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Nuclear receptors (NRs) activate transcription of target genes in response to binding of ligands to their ligand-binding domains (LBDs). Typically, in vitro assays use either gene expression or the recruitment of coactivators to the isolated LBD of the NR of interest to measure NR activation. However, this approach ignores that NRs function as homo- as well as heterodimers and that the LBD harbors the main dimerization interface. Cofactor recruitment is thereby interconnected with oligomerization status as well as ligand occupation of the partnering LBD through allosteric cross talk. Here we present a modular set of homogeneous time-resolved FRET–based assays through which we investigated the activation of PPARγ in response to ligands and the formation of heterodimers with its obligatory partner RXRα. We introduced mutations into the RXRα LBD that prevent coactivator binding but do not interfere with LBD dimerization or ligand binding. This enabled us to specifically detect PPARγ coactivator recruitment to PPARγ:RXRα heterodimers. We found that the RXRα agonist SR11237 destabilized the RXRα homodimer but promoted formation of the PPARγ:RXRα heterodimer, while being inactive on PPARγ itself. Of interest, incorporation of PPARγ into the heterodimer resulted in a substantial gain in affinity for coactivator CBP-1, even in the absence of ligands. Consequently, SR11237 indirectly promoted coactivator binding to PPARγ by shifting the oligomerization preference of RXRα toward PPARγ:RXRα heterodimer formation. These results emphasize that investigation of ligand-dependent NR activation should take NR dimerization into account. We envision these assays as the necessary assay tool kit for investigating NRs that partner with RXRα.
The interaction of fibroblast growth factors (FGFs) with their fibroblast growth factor receptors (FGFRs) are important in the signaling network of cell growth and development. SSR128129E (SSR),[1, 2] a ligand of small molecular weight with potential anti-cancer properties, acts allosterically on the extracellular domains of FGFRs. Up to now, the structural basis of SSR binding to the D3 domain of FGFR remained elusive. This work reports the structural characterization of the interaction of SSR with one specific receptor, FGFR3, by NMR spectroscopy. This information provides a basis for rational drug design for allosteric FGFR inhibitors.
The bile acid activated transcription factor farnesoid X receptor (FXR) regulates numerous metabolic processes and is a rising target for the treatment of hepatic and metabolic disorders. FXR agonists have revealed efficacy in treating non-alcoholic steatohepatitis (NASH), diabetes and dyslipidemia. Here we characterize imatinib as first-in-class allosteric FXR modulator and report the development of an optimized descendant that markedly promotes agonist induced FXR activation in a reporter gene assay and FXR target gene expression in HepG2 cells. Differential effects of imatinib on agonist-induced bile salt export protein and small heterodimer partner expression suggest that allosteric FXR modulation could open a new avenue to gene-selective FXR modulators.
The arachidonic acid cascade is a key player in inflammation, and numerous well-established drugs interfere with this pathway. Previous studies have suggested that simultaneous inhibition of 5-lipoxygenase (5-LO) and soluble epoxide hydrolase (sEH) results in synergistic anti-inflammatory effects. In this study, a novel prototype of a dual 5-LO/sEH inhibitor KM55 was rationally designed and synthesized. KM55 was evaluated in enzyme activity assays with recombinant enzymes. Furthermore, activity of KM55 in human whole blood and endothelial cells was investigated. KM55 potently inhibited both enzymes in vitro and attenuated the formation of leukotrienes in human whole blood. KM55 was also tested in a cell function-based assay. The compound significantly inhibited the LPS-induced adhesion of leukocytes to endothelial cells by blocking leukocyte activation.
We present a computational method for the reaction-based de novo design of drug-like molecules. The software DOGS (Design of Genuine Structures) features a ligand-based strategy for automated ‘in silico’ assembly of potentially novel bioactive compounds. The quality of the designed compounds is assessed by a graph kernel method measuring their similarity to known bioactive reference ligands in terms of structural and pharmacophoric features. We implemented a deterministic compound construction procedure that explicitly considers compound synthesizability, based on a compilation of 25'144 readily available synthetic building blocks and 58 established reaction principles. This enables the software to suggest a synthesis route for each designed compound. Two prospective case studies are presented together with details on the algorithm and its implementation. De novo designed ligand candidates for the human histamine H4 receptor and γ-secretase were synthesized as suggested by the software. The computational approach proved to be suitable for scaffold-hopping from known ligands to novel chemotypes, and for generating bioactive molecules with drug-like properties.
Background: Threonine Aspartase 1 (Taspase1) mediates cleavage of the mixed lineage leukemia (MLL) protein and leukemia provoking MLL-fusions. In contrast to other proteases, the understanding of Taspase1's (patho)biological relevance and function is limited, since neither small molecule inhibitors nor cell based functional assays for Taspase1 are currently available. Methodology/Findings: Efficient cell-based assays to probe Taspase1 function in vivo are presented here. These are composed of glutathione S-transferase, autofluorescent protein variants, Taspase1 cleavage sites and rational combinations of nuclear import and export signals. The biosensors localize predominantly to the cytoplasm, whereas expression of biologically active Taspase1 but not of inactive Taspase1 mutants or of the protease Caspase3 triggers their proteolytic cleavage and nuclear accumulation. Compared to in vitro assays using recombinant components the in vivo assay was highly efficient. Employing an optimized nuclear translocation algorithm, the triple-color assay could be adapted to a high-throughput microscopy platform (Z'factor = 0.63). Automated high-content data analysis was used to screen a focused compound library, selected by an in silico pharmacophor screening approach, as well as a collection of fungal extracts. Screening identified two compounds, N-[2-[(4-amino-6-oxo-3H-pyrimidin-2-yl)sulfanyl]ethyl]benzenesulfonamideand 2-benzyltriazole-4,5-dicarboxylic acid, which partially inhibited Taspase1 cleavage in living cells. Additionally, the assay was exploited to probe endogenous Taspase1 in solid tumor cell models and to identify an improved consensus sequence for efficient Taspase1 cleavage. This allowed the in silico identification of novel putative Taspase1 targets. Those include the FERM Domain-Containing Protein 4B, the Tyrosine-Protein Phosphatase Zeta, and DNA Polymerase Zeta. Cleavage site recognition and proteolytic processing of these substrates were verified in the context of the biosensor. Conclusions: The assay not only allows to genetically probe Taspase1 structure function in vivo, but is also applicable for high-content screening to identify Taspase1 inhibitors. Such tools will provide novel insights into Taspase1's function and its potential therapeutic relevance.
Poster presentation at 5th German Conference on Cheminformatics: 23. CIC-Workshop Goslar, Germany. 8-10 November 2009 We demonstrate the theoretical and practical application of modern kernel-based machine learning methods to ligand-based virtual screening by successful prospective screening for novel agonists of the peroxisome proliferator-activated receptor gamma (PPARgamma) [1]. PPARgamma is a nuclear receptor involved in lipid and glucose metabolism, and related to type-2 diabetes and dyslipidemia. Applied methods included a graph kernel designed for molecular similarity analysis [2], kernel principle component analysis [3], multiple kernel learning [4], and, Gaussian process regression [5]. In the machine learning approach to ligand-based virtual screening, one uses the similarity principle [6] to identify potentially active compounds based on their similarity to known reference ligands. Kernel-based machine learning [7] uses the "kernel trick", a systematic approach to the derivation of non-linear versions of linear algorithms like separating hyperplanes and regression. Prerequisites for kernel learning are similarity measures with the mathematical property of positive semidefiniteness (kernels). The iterative similarity optimal assignment graph kernel (ISOAK) [2] is defined directly on the annotated structure graph, and was designed specifically for the comparison of small molecules. In our virtual screening study, its use improved results, e.g., in principle component analysis-based visualization and Gaussian process regression. Following a thorough retrospective validation using a data set of 176 published PPARgamma agonists [8], we screened a vendor library for novel agonists. Subsequent testing of 15 compounds in a cell-based transactivation assay [9] yielded four active compounds. The most interesting hit, a natural product derivative with cyclobutane scaffold, is a full selective PPARgamma agonist (EC50 = 10 ± 0.2 microM, inactive on PPARalpha and PPARbeta/delta at 10 microM). We demonstrate how the interplay of several modern kernel-based machine learning approaches can successfully improve ligand-based virtual screening results.
We developed the Pharmacophore Alignment Search Tool (PhAST), a text-based technique for rapid hit and lead structure searching in large compound databases. For each molecule, a two-dimensional graph of potential pharmacophoric points (PPPs) is created, which has an identical topology as the original molecule with implicit hydrogen atoms. Each vertex is coloured by a symbol representing the corresponding PPP. The vertices of the graph are canonically labelled. The symbols associated with the vertices are combined to a so-called PhAST-Sequence beginning with the vertex with the lowest canonical label. Due to the canonical labelling the created PhAST-Sequence is characteristic for each molecule. For similarity assessment, PhAST-Sequences are compared using the sequence identity in their global pairwise alignment. The alignment score lies between 0 (no similarity) and 1 (identical PhAST-Sequences). In order to use global pairwise sequence alignment, a score matrix for pharmacophoric symbols was developed and gap penalties were optimized. PhAST performed comparably and sometimes superior to other similarity search tools (CATS2D, MOE pharmacophore quadruples) in retrospective virtual screenings using the COBRA collection of drugs and lead structures. Most importantly, the PhAST alignment technique allows for the computation of significance estimates that help prioritize a virtual hit list.