Functional topology in a network of protein interactions

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: W...

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Bibliographic Details
Published in:	Bioinformatics 2004-02, Vol.20 (3), p.340-348
Main Authors:	Pržulj, N., Wigle, D.A., Jurisica, I.
Format:	Article
Language:	eng
Subjects:	Algorithms Biological and medical sciences Cluster Analysis Computer Simulation Fundamental and applied biological sciences. Psychology General aspects Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) Models, Biological Multienzyme Complexes - metabolism Protein Interaction Mapping - methods Saccharomyces cerevisiae Saccharomyces cerevisiae - physiology Saccharomyces cerevisiae Proteins - metabolism Signal Transduction - physiology
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Summary:	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
ISSN:	1367-4803 1460-2059 1367-4811