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Kinase inhibitors are an important class of anticancer drugs, with 80 inhibitors clinically approved and >100 in active clinical testing. Most bind competitively in the ATP-binding site, leading to challenges with selectivity for a specific kinase, resulting in risks for toxicity and general off-target effects. Assessing the binding of an inhibitor for the entire kinome is experimentally possible but expensive. A reliable and interpretable computational prediction of kinase selectivity would greatly benefit the inhibitor discovery and optimization process. Here, we use machine learning on docked poses to address this need. To this end, we aggregated all known inhibitor-kinase affinities and generated the complete accompanying 3D interactome by docking all inhibitors to the respective high-quality X-ray structures. We then used this resource to train a neural network as a kinase-specific scoring function, which achieved an overall performance ( R 2 ) of 0.63-0.74 on unseen inhibitors across the kinome. The entire pipeline from molecule to 3D-based affinity prediction has been fully automated and wrapped in a freely available package. This has a graphical user interface that is tightly integrated with PyMOL to allow immediate adoption in the medicinal chemistry practice.

This study explores the use of activity-based protein profiling to study protein tyrosine phosphatases. With the discovery of allosteric SHP2 inhibitors, this enzyme family has resurfaced as interesting drug targets. Therefore, we envisioned that previously described direct electrophiles and quinone methide-based traps targeting phosphatases could be applied in competitive activity-based protein profiling assays. This study evaluates three direct electrophiles, specifically, a vinyl sulfonate, a vinyl sulfone, and an α-bromobenzylphosphonate as well as three quinone methide-based traps as activity-based probes. For all these moieties it was previously shown that they could selectively engage in assays with purified or overexpressed phosphatases in bacterial lysates. However, this study demonstrates that probes based on these moieties all suffer from unspecific labelling. Direct electrophiles were either unspecific or not activity-based, while quinone methide-based traps showed dependence on phosphatase activity but also resulted in unspecific labelling due to diffusion after activation. This phenomenon, termed 'bystander' labelling, occurred even with catalytically inactive SHP2 mutants. We concluded that alternative strategies or chemistries are needed to apply activity-based protein profiling in phosphatase research. Moreover, this study shows that quinone methide-based designs have limited potential in probe and inhibitor development strategies due to their intrinsic reactivity., (© 2024 The Author(s). ChemBioChem published by Wiley-VCH GmbH.)

Recent advancements in deep learning and generative models have significantly expanded the applications of virtual screening for drug-like compounds. Here, we introduce a multitarget transformer model, PCMol , that leverages the latent protein embeddings derived from AlphaFold2 as a means of conditioning a de novo generative model on different targets. Incorporating rich protein representations allows the model to capture their structural relationships, enabling the chemical space interpolation of active compounds and target-side generalization to new proteins based on embedding similarities. In this work, we benchmark against other existing target-conditioned transformer models to illustrate the validity of using AlphaFold protein representations over raw amino acid sequences. We show that low-dimensional projections of these protein embeddings cluster appropriately based on target families and that model performance declines when these representations are intentionally corrupted. We also show that the PCMol model generates diverse, potentially active molecules for a wide array of proteins, including those with sparse ligand bioactivity data. The generated compounds display higher similarity known active ligands of held-out targets and have comparable molecular docking scores while maintaining novelty. Additionally, we demonstrate the important role of data augmentation in bolstering the performance of generative models in low-data regimes. Software package and AlphaFold protein embeddings are freely available at https://github.com/CDDLeiden/PCMol.

Bacteria have evolved resistance to nearly all known antibacterials, emphasizing the need to identify antibiotics that operate via novel mechanisms. Here we report a class of allosteric inhibitors of DNA gyrase with antibacterial activity against fluoroquinolone-resistant clinical isolates of Escherichia coli. Screening of a small-molecule library revealed an initial isoquinoline sulfonamide hit, which was optimized via medicinal chemistry efforts to afford the more potent antibacterial LEI-800. Target identification studies, including whole-genome sequencing of in vitro selected mutants with resistance to isoquinoline sulfonamides, unanimously pointed to the DNA gyrase complex, an essential bacterial topoisomerase and an established antibacterial target. Using single-particle cryogenic electron microscopy, we determined the structure of the gyrase-LEI-800-DNA complex. The compound occupies an allosteric, hydrophobic pocket in the GyrA subunit and has a mode of action that is distinct from the clinically used fluoroquinolones or any other gyrase inhibitor reported to date. LEI-800 provides a chemotype suitable for development to counter the increasingly widespread bacterial resistance to fluoroquinolones., (© 2024. The Author(s).)

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.

The cannabinoid receptor type 2 (CB 2 R) is a G protein-coupled receptor with therapeutic potential for the treatment of inflammatory disorders. Fluorescent probes are desirable to study its receptor localization, expression and occupancy. Previously, we have reported a photoaffinity probe LEI-121 that stabilized the inactive conformation of the CB 2 R. Here, we report the structure-based design of a novel bifunctional probe that captures the active conformation of the CB 2 R upon irradiation with light. An alkyne handle was incorporated to visualize the receptor using click-chemistry with fluorophore-azides. These probes may hold promise to study different receptor conformations in relation to their cellular localization and function., (© 2024 The Authors. ChemBioChem published by Wiley-VCH GmbH.)

Monoacylglycerol lipase (MAGL) regulates endocannabinoid 2-arachidonoylglycerol (2-AG) and eicosanoid signalling. MAGL inhibition provides therapeutic opportunities but clinical potential is limited by central nervous system (CNS)-mediated side effects. Here, we report the discovery of LEI-515, a peripherally restricted, reversible MAGL inhibitor, using high throughput screening and a medicinal chemistry programme. LEI-515 increased 2-AG levels in peripheral organs, but not mouse brain. LEI-515 attenuated liver necrosis, oxidative stress and inflammation in a CCl 4 -induced acute liver injury model. LEI-515 suppressed chemotherapy-induced neuropathic nociception in mice without inducing cardinal signs of CB 1 activation. Antinociceptive efficacy of LEI-515 was blocked by CB 2 , but not CB 1 , antagonists. The CB 1 antagonist rimonabant precipitated signs of physical dependence in mice treated chronically with a global MAGL inhibitor (JZL184), and an orthosteric cannabinoid agonist (WIN55,212-2), but not with LEI-515. Our data support targeting peripheral MAGL as a promising therapeutic strategy for developing safe and effective anti-inflammatory and analgesic agents., (© 2023. The Author(s).)

The peptide spanning residues 35 to 55 of the protein myelin oligodendrocyte glycoprotein (MOG) has been studied extensively in its role as a key autoantigen in the neuroinflammatory autoimmune disease multiple sclerosis. Rodents and nonhuman primate species immunized with this peptide develop a neuroinflammatory condition called experimental autoimmune encephalomyelitis, often used as a model for multiple sclerosis. Over the last decade, the role of citrullination of this antigen in the disease onset and progression has come under increased scrutiny. We recently reported on the ability of these citrullinated MOG35-55 peptides to aggregate in an amyloid-like fashion, suggesting a new potential pathogenic mechanism underlying this disease. The immunodominant region of MOG is highly conserved between species, with the only difference between the murine and human protein, a polymorphism on position 42, which is serine in mice and proline for humans. Here, we show that the biophysical and biochemical behavior we previously observed for citrullinated murine MOG35-55 is fundamentally different for human and mouse MOG35-55. The citrullinated human peptides do not show amyloid-like behavior under the conditions where the murine peptides do. Moreover, we tested the ability of these peptides to stimulate lymphocytes derived from MOG immunized marmoset monkeys. While the citrullinated murine peptides did not produce a proliferative response, one of the citrullinated human peptides did. We postulate that this unexpected difference is caused by disparate antigen processing. Taken together, our results suggest that further study on the role of citrullination in MOG-induced experimental autoimmune encephalomyelitis is necessary., Competing Interests: Conflict of interest The authors declare that they have no conflicts of interest with the contents of this article., (Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.)

Cannabinoid CB 2 receptor (CB 2 R) agonists are investigated as therapeutic agents in the clinic. However, their molecular mode-of-action is not fully understood. Here, we report the discovery of LEI-102, a CB 2 R agonist, used in conjunction with three other CBR ligands (APD371, HU308, and CP55,940) to investigate the selective CB 2 R activation by binding kinetics, site-directed mutagenesis, and cryo-EM studies. We identify key residues for CB 2 R activation. Highly lipophilic HU308 and the endocannabinoids, but not the more polar LEI-102, APD371, and CP55,940, reach the binding pocket through a membrane channel in TM1-TM7. Favorable physico-chemical properties of LEI-102 enable oral efficacy in a chemotherapy-induced nephropathy model. This study delineates the molecular mechanism of CB 2 R activation by selective agonists and highlights the role of lipophilicity in CB 2 R engagement. This may have implications for GPCR drug design and sheds light on their activation by endogenous ligands., (© 2023. The Author(s).)

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.

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., (Copyright © 2022 The Author(s). Published by Elsevier Ltd.. All rights reserved.)