Target deconvolution is a vital initial step in preclinical drug development to determine research focus and strategy. In this respect, computational target prediction is used to identify the most probable targets of an orphan ligand or the most similar targets to a protein under investigation. Applications range from the fundamental analysis of the mode-of-action over polypharmacology or adverse effect predictions to drug repositioning. Here, we provide a review on published ligand- and target-based as well as hybrid approaches for computational target prediction, together with current limitations and future directions.

Phospholipase A2, group XVI (PLA2G16) is a thiol hydrolase from the HRASLS family that regulates lipolysis in adipose tissue and has been identified as a host factor enabling the cellular entry of picornaviruses. Chemical tools are essential to visualize and control PLA2G16 activity, but they have not been reported to date. Here, we show that MB064, which is a fluorescent lipase probe, also labels recombinant and endogenously expressed PLA2G16. Competitive activity-based protein profiling (ABPP) using MB064 enabled the discovery of α-ketoamides as the first selective PLA2G16 inhibitors. LEI110 was identified as a potent PLA2G16 inhibitor (Ki = 20 nM) that reduces cellular arachidonic acid levels and oleic acid-induced lipolysis in human HepG2 cells. Gel-based ABPP and chemical proteomics showed that LEI110 is a selective pan-inhibitor of the HRASLS family of thiol hydrolases (i.e., PLA2G16, HRASLS2, RARRES3 and iNAT). Molecular dynamic simulations of LEI110 in the reported crystal structure of PLA2G16 provided insight in the potential ligand–protein interactions to explain its binding mode. In conclusion, we have developed the first selective inhibitor that can be used to study the cellular role of PLA2G16.

Retinaldehyde dehydrogenases belong to a superfamily of enzymes that regulate cell differentiation and are responsible for detoxification of anticancer drugs. Chemical tools and methods are of great utility to visualize and quantify aldehyde dehydrogenase (ALDH) activity in health and disease. Here, we present the discovery of a first-in-class chemical probe based on retinal, the endogenous substrate of retinal ALDHs. We unveil the utility of this probe in quantitating ALDH isozyme activity in a panel of cancer cells via both fluorescence and chemical proteomic approaches. We demonstrate that our probe is superior to the widely used ALDEFLUOR assay to explain the ability of breast cancer (stem) cells to produce all-trans retinoic acid. Furthermore, our probe revealed the cellular selectivity profile of an advanced ALDH1A1 inhibitor, thereby prompting us to investigate the nature of its cytotoxicity. Our results showcase the application of substrate-based probes in interrogating pathologically relevant enzyme activities. They also highlight the general power of chemical proteomics in driving the discovery of new biological insights and its utility to guide drug discovery efforts.