A pooling-deconvolution strategy for biological network elucidation.
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 2(n) probe proteins (baits) in 2 x 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 x 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.
Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, and the Molecular Biology Institute, University of California, Los Angeles, California 90095, USA.
Fulai
F
Jin
Tony
T
Hazbun
Gregory A
GA
Michaud
Michael
M
Salcius
Paul F
PF
Predki
Stanley
S
Fields
Jing
J
Huang
D019295:Computational Biology / Q000379:methods*; D004351:Drug Resistance; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D040081:Protein Array Analysis / Q000379:methods*; D011506:Proteins / Q000737:chemistry; D012680:Sensitivity and Specificity; D049490:Systems Biology; D020798:Two-Hybrid System Techniques
D011506:Proteins