Article Abstract

	Title:	Inferring protein-protein interaction networks from protein complex data
	Author:	Shawn Martin, Zisu Mao, Linda S. Chan, Suraiya Rasheed
	Address:	Department of Computational Biology,Sandia National Laboratories, Albuquerque, NM 87185-1316, USA. ' Laboratory of Viral Oncology and Proteomics Research, Department of Pathology, University of Southern California, Los Angeles, CA 90032-3626, USA. ' Department of Pediatrics, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032-3626, USA. ' Laboratory of Viral Oncology and Proteomics Research, Department of Pathology, University of Southern California, Los Angeles, CA 90032-3626, USA
	Journal:	International Journal of Bioinformatics Research and Applications 2007 - Vol. 3, No.4  pp. 480 - 492
	Abstract:	Present day approaches for the determination of protein-protein interaction networks are usually based on two hybrid experimental measurements. Here we consider a computational method that uses another type of experimental data: instead of direct information about protein-protein interactions, we consider data in the form of protein complexes. We propose a method for using these complexes to provide predictions of protein-protein interactions. When applied to a dataset obtained from a cat melanoma cell line we find that we are able to predict when a protein pair belongs to a complex with ∼96% accuracy. Further, we are able to extrapolate the experimentally identified interaction pairs to the entire cat proteome.
	Keywords:	protein complexes; protein-protein interactions; feline protein interaction networks; bioinformatics; interaction prediction; cat proteome.
	DOI:	10.1504/IJBRA.2007.015416
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