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.
Jin
Fulai
F
Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, and the Molecular Biology Institute, University of California, Los Angeles, California 90095, USA.
Hazbun
Tony
T
Michaud
Gregory A
GA
Salcius
Michael
M
Predki
Paul F
PF
Fields
Stanley
S
Huang
Jing
J
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