Structure–activity relationship of novel DAPK inhibitors identified by structure-based virtual screening

Death-associated protein kinase (DAPK) is a serine/threonine protein kinase implicated in diverse programmed cell death pathways. DAPK is a promising target protein for the treatment of ischemic diseases. We identified novel potent and selective DAPK inhibitors efficiently by structure-based virtual screening, then further developed the hit compounds. In this paper, we describe the development of the hit compounds and the structure–activity relationship studies of the DAPK inhibitors in detail, including calculation of the solvated interaction energy (SIE), and verification of selectivity using a kinase panel.     

Development of a novel PPARγ ligand screening system using pinpoint fluorescence-probed protein

The activation of peroxisome-proliferator-activated receptor-γ (PPARγ), which plays a central role in adipocyte differentiation, depends on ligand-dependent co-activator recruitment. In this study, we developed a novel method of PPARγ ligand screening by measuring the increase in fluorescent polarization accompanied by the interaction of a fluorescent co-activator and PPARγ. Sterol receptor co-activator-1 (SRC-1), a major PPARγ co-activator, was probed by fluorescent TAMRA by the Amber codon fluorescence probe method. Polarization was increased by adding PPARγ ligands to a solution containing labeled SRC-1 (designated TAMRA-SRC-S) and PPARγ. The disassociation constants (Kd) of the PPARγ synthesized ligands, pioglitazone (221 nM), troglitazone (83.0 nM), and 15-deoxy-Δ12,14-prostaglandin J(2) (15d-ΔPGJ(2)) (156 nM), were determined by this method. Farnesol (2.89 μM) and bixin (21.1 μM), which we have reported to be PPARγ ligands, increased the fluorescent polarization. Their Kd values were in agreement with the ED(50) values obtained in the luciferase assay. The results indicate that the method is valuable for screening natural PPARγ ligands.     

Discovery of novel inhibitors of vascular endothelial growth factor-A–Neuropilin-1 interaction by structure-based virtual screening

Neuropilin-1 (NRP-1), one of the most important co-receptors of vascular endothelial growth factor-A (VEGF-A), increases its angiogenic action in several chronic diseases including cancer by increasing the activity of associated tyrosine kinase receptors, VEGFR1 and VEGFR2. Binding of VEGF-A to NRP-1 plays a critical role in pathological angiogenesis and tumor progression. Today, targeting this interaction is a validated approach to fight against angiogenesis-dependent diseases. Only anti-NRP-1 antibodies, peptide and peptidomimetic drug-candidates or hits have been developed thus far. In order to identify potent orally active small organic molecules various experimental and in silico approaches can be used. Here we report, novel promising small drug-like molecules disrupting the binding of VEGF-A₁₆₅ to NRP-1. We carried out structure-based virtual screening experiments using the ChemBridge compound collection on the VEGF-A₁₆₅ binding pocket of NRP-1. After docking and two rounds of similarity search computations, we identified 4 compounds that inhibit the biotinylated VEGF-A₁₆₅ binding to recombinant NRP-1 with K_i of about 10 μM. These compounds contain a common chlorobenzyloxy alkyloxy halogenobenzyl amine scaffold that can serve as a base for further development of new NRP-1 inhibitors.     

IKKβ inhibitors identification part I: Homology model assisted structure based virtual screening

Control of NF-κB release through the inhibition of IKKβ has been identified as a potential target for the treatment of inflammatory and autoimmune diseases. We have employed structure based virtual screening scheme to identify lead like molecule from ChemDiv database. Homology models of IKKβ enzyme were developed based on the crystal structures of four kinases. The efficiency of the homology model has been validated at different levels. Docking of known inhibitors library revealed the possible binding mode of inhibitors. Besides, the docking sequence analyses results indicate the responsibility of Glu172 in selectivity. Structure based virtual screening of ChemDiv database has yielded 277 hits. Top scoring 75 compounds were selected and purchased for the IKKβ enzyme inhibition test. From the combined approach of virtual screening followed by biological screening, we have identified six novel compounds that can work against IKKβ, in which 1 compound had highest inhibition rate 82.09% at 10 μM and IC₅₀ 1.76 μM and 5 compounds had 25.35–48.80% inhibition.     

Identification of novel scaffold of benzothiazepinones as non-ATP competitive glycogen synthase kinase-3β inhibitors through virtual screening

Glycogen synthase kinase-3β (GSK-3β) is an important serine/threonine kinase that has been proved as a key target for neurodegenerative diseases and diabetes. Up to date, most of known inhibitors are bound to the ATP-binding pocket of GSK-3β, which might lead widespread effects due to the high homology between kinases. Recently, some of its non-ATP competitive inhibitors had been confirmed having therapeutical effects owing to their high selectivity. This finding opens a new pathway to study hopeful drugs for treatment of these diseases. However, it is still a challenge nowadays on how to efficiently find non-ATP competitors. Here, we successfully discovered a novel scaffold of benzothiazepinones (BTZs) as selective non-ATP competitive GSK-3β inhibitors through virtual screening approach. A 3D receptor model of substrate binding site of GSK-3β was constructed and applied to screen against drug-like Maybridge database through Autodock program. BTZ compounds were top ranked as efficient hits and were then synthesized for further screening. Among them, the representative compound 4j showed activity to GSK-3β (IC₅₀: 25 μM) in non-ATP competitive mechanism, and nearly no inhibitory effect on other 10 related protein kinases. Overall, the results point out that BTZ compounds might be useful in treatment of Alzheimer’s disease and diabetes mellitus as novel GSK-3β inhibitors. It also suggests, on the other hand, that virtual screening would provide a valuable tool in combination with in vitro assays for the identification of novel selective and potent inhibitors.     

The identification of novel 5′-amino gemcitabine analogs as potent RRM1 inhibitors

The ribonucleotide reductase (RNR) enzyme is a heteromer of RRM1 and RRM2 subunits. The active enzyme catalyzes de novo reduction of ribonucleotides to generate deoxyribonucleotides (dNTPs), which are required for DNA replication and DNA repair processes. Complexity in the generation of physiologically relevant, active RRM1/RRM2 heterodimers was perceived as limiting to the identification of selective RRM1 inhibitors by high-throughput screening of compound libraries and led us to seek alternative methods to identify lead series. In short, we found that gemcitabine, as its diphosphate metabolite, represents one of the few described active site inhibitors of RRM1. We herein describe the identification of novel 5′-amino gemcitabine analogs as potent RRM1 inhibitors through in-cell phenotypic screening.     

Discovery of new GSK-3β inhibitors through structure-based virtual screening

Glycogen synthase kinase-3β (GSK-3β) is an attractive therapeutic target for human diseases, such as diabetes, cancer, neurodegenerative diseases, and inflammation. Thus, structure-based virtual screening was performed to identify novel scaffolds of GSK-3β inhibitors, and we observed that conserved water molecules of GSK-3β were suitable for virtual screening. We found 14 hits and D1 (IC₅₀ of 0.71?μM) were identified. Furthermore, the neuroprotection activity of D1–D3 was validated on a cellular level. 2D similarity searches were used to find derivatives of high inhibitory compounds and an enriched structure–activity relationship suggested that these skeletons were worthy of study as potent GSK-3β inhibitors.     

Identification of small molecule inhibitors of neurite loss induced by Aβ peptide using high content screening

Multiple lines of evidence indicate a strong relationship between Αβ peptide-induced neurite degeneration and the progressive loss of cognitive functions in Alzheimer disease (AD) patients and in AD animal models. This prompted us to develop a high content screening assay (HCS) and Neurite Image Quantitator (NeuriteIQ) software to quantify the loss of neuronal projections induced by Aβ peptide neurons and enable us to identify new classes of neurite-protective small molecules, which may represent new leads for AD drug discovery. We identified thirty-six inhibitors of Aβ-induced neurite loss in the 1,040-compound National Institute of Neurological Disorders and Stroke (NINDS) custom collection of known bioactives and FDA approved drugs. Activity clustering showed that non-steroidal anti-inflammatory drugs (NSAIDs) were significantly enriched among the hits. Notably, NSAIDs have previously attracted significant attention as potential drugs for AD; however their mechanism of action remains controversial. Our data revealed that cyclooxygenase-2 (COX-2) expression was increased following Aβ treatment. Furthermore, multiple distinct classes of COX inhibitors efficiently blocked neurite loss in primary neurons, suggesting that increased COX activity contributes to Aβ peptide-induced neurite loss. Finally, we discovered that the detrimental effect of COX activity on neurite integrity may be mediated through the inhibition of peroxisome proliferator-activated receptor γ (PPARγ) activity. Overall, our work establishes the feasibility of identifying small molecule inhibitors of Aβ-induced neurite loss using the NeuriteIQ pipeline and provides novel insights into the mechanisms of neuroprotection by NSAIDs.     

Receptor-based virtual screening evaluation for the identification of estrogen receptor β ligands

Abstract

In this paper, a receptor-based virtual screening study for the identification of estrogen receptor β (ERβ) ligands was developed. Starting from a commercial database of 400?000 molecules, only six compounds resulted to be potential active ligands of ERβ. Interestingly, all the six molecules possess scaffolds that had already been reported in known ERβ ligands. Therefore, the results obtained herein confirm the reliability of our virtual screening procedure, thus encouraging the application of this protocol to larger commercial databases in order to identify new ERβ ligands.     

In silico screening and identification of potential GSK3β inhibitors

Abstract

Glycogen synthase kinase-3β (GSK3β) has been reported for its impact on multitude biological processes from cell proliferation to apoptosis. The increase in the ratio of active/inactive GSK3β is the major factor associated in the etiology of several psychiatric diseases, diabetes, muscle hypertrophy, neurodegenerative diseases, and some cancers. These findings made GSK3β a promising therapeutic target, and the interest in the discovery, synthesis of novel drugs to effectively attenuate its function with probably no side effects has been increasing in the chronology of GSK3β drug discovery. In the present study, we applied a combination of computational tools on a chemical library for the virtual discovery of their potency to inhibit GSK3β. The chemical library was screened against a set of filters at different levels. Finally, five compounds in the chemical library were found to potentially inhibit GSK3β with no toxic effects. Furthermore, binding mode analysis revealed that all the compounds bound to the ATP site and most of the hydrogen bonding interactions are conserved as in GSK3β structures deposited in PDB.     

Identification of chemically diverse, novel inhibitors of 17β-hydroxysteroid dehydrogenase type 3 and 5 by pharmacophore-based virtual screening

17β-Hydroxysteroid dehydrogenase type 3 and 5 (17β-HSD3 and 17β-HSD5) catalyze testosterone biosynthesis and thereby constitute therapeutic targets for androgen-related diseases or endocrine-disrupting chemicals. As a fast and efficient tool to identify potential ligands for 17βHSD3/5, ligand- and structure-based pharmacophore models for both enzymes were developed. The models were evaluated first by in silico screening of commercial compound databases and further experimentally validated by enzymatic efficacy tests of selected virtual hits. Among the 35 tested compounds, 11 novel inhibitors with distinct chemical scaffolds, e.g. sulfonamides and triazoles, and with different selectivity properties were discovered. Thereby, we provide several potential starting points for further 17β-HSD3 and 17β-HSD5 inhibitor development. Article from the Special issue on Targeted Inhibitors.     

E-pharmacophore-based virtual screening to identify GSK-3β inhibitors

Glycogen synthase kinase-3β (GSK-3β) is a serine/threonine kinase which has attracted significant attention during recent years in drug design studies. The deregulation of GSK-3β increased the loss of hippocampal neurons by triggering apoptosis-mediating production of neurofibrillary tangles and alleviates memory deficits in Alzheimer’s disease (AD). Given its role in the formation of neurofibrillary tangles leading to AD, it has been a major therapeutic target for intervention in AD, hence was targeted in the present study. Twenty crystal structures were refined to generate pharmacophore models based on energy involvement in binding co-crystal ligands. Four common e-pharmacophore models were optimized from the 20 pharmacophore models. Shape-based screening of four e-pharmacophore models against nine established small molecule databases using Phase v3.9 had resulted in 1800 compounds having similar pharmacophore features. Rigid receptor docking (RRD) was performed for 1800 compounds and 20 co-crystal ligands with GSK-3β to generate dock complexes. Interactions of the best scoring lead obtained through RRD were further studied with quantum polarized ligand docking (QPLD), induced fit docking (IFD) and molecular mechanics/generalized Born surface area. Comparing the obtained leads to 20 co-crystal ligands resulted in 18 leads among them, lead1 had the lowest docking score, lower binding free energy and better binding orientation toward GSK-3β. The 50?ns MD simulations run confirmed the stable nature of GSK-3β-lead1 docking complex. The results from RRD, QPLD, IFD and MD simulations confirmed that lead1 might be used as a potent antagonist for GSK-3β.     

Chemoinformatic identification of novel inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase

L-aspartate α-decarboxylase (ADC) belongs to a class of pyruvoyl dependent enzymes and catalyzes the conversion of aspartate to β-alanine in the pantothenate pathway, which is critical for the growth of several micro-organisms, including Mycobacterium tuberculosis (Mtb). Its presence only in micro-organisms, fungi and plants and its absence in animals, particularly human, make it a promising drug target. We have followed a chemoinformatics-based approach to identify potential drug-like inhibitors against Mycobacterium tuberculosis L-aspartate α-decarboxylase (MtbADC). The structure-based high throughput virtual screening (HTVS) mode of the Glide program was used to screen 333,761 molecules of the Maybridge, National Cancer Institute (NCI) and Food and Drug Administration (FDA) approved drugs databases. Ligands were rejected if they cross-reacted with S-adenosylmethionine (SAM) decarboxylase, a human pyruvoyl dependent enzyme. The lead molecules were further analyzed for physicochemical and pharmacokinetic parameters, based on Lipinski's rule of five, and ADMET (absorption, distribution, metabolism, excretion and toxicity) properties. This analysis resulted in eight small potential drug-like inhibitors that are in agreement with the binding poses of the crystallographic ADC:fumarate and ADC:isoasparagine complex structures and whose backbone scaffolds seem to be suitable for further experimental studies in therapeutic development against tuberculosis.     

Identification of novel PPARα/γ dual agonists by virtual screening,ADMET prediction and molecular dynamics simulations

PPARα and PPARγ have been the most widely studied Peroxisome proliferator-activated receptor (PPAR) subtypes due to their important roles in regulating glucose, lipids, and cholesterol metabolism. By combining the lowering serum triglyceride levels benefit of PPARα agonists (such as fibrates) with the glycemic advantages of the PPARγ agonists (such as TZD), the dual PPAR agonists approach can both improve the metabolic effects and minimize the side effects caused by either agent alone, and hence, has become a promising strategy for designing effective drugs against type-2 diabetes. In this study, by means of virtual screening, ADMET prediction and molecular dynamics (MD) simulations techniques, one compound-ASN15761007 with high binding score, low toxicity were gained. It was observed by MD simulations that ASN15761007 not only possessed the same function as AZ242 did in activating PPARα and BRL did in activating PPARγ, but also had more favorable conformation for binding to the two receptors. Our results provided an approach to rapidly produce novel PPARα/γ dual agonists which might be a potential lead compound to develop against insulin resistance and hyperlipidemia.     

Discovery of a novel IKK-β inhibitor by ligand-based virtual screening techniques

Various inflammatory stimuli that activate the nuclear factor kappa B (NF-κB) signaling pathway converge on a serine/threonine kinase that displays a key role in the activation of NF-κB: the I kappa B kinase β (IKK-β). Therefore, IKK-β is considered an interesting target for combating inflammation and cancer. In our study, we developed a ligand-based pharmacophore model for IKK-β inhibitors. This model was employed to virtually screen commercial databases, giving a focused hit list of candidates. Subsequently, we scored by molecular shape to rank and further prioritized virtual hits by three-dimensional shape-based alignment. One out of ten acquired and biologically tested compounds showed inhibitory activity in the low micromolar range on IKK-β enzymatic activity in vitro and on NF-κB transactivation in intact cells. Compound 8 (2-(1-adamantyl)ethyl 4-[(2,5-dihydroxyphenyl)methylamino]benzoate) represents a novel chemical class of IKK-β inhibitors and shows that the presented model is a valid approach for identification and development of new IKK-β ligands.     

Potent and novel 11β-HSD1 inhibitors identified from shape and docking based virtual screening

Several potent and novel 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) inhibitors were discovered from in silico screening the commercially available Maybridge database. Among them, seven hit compounds showed good affinity, with IC(50) values lower than 100 nM and the best one 3.7 nM. To select the lead for further optimization, computational ADME/T prediction, the CYP3A4 inhibition and 11β-HSD1 over 11β-HSD2 selectivity test were also performed. Taking all of the above factors into consideration, two promising compounds were selected as lead structures for further development. The employed hierarchical virtual screening protocol not only demonstrates its efficiency, but also provides novel and selective compounds for developing 11β-HSD1 inhibitors to protect against metabolic syndrome.     

Development of a novel high-throughput screen and identification of small-molecule inhibitors of the Gα-RGS17 protein-protein interaction using AlphaScreen

In this study, the authors used AlphaScreen technology to develop a high-throughput screening method for interrogating small-molecule libraries for inhibitors of the Gα(o)-RGS17 interaction. RGS17 is implicated in the growth, proliferation, metastasis, and the migration of prostate and lung cancers. RGS17 is upregulated in lung and prostate tumors up to a 13-fold increase over patient-matched normal tissues. Studies show RGS17 knockdown inhibits colony formation and decreases tumorigenesis in nude mice. The screen in this study uses a measurement of the Gα(o)-RGS17 protein-protein interaction, with an excellent Z score exceeding 0.73, a signal-to-noise ratio >70, and a screening time of 1100 compounds per hour. The authors screened the NCI Diversity Set II and determined 35 initial hits, of which 16 were confirmed after screening against controls. The 16 compounds exhibited IC(50) <10 μM in dose-response experiments. Four exhibited IC(50) values <6 μM while inhibiting the Gα(o)-RGS17 interaction >50% when compared to a biotinylated glutathione-S-transferase control. This report describes the first high-throughput screen for RGS17 inhibitors, as well as a novel paradigm adaptable to many other RGS proteins, which are emerging as attractive drug targets for modulating G-protein-coupled receptor signaling.     

Pharmacophore modeling and hybrid virtual screening for the discovery of novel IκB kinase 2 (IKK2) inhibitors

IKK2 (IκB kinase 2) inhibitors have been identified as potential drug candidates in the treatment of various immune/inflammatory disorders as well as cancer. So far more than one hundred small molecule inhibitors against IKK2 have been reported publicly. In this investigation, pharmacophore modeling was carried out to clarify the essential structure-activity relationship for the known IKK2 inhibitors. One of the established pharmacophore hypotheses, namely Hypo8, which has the best prediction ability to an external test data set, was suggested as a template for virtual screening. Evaluation of the performances of Hypo8 and a hybrid method (Hypo81docking) in virtual screening indicated that the use of the hybrid virtual screening considerably increased the hit rate and enrichment factor. The hybrid method was therefore adopted for screening several commercially available chemical databases, including Specs, NCI, Maybridge and Chinese Nature Product Database (CNPD), for novel potent IKK2 inhibitors. The hit compounds were subsequently subjected to filtering by Lipinski's rule of five. Finally some of the final hit compounds were selected and suggested for further experimental investigations.