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 generate a benchmark dataset of 8,128 pairwise chemical-chemical interaction tests for synergy prediction, termed the cryptagen matrix (CM). An accompanying database resource called ChemGRID was developed to enable analysis, visualisation and downloads of all data. The CGM and CM datasets will facilitate the benchmarking of computational approaches for synergy prediction, as well as chemical structure-activity relationship models for anti-fungal drug discovery.

Chemogenomic model identifies synergistic drug combinations robust to the pathogen microenvironment

“…Similar large compendiums exist for S . cerevisae [40]. This approach could hence be potentially applied to a wide range of drugs in both prokaryotic and eukaryotic systems.…”

Leveraging genetic interactions for adverse drug-drug interaction prediction

“…Indeed, current combination therapy for cancers have typically been developed to induce synthetic lethal genetic interactions in cancer cells [58,59]. While there have been some efforts aimed at predicting synergistic drug effects [60,61] or directly predicting drug combinations for disease therapy, especially cancer treatment [62–64], incorporating cell type-specific genetic interaction data from the matching cell type can be crucial for developing combination therapies that specifically target certain cell types. With the continuous advancement of technologies for probing human genetic interactions including CRISPR interference, we anticipate that more comprehensive maps of human genetic interactions for multiple cell lineages will become available in the near future, which could illuminate predictions of adverse DDIs and beneficial drug combinations to a larger extent.…”

onlineFDR: an R package to control the false discovery rate for growing data repositories

“…The second dataset, described by Wildenhain et al (2016), contains phenotypic growth data for 240 diverse yeast gene deletion strains grown in the presence of about 5500 unique compounds. This collection has been generated to investigate how small molecule chemical-genetic fingerprints could be used to predict synergistic chemical–chemical combinations that induce lethal phenotypes.…”