Nature Methods
- 3, 183 - 189 (2006)
Published online: 17 February 2006; | doi:10.1038/nmeth859
A pooling-deconvolution strategy for biological network elucidationFulai Jin1, Tony Hazbun2, 4, Gregory A Michaud3, Michael Salcius3, Paul F Predki3, Stanley Fields2 & Jing Huang11
Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, and the Molecular Biology Institute, University of California, Los Angeles, California 90095, USA. 2
Howard Hughes Medical Institute (HHMI), Departments of Genome Sciences and Medicine, University of Washington, Box 357730, Seattle, Washington 98195, USA. 3
Protein Microarray Center, Invitrogen Life Technologies, 688 East Main Street, Branford, Connecticut 06405, USA. 4
Present address: Department of Medicinal Chemistry and Molecular Pharmacology, Purdue University, West Lafayette, Indiana 47907, USA.
Correspondence should be addressed to Jing Huang jinghuang@mednet.ucla.edu or Stanley Fields fields@u.washington.edu or Paul F Predki paul.predki@invitrogen.com The generation of large-scale data sets is a fundamental requirement of systems biology. But despite recent advances, generation of such high-coverage data remains a major challenge. We developed a pooling-deconvolution strategy that can dramatically decrease the effort required. This strategy, pooling with imaginary tags followed by deconvolution (PI-deconvolution), allows the screening of 2n probe proteins (baits) in 2  n pools, with n replicates for each bait. Deconvolution of baits with their binding partners (preys) can be achieved by reading the prey's profile from the 2 n experiments. We validated this strategy for protein-protein interaction mapping using both proteome microarrays and a yeast two-hybrid array, demonstrating that PI-deconvolution can be used to identify interactions accurately with fewer experiments and better coverage. We also show that PI-deconvolution can be used to identify protein-small molecule interactions inferred from profiling the yeast deletion collection. PI-deconvolution should be applicable to a wide range of library-against-library approaches and can also be used to optimize array designs.
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