Development of Inhibitors, Probes, and PROTAC Provides a Complete Toolbox to Study PARK7 in the Living Cell.

The integration of diverse chemical tools like small-molecule inhibitors, activity-based probes (ABPs), and proteolysis targeting chimeras (PROTACs) advances clinical drug discovery and facilitates the exploration of various biological facets of targeted proteins. Here, we report the development of such a chemical toolbox for the human Parkinson disease protein 7 (PARK7/DJ-1) implicated in Parkinson's disease and cancers. By combining structure-guided design, miniaturized library synthesis, and high-throughput screening, we identified two potent compounds, JYQ-164 and JYQ-173, inhibiting PARK7 in vitro and in cells by covalently and selectively targeting its critical residue, Cys106. Leveraging JYQ-173, we further developed a cell-permeable Bodipy probe, JYQ-196, for covalent labeling of PARK7 in living cells and a first-in-class PARK7 degrader JYQ-194 that selectively induces its proteasomal degradation in human cells. Our study provides a valuable toolbox to enhance the understanding of PARK7 biology in cellular contexts and opens new opportunities for therapeutic interventions.

Chemical Proteomics Reveals Antibiotic Targets of Oxadiazolones in MRSA.

Phenotypic screening is a powerful approach to identify novel antibiotics, but elucidation of the targets responsible for the antimicrobial activity is often challenging in the case of compounds with a polypharmacological mode of action. Here, we show that activity-based protein profiling maps the target interaction landscape of a series of 1,3,4-oxadiazole-3-ones identified in a phenotypic screen to have high antibacterial potency against multidrug-resistant Staphylococcus aureus. In situ competitive and comparative chemical proteomics with a tailor-made activity-based probe, in combination with transposon and resistance studies, revealed several cysteine and serine hydrolases as relevant targets. Our data showcase oxadiazolones as a novel antibacterial chemotype with a polypharmacological mode of action, in which FabH, FphC, and AdhE play a central role.

Oncological drug discovery: AI meets structure-based computational research.

The integration of machine learning and structure-based methods has proven valuable in the past as a way to prioritize targets and compounds in early drug discovery. In oncological research, these methods can be highly beneficial in addressing the diversity of neoplastic diseases portrayed by the different hallmarks of cancer. Here, we review six use case scenarios for integrated computational methods, namely driver prediction, computational mutagenesis, (off)-target prediction, binding site prediction, virtual screening, and allosteric modulation analysis. We address the heterogeneity of integration approaches and individual methods, while acknowledging their current limitations and highlighting their potential to bring drugs for personalized oncological therapies to the market faster.

Bioorthogonal protein labelling enables the study of antigen processing of citrullinated and carbamylated auto-antigens.

Proteolysis is fundamental to many biological processes. In the immune system, it underpins the activation of the adaptive immune response: degradation of antigenic material into short peptides and presentation thereof on major histocompatibility complexes, leads to activation of T-cells. This initiates the adaptive immune response against many pathogens. Studying proteolysis is difficult, as the oft-used polypeptide reporters are susceptible to proteolytic sequestration themselves. Here we present a new approach that allows the imaging of antigen proteolysis throughout the processing pathway in an unbiased manner. By incorporating bioorthogonal functionalities into the protein in place of methionines, antigens can be followed during degradation, whilst leaving reactive sidechains open to templated and non-templated post-translational modifications, such as citrullination and carbamylation. Using this approach, we followed and imaged the post-uptake fate of the commonly used antigen ovalbumin, as well as the post-translationally citrullinated and/or carbamylated auto-antigen vinculin in rheumatoid arthritis, revealing differences in antigen processing and presentation.

Discovery of a NAPE-PLD inhibitor that modulates emotional behavior in mice.

N-acylethanolamines (NAEs), which include the endocannabinoid anandamide, represent an important family of signaling lipids in the brain. The lack of chemical probes that modulate NAE biosynthesis in living systems hamper the understanding of the biological role of these lipids. Using a high-throughput screen, chemical proteomics and targeted lipidomics, we report here the discovery and characterization of LEI-401 as a CNS-active N-acylphosphatidylethanolamine phospholipase D (NAPE-PLD) inhibitor. LEI-401 reduced NAE levels in neuroblastoma cells and in the brain of freely moving mice, but not in NAPE-PLD KO cells and mice, respectively. LEI-401 activated the hypothalamus-pituitary-adrenal axis and impaired fear extinction, thereby emulating the effect of a cannabinoid CB1 receptor antagonist, which could be reversed by a fatty acid amide hydrolase inhibitor. Our findings highlight the distinctive role of NAPE-PLD in NAE biosynthesis in the brain and suggest the presence of an endogenous NAE tone controlling emotional behavior.

ABHD2 Inhibitor Identified by Activity-Based Protein Profiling Reduces Acrosome Reaction.

ABHD2 is a serine hydrolase that belongs to the subgroup of the α,ß-hydrolase fold-containing proteins, which is involved in virus propagation, immune response, and fertilization. Chemical tools to selectively modulate the activity of ABHD2 in an acute setting are highly desired to investigate its biological role, but are currently lacking. Here, we report a library-versus-library screening using activity-based protein profiling (ABPP) to evaluate in parallel the selectivity and activity of a focused lipase inhibitor library against ABHD2 and a panel of closely related ABHD proteins. This screen resulted in the rapid identification of novel inhibitors for ABHD2. The selectivity of the inhibitor was further investigated in native mouse testis proteome by competitive ABPP, revealing a highly restricted off-target profile. The progesterone-induced acrosome reaction was reduced in a dose-dependent manner by the newly identified inhibitor, which provides further support for the key-role of ABHD2 in the P4-stimulated acrosome reaction. On this basis, the ABHD2 inhibitor is an excellent starting point for further optimization of ABHD2 inhibitors that can modulate sperm fertility and may lead to novel contraceptives.

Drug Discovery Maps, a Machine Learning Model That Visualizes and Predicts Kinome-Inhibitor Interaction Landscapes.

The interpretation of high-dimensional structure-activity data sets in drug discovery to predict ligand-protein interaction landscapes is a challenging task. Here we present Drug Discovery Maps (DDM), a machine learning model that maps the activity profile of compounds across an entire protein family, as illustrated here for the kinase family. DDM is based on the t-distributed stochastic neighbor embedding (t-SNE) algorithm to generate a visualization of molecular and biological similarity. DDM maps chemical and target space and predicts the activities of novel kinase inhibitors across the kinome. The model was validated using independent data sets and in a prospective experimental setting, where DDM predicted new inhibitors for FMS-like tyrosine kinase 3 (FLT3), a therapeutic target for the treatment of acute myeloid leukemia. Compounds were resynthesized, yielding highly potent, cellularly active FLT3 inhibitors. Biochemical assays confirmed most of the predicted off-targets. DDM is further unique in that it is completely open-source and available as a ready-to-use executable to facilitate broad and easy adoption.

Two-step activity-based protein profiling of diacylglycerol lipase.

Diacylglycerol lipases (DAGL) produce the endocannabinoid 2-arachidonoylglycerol, a key modulator of neurotransmitter release. Chemical tools that visualize endogenous DAGL activity are desired. Here, we report the design, synthesis and application of a triazole urea probe for DAGL equipped with a norbornene as a biorthogonal handle. The activity and selectivity of the probe was assessed with activity-based protein profiling. This probe was potent against endogenous DAGLα (IC50 = 5 nM) and it was successfully applied as a two-step activity-based probe for labeling of DAGLα using an inverse electron-demand Diels-Alder ligation in living cells.

Activity-based protein profiling reveals off-target proteins of the FAAH inhibitor BIA 10-2474.

A recent phase 1 trial of the fatty acid amide hydrolase (FAAH) inhibitor BIA 10-2474 led to the death of one volunteer and produced mild-to-severe neurological symptoms in four others. Although the cause of the clinical neurotoxicity is unknown, it has been postulated, given the clinical safety profile of other tested FAAH inhibitors, that off-target activities of BIA 10-2474 may have played a role. Here we use activity-based proteomic methods to determine the protein interaction landscape of BIA 10-2474 in human cells and tissues. This analysis revealed that the drug inhibits several lipases that are not targeted by PF04457845, a highly selective and clinically tested FAAH inhibitor. BIA 10-2474, but not PF04457845, produced substantial alterations in lipid networks in human cortical neurons, suggesting that promiscuous lipase inhibitors have the potential to cause metabolic dysregulation in the nervous system.