 University Avenue, Toronto, ON, M5G 2M9, Canada.
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