PPARγ is involved in expression of genes that control glucose and lipid metabolism. PPARγ is the molecular target of the thiazolidinedione (TZD) class of antidiabetic drugs. However, despite their clinical use these drugs are related to numerous adverse effects, which are related to their full activation of PPARγ transcriptional responses. PPARγ partial agonists are the focus of development efforts towards second-generation PPARγ modulators with favourable pharmacology, potent insulin sensitization without the severe full agonists’ adverse effects. In order to identify novel PPARγ partial agonist lead compounds, we developed a virtual screening protocol based on 3D-ligand shape similarity and docking. 235 compounds were prioritized for experimental screening from a 340,000 MLSMR chemical library. Seven novel potent partial agonists were confirmed in cell-based transactivation and competitive binding assays. Our results illustrate a well-designed virtual screening campaign successfully identifying novel lead compounds as potential entry points for the development of antidiabetic drugs.

High-throughput screening data repositories, such as PubChem, represent valuable resources for the development of small molecule chemical probes and can serve as entry points for drug discovery programs. While the loose data format offered by PubChem allows for great flexibility, important annotations, such as the assay format and technologies employed, are not explicitly indexed. We have previously developed a BioAssay Ontology (BAO) and curated over 350 assays with standardized BAO terms. Here we describe the use of BAO annotations to analyze a large set of assays that employ luciferase- and β-lactamase-based technologies. We identified promiscuous chemotypes pertaining to different sub-categories of assays and specific mechanisms by which these chemotypes interfere in reporter gene assays. Our results show that the data in PubChem can be used to identify promiscuous compounds that interfere non-specifically with particular technologies. Furthermore, we show that BAO is a valuable toolset for the identification of related assays and for the systematic generation of insights that are beyond the scope of individual assays or screening campaigns.

PPARγ is the functioning receptor for the thiazolidinedione (TZD) class of anti-diabetes drugs including rosiglitazone and pioglitazone1. These drugs are full classical agonists for this nuclear receptor, but recent data has shown that many PPARγ-based drugs have a separate biochemical activity, blocking the obesity-linked phosphorylation of PPARγ by Cdk52. Here we describe novel synthetic compounds that have a unique mode of binding to PPARγ, completely lack classical transcriptional agonism and block the Cdk5-mediated phosphorylation in cultured adipocytes and in insulin-resistant mice. Moreover, one such compound, SR1664, has potent anti-diabetic activity while not causing the fluid retention and weight gain that are serious side effects of many of the PPARγ drugs. Unlike TZDs, SR1664 also does not interfere with bone formation in culture. These data illustrate that new classes of anti-diabetes drugs can be developed by specifically targeting the Cdk5-mediated phosphorylation of PPARγ.

The lymphoid tyrosine phosphatase (Lyp, PTPN22) is a critical negative regulator of T cell antigen receptor (TCR) signaling. A single-nucleotide polymorphism (SNP) in the ptpn22 gene correlates with the incidence of various autoimmune diseases, including type 1 diabetes, rheumatoid arthritis, and systemic lupus erythematosus. Since the disease-associated allele is a more potent inhibitor of TCR signaling, specific Lyp inhibitors may become valuable in treating autoimmunity. Using a structure-based approach, we synthesized a library of 34 compounds that inhibited Lyp with IC50 values between 0.27 and 6.2 μM. A reporter assay was employed to screen for compounds that enhanced TCR signaling in cells, and several inhibitors displayed a dose-dependent, activating effect. Subsequent probing for Lyp's direct physiological targets by immunoblot analysis confirmed the ability of the compounds to inhibit Lyp in T cells. Selectivity profiling against closely related tyrosine phosphatases and in silico docking studies with the crystal structure of Lyp yielded valuable information for the design of Lyp-specific compounds.

T helper cells that produce Interleukin-17 (IL-17) (TH17 cells) are a recently identified CD4+ T-cell subset with characterized pathological roles in autoimmune diseases1–3. The nuclear receptors retinoic acid receptor-related orphan receptors α and γt (RORα and RORγt) have indispensible roles in the development of this cell type4–7. Here we present a first-in-class, high-affinity synthetic ligand, SR1001, specific to both RORα and RORγt that inhibits TH17 cell differentiation and function. SR1001 binds specifically to the ligand binding domains (LBDs) of RORα and RORγt inducing a conformational change within the LBD that encompasses repositioning of helix 12 leading to diminished affinity for coactivators and increased affinity for corepressors resulting in suppression of the receptors transcriptional activity. SR1001 inhibited the development of murine TH17 cells as demonstrated by inhibition of IL-17A gene expression and protein production. Additionally, SR1001 inhibited the expression of cytokines when added to differentiated murine or human TH17 cells. Finally, SR1001 effectively suppressed the clinical severity of autoimmune disease in mice. Thus, our data demonstrates the feasibility of targeting the orphan receptors RORα and RORγt to specifically inhibit TH17 cell differentiation and function and indicates that this novel class of compound has potential utility in the treatment of autoimmune diseases.

Background

High-throughput screening (HTS) is one of the main strategies to identify novel entry points for the development of small molecule chemical probes and drugs and is now commonly accessible to public sector research. Large amounts of data generated in HTS campaigns are submitted to public repositories such as PubChem, which is growing at an exponential rate. The diversity and quantity of available HTS assays and screening results pose enormous challenges to organizing, standardizing, integrating, and analyzing the datasets and thus to maximize the scientific and ultimately the public health impact of the huge investments made to implement public sector HTS capabilities. Novel approaches to organize, standardize and access HTS data are required to address these challenges.

Results

We developed the first ontology to describe HTS experiments and screening results using expressive description logic. The BioAssay Ontology (BAO) serves as a foundation for the standardization of HTS assays and data and as a semantic knowledge model. In this paper we show important examples of formalizing HTS domain knowledge and we point out the advantages of this approach. The ontology is available online at the NCBO bioportal http://bioportal.bioontology.org/ontologies/44531.

Conclusions

After a large manual curation effort, we loaded BAO-mapped data triples into a RDF database store and used a reasoner in several case studies to demonstrate the benefits of formalized domain knowledge representation in BAO. The examples illustrate semantic querying capabilities where BAO enables the retrieval of inferred search results that are relevant to a given query, but are not explicitly defined. BAO thus opens new functionality for annotating, querying, and analyzing HTS datasets and the potential for discovering new knowledge by means of inference.

Tyrosine phosphorylation, controlled by the coordinated action of protein-tyrosine kinases (PTKs) and protein-tyrosine phosphatases (PTPs), is a fundamental regulatory mechanism of numerous physiological processes. PTPs are implicated in a number of human diseases and their potential as prospective drug targets is increasingly being recognized. Despite their biological importance, until now no comprehensive overview has been reported describing how all members of the human PTP family are related. Here we review the entire human PTP family and present a systematic knowledge-based characterization of global and local similarity relationships, which are relevant for the development of small molecule inhibitors. We use parallel homology modeling to expand the current PTP structure space and analyze the human PTPs based on local three-dimensional catalytic sites and domain sequences. Furthermore, we demonstrate the importance of binding site similarities in understanding cross-reactivity and inhibitor selectivity in the design of small molecule inhibitors.

We have studied the Sphingosine 1-phosphate (S1P) receptor system to better understand why certain molecular targets within a closely related family are much more tractable when identifying compelling chemical leads. Five medically important G protein-coupled receptors for S1P regulate heart rate, coronary artery caliber, endothelial barrier integrity, and lymphocyte trafficking. Selective S1P receptor agonist probes would be of great utility to study receptor subtype-specific function. Through systematic screening of the same libraries, we identified novel selective agonists chemotypes for each of the S1P1 and S1P3 receptors. uHTS for S1P1 was more effective than for S1P3, with many selective, low nanomolar hits of proven mechanism emerging for. Receptor structure modeling and ligand docking reveal differences between the receptor binding pockets, which are the basis for sub-type selectivity. Novel selective agonists interact primarily in the hydrophobic pocket of the receptor in the absence of head-group interactions. Chemistry-space and shape-based analysis of the screening libraries in combination with the binding models explain the observed differential hit rates and enhanced efficiency for lead discovery for S1P1 vs. S1P3 in this closely related receptor family.

Huge amounts of high-throughput screening (HTS) data for probe and drug development projects are being generated in the pharmaceutical industry and more recently in the public sector. The resulting experimental datasets are increasingly being disseminated via publically accessible repositories. However, existing repositories lack sufficient metadata to describe the experiments and are often difficult to navigate by non-experts. The lack of standardized descriptions and semantics of biological assays and screening results hinder targeted data retrieval, integration, aggregation, and analyses across different HTS datasets, for example to infer mechanisms of action of small molecule perturbagens. To address these limitations, we created the BioAssay Ontology (BAO). BAO has been developed with a focus on data integration and analysis enabling the classification of assays and screening results by concepts that relate to format, assay design, technology, target, and endpoint. Previously, we reported on the higher-level design of BAO and on the semantic querying capabilities offered by the ontology-indexed triple store of HTS data. Here, we report on our detailed design, annotation pipeline, substantially enlarged annotation knowledgebase, and analysis results. We used BAO to annotate assays from the largest public HTS data repository, PubChem, and demonstrate its utility to categorize and analyze diverse HTS results from numerous experiments. BAO is publically available from the NCBO BioPortal at http://bioportal.bioontology.org/ontologies/1533. BAO provides controlled terminology and uniform scope to report probe and drug discovery screening assays and results. BAO leverages description logic to formalize the domain knowledge and facilitate the semantic integration with diverse other resources. As a consequence, BAO offers the potential to infer new knowledge from a corpus of assay results, for example molecular mechanisms of action of perturbagens.