Protease-activated receptors (PARs) are a family of G-protein-coupled receptors (GPCRs) that are irreversibly activated by proteolytic cleavage of the N terminus, which unmasks a tethered peptide ligand that binds and activates the transmembrane receptor domain, eliciting a cellular cascade in response to inflammatory signals and other stimuli. PARs are implicated in a wide range of diseases, such as cancer and inflammation. PARs have been the subject of major pharmaceutical research efforts but the discovery of small-molecule antagonists that effectively bind them has proved challenging. The only marketed drug targeting a PAR is vorapaxar, a selective antagonist of PAR1 used to prevent thrombosis. The structure of PAR1 in complex with vorapaxar has been reported previously. Despite sequence homology across the PAR isoforms, discovery of PAR2 antagonists has been less successful, although GB88 has been described as a weak antagonist. Here we report crystal structures of PAR2 in complex with two distinct antagonists and a blocking antibody. The antagonist AZ8838 binds in a fully occluded pocket near the extracellular surface. Functional and binding studies reveal that AZ8838 exhibits slow binding kinetics, which is an attractive feature for a PAR2 antagonist competing against a tethered ligand. Antagonist AZ3451 binds to a remote allosteric site outside the helical bundle. We propose that antagonist binding prevents structural rearrangements required for receptor activation and signalling. We also show that a blocking antibody antigen-binding fragment binds to the extracellular surface of PAR2, preventing access of the tethered ligand to the peptide-binding site. These structures provide a basis for the development of selective PAR2 antagonists for a range of therapeutic uses.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking, rigid receptor docking, and protein structure prediction with explicit solvent molecular dynamics simulations. This novel methodology in detailed retrospective and prospective testing succeeded to determine proteinligand binding modes with a root-mean-square-deviation within 2.5 Å in over 90% of cross-docking cases. We further demonstrate these predicted ligand-receptor structures were sufficiently accurate to prospectively enable predictive structure-based drug discovery for challenging targets, substantially expanding the domain of applicability for such methods.

ATAD2 (ANCCA) is an epigenetic regulator and transcriptional cofactor, whose overexpression has been linked to the progress of various cancer types. Here, we report a DNA-encoded library screen leading to the discovery of BAY-850, a potent and isoform selective inhibitor that specifically induces ATAD2 bromodomain dimerization and prevents interactions with acetylated histones in vitro, as well as with chromatin in cells. These features qualify BAY-850 as a chemical probe to explore ATAD2 biology.

Millions of individuals are infected with and die from tuberculosis (TB) each year, and multidrug-resistant (MDR) strains of TB are increasingly prevalent. As such, there is an urgent need to identify novel drugs to treat TB infections. Current frontline therapies include the drug isoniazid, which inhibits the essential NADH-dependent enoyl–acyl-carrier protein (ACP) reductase, InhA. To inhibit InhA, isoniazid must be activated by the catalase-peroxidase KatG. Isoniazid resistance is linked primarily to mutations in the katG gene. Discovery of InhA inhibitors that do not require KatG activation is crucial to combat MDR TB. Multiple discovery efforts have been made against InhA in recent years. Until recently, despite achieving high potency against the enzyme, these efforts have been thwarted by lack of cellular activity. We describe here the use of DNA-encoded X-Chem (DEX) screening, combined with selection of appropriate physical properties, to identify multiple classes of InhA inhibitors with cell-based activity. The utilization of DEX screening allowed the interrogation of very large compound libraries (1011 unique small molecules) against multiple forms of the InhA enzyme in a multiplexed format. Comparison of the enriched library members across various screening conditions allowed the identification of cofactor-specific inhibitors of InhA that do not require activation by KatG, many of which had bactericidal activity in cell-based assays.

Mcl-1 is a pro-apoptotic BH3 protein family member similar to Bcl-2 and Bcl-xL. Overexpression of Mcl-1 is often seen in various tumors and allows cancer cells to evade apoptosis. Here we report the discovery and optimization of a series of non-natural peptide Mcl-1 inhibitors. Screening of DNA-encoded libraries resulted in hit compound , a 1.5 μM Mcl-1 inhibitor. A subsequent crystal structure demonstrated that compound bound to Mcl-1 in a β-turn conformation, such that the two ends of the peptide were close together. This proximity allowed for the linking of the two ends of the peptide to form a macrocycle. Macrocyclization resulted in an approximately 10-fold improvement in binding potency. Further exploration of a key hydrophobic interaction with Mcl-1 protein and also with the moiety that engages Arg256 led to additional potency improvements. The use of protein-ligand crystal structures and binding kinetics contributed to the design and understanding of the potency gains. Optimized compound is a<3 nM Mcl-1 inhibitor, while inhibiting Bcl-2 at only 5 μM and Bcl-xL at >99 μM, and induces cleaved caspase-3 in MV4-11 cells with an IC of 3 μM after 6 h.

A combination of physical organic experiments and quantum chemical calculations were used to construct a detailed mechanistic model for the Ni(0)-N-heterocyclic carbene-catalyzed vinylcyclopropane-cyclopentene rearrangement that involves a muti-step oxidative addition/ haptotropic shift/reductive elimination pathway. No evidence for the intermediacy of radicals or zwitterions was found. The roles of substituents on the vinylcyclopropane substrate and variations in the ligands on Ni were evaluated. It is postulated that bulky carbene ligands facilitate formation of the active catalyst species.

The discovery of ligands via affinity-mediated selection of DNA-encoded chemical libraries is driven by the quality and concentration of the protein target. G-protein-coupled receptors (GPCRs) and other membrane-bound targets can be difficult to isolate in their functional state and at high concentrations, and therefore have been challenging for affinity-mediated selection. Here, we report a successful selection campaign against protease-activated receptor 2 (PAR2). Using a thermo-stabilized mutant of PAR2, we conducted affinity selection using our >100-billion-compound DNA-encoded library. We observed a number of putative ligands enriched upon selection, and subsequent cellular profiling revealed these ligands to comprise both agonists and antagonists. The agonist series shared structural similarity with known agonists. The antagonists were shown to bind in a novel allosteric binding site on the PAR2 protein. This report serves to demonstrate that cell-free affinity selection against GPCRs can be achieved with mutant stabilized protein targets.

We present a reliable and accurate solution to the induced fit docking problem for protein-ligand binding by combining ligand-based pharmacophore docking (Phase), rigid receptor docking (Glide), and protein structure prediction (Prime) with explicit solvent molecular dynamics simulations. We provide an in-depth description of our novel methodology and present results for 41 targets consisting of 415 cross-docking cases divided amongst a training and test set. For both the training and test-set, we compute binding modes with a ligand-heavy atom RMSD to within 2.5 Å or better in over 90% of cross-docking cases compared to less than 70% of cross-docking cases using our previously published induced-fit docking algorithm and less than 41% using rigid receptor docking. Applications of the predicted ligand-receptor structure in free energy perturbation calculations is demonstrated for both public data and in active drug discovery projects, both retrospectively and prospectively.