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.
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