Unraveling protein interaction networks with near-optimal efficiency
Published online: 7 December 2003, doi:10.1038/nbt921
January 2004 Volume 22 Number 1 pp 98 - 103
Michael Lappe1, 3 & Liisa Holm2
1. EMBL Outstation, European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton, Cambridge CB10 1SD, UK.
2. Institute of Biotechnology, PO Box 56 (Viikinkaari 5), FI-00014 University of Helsinki, Finland.
3. Present address: University of Cambridge, Department of Biochemistry, Tennis Court Road, Cambridge CB2 1GA, UK.
Correspondence should be addressed to M Lappe. e-mail: lappe@ebi.ac.uk
The functional characterization of genes and their gene products is the main challenge of the genomic era. Examining interaction information for every gene product is a direct way to assemble the jigsaw puzzle of proteins into a functional map. Here we demonstrate a method in which the information gained from pull-down experiments, in which single proteins act as baits to detect interactions with other proteins, is maximized by using a network-based strategy to select the baits. Because of the scale-free distribution of protein interaction networks, we were able to obtain fast coverage by focusing on highly connected nodes (hubs) first. Unfortunately, locating hubs requires prior global information about the network one is trying to unravel. Here, we present an optimized 'pay-as-you-go' strategy that identifies highly connected nodes using only local information that is collected as successive pull-down experiments are performed. Using this strategy, we estimate that 90% of the human interactome can be covered by 10,000 pull-down experiments, with 50% of the interactions confirmed by reciprocal pull-down experiments.
ie, about 3x as fast as a random strategy. |
