Drosophila melanogaster is a proven model system for many aspects of human biology. Here we present a two-hybrid-based protein-interaction map of the fly proteome. A total of 10,623 predicted transcripts were isolated and screened against standard and normalized complementary DNA libraries to produce a draft map of 7048 proteins and 20,405 interactions. A computational method of rating two-hybrid interaction confidence was developed to refine this draft map to a higher confidence map of 4679 proteins and 4780 interactions. Statistical modeling of the network showed two levels of organization: a short-range organization, presumably corresponding to multiprotein complexes, and a more global organization, presumably corresponding to intercomplex connections. The network recapitulated known pathways, extended pathways, and uncovered previously unknown pathway components. This map serves as a starting point for a systems biology modeling of multicellular organisms, including humans.

Rac and Cdc42 regulate a variety of responses in mammalian cells including formation of lamellipodia and filopodia, activation of the JNK MAP kinase cascade, and induction of G1 cell cycle progression. Rac is also one of the downstream targets required for Ras-induced malignant transformation. Rac and Cdc42 containing a Y40C effector site substitution no longer intact with the Ser/Thr kinase p65PAK and are unable to activate the JNK MAP kinase pathway. However, they still induce cytoskeletal changes and G1 cell cycle progression. Rac containing an F37A effector site substitution, on the other hand, no longer interacts with the Ser/Thr kinase p160ROCK and is unable to induce lamellipodia or G1 progression. We conclude that Rac and Cdc42 control MAP kinase pathways and actin cytoskeleton organization independently through distinct downstream targets.

Although genome-scale technologies have benefited from statistical measures of data quality, extracting biologically relevant pathways from high-throughput proteomics data remains a challenge. Here we develop a quantitative method for evaluating proteomics data. We present a logistic regression approach that uses statistical and topological descriptors to predict the biological relevance of protein-protein interactions obtained from high-throughput screens for yeast. Other sources of information, including mRNA expression, genetic interactions and database annotations, are subsequently used to validate the model predictions without bias or cross-pollution. Novel topological statistics show hierarchical organization of the network of high-confidence interactions: protein complex interactions extend one to two links, and genetic interactions represent an even finer scale of organization. Knowledge of the maximum number of links that indicates a significant correlation between protein pairs (correlation distance) enables the integrated analysis of proteomics data with data from genetics and gene expression. The type of analysis presented will be essential for analyzing the growing amount of genomic and proteomics data in model organisms and humans.