Discovery of Protease-Activated Receptor 4 (PAR4)-Tethered Ligand Antagonists Using Ultralarge Virtual Screening.

Here, we demonstrate a structure-based small molecule virtual screening and lead optimization pipeline using a homology model of a difficult-to-drug G-protein-coupled receptor (GPCR) target. Protease-activated receptor 4 (PAR4) is activated by thrombin cleavage, revealing a tethered ligand that activates the receptor, making PAR4 a challenging target. A virtual screen of a make-on-demand chemical library yielded a one-hit compound. From the single-hit compound, we developed a novel series of PAR4 antagonists. Subsequent lead optimization via simultaneous virtual library searches and structure-based rational design efforts led to potent antagonists of thrombin-induced activation. Interestingly, this series of antagonists was active against PAR4 activation by the native protease thrombin cleavage but not the synthetic PAR4 agonist peptide AYPGKF.

Groebke Blackburn Bienaymé-mediated multi-component synthesis of selective HDAC6 inhibitors with anti-inflammatory properties.

Histone deacetylases (HDACs) are a class of enzymes that cleave acyl groups from lysine residues of histone and non-histone proteins. There are 18 human HDAC isoforms with different cellular targets and functions. Among them, HDAC6 was found to be overexpressed in different types of cancer. However, when used in monotherapy, HDAC6 inhibition by selective inhibitors fails to show pronounced anti-cancer effects. The HDAC6 enzyme also addresses non-histone proteins like α-tubulin and cortactin, making it important for cell migration and angiogenesis. Recently, the NLRP3 inflammasome was identified as an important regulator of inflammation and immune responses and, importantly, HDAC6 is critically involved the activation of the inflammasome. We herein report the design, synthesis and biological evaluation of a library of selective HDAC6 inhibitors. Starting from the previously published crystal structure of MAIP-032 in complex with CD2 of zHDAC6, we performed docking studies to evaluate additional possible interactions of the cap group with the L1-loop pocket. Based on the results we synthesized 13 novel HDAC6 inhibitors via the Groebke-Blackburn-Bienaymé three component reaction as the key step. Compounds 8k (HDAC1 IC50: 5.87 µM; HDAC6 IC50: 0.024 µM; selectivity factor (SF1/6): 245) and 8m (HDAC1 IC50: 3.07 µM; HDAC6 IC50: 0.026 µM; SF1/6: 118) emerged as the most potent and selective inhibitors of HDAC6 and outperformed the lead structure MAIP-032 (HDAC1 IC50: 2.20 µM; HDAC6 IC50: 0.058 µM; SF1/6: 38) both in terms of inhibitory potency and selectivity. Subsequent immunoblot analysis confirmed the high selectivity of 8k and 8m for HDAC6 in a cellular environment. While neither 8k and 8m nor the selectivity HDAC6 inhibitor tubastatin A showed antiproliferative effects in the U-87 MG glioblastoma cell line, compound 8m attenuated cell migration significantly in wound healing assays in U-87 MG cells. Moreover, in macrophages compounds 8k and 8m demonstrated significant inhibition of LPS-induced IL1B mRNA expression and TNF release. These findings suggest that our imidazo[1,2-a]pyridine-capped HDAC6 inhibitors may serve as promising candidates for the development of drugs to effectively treat NLRP3 inflammasome-driven inflammatory diseases.

Rosetta's Predictive Ability for Low-Affinity Ligand Binding in Fragment-Based Drug Discovery.

Fragment-based drug discovery begins with the identification of small molecules with a molecular weight of usually less than 250 Da which weakly bind to the protein of interest. This technique is challenging for computational docking methods as binding is determined by only a few specific interactions. Inaccuracies in the energy function or slight deviations in the docking pose can lead to the prediction of incorrect binding or difficulties in ranking fragments in in silico screening. Here, we test RosettaLigand by docking a series of fragments to a cysteine-depleted variant of the TIM-barrel protein, HisF (UniProtKB Q9X0C6). We compare the computational results with experimental NMR spectroscopy screens. NMR spectroscopy gives details on binding affinities of individual ligands, which allows assessment of the ligand-ranking ability using RosettaLigand and also provides feedback on the location of the binding pocket, which serves as a reliable test of RosettaLigand's ability to identify plausible binding poses. From a library screen of 3456 fragments, we identified a set of 31 ligands with intrinsic affinities to HisF with dissociation constants as low as 400 µM. The same library of fragments was blindly screened in silico. RosettaLigand was able to rank binders before non-binders with an area under the curve of the receiver operating characteristics of 0.74. The docking poses observed for binders agreed with the binding pocket identified by NMR chemical shift perturbations for all fragments. Taken together, these results provide a baseline performance of RosettaLigand in a fragment-based drug discovery setting.

PlaceWaters: Real-time, explicit interface water sampling during Rosetta ligand docking.

Water molecules at the protein-small molecule interface often form hydrogen bonds with both the small molecule ligand and the protein, affecting the structural integrity and energetics of a binding event. The inclusion of these 'bridging waters' has been shown to improve the accuracy of predicted docked structures; however, due to increased computational costs, this step is typically omitted in ligand docking simulations. In this study, we introduce a resource-efficient, Rosetta-based protocol named "PlaceWaters" to predict the location of explicit interface bridging waters during a ligand docking simulation. In contrast to other explicit water methods, this protocol is independent of knowledge of number and location of crystallographic waters in homologous structures. We test this method on a diverse protein-small molecule benchmark set in comparison to other Rosetta-based protocols. Our results suggest that this coarse-grained, structure-based approach quickly and accurately predicts the location of bridging waters, improving our ability to computationally screen drug candidates.

Structural Comparative Modeling of Multi-Domain F508del CFTR.

Cystic fibrosis (CF) is a rare genetic disease caused by mutations in the cystic fibrosis transmembrane conductance regulator (CFTR), an epithelial anion channel expressed in several vital organs. Absence of functional CFTR results in imbalanced osmotic equilibrium and subsequent mucus build up in the lungs-which increases the risk of infection and eventually causes death. CFTR is an ATP-binding cassette (ABC) transporter family protein composed of two transmembrane domains (TMDs), two nucleotide binding domains (NBDs), and an unstructured regulatory domain. The most prevalent patient mutation is the deletion of F508 (F508del), making F508del CFTR the primary target for current FDA approved CF therapies. However, no experimental multi-domain F508del CFTR structure has been determined and few studies have modeled F508del using multi-domain WT CFTR structures. Here, we used cryo-EM density data and Rosetta comparative modeling (RosettaCM) to compare a F508del model with published experimental data on CFTR NBD1 thermodynamics. We then apply this modeling method to generate multi-domain WT and F508del CFTR structural models. These models demonstrate the destabilizing effects of F508del on NBD1 and the NBD1/TMD interface in both the inactive and active conformation of CFTR. Furthermore, we modeled F508del/R1070W and F508del bound to the CFTR corrector VX-809. Our models reveal the stabilizing effects of VX-809 on multi-domain models of F508del CFTR and pave the way for rational design of additional drugs that target F508del CFTR for treatment of CF.

Discovery and Optimization of a Novel Series of Competitive and Central Nervous System-Penetrant Protease-Activated Receptor 4 (PAR4) Inhibitors.

The detailed pharmacology and therapeutic potential of the central PAR4 receptors are poorly understood due to a lack of potent, selective, and brain-penetrant tool compounds. Despite this, robust data with biochemical and genetic tools show the therapeutic potential of PAR4 antagonists in traumatic brain injury, Alzheimer's disease, Parkinson's disease, and other neurodegenerative disorders with a neuroinflammatory component. Thus, we performed a functional HTS campaign, identified a fundamentally new PAR4 competitive inhibitor chemotype, optimized this new series (increased potency >45-fold), discovered enantiospecific activity (though opposing preference for human versus mouse PAR4), and engendered high central nervous system penetration (rat Kp's of 0.52 to 4.2 and Kp,uu's of 0.52 to 1.2).

Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks.

Each year vast international resources are wasted on irreproducible research. The scientific community has been slow to adopt standard software engineering practices, despite the increases in high-dimensional data, complexities of workflows, and computational environments. Here we show how scientific software applications can be created in a reproducible manner when simple design goals for reproducibility are met. We describe the implementation of a test server framework and 40 scientific benchmarks, covering numerous applications in Rosetta bio-macromolecular modeling. High performance computing cluster integration allows these benchmarks to run continuously and automatically. Detailed protocol captures are useful for developers and users of Rosetta and other macromolecular modeling tools. The framework and design concepts presented here are valuable for developers and users of any type of scientific software and for the scientific community to create reproducible methods. Specific examples highlight the utility of this framework, and the comprehensive documentation illustrates the ease of adding new tests in a matter of hours.

Modeling Immunity with Rosetta: Methods for Antibody and Antigen Design.

Structure-based antibody and antigen design has advanced greatly in recent years, due not only to the increasing availability of experimentally determined structures but also to improved computational methods for both prediction and design. Constant improvements in performance within the Rosetta software suite for biomolecular modeling have given rise to a greater breadth of structure prediction, including docking and design application cases for antibody and antigen modeling. Here, we present an overview of current protocols for antibody and antigen modeling using Rosetta and exemplify those by detailed tutorials originally developed for a Rosetta workshop at Vanderbilt University. These tutorials cover antibody structure prediction, docking, and design and antigen design strategies, including the addition of glycans in Rosetta. We expect that these materials will allow novice users to apply Rosetta in their own projects for modeling antibodies and antigens.

Assessing multiple score functions in Rosetta for drug discovery.

Rosetta is a computational software suite containing algorithms for a wide variety of macromolecular structure prediction and design tasks including small molecule protocols commonly used in drug discovery or enzyme design. Here, we benchmark RosettaLigand score functions and protocols in comparison to results of other software recently published in the Comparative Assessment of Score Functions (CASF-2016). The CASF-2016 benchmark covers a wide variety of tests including scoring and ranking multiple compounds against a target, ligand docking of a small molecule to a target, and virtual screening to extract binders from a compound library. Direct comparison to the score functions provided by CASF-2016 results shows that the original RosettaLigand score function ranks among the top software for scoring, ranking, docking and screening tests. Most notably, the RosettaLigand score function ranked 2/34 among other report score functions in CASF-2016. We additionally perform a ligand docking test with full sampling to mimic typical use cases. Despite improved performance of newer score functions in canonical protein structure prediction and design, we demonstrate here that more recent Rosetta score functions have reduced performance across all small molecule benchmarks. The tests described here have also been uploaded to the Rosetta scientific benchmarking server and will be run weekly to track performance as the code is continually being developed.

Pharmacological blockade of ASCT2-dependent glutamine transport leads to antitumor efficacy in preclinical models.

The unique metabolic demands of cancer cells underscore potentially fruitful opportunities for drug discovery in the era of precision medicine. However, therapeutic targeting of cancer metabolism has led to surprisingly few new drugs to date. The neutral amino acid glutamine serves as a key intermediate in numerous metabolic processes leveraged by cancer cells, including biosynthesis, cell signaling, and oxidative protection. Herein we report the preclinical development of V-9302, a competitive small molecule antagonist of transmembrane glutamine flux that selectively and potently targets the amino acid transporter ASCT2. Pharmacological blockade of ASCT2 with V-9302 resulted in attenuated cancer cell growth and proliferation, increased cell death, and increased oxidative stress, which collectively contributed to antitumor responses in vitro and in vivo. This is the first study, to our knowledge, to demonstrate the utility of a pharmacological inhibitor of glutamine transport in oncology, representing a new class of targeted therapy and laying a framework for paradigm-shifting therapies targeting cancer cell metabolism.

Intentional inattention: Detecting feigned attention-deficit/hyperactivity disorder on the Personality Assessment Inventory.

Given the increasing number of college students seeking Attention-Deficit/Hyperactivity Disorder (ADHD) diagnoses as well as the potential secondary gains associated with this disorder (e.g., access to stimulant medication, academic accommodations), the detection of malingered symptom presentations in this population is a major concern. The present study examined the ability of validity indicators on the widely used Personality Assessment Inventory (PAI; Morey, 1991) to distinguish between individuals experiencing genuine ADHD symptoms and individuals instructed to present with ADHD symptomatology for secondary gain. Sixty-six participants who successfully simulated ADHD (based on elevations on the Conners' Adult ADHD Rating Scale; Conners, Erhardt, & Sparrow, 1998) were compared with a sample of undergraduate students meeting diagnostic criteria for ADHD (N = 22) and an archival sample of adults who received an ADHD diagnosis at a university psychology clinic following a comprehensive psychological evaluation (N = 41). Successful simulators obtained significantly higher scores on all relevant PAI validity indicators compared with the clinical and archival comparison samples, with the Rogers Discriminant Function demonstrating the highest predictive accuracy (AUC = .86). Traditional cut scores on the Negative Impression (NIM) validity scale used to designate probable malingering, however, were not sensitive to simulated ADHD symptoms, although they did demonstrate excellent specificity. The PAI may be informative as an indicator of potentially exaggerated or malingered symptom presentation, but alternative cut scores for symptom validity indicators may be necessary to maximize its utility in these particular types of psychological evaluations. (PsycINFO Database Record