Systematic chemical-genetic and chemical-chemical interaction datasets for prediction of compound synergism.

ABSTRACT: The network structure of biological systems suggests that effective therapeutic intervention may require combinations of agents that act synergistically. However, a dearth of systematic chemical combination datasets have limited the development of predictive algorithms for chemical synergism. Here, we report two large datasets of linked chemical-genetic and chemical-chemical interactions in the budding yeast Saccharomyces cerevisiae. We screened 5,518 unique compounds against 242 diverse yeast gene deletion strains to generate an extended chemical-genetic matrix (CGM) of 492,126 chemical-gene interaction measurements. This CGM dataset contained 1,434 genotype-specific inhibitors, termed cryptagens. We selected 128 structurally diverse cryptagens and tested all pairwise combinations to gene

SUBMITTER: Wildenhain J 

PROVIDER: S-EPMC5127411 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature