In Silico and In Vitro Approach to Assess Direct Allosteric AMPK Activators from Nature.

The 5'-adenosine monophosphate-activated protein kinase (AMPK) is an important metabolic regulator. Its allosteric drug and metabolite binding (ADaM) site was identified as an attractive target for direct AMPK activation and holds promise as a novel mechanism for the treatment of metabolic diseases. With the exception of lusianthridin and salicylic acid, no natural product (NP) is reported so far to directly target the ADaM site. For the streamlined assessment of direct AMPK activators from the pool of NPs, an integrated workflow using in silico and in vitro methods was applied. Virtual screening combining a 3D shape-based approach and docking identified 21 NPs and NP-like molecules that could potentially activate AMPK. The compounds were purchased and tested in an in vitro AMPK α 1 ß 1 γ 1 kinase assay. Two NP-like virtual hits were identified, which, at 30 µM concentration, caused a 1.65-fold (± 0.24) and a 1.58-fold (± 0.17) activation of AMPK, respectively. Intriguingly, using two different evaluation methods, we could not confirm the bioactivity of the supposed AMPK activator lusianthridin, which rebuts earlier reports.

Support Vector Machine as a Supervised Learning for the Prioritization of Novel Potential SARS-CoV-2 Main Protease Inhibitors.

In the last year, the COVID-19 pandemic has highly affected the lifestyle of the world population, encouraging the scientific community towards a great effort on studying the infection molecular mechanisms. Several vaccine formulations are nowadays available and helping to reach immunity. Nevertheless, there is a growing interest towards the development of novel anti-covid drugs. In this scenario, the main protease (Mpro) represents an appealing target, being the enzyme responsible for the cleavage of polypeptides during the viral genome transcription. With the aim of sharing new insights for the design of novel Mpro inhibitors, our research group developed a machine learning approach using the support vector machine (SVM) classification. Starting from a dataset of two million commercially available compounds, the model was able to classify two hundred novel chemo-types as potentially active against the viral protease. The compounds labelled as actives by SVM were next evaluated through consensus docking studies on two PDB structures and their binding mode was compared to well-known protease inhibitors. The best five compounds selected by consensus docking were then submitted to molecular dynamics to deepen binding interactions stability. Of note, the compounds selected via SVM retrieved all the most important interactions known in the literature.

A Novel Series of [1,2,4]Triazolo[4,3-a]Pyridine Sulfonamides as Potential Antimalarial Agents: In Silico Studies, Synthesis and In Vitro Evaluation.

For the development of new and potent antimalarial drugs, we designed the virtual library with three points of randomization of novel [1,2,4]triazolo[4,3-a]pyridines bearing a sulfonamide fragment. The library of 1561 compounds has been investigated by both virtual screening and molecular docking methods using falcipain-2 as a target enzyme. 25 chosen hits were synthesized and evaluated for their antimalarial activity in vitro against Plasmodium falciparum. 3-Ethyl-N-(3-fluorobenzyl)-N-(4-methoxyphenyl)-[1,2,4]triazolo[4,3-a]pyridine-6-sulfonamide and 2-(3-chlorobenzyl)-8-(piperidin-1-ylsulfonyl)-[1,2,4]triazolo[4,3-a]pyridin-3(2H)-one showed in vitro good antimalarial activity with inhibitory concentration IC50 = 2.24 and 4.98 µM, respectively. This new series of compounds may serve as a starting point for future antimalarial drug discovery programs.

A Combination of Pharmacophore and Docking-based Virtual Screening to Discover new Tyrosinase Inhibitors.

Melanogenesis controls the formation of melanin pigment whose overproduction is related to various hyperpigmentary disorders in humans. Tyrosinase is a type-3 copper enzyme involved in the rate limiting step of melanin synthesis, therefore its inhibition could represent an efficient way for the development of depigmenting agents. In this work, a combination of pharmacophore and docking-based studies has been employed to screen two in-house 3D compound databases containing about 2,000 molecules from natural and synthetic sources. As result we selected two "hit compounds" which proved to inhibit tyrosinase activity showing IC50 values in the micromolar range.

Discovery of Potent Inhibitors for the Large Neutral Amino Acid Transporter 1 (LAT1) by Structure-Based Methods.

The large neutral amino acid transporter 1 (LAT1) is a promising anticancer target that is required for the cellular uptake of essential amino acids that serve as building blocks for cancer growth and proliferation. Here, we report a structure-based approach to identify chemically diverse and potent inhibitors of LAT1. First, a homology model of LAT1 that is based on the atomic structures of the prokaryotic homologs was constructed. Molecular docking of nitrogen mustards (NMs) with a wide range of affinity allowed for deriving a common binding mode that could explain the structure-activity relationship pattern in NMs. Subsequently, validated binding hypotheses were subjected to molecular dynamics simulation, which allowed for extracting a set of dynamic pharmacophores. Finally, a library of ~1.1 million molecules was virtually screened against these pharmacophores, followed by docking. Biological testing of the 30 top-ranked hits revealed 13 actives, with the best compound showing an IC50 value in the sub-µM range.

A daily single dose of a novel modafinil analogue CE-123 improves memory acquisition and memory retrieval.

Dopamine reuptake inhibitors have been shown to improve cognitive parameters in various tasks and animal models. We recently reported a series of modafinil analogues, of which the most promising, 5-((benzhydrylsulfinyl)methyl) thiazole (CE-123), was selected for further development. The present study aims to characterize pharmacological properties of CE-123 and to investigate the potential to enhance memory performance in a rat model. In vitro transporter assays were performed in cells expressing human transporters. CE-123 blocked uptake of [3H] dopamine (IC50 = 4.606 µM) while effects on serotonin (SERT) and the norepinephrine transporter (NET) were negligible. Blood-brain barrier and pharmacokinetic studies showed that the compound reached the brain and lower elimination than R-modafinil. The Pro-cognitive effect was evaluated in a spatial hole-board task in male Sprague-Dawley rats and CE-123 enhances memory acquisition and memory retrieval, represented by significantly increased reference memory indices and shortened latency. Since DAT blockers can be considered as indirect dopamine receptor agonists, western blotting was used to quantify protein levels of dopamine receptors D1R, D2R and D5R and DAT in the synaptosomal fraction of hippocampal subregions CA1, CA3 and dentate gyrus (DG). CE-123 administration in rats increased total DAT levels and D1R protein levels were significantly increased in CA1 and CA3 in treated/trained groups. The increase of D5R was observed in DG only. Dopamine receptors, particularly D1R, seem to play a role in mediating CE-123-induced memory enhancement. Dopamine reuptake inhibition by CE-123 may represent a novel and improved stimulant therapeutic for impairments of cognitive functions.

R-Modafinil exerts weak effects on spatial memory acquisition and dentate gyrus synaptic plasticity.

Modafinil is a wake promoting drug approved for clinical use and also has cognitive enhancing properties. Its enantiomer R-Modafinil (R-MO) is not well studied in regard to cognitive enhancing properties. Hence we studied its effect in a spatial memory paradigm and its possible effects on dentate gyrus long-term potentiation (DG-LTP). Clinically relevant doses of R-MO, vehicle dimethyl sulfoxide (DMSO) or saline were administered for three days during the hole-board test and in in vivo DG-LTP. Synaptic levels of dopamine receptors D1R, D2R, dopamine transporter (DAT), and its phosphorylated form (ph-DAT) in DG tissue 4 h after LTP induction were quantified by western blot analysis. Monoamine reuptake and release assays were performed by using transfected HEK-293 cells. Possible neurotoxic side effects on general behaviour were also studied. R-MO at both doses significantly enhanced spatial reference memory during the last training session and during memory retrieval compared to DMSO vehicle but not when compared to saline treated rats. Similarly, R-MO rescues DG-LTP from impairing effects of DMSO. DMSO reduced memory performance and LTP magnitude when compared to saline treated groups. The synaptic DR1 levels in R-MO groups were significantly decreased compared to DMSO group but were comparable with saline treated animals. We found no effect of R-MO in neurotoxicity tests. Thus, our results support the notion that LTP-like synaptic plasticity processes could be one of the factors contributing to the cognitive enhancing effects of spatial memory traces. D1R may play an important regulatory role in these processes.

Common Hits Approach: Combining Pharmacophore Modeling and Molecular Dynamics Simulations.

We present a new approach that incorporates flexibility based on extensive MD simulations of protein-ligand complexes into structure-based pharmacophore modeling and virtual screening. The approach uses the multiple coordinate sets saved during the MD simulations and generates for each frame a pharmacophore model. Pharmacophore models with the same pharmacophore features are pooled. In this way the high number of pharmacophore models that results from the MD simulation is reduced to only a few hundred representative pharmacophore models. Virtual screening runs are performed with every representative pharmacophore model; the screening results are combined and rescored to generate a single hit-list. The score for a particular molecule is calculated based on the number of representative pharmacophore models which classified it as active. Hence, the method is called common hits approach (CHA). The steps between the MD simulation and the final hit-list are performed automatically and without user interaction. We test the performance of CHA for virtual screening using screening databases with active and inactive compounds for 40 protein-ligand systems. The results of the CHA are compared to the (i) median screening performance of all representative pharmacophore models of protein-ligand systems, as well as to the virtual screening performance of (ii) a random classifier, (iii) the pharmacophore model derived from the experimental structure in the PDB, and (iv) the representative pharmacophore model appearing most frequently during the MD simulation. For the 34 (out of 40) protein-ligand complexes, for which at least one of the approaches was able to perform better than a random classifier, the highest enrichment was achieved using CHA in 68% of the cases, compared to 12% for the PDB pharmacophore model and 20% for the representative pharmacophore model appearing most frequently. The availabilithy of diverse sets of different pharmacophore models is utilized to analyze some additional questions of interest in 3D pharmacophore-based virtual screening.

Pharmacophore Models Derived From Molecular Dynamics Simulations of Protein-Ligand Complexes: A Case Study.

A single, merged pharmacophore hypothesis is derived combining 2000 pharmacophore models obtained during a 20 ns molecular dynamics simulation of a protein-ligand complex with one pharmacophore model derived from the initial PDB structure. This merged pharmacophore model contains all features that are present during the simulation and statistical information about the dynamics of the pharmacophore features. Based on the dynamics of the pharmacophore features we derive two distinctive feature patterns resulting in two different pharmacophore models for the analyzed system - the first model consists of features that are obtained from the PDB structure and the second uses two features that can only be derived from the molecular dynamics simulation. Both models can distinguish between active and decoy molecules in virtual screening. Our approach represents an objective way to add/remove features in pharmacophore models and can be of interest for the investigation of any naturally occurring system that relies on ligand-receptor interactions for its biological activity.

The Use of the Comins-Meyers Amide in Synthetic Chemistry: an Overview.

Formylation reactions are fundamental operations in synthetic chemistry allowing the incorporation into a given structure formyl groups amenable to further deiivatization. Conceptually, the introduction of such groups through the reaction between an electrophilic donor and a nucleophilic acceptor (i.e. organometallic reagent) constitutes a reliable technique with widespread applications. In this Highlight, we summarize the effectiveness of the so called Comnins-Meyers amide - [2-(N-methyl-N-formylamino]pyridine - in such a chemistry with vistas to the synthesis of natural products and biologically active substrates.

Design, Virtual Screening, and Synthesis of Antagonists of αIIbß3 as Antiplatelet Agents.

This article describes design, virtual screening, synthesis, and biological tests of novel αIIbß3 antagonists, which inhibit platelet aggregation. Two types of αIIbß3 antagonists were developed: those binding either closed or open form of the protein. At the first step, available experimental data were used to build QSAR models and ligand- and structure-based pharmacophore models and to select the most appropriate tool for ligand-to-protein docking. Virtual screening of publicly available databases (BioinfoDB, ZINC, Enamine data sets) with developed models resulted in no hits. Therefore, small focused libraries for two types of ligands were prepared on the basis of pharmacophore models. Their screening resulted in four potential ligands for open form of αIIbß3 and four ligands for its closed form followed by their synthesis and in vitro tests. Experimental measurements of affinity for αIIbß3 and ability to inhibit ADP-induced platelet aggregation (IC50) showed that two designed ligands for the open form 4c and 4d (IC50 = 6.2 nM and 25 nM, respectively) and one for the closed form 12b (IC50 = 11 nM) were more potent than commercial antithrombotic Tirofiban (IC50 = 32 nM).

Accessing biological actions of Ganoderma secondary metabolites by in silico profiling.

The species complex around the medicinal fungus Ganoderma lucidum Karst. (Ganodermataceae) is widely known in traditional medicines, as well as in modern applications such as functional food or nutraceuticals. A considerable number of publications reflects its abundance and variety in biological actions either provoked by primary metabolites, such as polysaccharides, or secondary metabolites, such as lanostane-type triterpenes. However, due to this remarkable amount of information, a rationalization of the individual Ganoderma constituents to biological actions on a molecular level is quite challenging. To overcome this issue, a database was generated containing meta-information, i.e., chemical structures and biological actions of hitherto identified Ganoderma constituents (279). This was followed by a computational approach subjecting this 3D multi-conformational molecular dataset to in silico parallel screening against an in-house collection of validated structure- and ligand-based 3D pharmacophore models. The predictive power of the evaluated in silico tools and hints from traditional application fields served as criteria for the model selection. Thus, the focus was laid on representative druggable targets in the field of viral infections (5) and diseases related to the metabolic syndrome (22). The results obtained from this in silico approach were compared to bioactivity data available from the literature. 89 and 197 Ganoderma compounds were predicted as ligands of at least one of the selected pharmacological targets in the antiviral and the metabolic syndrome screening, respectively. Among them only a minority of individual compounds (around 10%) has ever been investigated on these targets or for the associated biological activity. Accordingly, this study discloses putative ligand target interactions for a plethora of Ganoderma constituents in the empirically manifested field of viral diseases and metabolic syndrome which serve as a basis for future applications to access yet undiscovered biological actions of Ganoderma secondary metabolites on a molecular level.

Epidermis-type lipoxygenase 3 regulates adipocyte differentiation and peroxisome proliferator-activated receptor gamma activity.

The nuclear receptor peroxisome proliferator-activated receptor gamma (PPAR gamma) is essential for adipogenesis. Although several fatty acids and their derivatives are known to bind and activate PPAR gamma, the nature of the endogenous ligand(s) promoting the early stages of adipocyte differentiation has remained enigmatic. Previously, we showed that lipoxygenase (LOX) activity is involved in activation of PPAR gamma during the early stages of adipocyte differentiation. Of the seven known murine LOXs, only the unconventional LOX epidermis-type lipoxygenase 3 (eLOX3) is expressed in 3T3-L1 preadipocytes. Here, we show that forced expression of eLOX3 or addition of eLOX3 products stimulated adipogenesis under conditions that normally require an exogenous PPAR gamma ligand for differentiation. Hepoxilins, a group of oxidized arachidonic acid derivatives produced by eLOX3, bound to and activated PPAR gamma. Production of hepoxilins was increased transiently during the initial stages of adipogenesis. Furthermore, small interfering RNA-mediated or retroviral short hairpin RNA-mediated knockdown of eLOX3 expression abolished differentiation of 3T3-L1 preadipocytes. Finally, we demonstrate that xanthine oxidoreductase (XOR) and eLOX3 synergistically enhanced PPAR gamma-mediated transactivation. Collectively, our results indicate that hepoxilins produced by the concerted action of XOR and eLOX3 may function as PPAR gamma activators capable of promoting the early PPAR gamma-dependent steps in the conversion of preadipocytes into adipocytes.

In silico target fishing for rationalized ligand discovery exemplified on constituents of Ruta graveolens.

The identification of targets whose interaction is likely to result in the successful treatment of a disease is of growing interest for natural product scientists. In the current study we performed an exemplary application of a virtual parallel screening approach to identify potential targets for 16 secondary metabolites isolated and identified from the aerial parts of the medicinal plant RUTA GRAVEOLENS L. Low energy conformers of the isolated constituents were simultaneously screened against a set of 2208 pharmacophore models generated in-house for the IN SILICO prediction of putative biological targets, i. e., target fishing. Based on the predicted ligand-target interactions, we focused on three biological targets, namely acetylcholinesterase (AChE), the human rhinovirus (HRV) coat protein and the cannabinoid receptor type-2 (CB (2)). For a critical evaluation of the applied parallel screening approach, virtual hits and non-hits were assayed on the respective targets. For AChE the highest scoring virtual hit, arborinine, showed the best inhibitory IN VITRO activity on AChE (IC (50) 34.7 muM). Determination of the anti-HRV-2 effect revealed 6,7,8-trimethoxycoumarin and arborinine to be the most active antiviral constituents with IC (50) values of 11.98 muM and 3.19 muM, respectively. Of these, arborinine was predicted virtually. Of all the molecules subjected to parallel screening, one virtual CB (2) ligand was obtained, i. e., rutamarin. Interestingly, in experimental studies only this compound showed a selective activity to the CB (2) receptor ( Ki of 7.4 muM) by using a radioligand displacement assay. The applied parallel screening paradigm with constituents of R. GRAVEOLENS on three different proteins has shown promise as an IN SILICO tool for rational target fishing and pharmacological profiling of extracts and single chemical entities in natural product research.

Discovery of novel PPAR ligands by a virtual screening approach based on pharmacophore modeling, 3D shape, and electrostatic similarity screening.

Peroxisome proliferator-activated receptors (PPARs) are important targets for drugs used in the treatment of atherosclerosis, dyslipidaemia, obesity, type 2 diabetes, and other diseases caused by abnormal regulation of the glucose and lipid metabolism. We applied a virtual screening workflow based on a combination of pharmacophore modeling with 3D shape and electrostatic similarity screening techniques to discover novel scaffolds for PPAR ligands. From the resulting 10 virtual screening hits, five tested positive in human PPAR ligand-binding domain (hPPAR-LBD) transactivation assays and showed affinities for PPAR in a competitive binding assay. Compounds 5, 7, and 8 were identified as PPAR-alpha agonists, whereas compounds 2 and 9 showed agonistic activity for hPPAR-gamma. Moreover, compound 9 was identified as a PPAR-delta antagonist. These results demonstrate that our virtual screening protocol is able to enrich novel scaffolds for PPAR ligands that could be useful for drug development in the area of atherosclerosis, dyslipidaemia, and type 2 diabetes.

Discovery of novel cathepsin S inhibitors by pharmacophore-based virtual high-throughput screening.

The cysteine protease cathepsin S (CatS) is involved in the pathogenesis of autoimmune disorders, atherosclerosis, and obesity. Therefore, it represents a promising pharmacological target for drug development. We generated ligand-based and structure-based pharmacophore models for noncovalent and covalent CatS inhibitors to perform virtual high-throughput screening of chemical databases in order to discover novel scaffolds for CatS inhibitors. An in vitro evaluation of the resulting 15 structures revealed seven CatS inhibitors with kinetic constants in the low micromolar range. These compounds can be subjected to further chemical modifications to obtain drugs for the treatment of autoimmune disorders and atherosclerosis.

Discovery of nonsteroidal 17beta-hydroxysteroid dehydrogenase 1 inhibitors by pharmacophore-based screening of virtual compound libraries.

17Beta-hydroxysteroid dehydrogenase type 1 (17beta-HSD1) plays a pivotal role in the local synthesis of the most potent estrogen estradiol. Its expression is a prognostic marker for the outcome of patients with breast cancer and inhibition of 17beta-HSD1 is currently under consideration for breast cancer prevention and treatment. We aimed to identify nonsteroidal 17beta-HSD1 inhibitor scaffolds by virtual screening with pharmacophore models built from crystal structures containing steroidal compounds. The most promising model was validated by comparing predicted and experimentally determined inhibitory activities of several flavonoids. Subsequently, a virtual library of nonsteroidal compounds was screened against the 3D pharmacophore. Analysis of 14 selected compounds yielded four that inhibited the activity of human 17beta-HSD1 (IC 50 below 50 microM). Specificity assessment of identified 17beta-HSD1 inhibitors emphasized the importance of including related short-chain dehydrogenase/reductase (SDR) members to analyze off-target effects. Compound 29 displayed at least 10-fold selectivity over the related SDR enzymes tested.

Structure-based virtual screening for the discovery of natural inhibitors for human rhinovirus coat protein.

Inhibitors of the human rhinovirus (HRV) coat protein are promising candidates to treat and prevent a number of upper respiratory diseases. The aim of this study was to find antiviral compounds from nature, focusing on the HRV coat protein. Through computational structure-based screening of an in-house 3D database containing 9676 individual plant metabolites from ancient herbal medicines, combined with knowledge from traditional use, we selected sesquiterpene coumarins from the gum resin asafetida as promising natural products. Chromatographic separation steps resulted in the isolation of microlobidene (1), farnesiferol C (2), farnesiferol B (3), and kellerin (4). Determination of the inhibition of the HRV-induced cytopathic effect for serotypes 1A, 2, 14, and 16 revealed a dose-dependent and selective antirhinoviral activity against serotype 2 for asafetida (IC50 = 11.0 microg/mL) and its virtually predicted constituents 2 (IC50 = 2.5 microM) and 3 (IC50 = 2.6 microM). Modeling studies helped to rationalize the retrieved results.

Virtual screening for the discovery of bioactive natural products.

In this survey the impact of the virtual screening concept is discussed in the field of drug discovery from nature. Confronted by a steadily increasing number of secondary metabolites and a growing number of molecular targets relevant in the therapy of human disorders, the huge amount of information needs to be handled. Virtual screening filtering experiments already showed great promise for dealing with large libraries of potential bioactive molecules. It can be utilized for browsing databases for molecules fitting either an established pharmacophore model or a three dimensional (3D) structure of a macromolecular target. However, for the discovery of natural lead candidates the application of this in silico tool has so far almost been neglected. There are several reasons for that. One concerns the scarce availability of natural product (NP) 3D databases in contrast to synthetic libraries; another reason is the problematic compatibility of NPs with modern robotized high throughput screening (HTS) technologies. Further arguments deal with the incalculable availability of pure natural compounds and their often too complex chemistry. Thus research in this field is time-consuming, highly complex, expensive and ineffective. Nevertheless, naturally derived compounds are among the most favorable source of drug candidates. A more rational and economic search for new lead structures from nature must therefore be a priority in order to overcome these problems. Here we demonstrate some basic principles, requirements and limitations of virtual screening strategies and support their applicability in NP research with already performed studies. A sensible exploitation of the molecular diversity of secondary metabolites however asks for virtual screening concepts that are interfaced with well-established strategies from classical pharmacognosy that are used in an effort to maximize their efficacy in drug discovery. Such integrated virtual screening workflows are outlined here and shall help to motivate NP researchers to dare a step towards this powerful in silico tool.

Pharmacophore modeling and parallel screening for PPAR ligands.

We describe the generation and validation of pharmacophore models for PPARs, as well as a large scale validation of the parallel screening approach by screening PPAR ligands against a large database of structure-based models. A large test set of 357 PPAR ligands was screened against 48 PPAR models to determine the best models for agonists of PPAR-alpha, PPAR-delta, and PPAR-gamma. Afterwards, a parallel screen was performed using the 357 PPAR ligands and 47 structure-based models for PPARs, which were integrated into a 1537 models comprising in-house pharmacophore database, to assess the enrichment of PPAR ligands within the PPAR hypotheses. For these purposes, we categorized the 1537 database models into 181 protein targets and developed a score that ranks the retrieved targets for each ligand. Thus, we tried to find out if the concept of parallel screening is able to predict the correct pharmacological target for a set of compounds. The PPAR target was ranked first more often than any other target. This confirms the ability of parallel screening to forecast the pharmacological active target for a set of compounds.