Finding molecular complexes through multiple layer clustering of protein interaction networks

Authors:
Bill Andreopoulos;Aijun An;Xiangji Huang;Xiaogang Wang
Affiliations:
Department of Computer Science and Engineering, York University, Toronto, Ontario M3J 1P3, Canada.;Department of Computer Science and Engineering, York University, Toronto, Ontario M3J 1P3, Canada.;School of Information Technology, York University, Toronto, Ontario M3J 1P3, Canada.;Department of Mathematics and Statistics, York University, Toronto, Ontario M3J 1P3, Canada
Venue:
International Journal of Bioinformatics Research and Applications
Year:
2007

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Abstract

Clustering protein-protein interaction networks (PINs) helps to identify complexes that guide the cell machinery. Clustering algorithms often create a flat clustering, without considering the layered structure of PINs. We propose the MULIC clustering algorithm that produces layered clusters. We applied MULIC to five PINs. Clusters correlate with known MIPS protein complexes. For example, a cluster of 79 proteins overlaps with a known complex of 88 proteins. Proteins in top cluster layers tend to be more representative of complexes than proteins in bottom layers. Lab work on finding unknown complexes or determining drug effects can be guided by top layer proteins.