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.

Steroid receptor drugs have been available for more than half a century, but details of the ligand binding mechanism have remained elusive. We solved X-ray structures of the glucocorticoid and mineralocorticoid receptors to identify a conserved plasticity at the helix 6-7 region that extends the ligand binding pocket toward the receptor surface. Since none of the endogenous ligands exploit this region, we hypothesized that it constitutes an integral part of the binding event. Extensive all-atom unbiased ligand exit and entrance simulations corroborate a ligand binding pathway that gives the observed structural plasticity a key functional role. Kinetic measurements reveal that the receptor residence time correlates with structural rearrangements observed in both structures and simulations. Ultimately, our findings reveal why nature has conserved the capacity to open up this region, and highlight how differences in the details of the ligand entry process result in differential evolutionary constraints across the steroid receptors.

Recent advances in interaction design have created new ways to use computers. One example is the ability to create enhanced 3D environments that simulate physical presence in the real world--a virtual reality. This is relevant to drug discovery since molecular models are frequently used to obtain deeper understandings of, say, ligand-protein complexes. We have developed a tool (Molecular Rift), which creates a virtual reality environment steered with hand movements. Oculus Rift, a head-mounted display, is used to create the virtual settings. The program is controlled by gesture-recognition, using the gaming sensor MS Kinect v2, eliminating the need for standard input devices. The Open Babel toolkit was integrated to provide access to powerful cheminformatics functions. Molecular Rift was developed with a focus on usability, including iterative test-group evaluations. We conclude with reflections on virtual reality's future capabilities in chemistry and education. Molecular Rift is open source and can be downloaded from GitHub.

Virtual screening is a standard tool in Computer-Assisted Drug Design (CADD). Early in a project, it is typical to use ligand-based similarity search methods to find suitable hit molecules. However, the number of compounds which can be screened and the time required are usually limited by computational resources. We describe here a high-throughput virtual screening project using 3D similarity (FastROCS) and automated evaluation workflows on Orion, a cloud computing platform. Cloud resources make this approach fully scalable and flexible, allowing the generation and search of billions of virtual molecules, and give access to an explicit 3D virtual chemistry space not available before. We discuss the impact of the size of the search space with respect to finding novel chemical hits and the size of the required hit list, as well as computational and economical aspects of resource scaling. Article pubs.acs.org/jcim

Artificial intelligence offers promising solutions for property prediction, compound design, and retrosynthetic planning, which are expected to significantly accelerate the search for pharmacologically relevant molecules. Here, we investigate aspects of artificial intelligence based de novo design pertaining to its integration into real-life workflows. First, different chemical spaces were used as training sets for reinforcement learning (RL) in combination with different reward functions. With the trained neuronal networks different biologically active molecules could be regenerated. Excluding molecules with substructures such as five-membered rings from training spaces nevertheless produced results containing these moieties. Furthermore, different scoring functions in RL were investigated and produced different design ensembles. In summary, some of these design proposals are close in chemical space to the query, thus supporting lead optimization, while 3D-shape or QSAR (quantitative structure−activity relationship) models produced significantly different proposals by sampling a broader region of the chemical space, thus supporting lead generation. Therefore, RL provides a good framework to tailored design approaches for different discovery phases.