The deubiquitinases, or DUBs, are associated with various human diseases, including neurological disorders, cancer, and viral infection, making them excellent candidates for pharmacological intervention. Drug discovery campaigns against DUBs require enzymatic deubiquitination assays amenable for high-throughput screening (HTS). Although several DUB substrates and assays have been developed in recent years, they are largely limited to recombinantly purified DUBs. Many DUBs are large multidomain proteins that are difficult to obtain recombinantly in sufficient quantities for HTS. Therefore, an assay that obviates the need of recombinant protein generation and also recapitulates a physiologically relevant environment is highly desirable. Such an assay will open doors for drug discovery against many therapeutically relevant, but currently inaccessible, DUBs. Here, we report a cell lysate DUB assay based on AlphaLISA technology for high throughput screening. This assay platform uses a biotin-tagged ubiquitin probe and a HA-tagged DUB expressed in human cells. The assay was validated and adapted to a 1536-well format, which enabled a screening against UCHL1 as proof of principle using a library of 15 000 compounds. We expect that the new platform can be readily adapted to other DUBs to allow the identification of more potent and selective small molecule inhibitors and chemical probes.

We describe the parallel synthesis of novel analogs of GW0742, a peroxisome proliferator-activated receptor δ (PPARδ) agonist. For that purpose, modified reaction conditions were applied, such as a solid-phase palladium-catalyzed Suzuki coupling. In addition, tetrazole-based compounds were generated as a bioisostere for carboxylic acid-containing ligand GW0742. The new compounds were investigated for their ability to activate PPARδ mediated transcription and their cross-reactivity with the vitamin D receptor (VDR), another member of the nuclear receptor superfamily. We identified many potent PPARδ agonists that were less toxic than GW0742, where ∼65 of the compounds synthesized exhibited partial PPARδ activity (23-98%) with EC50 values ranging from 0.007-18.2 μM. Some ligands, such as compound 32, were more potent inhibitors of VDR-mediated transcription with significantly reduced PPARδ activity than GW0742, however, none of the ligands were completely selective for VDR inhibition over PPARδ activation of transcription.

OBJECTIVE: Adequate platelet reactivity is required for maintaining hemostasis. However, excessive platelet reactivity can also lead to the formation of occlusive thrombi. Platelet 12(S)-lipoxygenase (12-LOX), an oxygenase highly expressed in the platelet, has been demonstrated to regulate platelet function and thrombosis ex vivo, supporting a key role for 12-LOX in the regulation of in vivo thrombosis. However, the ability to pharmacologically target 12-LOX in vivo has not been established to date. Here, we studied the effect of the first highly selective 12-LOX inhibitor, ML355, on in vivo thrombosis and hemostasis. APPROACH AND RESULTS: ML355 dose-dependently inhibited human platelet aggregation and 12-LOX oxylipin production, as confirmed by mass spectrometry. Interestingly, the antiplatelet effects of ML355 were reversed after exposure to high concentrations of thrombin in vitro. Ex vivo flow chamber assays confirmed that human platelet adhesion and thrombus formation at arterial shear over collagen were attenuated in whole blood treated with ML355 comparable to aspirin. Oral administration of ML355 in mice showed reasonable plasma drug levels by pharmacokinetic assessment. ML355 treatment impaired thrombus growth and vessel occlusion in FeCl3-induced mesenteric and laser-induced cremaster arteriole thrombosis models in mice. Importantly, hemostatic plug formation and bleeding after treatment with ML355 was minimal in mice in response to laser ablation on the saphenous vein or in a cremaster microvasculature laser-induced rupture model. CONCLUSIONS: Our data strongly support 12-LOX as a key determinant of platelet reactivity in vivo, and inhibition of platelet 12-LOX with ML355 may represent a new class of antiplatelet therapy.

CD8(+) T cell immunosurveillance is based on recognizing oligopeptides presented by MHC class I molecules. Despite decades of study, the importance of protein ubiquitylation to peptide generation remains uncertain. In this study, we examined the ability of MLN7243, a recently described ubiquitin-activating enzyme E1 inhibitor, to block overall cytosolic peptide generation and generation of specific peptides from vaccinia- and influenza A virus-encoded proteins. We show that MLN7243 rapidly inhibits ubiquitylation in a variety of cell lines and can profoundly reduce the generation of cytosolic peptides. Kinetic analysis of specific peptide generation reveals that ubiquitylation of defective ribosomal products is rate limiting in generating class I peptide complexes. More generally, our findings demonstrate that the requirement for ubiquitylation in MHC class I-restricted Ag processing varies with class I allomorph, cell type, source protein, and peptide context. Thus, ubiquitin-dependent and -independent pathways robustly contribute to MHC class I-based immunosurveillance.

X-chromosome inactivation is a mechanism of dosage compensation in which one of the two X chromosomes in female mammals is transcriptionally silenced. Once established, silencing of the inactive X (Xi) is robust and difficult to reverse pharmacologically. However, the Xi is a reservoir of >1,000 functional genes that could be potentially tapped to treat X-linked disease. To identify compounds that could reactivate the Xi, here we screened ∼367,000 small molecules in an automated high-content screen using an Xi-linked GFP reporter in mouse fibroblasts. Given the robust nature of silencing, we sensitized the screen by "priming" cells with the DNA methyltransferase inhibitor, 5-aza-2'-deoxycytidine (5azadC). Compounds that elicited GFP activity include VX680, MLN8237, and 5azadC, which are known to target the Aurora kinase and DNA methylation pathways. We demonstrate that the combinations of VX680 and 5azadC, as well as MLN8237 and 5azadC, synergistically up-regulate genes on the Xi. Thus, our work identifies a synergism between the DNA methylation and Aurora kinase pathways as being one of interest for possible pharmacological reactivation of the Xi.

Aldehyde dehydrogenase enzymes (ALDHs) have a broad spectrum of biological activities through the oxidation of both endogenous and exogenous aldehydes. Increased expression of ALDH1A1 has been identified in a wide-range of human cancer stem cells and is associated with cancer relapse and poor prognosis, raising the potential of ALDH1A1 as a therapeutic target. To facilitate quantitative high-throughput screening (qHTS) campaigns for the discovery, characterization and structure-activity-relationship (SAR) studies of small molecule ALDH1A1 inhibitors with cellular activity, we show herein the miniaturization to 1536-well format and automation of a high-content cell-based ALDEFLUOR assay. We demonstrate the utility of this assay by generating dose-response curves on a comprehensive set of prior art inhibitors as well as hundreds of ALDH1A1 inhibitors synthesized in house. Finally, we established a screening paradigm using a pair of cell lines with low and high ALDH1A1 expression, respectively, to uncover novel cell-active ALDH1A1-specific inhibitors from a collection of over 1,000 small molecules.

Cell membrane permeability is an important determinant for oral absorption and bioavailability of a drug molecule. An in silico model predicting drug permeability is described, which is built based on a large permeability dataset of 7488 compound entries or 5435 structurally unique molecules measured by the same lab using parallel artificial membrane permeability assay (PAMPA). On the basis of customized molecular descriptors, the support vector regression (SVR) model trained with 4071 compounds with quantitative data is able to predict the remaining 1364 compounds with the qualitative data with an area under the curve of receiver operating characteristic (AUC-ROC) of 0.90. The support vector classification (SVC) model trained with half of the whole dataset comprised of both the quantitative and the qualitative data produced accurate predictions to the remaining data with the AUC-ROC of 0.88. The results suggest that the developed SVR model is highly predictive and provides medicinal chemists a useful in silico tool to facilitate design and synthesis of novel compounds with optimal drug-like properties, and thus accelerate the lead optimization in drug discovery.

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD.

The endoplasmic reticulum (ER) is involved in Ca2+ signaling and protein folding. ER Ca2+ depletion and accumulation of unfolded proteins activate the molecular chaperone GRP78 (glucose-regulated protein 78) which in turn triggers the ER stress response (ERSR) pathway aimed to restore ER homeostasis. Failure to adapt to stress, however, results in apoptosis. We and others have shown that malignant cells are more susceptible to ERSR-induced apoptosis than their normal counterparts, implicating the ERSR as a potential target for cancer therapeutics. Predicated on these findings, we developed an assay that uses a GRP78 biosensor to identify small molecule activators of ERSR in glioma cells. We performed a quantitative high-throughput screen (qHTS) against a collection of ~425,000 compounds and a comprehensive panel of orthogonal secondary assays was formulated for stringent compound validation. We identified novel activators of ERSR, including a compound with a 2,9-diazaspiro[5.5]undecane core, which depletes intracellular Ca2+ stores and induces apoptosis-mediated cell death in several cancer cell lines, including patient-derived and 3D cultures of glioma cells. This study demonstrates that our screening platform enables the identification and profiling of ERSR inducers with cytotoxic activity and advocates for characterization of these compound in in vivo models.

The potential of the diverse chemistries present in natural products (NP) for biotechnology and medicine remains untapped because NP databases are not searchable with raw data and the NP community has no way to share data other than in published papers. Although mass spectrometry (MS) techniques are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations. We also introduce the concept of 'living data' through continuous reanalysis of deposited data.