Ligand-based pharmacophore modelling, structure optimisation, and biological evaluation for the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors

Targeting Heat shock protein 90 (HSP90) C-terminus is an important strategy to develop HSP90 inhibitors without inducing heat shock response. The development of C-terminal inhibitors, however, is hampered by a lack of understanding regarding the interaction between the HSP90 C-terminus and the present inhibitors. We collected seven classical and structurally diverse HSP90 C-terminal inhibitors and constructed a ligand-based pharmacophore model. The subsequent virtual screening and structural optimisation led to the identification of 2-heteroarylthio-N-arylacetamides as novel HSP90 C-terminal inhibitors. 9 and 27 exhibited strong antitumour activity in vitro by inhibiting proliferation and inducing apoptosis in multiple cancer cell lines. These compounds disrupted the interaction between HSP90 C-terminus and peptidylprolyl isomerase D, exerting a stronger inhibitory effect than novobiocin. 27 significantly induced the degradation of HSP90 clients without triggering heat shock response. In an in vivo study using 4T1 mice breast cancer models, 9 showed a potent antitumour effect without obvious toxicity.

Structure-Based Drug Design for TSPO: Challenges and Opportunities

The translocator protein 18 kDa (TSPO) is an evolutionarily conserved mitochondrial transmembrane protein implicated in various neuropathologies and inflammatory conditions, making it a longstanding diagnostic and therapeutic target of interest. Despite the development of various classes of TSPO ligand chemotypes, and the elucidation of bacterial and non-human mammalian experimental structures, many unknowns exist surrounding its differential structural and functional features in health and disease. There are several limitations associated with currently used computational methodologies for modelling the native structure and ligand-binding behaviour of this enigmatic protein. In this perspective, we provide a critical analysis of the developments in the uses of these methods, outlining their uses, inherent limitations, and continuing challenges. We offer suggestions of unexplored opportunities that exist in the use of computational methodologies which offer promise for enhancing our understanding of the TSPO.

Elucidating the binding mechanisms of GABA and Muscimol as an avenue to discover novel GABA-mimetic small molecules

Gamma-aminobutyric acid (GABA) signaling is the principal inhibitory pathway in the central nervous system. It is critical in neuronal cell proliferation and fate determination. Any aberration in GABA inhibition results in psychiatric and neurological diseases. Thus, modulating GABAergic neurotransmission has become the basis of drug therapy for psychiatric and several neurological diseases. Though GABA and muscimol are classical inhibitors of GABA receptors, the search for novel inhibitors continues unabated. In this study, the binding mechanism of GABA and muscimol was elucidated and applied in the search for small molecule GABAergic inhibitors using comprehensive computational techniques. It was revealed that a high-affinity binding of GABA and muscimol was mediated by a water molecule involving α1Thr129 and then stabilized by strong interactions including salt bridges with β2Glu155 and α1Arg66 amidst hydrogen bonds, π-π stacking, and π -cation interactions with other residues. The binding of GABA and muscimol was also characterized by stability and deeper penetration into the hydrophobic core of the protein which resulted in conformational changes of the binding pocket and domain, by inducing correlated motions of the residues. Thermodynamics analysis showed GABA and muscimol exhibited total binding free energies of -19.85 ± 8.83 Kcal/mol and -26.55 ± 3.42 Kcal/mol, respectively. A pharmacophore model search, based on the energy contributions of implicating binding residues, resulted in the identification of ZINC68604167, ZINC19735138, ZINC04202466, ZINC00901626, and ZINC01532854 as potential GABA-mimetic compounds from metabolites and natural products libraries. This study has elucidated the binding mechanisms of GABA and muscimol and successfully applied in the identification of GABA-mimetic compounds.Communicated by Ramaswamy H. Sarma.

3D-QSAR-based pharmacophore modelling of quinazoline derivatives for the identification of acetylcholinesterase inhibitors through virtual screening, molecular docking, molecular dynamics and DFT studies

Alzheimer's disease (AD) is a progressive neurological disorder responsible for the cognitive dysfunction and cognitive impairment in the patients. Acetylcholinesterase inhibitors (AChEIs) are used to treat AD however, these only provided symptomatic relief and more efficient drug molecules are desired for the effective treatment of the disease. In this article, ligand-based drug-designing strategy was used to develop and validate a field-based 3D-QSAR pharmacophore model on quinazoline-based AChEIs reported in the literature. The validated pharmacophore model (AAAHR_1) was used as a prefilter to screen an ASINEX database via virtual screening workflow (VSW). The hits generated were subjected to MM-GBSA to identify potential AChEIs and top three scoring molecules (BAS 05264565, LEG 12727144 and SYN 22339886) were evaluated for thermodynamic stability at the target site using molecular dynamic simulations. Additionally, DFT study was performed to predict the reactivity of lead molecules towards acetylcholinesterase (AChE). Thus, by utilising various computational tools, three molecules were identified as potent AChEIs that can be developed as potential drug candidates for the treatment of AD.Communicated by Ramaswamy H. Sarma.

Pharmacophore based virtual screening & molecular docking approach for identification of mycobacterial membrane protein large 3 (MmpL3) inhibitors

Tuberculosis (TB) disease continues to remain one of the global threats for mankind. Till date many antibacterial compounds have been identified to target mycobacterium tuberculosis (MTB). However, the mutating nature of the mycobacteria has always posed a challenge for designing newer drugs which can target both the non-mutating and mutating forms of TB. In this process, Mycobacterial membrane protein Large 3 (MmpL3) transporter was identified as one of the key targets for inhibiting tuberculosis. Herein we have made an effort to find potential inhibitors against MmpL3 by using a pharmacophore-based virtual screening workflow, followed by molecular docking studies and molecular dynamic simulations. Based on a set of 220 compounds showing anti-tubercular activity proposed to target MmpL3 transporter with MIC values ranging from 0.003 to 737 μM, a 5-point pharmacophore ADHHR_2 model possessing one hydrogen acceptor, one hydrogen donor, two hydrophobic groups and an aromatic ring system was generated. The model validated by enrichment study was used to screen Asinex and DrugBank database to identify a potential lead compound such as DrugBank_6059 that was found to show better binding affinity (-11.36) and hydrophobic interactions with target protein in comparison to standard drug SQ109.Communicated by Ramaswamy H. Sarma.

Computational studies to explore inhibitors against the cyclin-dependent kinase 12/13 enzyme: an insilco pharmacophore modeling, molecular docking and dynamics approach

Cancer is enlisted among the deadliest disease all over the world. The cyclin-dependent kinases 12 and 13 have been identified as cell cycle regulators. They conduct transcription and co-transcriptional processes by phosphorylating the C-terminal of RNA polymerase-II. Inhibition of CDK12 and 13 selectively presents a novel strategy to treat triple-negative breast cancer, but dual inhibitors are still lacking. Here, we report the screening of the natural product compound class against the dual CDK12/13 enzyme by employing various in silico methods. Complexes of CDK12 enzymes are used to form common feature pharmacophore models, whereas we perform receptor-based pharmacophore modelling on CDK13 enzyme owing to the availability of a single PDB. On conducting screening over the representative pharmacophores, the common drug-like screened natural products were shortlisted for conducting molecular docking studies. After molecular docking calculations, the candidates that showed crucial interaction with CDK12 and CDK13 enzymes were shortlisted for simulation studies. Five common docked candidates were selected for molecular dynamics simulations and free energy calculations. Based on the cut-off criteria of free energy calculations, one common hit was selected as the dual CDK12/13 inhibitor. The outcome concluded that the hit with ID CNP0386383 possesses drug-like properties, displays crucial interaction in the binding pocket, and shows stable dynamic behaviour and higher binding energy than the experimentally reported inhibitor of both CDK12 and CDK13 enzymes.Communicated by Ramaswamy H. Sarma.

The action of physiological and synthetic steroids on the calcium channel CatSper in human sperm

The sperm-specific channel CatSper (cation channel of sperm) controls the intracellular Ca2+ concentration ([Ca2+]i) and plays an essential role in sperm function. It is mainly activated by the steroid progesterone (P4) but is also promiscuously activated by a wide range of synthetic and physiological compounds. These compounds include diverse steroids whose action on the channel is so far still controversial. To investigate the effect of these compounds on CatSper and sperm function, we developed a high-throughput screening (HTS) assay to measure changes in [Ca2+]i in human sperm and screened 1,280 approved and off-patent drugs including 90 steroids from the Prestwick chemical library. More than half of the steroids tested (53%) induced an increase in [Ca2+]i and reduced the P4-induced Ca2+ influx in human sperm in a dose-dependent manner. Ten of the most potent steroids (activating and P4-inhibiting) were selected for a detailed analysis of their action on CatSper and their ability to act on sperm acrosome reaction (AR) and penetration in viscous media. We found that these steroids show an inhibitory effect on P4 but not on prostaglandin E1-induced CatSper activation, suggesting that they compete for the same binding site as P4. Pregnenolone, dydrogesterone, epiandrosterone, nandrolone, and dehydroepiandrosterone acetate (DHEA) were found to activate CatSper at physiologically relevant concentrations within the nanomolar range. Like P4, most tested steroids did not significantly affect the AR while stanozolol and estropipate slightly increased sperm penetration into viscous medium. Furthermore, using a hybrid approach integrating pharmacophore analysis and statistical modelling, we were able to screen in silico for steroids that can activate the channel and define the physicochemical and structural properties required for a steroid to exhibit agonist activity against CatSper. Overall, our results indicate that not only physiological but also synthetic steroids can modulate the activity of CatSper with varying potency and if bound to CatSper prior to P4, could impair the timely CatSper activation necessary for proper fertilization to occur.

Molecular docking, QSAR, pharmacophore modeling, and dynamics studies of some chromone derivatives for the discovery of anti-breast cancer agents against hormone-dependent breast cancer

In search of new anti-breast cancer agents, the present study envisaged the design and synthesis of a series of benzopyran-chalcones. All the synthesized compounds were assayed for their in-vitro anticancer activity against ER + MCF-7 and triple-negative MDA-MB-231 breast cancer cell lines using SRB assay. The synthesized compounds were found active against ER + MCF-7 cell lines. Based on the in-vitro data, in-silico analysis was performed using hormone-dependent breast cancer targets such as hER-α and aromatase because the compounds showed activity against MCF-7 cells and none was active against MDA-MB-231. The in-silico results supported the in-vitro anticancer activity suggesting the affinity of compounds toward hormone-dependant breast cancer. Compounds 4A1 to 4A3 were found to be most cytotoxic to MCF-7 cells with IC50 values of 31.87, 22.95, and 20.34 μg/ml, respectively (Doxorubicin IC50: <10 μg/ml). In addition, they showed the interactions with the amino acid residues of a binding cavity of an hER-α. Furthermore, quantitative structure-activity relationship (QSAR) studies were performed to reveal the vital structural features required for anticancer activity against breast cancer. Molecular dynamic simulation studies of hER-α and 4A3 in comparison with the raloxifene complex ensure the appropriate refinement of compounds in the dynamic system. Additionally, a generated pharmacophore model explored the essential pharmacophoric features of the synthesized scaffolds with respect to clinically used drug molecules for optimal hormone-dependant anti-breast cancer activity.Communicated by Ramaswamy H. Sarma.

Computational assessment of chemicals from Morinda citrifolia as potential inhibitors of B-Raf kinase in hepatocellular carcinoma treatment

Hepatocellular carcinoma (HCC) is a tumour pathology that lacks specific treatment and is predominantly resistant to chemotherapy. The inhibitory activity of Morinda citrifolia, an evergreen tree commonly called Noni, against various carcinomas especially HCC is widely acclaimed. This study was to assess the phytochemical constituents of the plant for inhibitory activity against B-Raf kinase (3C4C) in order to design drugs for HCC treatment. Molecular docking, pharmacophore modelling, induced-fit docking, molecular dynamics (MD) simulations and ADMET predictions were the computational techniques employed in this study to detect potential inhibitors of B-Raf kinase from 135 compounds of Morinda citrifolia. Soranjidiol, Thiamine, Lucidin, 2-Methyl-1,3,5-Trihydroxyanthraquinone and Rubiadin were the five top-scoring compounds ranging from -8.39 to -8.22 kcal/mol, however, the standard ligand, PLX4720, scored -11.26 kcal/mol. The five compounds, like PLX4720 demonstrated hydrogen bond interactions with active site amino acid residues such as GLN 530, CYS 532 and ASP 594. The main energy contributor to the interactions between the compounds and B-Raf kinase were pi-stacking, hydrogen bond, van der Waals and covalent energy. Better docking scores obtained in the induced-fit docking further validates the inhibitory potential of the Soranjidiol against the flexible protein. In MD simulations, Soranjidiol revealed good stability in the active site of the protein since significant conformational changes were not evident. These five compounds, unlike the standard compound, demonstrated adequate druglike properties and good safety profiles. Therefore, further studies should be undertaken so as to develop them into drugs against HCC.Communicated by Ramaswamy H. Sarma.

In silico prediction of SARS-CoV-2 main protease and polymerase inhibitors: 3D-Pharmacophore modelling

The outbreak of the second severe acute respiratory syndrome coronavirus (SARS-CoV-2) known as COVID-19 has caused global concern. No effective vaccine or treatment to control the virus has been approved yet. Social distancing and precautionary protocols are still the only way to prevent person-to-person transmission. We hope to identify anti-COVID-19 activity of the existing drugs to overcome this pandemic as soon as possible. The present study used HEX and AutoDock Vina softwares to predict the affinity of about 100 medicinal structures toward the active site of 3-chymotrypsin-like protease (3Clpro) and RNA-dependent RNA polymerase (RdRp), separately. Afterwards, MOE software and the pharmacophore-derived query methodology were employed to determine the pharmacophore model of their inhibitors. Tegobuvir (19) and compound 45 showed the best binding affinity toward RdRp and 3Clpro of SARS-CoV-2 in silico, respectively. Tegobuvir -previously applied for hepatitis C virus- formed highly stable complex with uncommon binding pocket of RdRp (E total: -707.91 Kcal/mol) in silico. In addition to compound 45, tipranavir (28) and atazanavir (26) as FDA-approved HIV protease inhibitors were tightly interacted with the active site of SARS-CoV-2 main protease as well. Based on pharmacophore modelling, a good structural pattern for potent candidates against SARS-CoV-2 main enzymes is suggested. Re-tasking or taking inspiration from the structures of tegobuvir and tipranavir can be a proper approach toward coping with the COVID-19 in the shortest possible time and at the lowest cost.Communicated by Ramaswamy H. Sarma.

Ligand-Enhanced Negative Images Optimized for Docking Rescoring

Despite the pivotal role of molecular docking in modern drug discovery, the default docking scoring functions often fail to recognize active ligands in virtual screening campaigns. Negative image-based rescoring improves docking enrichment by comparing the shape/electrostatic potential (ESP) of the flexible docking poses against the target protein’s inverted cavity volume. By optimizing these negative image-based (NIB) models using a greedy search, the docking rescoring yield can be improved massively and consistently. Here, a fundamental modification is implemented to this shape-focused pharmacophore modelling approach—actual ligand 3D coordinates are incorporated into the NIB models for the optimization. This hybrid approach, labelled as ligand-enhanced brute-force negative image-based optimization (LBR-NiB), takes the best from both worlds, i.e., the all-roundedness of the NIB models and the difficult to emulate atomic arrangements of actual protein-bound small-molecule ligands. Thorough benchmarking, focused on proinflammatory targets, shows that the LBR-NiB routinely improves the docking enrichment over prior iterations of the R-NiB methodology. This boost can be massive, if the added ligand information provides truly essential binding information that was lacking or completely missing from the cavity-based NIB model. On a practical level, the results indicate that the LBR-NiB typically works well when the added ligand 3D data originates from a high-quality source, such as X-ray crystallography, and, yet, the NIB model compositions can also sometimes be improved by fusing into them, for example, with flexibly docked solvent molecules. In short, the study demonstrates that the protein-bound ligands can be used to improve the shape/ESP features of the negative images for effective docking rescoring use in virtual screening.

Greedy 3-Point Search (G3PS)-A Novel Algorithm for Pharmacophore Alignment

Chemical features of small molecules can be abstracted to 3D pharmacophore models, which are easy to generate, interpret, and adapt by medicinal chemists. Three-dimensional pharmacophores can be used to efficiently match and align molecules according to their chemical feature pattern, which facilitates the virtual screening of even large compound databases. Existing alignment methods, used in computational drug discovery and bio-activity prediction, are often not suitable for finding matches between pharmacophores accurately as they purely aim to minimize RMSD or maximize volume overlap, when the actual goal is to match as many features as possible within the positional tolerances of the pharmacophore features. As a consequence, the obtained alignment results are often suboptimal in terms of the number of geometrically matched feature pairs, which increases the false-negative rate, thus negatively affecting the outcome of virtual screening experiments. We addressed this issue by introducing a new alignment algorithm, Greedy 3-Point Search (G3PS), which aims at finding optimal alignments by using a matching-feature-pair maximizing search strategy while at the same time being faster than competing methods.

Pharmacophore Modelling-Based Drug Repurposing Approaches for SARS-CoV-2 Therapeutics

The recent outbreak of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a devastating effect globally with no effective treatment. The swift strategy to find effective treatment against coronavirus disease 2019 (COVID-19) is to repurpose the approved drugs. In this pursuit, an exhaustive computational method has been used on the DrugBank compounds targeting nsp16/nsp10 complex (PDB code: 6W4H). A structure-based pharmacophore model was generated, and the selected model was escalated to screen DrugBank database, resulting in three compounds. These compounds were subjected to molecular docking studies at the protein-binding pocket employing the CDOCKER module available with the Discovery Studio v18. In order to discover potential candidate compounds, the co-crystallized compound S-adenosyl methionine (SAM) was used as the reference compound. Additionally, the compounds remdesivir and hydroxycholoroquine were employed for comparative docking. The results have shown that the three compounds have demonstrated a higher dock score than the reference compounds and were upgraded to molecular dynamics simulation (MDS) studies. The MDS results demonstrated that the three compounds, framycetin, kanamycin, and tobramycin, are promising candidate compounds. They have represented a stable binding mode at the targets binding pocket with an average protein backbone root mean square deviation below 0.3 nm. Additionally, they have prompted the hydrogen bonds during the entire simulations, inferring that the compounds have occupied the active site firmly. Taken together, our findings propose framycetin, kanamycin, and tobramycin as potent putative inhibitors for COVID-19 therapeutics.

Discovery of New Small Molecule Hits as Hepatitis B Virus Capsid Assembly Modulators: Structure and Pharmacophore-Based Approaches

Hepatitis B virus (HBV) capsid assembly modulators (CpAMs) have shown promise as potent anti-HBV agents in both preclinical and clinical studies. Herein, we report our efforts in identifying novel CpAM hits via a structure-based virtual screening against a small molecule protein-protein interaction (PPI) library, and pharmacophore-guided compound design and synthesis. Curated compounds were first assessed in a thermal shift assay (TSA), and the TSA hits were further evaluated in an antiviral assay. These efforts led to the discovery of two structurally distinct scaffolds, ZW-1841 and ZW-1847, as novel HBV CpAM hits, both inhibiting HBV in single-digit µM concentrations without cytotoxicity at 100 µM. In ADME assays, both hits displayed extraordinary plasma and microsomal stability. Molecular modeling suggests that these hits bind to the Cp dimer interfaces in a mode well aligned with known CpAMs.

In silico guided design of non-covalent inhibitors of DprE1: synthesis and biological evaluation

DprE1 is a potential target of resistant tuberculosis (TB), especially multidrug-resistant (MDR) and extensively drug-resistant (XDR) TB. 2-benzoxazolinone is a closely related bioisostere of some scaffolds such as benzoxazoles, benzimidazole, benzothiazolinone, and benzothiazoles that have been previously explored against DprE1. Thus, a ligand-based quantitative pharmacophore model (AHRR.8) of DprE1 was developed and this pharmacophore model was utilized in activity profiling of some 2-benzoxazolinones from an in-house database using virtual screening. Obtained hits were subject to molecular docking, molecular dynamics (MD), and MM/GBSA calculations, which resulted in benzoyl-substituted derivatives of 2-benzoxazolinone showing strong interactions with the key amino acid residues in the active site of DprE1. Based on in silico results, the top five hits were duly synthesized and evaluated against the XDR-TB strain. This study is an initial effort to explore 2-benzoxazolinones against XDR-TB, which can be submitted further to lead optimization for refining the results.

3D Pharmacophore-Based Discovery of Novel KV10.1 Inhibitors with Antiproliferative Activity

Simple Summary

A novel structural class of inhibitors of the voltage-gated potassium channel K_V10.1 was discovered by a ligand-based drug design method using a 3D pharmacophore model. The virtual screening hit compound ZVS-08 inhibited the channel in a voltage-dependent manner consistent with the action of a gating modifier. Structure–activity relationship studies revealed a nanomolar K_V10.1 inhibitor that is selective for some K_V and Na_V channels but exhibits significant inhibition of the hERG channel. K_V10.1 inhibitor 1 inhibited the growth of the MCF-7 cell line expressing high levels of K_V10.1 and low levels of hERG more potently than the Panc1 cell line (no K_V10.1 and high hERG expression). Moreover, the K_V10.1 inhibitor 1 induced significant apoptosis in tumour spheroids of Colo-357 cells. This study may provide a basis for the use of computational drug design methods for the discovery of novel K_V10.1 inhibitors as new promising anticancer drugs.

Abstract

(1) Background: The voltage-gated potassium channel K_V10.1 (Eag1) is considered a near- universal tumour marker and represents a promising new target for the discovery of novel anticancer drugs. (2) Methods: We utilized the ligand-based drug discovery methodology using 3D pharmacophore modelling and medicinal chemistry approaches to prepare a novel structural class of K_V10.1 inhibitors. Whole-cell patch clamp experiments were used to investigate potency, selectivity, kinetics and mode of inhibition. Anticancer activity was determined using 2D and 3D cell-based models. (3) Results: The virtual screening hit compound ZVS-08 discovered by 3D pharmacophore modelling exhibited an IC₅₀ value of 3.70 µM against K_V10.1 and inhibited the channel in a voltage-dependent manner consistent with the action of a gating modifier. Structural optimization resulted in the most potent K_V10.1 inhibitor of the series with an IC₅₀ value of 740 nM, which was potent on the MCF-7 cell line expressing high K_V10.1 levels and low hERG levels, induced significant apoptosis in tumour spheroids of Colo-357 cells and was not mutagenic. (4) Conclusions: Computational ligand-based drug design methods can be successful in the discovery of new potent K_V10.1 inhibitors. The main problem in the field of K_V10.1 inhibitors remains selectivity against the hERG channel, which needs to be addressed in the future also with target-based drug design methods.

Structure-Based Virtual Screening of Tumor Necrosis Factor-α Inhibitors by Cheminformatics Approaches and Bio-Molecular Simulation

Tumor necrosis factor-α (TNF-α) is a drug target in rheumatoid arthritis and several other auto-immune disorders. TNF-α binds with TNF receptors (TNFR), located on the surface of several immunological cells to exert its effect. Hence, the use of inhibitors that can hinder the complex formation of TNF-α/TNFR can be of medicinal significance. In this study, multiple chem-informatics approaches, including descriptor-based screening, 2D-similarity searching, and pharmacophore modelling were applied to screen new TNF-α inhibitors. Subsequently, multiple-docking protocols were used, and four-fold post-docking results were analyzed by consensus approach. After structure-based virtual screening, seventeen compounds were mutually ranked in top-ranked position by all the docking programs. Those identified hits target TNF-α dimer and effectively block TNF-α/TNFR interface. The predicted pharmacokinetics and physiological properties of the selected hits revealed that, out of seventeen, seven compounds (4, 5, 10, 11, 13-15) possessed excellent ADMET profile. These seven compounds plus three more molecules (7, 8 and 9) were chosen for molecular dynamics simulation studies to probe into ligand-induced structural and dynamic behavior of TNF-α, followed by ligand-TNF-α binding free energy calculation using MM-PBSA. The MM-PBSA calculations revealed that compounds 4, 5, 7 and 9 possess highest affinity for TNF-α; 8, 11, 13-15 exhibited moderate affinities, while compound 10 showed weaker binding affinity with TNF-α. This study provides valuable insights to design more potent and selective inhibitors of TNF-α, that will help to treat inflammatory disorders.

Computer-aided identification of human carbonic anhydrase isoenzyme VII inhibitors as potential antiepileptic agents

Human carbonic anhydrase (hCA) belongs to a superfamily of metalloenzymes that reversibly catalyse the hydration of carbon dioxide to give bicarbonate (HCO₃^-) and proton (H⁺). As HCO₃^- ions play an important role in neuronal signalling hence, hCA enzymes are an attractive target for antiepileptic drugs. Out of all the isoforms, hCA VII is predominantly expressed in the brain cortex and hippocampus region, which are the most affected area during seizure activity. Hence, we have identified some hCA VII inhibitors employing computational tools like atom-based 3D quantitative structure-activity relationship (QSAR), auto-QSAR, pharmacophore-based virtual screening, molecular docking, and molecular dynamics (MD) simulations. Atom-based 3D QSAR modelling outperformed auto-QSAR with an R² and Q² value of 0.9634 and 0.9646, respectively. A four-feature pharmacophore model (AADR_1) was developed and a focussed library of around 3,00,000 compounds was screened. Compounds with a phase screen score >2.40 were selected for docking studies. The activity of the selected hits was predicted employing the developed 3D QSAR model. Finally, three compounds were taken up for the MD simulation studies which also suggest that the identified hits might form a stable complex with hCA VII enzyme. A comparative docking study was also done with other hCA isoforms like I, II, IV, IX, and XII to examine the selectivity of the identified hits towards hCA VII. Based on these studies, three hits have been identified as potential hCA VII inhibitor which is drug-like molecules. Further, in vitro studies are required to develop leads from these identified hits.Communicated by Ramaswamy H. Sarma.

Critical Insight into the Design of PPAR-γ Agonists by Virtual Screening Techniques

BACKGROUND

Design of novel PPAR-γ modulators with better binding efficiency and fewer side effects to treat type 2 diabetes is still a challenge for medicinal chemists. Cost and time efficient computational methods have presently become an integral part of research in nuclear receptors and their ligands, enabling hit to lead identification and lead optimization. This review will focus on cutting-edge technologies used in most recent studies on the design of PPAR- γ agonists and will discuss the chemistry of few molecules which emerged successful.

METHODS

Literature review was carried out in google scholar using customized search from 2011- 2017. Computer-aided design methods presented in this article were used as search terms to retrieve corresponding literature.

RESULTS

Virtual screening of natural product libraries is an effective strategy to harness nature as the source of ligands for PPARs. Rigid and induced fit docking and core hopping approach in docking are rapid screening methods to predict the PPAR- γ and PPAR-α/ γ dual agonistic activity. Onedimensional drug profile matching is one of the recent virtual screening methods by which an antiprotozoal drug, Nitazoxanide was identified as a PPAR- γ agonist.

CONCLUSION

It is concluded that to achieve a convincing and reliable design of PPAR-γ agonist by virtual screening techniques, customized workflow comprising of appropriate models is essential in which methods may be applied either sequentially or simultaneously.

Combining Virtual Screening Protocol and In Vitro Evaluation towards the Discovery of BACE1 Inhibitors

The treatment options for a patient diagnosed with Alzheimer’s disease (AD) are currently limited. The cerebral accumulation of amyloid-β (Aβ) is a critical molecular event in the pathogenesis of AD. When the amyloidogenic β-secretase (BACE1) is inhibited, the production of Aβ peptide is reduced. Henceforth, the main goal of this study is the discovery of new small bioactive molecules that potentially reach the brain and inhibit BACE1. The work was conducted by a customized molecular modelling protocol, including pharmacophore-based and molecular docking-based virtual screening (VS). Structure-based (SB) and ligand-based (LB) pharmacophore models were designed to accurately screen several drug-like compound databases. The retrieved hits were subjected to molecular docking and in silico filtered to predict their ability to cross the blood–brain barrier (BBB). Additionally, 34 high-scoring compounds structurally distinct from known BACE1 inhibitors were selected for in vitro screening assay, which resulted in 13 novel hit-compounds for this relevant therapeutic target. This study disclosed new BACE1 inhibitors, proving the utility of combining computational and in vitro approaches for effectively predicting anti-BACE1 agents in the early drug discovery process.

In silico identification of novel 5-HT2A antagonists supported with ligand- and target-based drug design methodologies

A wide range of neuropsychological disorders is caused by serotonin 5-HT_2A receptor (5-HT_2AR) malfunction. Therefore, this receptor had been frequently used as target in CNS drug research. To design novel potent 5-HT_2AR antagonists, we have combined ligand-based and target-based approaches. This study was performed on wide range of structurally diverse antagonists that were divided into three different clusters: clozapine, ziprasidone, and ChEMBL240876 derivatives. By performing the 50 ns long molecular dynamic simulations with each cluster representative in complex with 5-HT_2A receptor, we have obtained virtually bioactive conformations of the ligands and three different antagonist-bound, inactive, conformations of the 5-HT_2AR. These three 5-HT_2AR conformations were further used for docking studies and generation of the bioactive conformations of the data set ligands in each cluster. Subsequently, selected conformers were used for 3D-Quantitative Structure Activity Relationship (3D-QSAR) modelling and pharmacophore analysis. The reliability and predictive power of the created model was assessed using an external test set compounds and showed reasonable external predictability. Statistically significant variables were used to define the most important structural features required for 5-HT_2A antagonistic activity. Conclusions obtained from performed ligand-based (3D-QSAR) and target-based (molecular docking and molecular dynamics) methods were compiled and used as guidelines for rational drug design of novel 5-HT_2AR antagonists.Communicated by Ramaswamy H. Sarma.

Modulating FOXO3 transcriptional activity by small, DBD-binding molecules

FOXO transcription factors are critical regulators of cell homeostasis and steer cell death, differentiation and longevity in mammalian cells. By combined pharmacophore-modeling-based in silico and fluorescence polarization-based screening we identified small molecules that physically interact with the DNA-binding domain (DBD) of FOXO3 and modulate the FOXO3 transcriptional program in human cells. The mode of interaction between compounds and the FOXO3-DBD was assessed via NMR spectroscopy and docking studies. We demonstrate that compounds S9 and its oxalate salt S9OX interfere with FOXO3 target promoter binding, gene transcription and modulate the physiologic program activated by FOXO3 in cancer cells. These small molecules prove the druggability of the FOXO-DBD and provide a structural basis for modulating these important homeostasis regulators in normal and malignant cells.

Design and Selection of Novel C1s Inhibitors by In Silico and In Vitro Approaches

The complement system is associated with various diseases such as inflammation or auto-immune diseases. Complement-targeted drugs could provide novel therapeutic intervention against the above diseases. C1s, a serine protease, plays an important role in the CS and could be an attractive target since it blocks the system at an early stage of the complement cascade. Designing C1 inhibitors is particularly challenging since known inhibitors are restricted to a narrow bioactive chemical space in addition selectivity over other serine proteases is an important requirement. The typical architecture of a small molecule inhibitor of C1s contains an amidine (or guanidine) residue, however, the discovery of non-amidine inhibitors might have high value, particularly if novel chemotypes and/or compounds displaying improved selectivity are identified. We applied various virtual screening approaches to identify C1s focused libraries that lack the amidine/guanidine functionalities, then the in silico generated libraries were evaluated by in vitro biological assays. While 3D structure-based methods were not suitable for virtual screening of C1s inhibitors, and a 2D similarity search did not lead to novel chemotypes, pharmacophore model generation allowed us to identify two novel chemotypes with submicromolar activities. In three screening rounds we tested altogether 89 compounds and identified 20 hit compounds (<10 μM activities; overall hit rate: 22.5%). The highest activity determined was 12 nM (1,2,4-triazole), while for the newly identified chemotypes (1,3-benzoxazin-4-one and thieno[2,3-d][1,3]oxazin-4-one) it was 241 nM and 549 nM, respectively.

Pharmacophore and docking-based sequential virtual screening for the identification of novel Sigma 1 receptor ligands

Sigma 1 receptor (σ1), a small transmembrane protein expressed in most human cells participates in modulating the function of other membrane proteins such as G protein coupled receptors and ion channels. Several ligands targeting this receptor are currently in clinical trials for the treatment of Alzheimer's disease, ischemic stroke and neuro-pathic pain. Hence, this receptor has emerged as an attractive target for the treatment of neuro-pathological diseases with unmet medical needs. It is of interest to identify and characterise novelσ1 receptor ligands with different chemical scaffolds using computer-aided drug designing approach. In this work, a GPCR-focused chemical library consisting of 8543 compounds was screened by pharmacophore and docking-based virtual screening methods using LigandScout 4.3 and Autodock Vina 1.1.2 in PyRx 0.8, respectively. The pharmacophore model was constructed based on the interactions of a selective agonist and another antagonist ligand with high binding affinity to the human σ1receptors. Candidate compounds were filtered sequentially by pharmacophore-fit scores, docking energy scores, drug-likeness filters and ADMET properties. The binding mode and pharmacophore mapping of candidate compounds were analysed by Autodock Vina 1.1.2 and LigandScout 4.3 programs, respectively. A pharmacophore model composed of three hydrophobic and positive ionizable features with recognized geometry was built and used as a 3D query for screening a GPCR-focused chemical library by LigandScout 4.3 program. Among the screened 8543 compounds, 159 candidate compounds were obtained from pharmacophore-based screening. 45 compounds among them bound to σ 1receptor with high binding-affinity scores in comparison to the co-crystallized ligand. Amongst these, top five candidate compounds with excellent druglikeness and ADMET properties were selected. These five candidate compounds may act as potential σ1 receptor ligands.

Virtual Screening for Type II B Inhibitors of B-RafV600E Kinase

BACKGROUND

B-RafV600E kinase was identified as an important target in current cancer treatment, and the type II B inhibitors show good qualities in preclinical studies. Therefore, it is very important to discover novel II B inhibitors of B-RafV600E kinase.

METHODS

In order to discover novel II B inhibitors of B-RafV600E kinase, virtual screening against ZINC database was performed by using a combination of pharmacophore modelling, molecular docking, 3DQSAR model and binding free energy (ΔGbind) calculation studies. The inhibitory activities against A375 cell lines of the hit compounds were tested by using MTT assay.

RESULTS

Five promising hit compounds were obtained after screening, and all the five hit compounds showed good inhibitory rates against A375 cell lines.

CONCLUSION

The combined approach of the virtual screening in our work is effective, which can be used to discover novel inhibitors with a new skeleton. In addition, the five compounds obtained from the screening showed good inhibitory rates against A375 cell lines, which can be considered to develop new II B inhibitors of B-RafV600E kinase.

Identification of potential CRAC channel inhibitors: Pharmacophore mapping, 3D-QSAR modelling, and molecular docking approach

Upregulation of store-operated Ca²⁺ influx via ORAI1, an integral component of the CRAC channel, is responsible for abnormal cytokine release in active rheumatoid arthritis, and therefore ORAI1 has been proposed as an attractive molecular target. In this study, we attempted to predict the mechanical insights of ORAI1 inhibitors through pharmacophore modelling, 3D-QSAR, molecular docking and free energy analysis. Various hypotheses of pharmacophores were generated and from that, a pharmacophore hypothesis with two hydrogen bond acceptors, one hydrogen bond donor and two aromatic rings (AADRR) resulted in a statistically significant 3D-QSAR model (r² = 0.84 and q² = 0.74). We believe that the obtained statistical model is a reliable QSAR model for the diverse dataset of inhibitors against the IL-2 production assay. The visualization of contours in active and inactive compounds generated from the 3D-QSAR models and molecular docking studies revealed major interaction with GLN108, HIS113 and ASP114, and interestingly, these residues are located near the Ca²⁺ selectivity filter region. Free energy binding analysis revealed that Coulomb energy, van der Waals energy and non-polar solvation terms are more favourable for ligand binding. Thus, the present study provides the physical and chemical requirements for the development of novel ORAI1 inhibitors with improved biological activity.

Natural Compound Modulates the Cervical Cancer Microenvironment-A Pharmacophore Guided Molecular Modelling Approaches

Cervical cancer is regarded as one of the major burdens noticed in women next to breast cancer. Although, human papilloma viruses (HPVs) are regarded as the principal causative agents, they require certain other factors such as oestrogen hormone to induce cervical cancer. Aromatase is an enzyme that converts androgens into oestrogens and hindering this enzyme could subsequently hamper the formation of oestrogen thereby alleviating the disease. Accordingly, in the current investigation, a structure based pharmacophore was generated considering two proteins bearing the Protein Data Bank (PDB) codes 3EQM (pharm 1) and 3S7S (pharm 2), respectively. The two models were employed as the 3D query to screen the in-house built natural compounds database. The obtained 51 compounds were escalated to molecular docking studies to decipher on the binding affinities and to predict the quintessential binding modes which were affirmed by molecular dynamics (MD) simulations. The compound has induced dose-dependent down regulation of PP2B, Nitric oxide synthase-2 (NOS2), and Interleukin 6 (IL-6) genes in the HeLa cells and has modulated the expression of apoptotic genes such as Bax, Bcl2, and caspases-3 at different concentrations. These results guide us to comprehend that the identified aromatase inhibitor was effective against the cervical cancer cells and additionally could server as scaffolds in designing new drugs.

Antimicrobial cell penetrating peptides with bacterial cell specificity: pharmacophore modelling, quantitative structure activity relationship and molecular dynamics simulation

Current research has shown cell-penetrating peptides and antimicrobial peptides (AMPs) as probable vectors for use in drug delivery and as novel antibiotics. It has been reported that the higher the therapeutic index (TI) the higher would be the bacterial cell penetrating ability. To the best of our knowledge, no in-silico study has been performed to determine bacterial cell specificity of the antimicrobial cell penetrating peptides (aCPP's) based on their TI. The aim of this study was to develop a quantitative structure activity relationship (QSAR) model, which can estimate antimicrobial potential and cell-penetrating ability of aCPPs against S. aureus, to confirm the relationship between the TI and aCPPs and to identify specific descriptors responsible for aCPPs penetrating ability. Molecular dynamics (MD) simulation was also performed to confirm the membrane insertion of the most active aCPPs obtained from the QSAR study. The most appropriate pharmacophore was identified to predict the aCPP's activity. The statistical results confirmed the validity of the model. The QSAR model was successful in identifying the optimal aCPP with high activity prediction and provided insights into the structural requirements to correlate their TI to cell penetrating ability. MD simulation of the best aCPP with 1-palmitoyl-2-oleoyl-sn-glycero-3-phosphocholine (POPC) bilayer confirmed its interaction with the membrane and the C-terminal residues of the aCPP played a key role in membrane penetration. The strategy of combining QSAR and molecular dynamics, allowed for optimal estimation of ligand-target interaction and confirmed the importance of Trp and Lys in interacting with the POPC bilayer. Communicated by Ramaswamy H. Sarma.

Identification of some novel pyrazolo[1,5-a]pyrimidine derivatives as InhA inhibitors through pharmacophore-based virtual screening and molecular docking

The InhA inhibitors play key role in mycolic acid synthesis by preventing the fatty acid biosynthesis pathway. In this present article, Pharmacophore modelling and molecular docking study followed by in silico virtual screening could be considered as effective strategy to identify newer enoyl-ACP reductase inhibitors. Pyrrolidine carboxamide derivatives were opted to generate pharmacophore models using HypoGen algorithm in Discovery studio 2.1. Further it was employed to screen Zinc and Minimaybridge databases to identify and design newer potent hit molecules. The retrieved newer hits were further evaluated for their drug likeliness and docked against enoyl acyl carrier protein reductase. Here, novel pyrazolo[1,5-a]pyrimidine analogues were designed and synthesized with good yields. Structural elucidation of synthesized final molecules was perform through IR, MASS, ¹H-NMR, ¹³C-NMR spectroscopy and further tested for its in vitro anti-tubercular activity against H37Rv strain using Microplate Alamar blue assay (MABA) method. Most of the synthesized compounds displayed strong anti-tubercular activities. Further, these potent compounds were gauged for MDR-TB, XDR-TB and cytotoxic study.

Combinatorial design and virtual screening of potent anti-tubercular fluoroquinolone and isothiazoloquinolone compounds utilizing QSAR and pharmacophore modelling

The virulence of tuberculosis infections resistant to conventional combination drug regimens cries for the design of potent fluoroquinolone compounds to be used as second line antimycobacterial chemotherapeutics. One of the most effective in silico methods is combinatorial design and high throughput screening by a ligand-based pharmacophore prior to experiment. The combinatorial design of a series of 3850 fluoroquinolone and isothiazoloquinolone compounds was then screened virtually by applying a topological descriptor based quantitative structure activity relationship (QSAR) for predicting highly active congeneric quinolone leads against Mycobacterium fortuitum and Mycobacterium smegmatis. The predicted highly active congeneric hits were then subjected to a comparative study between existing lead sparfloxacin with fluoroquinolone FQ hits as well as ACH-702 with predicted active isothiazoloquinolones, utilizing pharmacophore modelling to focus on the mechanism of drug binding against mycobacterial DNA gyrase. Finally, 68 compounds including 34 FQ and 34 isothiazoloquinolones were screened through high throughput screening comprising QSAR, the Lipinski rule of five and ligand-based pharmacophore modelling.

Pharmacophore modelling, virtual screening and molecular docking studies on PLD1 inhibitors

Lipid metabolism plays a significant role in influenza virus replication and subsequent infection. The regulatory mechanism governing lipid metabolism and viral replication is not properly understood to date, but both Phospholipase D (PLD1 and PLD2) activities are stimulated in viral infection. In vitro studies indicate that chemical inhibition of PLD1 delays viral entry and reduction of viral loads. The current study reports a three-dimensional pharmacophore model based on 35 known PLD1 inhibitors. A sub-set of 25 compounds was selected as the training set and the remaining 10 compounds were kept in the test set. One hundred and twelve pharmacophore models were generated; a six-featured pharmacophore model (AADDHR.57) with survival score (2.69) produced a statistically significant three-dimensional quantitative structure-activity relationship model with r² = 0.97 (internal training set), r² = 0.71 (internal test set) and Q² = 0.64. The predictive power of the pharmacophore model was validated with an external test set (r² = 0.73) and a systematic virtual screening work-flow was employed showing an enrichment factor of 23.68 at the top 2% of the dataset (active and decoys). Finally, the model was used for screening of the filtered PubChem database to fetch molecules which can be proposed as potential PLD1 inhibitors for blocking influenza infection.

A QSAR and molecular modelling study towards new lead finding: polypharmacological approach to Mycobacterium tuberculosis

Developing effective inhibitors against Mycobacterium tuberculosis (Mtb) is a challenging task, primarily due to the emergence of resistant strains. In this study, we have proposed and implemented an in silico guided polypharmacological approach, which is expected to be effective against resistant strains by simultaneously inhibiting several potential Mtb drug targets. A combination of pharmacophore and QSAR based virtual screening strategy taking three key targets such as InhA (enoyl-acyl-carrier-protein reductase), GlmU (N-acetyl-glucosamine-1-phosphate uridyltransferase) and DapB (dihydrodipicolinate reductase) have resulted in initial 784 hits from Asinex database of 435,000 compounds. These hits were further subjected to docking with 33 Mtb druggable targets. About 110 potential polypharmacological hits were taken by integrating the aforementioned screening protocols. Further screening was conducted by taking various parameters and properties such as cell permeability, drug-likeness, drug-induced phospholipidosisand structural alerts. A consensus analysis has yielded 59 potential hits that pass through all the filters and can be prioritized for effective drug-resistant tuberculosis. This study proposes about nine potential hits which are expected to be promising molecules, having not only drug-like properties, but also being effective against multiple Mtb targets.

Targeting the apoptotic Mcl-1-PUMA interface with a dual-acting compound

Despite intensive efforts in the search for small molecules with anti-cancer activity, it remains challenging to achieve both high effectiveness and safety, since many agents lack the selectivity to only act on cancer cells. The interface of two apoptotic proteins, myeloid cell leukemia-1 (Mcl-1) and p53 upregulated modulator of apoptosis (PUMA), has been recently affirmed as a target for treating cancers, as the disruption of Mcl-1-PUMA binding can reduce cancer cell survival and protect normal cells from apoptosis. However, therapeutic agents that target this interface are yet to be found. In this work, we combined pharmacophore modelling and biological tests to seek small molecules which target the Mcl-1-PUMA interface. For the first time, a small-molecule compound was identified. Its dual activity has been validated to reduce PUMA-dependent apoptosis while deactivating Mcl-1-mediated anti-apoptosis in cancer cells. Our results would provide a new avenue for the development of effective and safe anti-cancer agents.

Molecular dynamics and pharmacophore modelling studies of different subtype (ALK and EGFR (T790M)) inhibitors in NSCLC

Extensively validated 3D pharmacophore models for ALK (anaplastic lymphoma kinase) and EGFR (T790M) (epithelial growth factor receptor with acquired secondary mutation) were developed. The pharmacophore model for ALK (r² = 0.96, q² = 0.692) suggested that two hydrogen bond acceptors and three hydrophobic groups arranged in 3-D space are essential for the binding affinity of ALK inhibitors. Similarly, the pharmacophore model for EGFR (T790M) (r² = 0.92, q² = 0.72) suggested that the presence of a hydrogen bond acceptor, two hydrogen bond donors and a hydrophobic group plays vital role in binding of an inhibitor of EGFR (T790M). These pharmacophore models allowed searches for novel ALK and EGFR (T790M) dual inhibitors from multiconformer 3D databases (Asinex, Chembridge and Maybridge). Finally, the eight best hits were selected for molecular dynamics simulation, to study the stability of their complexes with both proteins and final binding orientations of these molecules. After molecular dynamics simulations, one hit has been predicted to possess good binding affinity for both ALK and EGFR (T790M), which can be further investigated for its experimental in-vitro/in-vivo activities.

Identification of novel PfDHODH inhibitors as antimalarial agents via pharmacophore-based virtual screening followed by molecular docking and in vivo antimalarial activity

Plasmodium falciparum dihydroorotate dehydrogenase (PfDHODH) catalyses the fourth reaction of de novo pyrimidine biosynthesis in parasites, and represents an important target for the treatment of malaria. In this study, we describe pharmacophore-based virtual screening combined with docking study and biological evaluation as a rational strategy for identification of novel hits as antimalarial agents. Pharmacophore models were established from known PfDHODH inhibitors using the GALAHAD module with IC50 values ranging from 0.033 μM to 142 μM. The best pharmacophore model consisted of three hydrogen bond acceptor, one hydrogen bond donor and one hydrophobic features. The pharmacophore models were validated through receiver operating characteristic and Günere-Henry scoring methods. The best pharmacophore model as a 3D search query was searched against the IBS database. Several compounds with different structures (scaffolds) were retrieved as hit molecules. Among these compounds, those with a QFIT value of more than 81 were docked in the PfDHODH enzyme to further explore the binding modes of these compounds. In silico pharmacokinetic and toxicities were predicted for the best docked molecules. Finally, the identified hits were evaluated in vivo for their antimalarial activity in a parasite inhibition assay. The hits reported here showed good potential to become novel antimalarial agents.

Binding mode of inhibitors and Cryptosporidium parvum IMP dehydrogenase: A combined ligand- and receptor-based study

A combined ligand- and target-based approach was used to analyse the interaction models of Cryptosporidium parvum inosine 5'-monophosphate dehydrogenase (CpIMPDH) with selective inhibitors. First, a ligand-based pharmacophore model was generated from 20 NAD(+) competitive CpIMPDH inhibitors with the HipHop module. The characteristic of the NAD(+) binding site of CpIMPDH was then described, and the binding modes of the representative inhibitors were studied by molecular docking. The combination of the pharmacophore model and the docking results allowed us to evaluate the pharmacophore features and structural information of the NAD(+) binding site of CpIMPDH. This research supports the proposal of an interaction model inside the NAD(+) binding site of CpIMPDH, consisting of four key interaction points: two hydrophobic-aromatic groups, a hydrophobic-aliphatic group and a hydrogen bond donor. This study also provides guidance for the design of more potent CpIMPDH inhibitors for the treatment of Cryptosporidium infections.

Ligand-based pharmacophore modelling and screening of DNA minor groove binders targeting Staphylococcus aureus

The recognition of DNA by small molecules is of special importance in the design of new drugs. Many natural and synthetic compounds have the ability to interact with the minor groove of DNA. In the present study, identification of minor groove binding compounds was attained by the combined approach of pharmacophore modelling, virtual screening and molecular dynamics approach. Experimentally reported 32 minor groove binding compounds were used to develop the pharmacophore model. Based on the fitness score, best three pharmacophore hypotheses were selected and used as template for screening the compounds from drug bank database. This pharmacophore-based screening provides many compounds with the same pharmacological properties. All these compounds were subjected to four phases of docking protocols with combined Glide-quantum-polarized ligand docking approach. Molecular dynamics results indicated that selected compounds are more active and showed good interaction in the binding site of DNA. Based on the scoring parameters and energy values, the best compounds were selected, and antibacterial activity of these compounds was identified using in vitro antimicrobial techniques.

Modelling the binding affinity of steroids to zebrafish sex hormone-binding globulin

The circulating endogenous steroids are transported in the bloodstream. These are bound to a highly specific sex hormone-binding globulin (SHBG) and in lower affinity to proteins such as the corticosteroid-binding protein and albumin in vertebrates, including fish. It is generally believed that the glycoprotein SHBG protects these steroids from rapid metabolic degradation and thus intervenes in its availability at the target tissues. Endocrine disrupters binding to SHBG affect the normal activity of natural steroids. Since xenobiotics are primarily released in the aquatic environment, there is a need to evaluate the binding affinity of xenosteroid mimics on fish SHBG, especially in zebrafish (Danio rerio), a small freshwater fish originating in India and widely employed in ecotoxicology, toxicology, and genetics. In this context, a zebrafish SHBG (zfSHBG) homology model was developed using the human SHBG (hSHBG) receptor structure as template. It was shown that interactions with amino acids Ser-36, Asp-59 and Thr-54 were important for binding affinity. A ligand-based pharmacophore model was also developed for both zfSHBG and hSHBG inhibitors that differentiated binders from non-binders, but also demonstrated structural requirements for zfSHBG and hSHBG ligands. The study provides insights into the mechanism of action of endocrine disruptors in zebrafish as well as providing a useful tool for identifying anthropogenic compounds inhibiting zfSHBG.

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.