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Bioinformatics Advance Access published online on June 4, 2004
Bioinformatics, doi:10.1093/bioinformatics/bth351
Bioinformatics © Oxford University Press 2004; all rights reserved
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Received April 6, 2004
Revised May 5, 2004
Accepted May 14, 2004

Article

Protein complex prediction via cost-based clustering

A. D. King 1, N. Przulj 1, I. Jurisica 2*

1 Department of Computer Science, University of Toronto, Toronto, M5S 3G4, Canada
2 Ontario Cancer Institute, Division of Cancer Informatics, Toronto, M5G 2M9, Canada

* To whom correspondence should be addressed. E-mail: ij{at}uhnres.utoronto.ca.


  Abstract

Motivation: Understanding principles of cellular organization and function can be enhanced if we detect known and predict still undiscovered protein complexes within the cell's protein-protein interaction (PPI) network. Such predictions may be used as an inexpensive tool to direct biological experiments. The increasing amount of available PPI data necessitates an accurate and scalable approach to protein complex identification.

Results: We have developed the Restricted Neighbourhood Search Clustering Algorithm (RNSC) to efficiently partition networks into clusters using a cost function. We applied this cost-based clustering algorithm to PPI networks of S. cerevisiae, D. melanogaster, and C. elegans to identify and predict protein complexes. We have determined functional and graph-theoretic properties of true protein complexes from the MIPS database. Based on these properties we defined filters to distinguish between identified network clusters and true protein complexes.

Conclusions: Our application of the cost-based clustering algorithm provides an accurate and scalable method of detecting and predicting protein complexes within a PPI network.

Availability: The RNSC algorithm and data processing code is available upon request from the authors.

Supplementary Information: Supplementary data is available at: http://www.cs.utoronto.ca/.juris/data/ppi04/.


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