UCSF

Small molecule drugs target many small molecule metabolic enzymes in humans and pathogens, often mimicking endogenous ligands. The effects may be therapeutic or toxic, but are frequently unexpected. A large-scale mapping of the intersection between drugs and metabolism is needed to better guide drug discovery. To map the intersection between drugs and metabolism, we have grouped drugs and metabolites by their associated targets and enzymes using ligand-based set signatures created to quantify their degree of similarity in chemical space. The results reveal the chemical space that has been explored for metabolic targets, where successful drugs have been found, and what novel territory remains. Chemical similarity links between drugs and metabolites predict potential toxicity, suggest routes of metabolism, and reveal drug polypharmacology. To aid other researchers in their drug discovery efforts, we have created an online resource of interactive maps linking drugs to metabolism that enable easy navigation of the vast biological data on potential metabolic drug targets and the drug chemistry currently available to prosecute those targets. Thus, this work provides a large-scale approach to ligand-based prediction of drug action in small molecule metabolism.

Furthermore, this work challenges a fundamental dogma in modern molecular biology - the presumption that individual protein structural and chemical requirements are the dominant constraints in small molecule metabolic enzyme evolution. We directly test that assumption by weighing the absolute and relative constraints imposed by structural homology, metabolic pathway context, and transcriptional coregulation. We believe this work is the first to explicitly argue - from the molecular level perspective of genomic data - that selection constrains enzyme evolution as much at the level of metabolic pathway organization as it does at the level of individual protein structure.