Exploring features of interactome networks: Binary protein interaction maps and network pharmacology.

摘要

A crucial step towards understanding cellular systems properties is mapping networks of physical DNA-, RNA-, metabolite-, drug- and protein-protein interactions, the "interactome network", of an organism of interest as completely and accurately as possible. Current yeast interactome network maps contain several hundred molecular complexes with limited and somewhat controversial representation of direct binary interactions. We carried out a comparative quality assessment of current yeast interactome datasets, demonstrating that high-throughput yeast two-hybrid (Y2H) provides high-quality binary interaction information. As most of the yeast binary interactome remains to be mapped, we developed an empirically-controlled mapping framework to produce a "second-generation" high-quality high-throughput Y2H dataset, covering ∼20% of all yeast binary interactions. Both Y2H and affinity-purification followed by mass spectrometry (AP/MS) data are of equally high quality but of a fundamentally different and complementary nature resulting in networks with different topological and biological properties. Compared to co-complex interactome models, this binary map is enriched for transient signaling interactions and inter-complex connections with a highly significant clustering between essential proteins. Rather than correlating with essentiality, protein connectivity correlates with genetic pleiotropy.;Diseases cause changes in the cellular networks and drugs perturb the interactome networks by binding to proteins to reverse or eliminate the adverse affects of diseases. Nevertheless the global set of relationships between protein targets of all drugs and all disease gene products in the human interactome network remains uncharacterized. We built a bipartite graph composed of FDA-approved drugs and proteins linked by drug-target binary associations. The resulting network connects most drugs into a highly interlinked giant component, with strong local clustering of drugs of similar types. Topological analyses of this network quantitatively showed an over-abundance of "follow-on" drugs, i.e., drugs that target already targeted proteins. By including drugs currently under investigation, we identified a trend towards more functionally diverse targets improving polypharmacology. To analyze the relationships between drug targets and disease gene products, the shortest distance between both sets of proteins was measured in the human interactome network. Significant differences in distance were found between etiological and palliative drugs, with a recent trend towards more rational drug design.