Ligand-based pharmacophore modeling and virtual screening for the discovery of novel 17β-hydroxysteroid dehydrogenase 2 inhibitors

Development of pharmacophore models for AcrB protein and the identification of potential adjuvant candidates for overcoming efflux-mediated colistin resistance

Growing multi-drug resistance (MDR) among ESKAPE pathogens is a huge challenge. Increased resistance to last-resort antibiotics, like colistin, has further aggravated this. Efflux is identified as a major route of colistin resistance. So, finding an FDA-approved efflux inhibitor for potential application as an adjuvant to colistin was the primary objective of this study. E. coli-AcrB pump inhibitors and substrates were used to develop and validate the pharmacophoric model. Drugs confirming this pharmacophore were subjected to molecular docking to identify hits for the AcrB binding pocket. The efflux inhibition potential of the top hit was validated through the in vitro evaluation of the minimum inhibitory concentration (MIC) in combination with colistin. The checkerboard assay was done to demonstrate synergism, which was further corroborated by the Time-kill assay. Ten common pharmacophore hypotheses were successfully generated using substrate/inhibitors. Following enrichment analysis, AHHNR.100 was identified as the top-ranked hypothesis, and 207 unique compounds were found to conform to this hypothesis. The multi-step docking of these compounds against the AcrB protein revealed argatroban as the top non-antibiotic hit. This significantly inhibited the efflux activity of colistin-resistant clinical isolates K. pneumoniae (n = 1) and M. morganii (n = 2). Further, their combination with colistin enhanced the susceptibility of these isolates, and the effect was found to be synergistic. Accordingly, the time-kill assay of this combination showed 8-log and 2-log reductions against K. pneumoniae and M. morganii, respectively. In conclusion, this study found argatroban as a bacterial efflux inhibitor that can be potentially used to overcome efflux-mediated resistance.

Harnessing systematic protein-ligand interaction fingerprints for drug discovery

Determining protein-ligand interaction characteristics and mechanisms is crucial to the drug discovery process. Here, we review recent progress and successful applications of a systematic protein-ligand interaction fingerprint (IFP) approach for investigating proteome-wide protein-ligand interactions for drug development. Specifically, we review the use of this IFP approach for revealing polypharmacology across the kinome, predicting promising targets from which to design allosteric inhibitors and covalent kinase inhibitors, uncovering the binding mechanisms of drugs of interest, and demonstrating resistant mechanisms of specific drugs. Together, we demonstrate that the IFP strategy is efficient and practical for drug design research for protein kinases as targets and is extensible to other protein families.

Interface inhibitory action on Interleukin-1β using selected anti-inflammatory compounds to mitigate the depression: A computational investigation

Interleukin-1β (IL1β) is a keynote mediator of inflammation with diverse physiological functions, playing a fundamental role in memory and mood regulation. The pleiotropic effects of IL-1β have been proposed to be implicated in the pathogenesis and etiology of depression. Thus, targeting IL-1β offers an inimitable opportunity to develop new strategies for an alternative therapy to treat depression. The focus of this study is to find out the potential inhibitors against IL-1β. Since, there is no oral specific drug reported yet thus, demanding an urgent need to develop new immunomodulatory drugs to combat chronic diseases. In this study, ligand-based pharmacophore modeling integrated with virtual screening and molecular docking strategy was designed to identify novel compounds capable of inhibiting the interactions towards cognitive receptor IL-1RI. In this connection, a set of 30,000 compounds were screened by a developed pharmacophore model that led to the retrieval of 2043 molecules from the in-house library and ZINC Database. Primarily, specific binding regions for IL-1β inhibitors have been explored by blind docking studies. After the selection of the binding site, the hits identified as actives based on the 3D-pharmacophore model were assessed by molecular docking studies. In a stepwise screening, six potential virtual hits were shortlisted for molecular dynamic simulation to acquire insights into their dynamic behavior. The obtained results highlighted that these compounds are stabilized in the targeted pocket of IL-1β and possibly block the formation of an active heterocomplex, subsequently locking the associated signaling cascade. Further in vitro experiments confirmed the inhibitory potential of Compound-157 and compound-283 with the IC₅₀ of 1.6 ± 0.1 and 9.1 ± 1.7 µg/mL respectively.

Metabolite Profiling of the Environmental-Controlled Growth of Marsilea crenata Presl. and Its In Vitro and In Silico Antineuroinflammatory Properties

This study was aimed to evaluate the metabolite contents and antineuroinflammatory potential of Marsilea crenata Presl. grown under a controlled environmental condition. The antineuroinflammatory test has been carried out in vitro using ethanolic extract of M. crenata leaves on HMC3 microglia cells. An in silico approach was applied to predict the active compounds of the extract. The HMC3 microglia cells were induced with IFNγ to create prolonged inflammatory conditions and then treated with 96% ethanolic extract of the M. crenata leaves of 62.5, 125, and 250 μg/mL. The expression of MHC II was analyzed using the ICC method with the CLSM instrument. Metabolites of the extract were profiled using UPLC-QToF-MS/MS instrument and MassLynx 4.1 software. In silico evaluation was conducted with molecular docking on 3OLS protein using PyRx 0.8 software, and physicochemical properties of the compounds were analyzed using SwissADME webtool. The ethanolic extract of M. crenata leaves could reduce the MHC II expression in HMC3 microglia cells in all concentrations with the values 97.458, 139.574, and 82.128 AU. The result of metabolite profiling found 79 compounds in the extract. In silico evaluation showed that 19 compounds gave agonist interaction toward 3OLS, and three met all parameters of physicochemical analysis. The ethanolic extract of the environmental-controlled growth of M. crenata leaves antineuroinflammatory activity on HMC3 microglia cells. The extract was predicted to contain some phytoestrogen compounds which act as 3OLS agonists.

Delineating binding potential, stability of Sulforaphane-N-acetyl-cysteine in the active site of histone deacetylase 2 and testing its cytotoxicity against distinct cancer lines through stringent molecular dynamics, DFT and cell-based assays

Histone deacetylase 2 (HDAC2), an isozyme of Class I HDACs has potent imputations in actuating neurodegenerative signaling. Currently, there are sizeable therapeutic disquiets with the use of synthetic histone deacetylase inhibitors in disease management. This strongly suggests the unfulfilled medical necessity of plant substitutes for therapeutic intervention. Sulforaphane-N-acetyl-cysteine (SFN-N-acetylcysteine or SFN-NAC), a sulforaphane metabolite has shown significantly worthier activity against HDACs under in vitro conditions. However, the atomistic studies of SFN-NAC against HDAC2 are currently lacking. Thus, the present study employed a hybrid strategy including extra-precision (XP) grid-based flexible molecular docking, molecular mechanics generalized born surface area (MM-GBSA), e-Pharmacophores method, and molecular dynamics simulation for exploring the binding strengh, mode of interaction, e-Pharmacophoric features, and stability of SFN-NAC towards HDAC2. Further, the globally acknowledged density functional theory (DFT) study was performed on SFN-NAC and entinostat individually in complex state with HDAC2. Apart from this, these inhibitors were tested against three distinct cancer cell models and one transformed cell line for cytotoxic activity. Moreover, double mutant of HDAC2 was generated and the binding orientation and interaction of SFN-NAC was scrutinized in this state. On the whole, this study unbosomed and explained the comparatively higher binding affinity of entinostat for HDAC2 and its wide spectrum cytotoxicity than SFN-NAC.

COVID-19: In silico identification of potent α-ketoamide inhibitors targeting the main protease of the SARS-CoV-2

The COVID-19 has been creating a global crisis, causing countless deaths and unbearable panic. Despite the progress made in the development of the vaccine, there is an urge need for the discovery of antivirals that may better work at different stages of SARS-CoV-2 reproduction. The main protease (M^pro) of the SARS-CoV-2 is a crucial therapeutic target due to its critical function in virus replication. The α-ketoamide derivatives represent an important class of inhibitors against the M^pro of the SARS-CoV. While there is 99% sequence similarity between SARS-CoV and SARS-CoV-2 main proteases, anti-SARS-CoV compounds may have a huge demonstration's prospect of their effectiveness against the SARS-CoV-2. In this study, we applied various computational approaches to investigate the inhibition potency of novel designed α-ketoamide-based compounds. In this regard, a set of 21 α-ketoamides was employed to construct a QSAR model, using the genetic algorithm-multiple linear regression (GA-MLR), as well as a pharmacophore fit model. Based on the GA-MLR model, 713 new designed molecules were reduced to 150 promising hits, which were later subject to the established pharmacophore fit model. Among the 150 compounds, the best selected compounds (3 hits) with greater pharmacophore fit score were further studied via molecular docking, molecular dynamic simulations along with the Absorption, distribution, metabolism, excretion, and toxicity (ADMET) analysis. Our approach revealed that the three hit compounds could serve as potential inhibitors against the SARS-CoV-2 M^pro target.

Computational Drug Repurposing Resources and Approaches for Discovering Novel Antifungal Drugs against Candida albicans N-Myristoyl Transferase

Candida albicans is a yeast that is an opportunistic fungal pathogen and also identified as ubiquitous polymorphic species that is mainly linked with major fungal infections in humans, particularly in the immunocompromised patients including transplant recipients, chemotherapy patients, HIV-infected patients as well as in low-birth-weight infants. Systemic Candida infections have a high mortality rate of around 29 to 76%. For reducing its infection, limited drugs are existing such as caspofungin, fluconazole, terbinafine, and amphotericin B, etc. which contain unlikable side effects and also toxic. This review intends to utilize advanced bioinformatics technologies such as Molecular docking, Scaffold hopping, Virtual screening, Pharmacophore modeling, Molecular dynamics (MD) simulation for the development of potentially new drug candidates with a drug-repurpose approach against Candida albicans within a limited time frame and also cost reductive.

Osteoporosis: Mechanism, Molecular Target and Current Status on Drug Development

CDATA[Osteoporosis is a pathological loss of bone mass due to an imbalance in bone remodeling where osteoclast-mediated bone resorption exceeds osteoblast-mediated bone formation resulting in skeletal fragility and fractures. Anti-resorptive agents, such as bisphosphonates and SERMs, and anabolic drugs that stimulate bone formation, including PTH analogues and sclerostin inhibitors, are current treatments for osteoporosis. Despite their efficacy, severe side effects and loss of potency may limit the long term usage of a single drug. Sequential and combinational use of current drugs, such as switching from an anabolic to an anti-resorptive agent, may provide an alternative approach. Moreover, there are novel drugs being developed against emerging new targets such as Cathepsin K and 17β-HSD2 that may have less side effects. This review will summarize the molecular mechanisms of osteoporosis, current drugs for osteoporosis treatment, and new drug development strategies.

Docking based screening and molecular dynamics simulations to identify potential selective PDE4B inhibitor

Inhibition of phosphodiesterase 4 (PDE4) is a promising therapeutic approach for the treatment of inflammatory pulmonary disorders, i.e. asthma and chronic obstructive pulmonary disease. However, the treatment with non-selective PDE4 inhibitors is associated with side effects such as nausea and vomiting. Among the subtypes of PDE4 inhibited by these inhibitors, PDE4B is expressed in immune, inflammatory and airway smooth muscle cells, whereas, PDE4D is expressed in the area postrema and nucleus of the solitary tract. Thus, PDE4D inhibition is responsible for the emetic response. In this regard, a selective PDE4B inhibitor is expected to be a potential drug candidate for the treatment of inflammatory pulmonary disorders. Therefore, a shared feature pharmacophore model was developed and used as a query for the virtual screening of Maybridge and SPECS databases. A number of filters were applied to ensure only compounds with drug-like properties were selected. Accordingly, nine compounds have been identified as final hits, where HTS04529 showed the highest affinity and selectivity for PDE4B over PDE4D in molecular docking. The docked complexes of HTS04529 with PDE4B and PDE4D were subjected to molecular dynamics simulations for 100ns to assess their binding stability. The results showed that HTS04529 was bound tightly to PDE4B and formed a more stable complex with it than with PDE4D.

Application of Various Molecular Modelling Methods in the Study of Estrogens and Xenoestrogens

In this review, applications of various molecular modelling methods in the study of estrogens and xenoestrogens are summarized. Selected biomolecules that are the most commonly chosen as molecular modelling objects in this field are presented. In most of the reviewed works, ligand docking using solely force field methods was performed, employing various molecular targets involved in metabolism and action of estrogens. Other molecular modelling methods such as molecular dynamics and combined quantum mechanics with molecular mechanics have also been successfully used to predict the properties of estrogens and xenoestrogens. Among published works, a great number also focused on the application of different types of quantitative structure-activity relationship (QSAR) analyses to examine estrogen's structures and activities. Although the interactions between estrogens and xenoestrogens with various proteins are the most commonly studied, other aspects such as penetration of estrogens through lipid bilayers or their ability to adsorb on different materials are also explored using theoretical calculations. Apart from molecular mechanics and statistical methods, quantum mechanics calculations are also employed in the studies of estrogens and xenoestrogens. Their applications include computation of spectroscopic properties, both vibrational and Nuclear Magnetic Resonance (NMR), and also in quantum molecular dynamics simulations and crystal structure prediction. The main aim of this review is to present the great potential and versatility of various molecular modelling methods in the studies on estrogens and xenoestrogens.

Structural dynamics and allostery of Rab proteins: strategies for drug discovery and design

Rab proteins represent the largest family of the Rab superfamily guanosine triphosphatase (GTPase). Aberrant human Rab proteins are associated with multiple diseases, including cancers and neurological disorders. Rab subfamily members display subtle conformational variations that render specificity in their physiological functions and can be targeted for subfamily-specific drug design. However, drug discovery efforts have not focused much on targeting Rab allosteric non-nucleotide binding sites which are subjected to less evolutionary pressures to be conserved, hence are likely to offer subfamily specificity and may be less prone to undesirable off-target interactions and side effects. To discover druggable allosteric binding sites, Rab structural dynamics need to be first incorporated using multiple experimentally and computationally obtained structures. The high-dimensional structural data may necessitate feature extraction methods to identify manageable representative structures for subsequent analyses. We have detailed state-of-the-art computational methods to (i) identify binding sites using data on sequence, shape, energy, etc., (ii) determine the allosteric nature of these binding sites based on structural ensembles, residue networks and correlated motions and (iii) identify small molecule binders through structure- and ligand-based virtual screening. To benefit future studies for targeting Rab allosteric sites, we herein detail a refined workflow comprising multiple available computational methods, which have been successfully used alone or in combinations. This workflow is also applicable for drug discovery efforts targeting other medically important proteins. Depending on the structural dynamics of proteins of interest, researchers can select suitable strategies for allosteric drug discovery and design, from the resources of computational methods and tools enlisted in the workflow.

Pharmmaker: Pharmacophore modeling and hit identification based on druggability simulations

Recent years have seen progress in druggability simulations, that is, molecular dynamics simulations of target proteins in solutions containing drug‐like probe molecules to characterize their drug‐binding abilities, if any. An important consecutive step is to analyze the trajectories to construct pharmacophore models (PMs) to use for virtual screening of libraries of small molecules. While considerable success has been observed in this type of computer‐aided drug discovery, a systematic tool encompassing multiple steps from druggability simulations to pharmacophore modeling, to identifying hits by virtual screening of libraries of compounds, has been lacking. We address this need here by developing a new tool, Pharmmaker, building on the DruGUI module of our ProDy application programming interface. Pharmmaker is composed of a suite of steps: (Step 1) identification of high affinity residues for each probe molecule type; (Step 2) selecting high affinity residues and hot spots in the vicinity of sites identified by DruGUI; (Step 3) ranking of the interactions between high affinity residues and specific probes; (Step 4) obtaining probe binding poses and corresponding protein conformations by collecting top‐ranked snapshots; and (Step 5) using those snapshots for constructing PMs. The PMs are then used as filters for identifying hits in structure‐based virtual screening. Pharmmaker, accessible online at http://prody.csb.pitt.edu/pharmmaker/, can be used in conjunction with other tools available in ProDy.

Discovery of potent apoptosis signal-regulating kinase 1 inhibitors via integrated computational strategy and biological evaluation

Apoptosis signal-regulating Kinase 1 (ASK1) has been confirmed as a potential therapeutic target for the treatment of non-alcoholic steatohepatitis (NASH) disorder and the discovery of ASK1 inhibitors has attracted increasing attention. In this work, a series of in silico methods including pharmacophore screening, docking binding site analysis, protein-ligand interaction fingerprint (PLIF) similarity investigation and molecular docking were applied to find the potential hits from commercial compound databases. Five compounds with potential inhibitory activity were purchased and submitted to biological activity validation. Thus, one hit compound was discovered with micromolar IC₅₀ value (10.59 μM) against ASK1. Results demonstrated that the integration of computation methods and biological test was quite reliable for the discovery of potent ASK1 inhibitors and the strategy could be extended to other similar targets of interest.

Role of HSD17B13 in the liver physiology and pathophysiology

17β-Hydroxysteroid dehydrogenases (HSD17Bs) comprise a large family of 15 members that are mainly involved in sex hormone metabolism. Some HSD17Bs enzymes also play key roles in cholesterol and fatty acid metabolism. Recent study showed that hydroxysteroid 17β-dehydrogenase 13 (HSD17B13), an enzyme with unknown biological function, is a novel liver-specific lipid droplet (LD)-associated protein in mouse and humans. HSD17B13 expression is markedly upregulated in patients and mice with non-alcoholic fatty liver disease (NAFLD). Hepatic overexpression of HSD17B13 promotes lipid accumulation in the liver. In this review, we summarize recent progress regarding the role of HSD17B13 in the regulation of hepatic lipid homeostasis and discuss genetic, genomic and proteomic evidence supporting the pathogenic role of HSD17B13 in NAFLD. We also emphasize its potential as a biomarker of advanced liver disease, such as non-alcoholic steatohepatitis (NASH) and liver cancer.

Inhibitors of 17β-hydroxysteroid dehydrogenase type 1, 2 and 14: Structures, biological activities and future challenges

During the past 25 years, the modulation of estrogen action by inhibition of 17β-hydroxysteroid dehydrogenase types 1 and 2 (17β-HSD1 and 17β-HSD2), respectively, has been pursued intensively. In the search for novel treatment options for estrogen-dependent diseases (EDD) and in order to explore estrogenic signaling pathways, a large number of steroidal and nonsteroidal inhibitors of these enzymes has been described in the literature. The present review gives a survey on the development of inhibitor classes as well as the structural formulas and biological properties of their most interesting representatives. In addition, rationally designed dual inhibitors of both 17β-HSD1 and steroid sulfatase (STS) as well as the first inhibitors of 17β-HSD14 are covered.

A Strength-Weaknesses-Opportunities-Threats (SWOT) Analysis of Cheminformatics in Natural Product Research

Cheminformatics-based techniques, such as molecular modeling, docking, virtual screening, and machine learning, are well accepted for their usefulness in drug discovery and development of therapeutically relevant small molecules. Although delayed by several decades, their application in natural product research has led to outstanding findings. Combining information obtained from different sources, i.e., virtual predictions, traditional medicine, structural, biochemical, and biological data, and handling big data effectively will open up new possibilities, but also challenges in the future. Strategies and examples will be presented on how to integrate cheminformatics in pharmacognostic workflows to benefit from these two highly complementary disciplines toward streamlining experimental efforts. While considering their limits and pitfalls and by exploiting their potential, computer-aided strategies should successfully guide future studies and thereby augment our knowledge of bioactive natural lead structures.

Highly Potent 17β-HSD2 Inhibitors with a Promising Pharmacokinetic Profile for Targeted Osteoporosis Therapy

Intracellular elevation of E2 levels in bone by inhibition of 17β hydroxysteroid dehydrogenase type 2 (17β-HSD2) without affecting systemic E2 levels is an attractive approach for a targeted therapy against osteoporosis, a disease which is characterized by loss of bone mineral density. Previously identified inhibitor A shows high potency on human and mouse 17β-HSD2, but poor pharmacokinetic properties when applied perorally in mice. A combinatorial chemistry approach was utilized to synthesize truncated derivatives of A, leading to highly potent compounds with activities in the low nanomolar to picomolar range. Compound 33, comparable to A in terms of inhibitor potency against both human and mouse enzymes, displays high in vitro metabolic stability in human and mouse liver S9 fraction as well as low toxicity and moderate hepatic CYP inhibition. Thus, compound 33 showed a highly improved peroral pharmacokinetic profile in comparison to A, making 33 a promising candidate for further development.

Design, synthesis, and biological evaluation of novel selective peptide inhibitors of 11β-hydroxysteroid dehydrogenase 1

The enzyme 11β-HSD1 plays a crucial role in the tissue-specific regulation of cortisol levels and it has been associated with various diseases. Inhibition of 11β-HSD1 is an attractive intervention strategy and the discovery of novel selective 11β-HSD1 inhibitors is of high relevance. In this study, we identified and evaluated a new series of selective peptide 11β-HSD1 inhibitors with potential for skin care applications. This novel scaffold was designed with the aid of molecular modeling and two previously reported inhibitors. SAR optimization yielded highly active peptides (IC₅₀ below 400 nM) that were inactive at 1 µM concentration against structurally related enzymes (11β-HSD2, 17β-HSD1 and 17β-HSD2). The best performing peptides inhibited the conversion of cortisone into cortisol in primary human keratinocytes and the most active compound, 5d, was further shown to reverse cortisone-induced collagen damage in human ex-vivo tissue.

3D QSAR Pharmacophore Based Virtual Screening for Identification of Potential Inhibitors for CDC25B

Owing to its fundamental roles in cell cycle phases, the cell division cycle 25B (CDC25B) was broadly considered as potent clinical drug target for cancers. In this study, 3D QSAR pharmacophore models for CDC25B inhibitors were developed by the module of Hypogen. Three methods (cost analysis, test set prediction, and Fisher's test) were applied to validate that the models could be used to predict the biological activities of compounds. Subsequently, 26 compounds satisfied Lipinski's rule of five were obtained by the virtual screening of the Hypo-1-CDC25B against ZINC databases. It was then discovered that 9 identified molecules had better binding affinity than a known CDC25B inhibitors-compound 1 using docking studies. The molecular dynamics simulations showed that the compound had favorable conformations for binding to the CDC25B. Thus, our findings here would be helpful to discover potent lead compounds for the treatment of cancers.

Combinatorial In Silico Strategy towards Identifying Potential Hotspots during Inhibition of Structurally Identical HDAC1 and HDAC2 Enzymes for Effective Chemotherapy against Neurological Disorders

Histone deacetylases (HDACs) regulate epigenetic gene expression programs by modulating chromatin architecture and are required for neuronal development. Dysregulation of HDACs and aberrant chromatin acetylation homeostasis have been implicated in various diseases ranging from cancer to neurodegenerative disorders. Histone deacetylase inhibitors (HDACi), the small molecules interfering HDACs have shown enhanced acetylation of the genome and are gaining great attention as potent drugs for treating cancer and neurodegeneration. HDAC2 overexpression has implications in decreasing dendrite spine density, synaptic plasticity and in triggering neurodegenerative signaling. Pharmacological intervention against HDAC2 though promising also targets neuroprotective HDAC1 due to high sequence identity (94%) with former in catalytic domain, culminating in debilitating off-target effects and creating hindrance in the defined intervention. This emphasizes the need of designing HDAC2-selective inhibitors to overcome these vicious effects and for escalating the therapeutic efficacy. Here we report a top-down combinatorial in silico approach for identifying the structural variants that are substantial for interactions against HDAC1 and HDAC2 enzymes. We used extra-precision (XP)-molecular docking, Molecular Mechanics Generalized Born Surface Area (MMGBSA) for predicting affinity of inhibitors against the HDAC1 and HDAC2 enzymes. Importantly, we employed a novel in silico strategy of coupling the state-of-the-art molecular dynamics simulation (MDS) to energetically-optimized structure based pharmacophores (e-Pharmacophores) method via MDS trajectory clustering for hypothesizing the e-Pharmacophore models. Further, we performed e-Pharmacophores based virtual screening against phase database containing millions of compounds. We validated the data by performing the molecular docking and MM-GBSA studies for the selected hits among the retrieved ones. Our studies attributed inhibitor potency to the ability of forming multiple interactions and infirm potency to least interactions. Moreover, our studies delineated that a single HDAC inhibitor portrays differential features against HDAC1 and HDAC2 enzymes. The high affinity and selective HDAC2 inhibitors retrieved through e-Pharmacophores based virtual screening will play a critical role in ameliorating neurodegenerative signaling without hampering the neuroprotective isoform (HDAC1).

Interference of Paraben Compounds with Estrogen Metabolism by Inhibition of 17β-Hydroxysteroid Dehydrogenases

Parabens are effective preservatives widely used in cosmetic products and processed food, with high human exposure. Recent evidence suggests that parabens exert estrogenic effects. This work investigated the potential interference of parabens with the estrogen-activating enzyme 17β-hydroxysteroid dehydrogenase (17β-HSD) 1 and the estrogen-inactivating 17β-HSD2. A ligand-based 17β-HSD2 pharmacophore model was applied to screen a cosmetic chemicals database, followed by in vitro testing of selected paraben compounds for inhibition of 17β-HSD1 and 17β-HSD2 activities. All tested parabens and paraben-like compounds, except their common metabolite p-hydroxybenzoic acid, inhibited 17β-HSD2. Ethylparaben and ethyl vanillate inhibited 17β-HSD2 with IC₅₀ values of 4.6 ± 0.8 and 1.3 ± 0.3 µM, respectively. Additionally, parabens size-dependently inhibited 17β-HSD1, whereby hexyl- and heptylparaben were most active with IC₅₀ values of 2.6 ± 0.6 and 1.8 ± 0.3 µM. Low micromolar concentrations of hexyl- and heptylparaben decreased 17β-HSD1 activity, and ethylparaben and ethyl vanillate decreased 17β-HSD2 activity. However, regarding the very rapid metabolism of these compounds to the inactive p-hydroxybenzoic acid by esterases, it needs to be determined under which conditions low micromolar concentrations of these parabens or their mixtures can occur in target cells to effectively disturb estrogen effects in vivo.

Toward a hierarchical virtual screening and toxicity risk analysis for identifying novel CA XII inhibitors

Carbonic anhydrase isoform XII (CA XII) is a potential target for cancer treatment. In this study, pharmacophore modeling, hierarchical virtual screening, and toxicity risk analysis were performed for identifying novel CA XII inhibitors. A pharmacophore model of two classes of CA XII inhibitors was generated. The pharmacophore model indicated the important features of inhibitors for the binding with the CA XII. The model was then utilized to screen the ZINC and CoCoCo databases for retrieving potential hit compounds of CA XII. For accurate conclusions about the selectivity of inhibitors, the retrieved molecules which obey of Lipinski's rule of five (RO5) and have no toxicity risk were docked in a CA XII 3D structure by smina. Finally, on the basis of binding affinity and the binding mode of the molecules, twelve molecules were prioritized as promising hits. It should be noted that two of hits H5 and H6 were previously reported in the CHEMBL database. This hierarchical method is worthy of reducing the time and using almost all information available. The final hits may be used as a lead to discovery novel CA XII inhibitors.

Virtual screening applications in short-chain dehydrogenase/reductase research

Several members of the short-chain dehydrogenase/reductase (SDR) enzyme family play fundamental roles in adrenal and gonadal steroidogenesis as well as in the metabolism of steroids, oxysterols, bile acids, and retinoids in peripheral tissues, thereby controlling the local activation of their cognate receptors. Some of these SDRs are considered as promising therapeutic targets, for example to treat estrogen-/androgen-dependent and corticosteroid-related diseases, whereas others are considered as anti-targets as their inhibition may lead to disturbances of endocrine functions, thereby contributing to the development and progression of diseases. Nevertheless, the physiological functions of about half of all SDR members are still unknown. In this respect, in silico tools are highly valuable in drug discovery for lead molecule identification, in toxicology screenings to facilitate the identification of hazardous chemicals, and in fundamental research for substrate identification and enzyme characterization. Regarding SDRs, computational methods have been employed for a variety of applications including drug discovery, enzyme characterization and substrate identification, as well as identification of potential endocrine disrupting chemicals (EDC). This review provides an overview of the efforts undertaken in the field of virtual screening supported identification of bioactive molecules in SDR research. In addition, it presents an outlook and addresses the opportunities and limitations of computational modeling and in vitro validation methods.

Potential Antiosteoporotic Natural Product Lead Compounds That Inhibit 17β-Hydroxysteroid Dehydrogenase Type 2

17β-Hydroxysteroid dehydrogenase type 2 (17β-HSD2) converts the active steroid hormones estradiol, testosterone, and 5α-dihydrotestosterone into their weakly active forms estrone, Δ⁴-androstene-3,17-dione, and 5α-androstane-3,17-dione, respectively, thereby regulating cell- and tissue-specific steroid action. As reduced levels of active steroids are associated with compromised bone health and onset of osteoporosis, 17β-HSD2 is considered a target for antiosteoporotic treatment. In this study, a pharmacophore model based on 17β-HSD2 inhibitors was applied to a virtual screening of various databases containing natural products in order to discover new lead structures from nature. In total, 36 hit molecules were selected for biological evaluation. Of these compounds, 12 inhibited 17β-HSD2 with nanomolar to low micromolar IC₅₀ values. The most potent compounds, nordihydroguaiaretic acid (1), IC₅₀ 0.38 ± 0.04 μM, (-)-dihydroguaiaretic acid (4), IC₅₀ 0.94 ± 0.02 μM, isoliquiritigenin (6), IC₅₀ 0.36 ± 0.08 μM, and ethyl vanillate (12), IC₅₀ 1.28 ± 0.26 μM, showed 8-fold or higher selectivity over 17β-HSD1. As some of the identified compounds belong to the same structural class, structure-activity relationships were derived for these molecules. Thus, this study describes new 17β-HSD2 inhibitors from nature and provides insights into the binding pocket of 17β-HSD2, offering a promising starting point for further research in this area.

Novel 11β-hydroxysteroid dehydrogenase 1 inhibitors reduce cortisol levels in keratinocytes and improve dermal collagen content in human ex vivo skin after exposure to cortisone and UV

Activity and selectivity assessment of new bi-aryl amide 11β-hydroxysteroid dehydrogenase 1 (11β-HSD1) inhibitors, prepared in a modular manner via Suzuki cross-coupling, are described. Several compounds inhibiting 11β-HSD1 at nanomolar concentrations were identified. Compounds 2b, 3e, 7b and 12e were shown to selectively inhibit 11β-HSD1 over 11β-HSD2, 17β-HSD1 and 17β-HSD2. These inhibitors also potently inhibited 11β-HSD1 activity in intact HEK-293 cells expressing the recombinant enzyme and in intact primary human keratinocytes expressing endogenous 11β-HSD1. Moreover, compounds 2b, 3e and 12e were tested for their activity in human skin biopsies. They were able to prevent, at least in part, both the cortisone- and the UV-mediated decreases in collagen content. Thus, inhibition of 11β-HSD1 by these compounds can be further investigated to delay or prevent UV-mediated skin damage and skin aging.

Inhibition of 11β-hydroxysteroid dehydrogenase 2 by the fungicides itraconazole and posaconazole

Impaired 11β-hydroxysteroid dehydrogenase type 2 (11β-HSD2)-dependent cortisol inactivation can lead to electrolyte dysbalance, hypertension and cardiometabolic disease. Furthermore, placental 11β-HSD2 essentially protects the fetus from high maternal glucocorticoid levels, and its impaired function has been associated with altered fetal growth and a higher risk for cardio-metabolic diseases in later life. Despite its important role, 11β-HSD2 is not included in current off-target screening approaches. To identify potential 11β-HSD inhibitors among approved drugs, a pharmacophore model was used for virtual screening, followed by biological assessment of selected hits. This led to the identification of several azole fungicides as 11β-HSD inhibitors, showing a significant structure-activity relationship between azole scaffold size, 11β-HSD enzyme selectivity and inhibitory potency. A hydrophobic linker connecting the azole ring to the other, more polar end of the molecule was observed to be favorable for 11β-HSD2 inhibition and selectivity over 11β-HSD1. The most potent 11β-HSD2 inhibition, using cell lysates expressing recombinant human 11β-HSD2, was obtained for itraconazole (IC₅₀ 139±14nM), its active metabolite hydroxyitraconazole (IC₅₀ 223±31nM) and posaconazole (IC₅₀ 460±98nM). Interestingly, experiments with mouse and rat kidney homogenates showed considerably lower inhibitory activity of these compounds towards 11β-HSD2, indicating important species-specific differences. Thus, 11β-HSD2 inhibition by these compounds is likely to be overlooked in preclinical rodent studies. Inhibition of placental 11β-HSD2 by these compounds, in addition to the known inhibition of cytochrome P450 enzymes and P-glycoprotein efflux transport, might contribute to elevated local cortisol levels, thereby affecting fetal programming.

Protein-Ligand Docking Methodologies and Its Application in Drug Discovery

Molecular docking is a key tool in structural biology and computer-assisted drug design. Molecular docking is a method which predicts the preferred orientation of a ligand when bound in an active site to form a stable complex. It is the most common method used as a structure-based drug design. Here, the authors intend to discuss the various types of docking methods and their development and applications in modern drug discovery. The important basic theories such as sampling algorithm and scoring functions have been discussed briefly. The performances of the different available docking software have also been discussed. This chapter also includes some application examples of docking studies in modern drug discovery such as targeted drug delivery using carbon nanotubes, docking of nucleic acids to find the binding modes and a comparative study between high-throughput screening and structure-based virtual screening.

Integration of pharmacophore mapping and molecular docking in sequential virtual screening: towards the discovery of novel JAK2 inhibitors

An integrated sequential virtual screening protocol by combining molecular docking and pharmacophore mapping was successfully constructed to identify novel small-molecule inhibitors of JAK2.

Structure-based discovery of potentially active semiochemicals for Cydia pomonella (L.)

The development of physiologically active semiochemicals is largely limited by the labor-consuming searching process. How to screen active semiochemicals efficiently is of significance to the extension of behavior regulation in pest control. Here pharmacophore modeling and shape-based virtual screening were combined to predict candidate ligands for Cydia pomonella pheromone binding protein 1 (CpomPBP1). Out of the predicted compounds, ETrME displayed the highest affinity to CpomPBP1. Further studies on the interaction between CpomPBP1 and ETrME, not only depicted the binding mode, but also revealed residues providing negative and positive contributions to the ETrME binding. Moreover, key residues involved in interacting with ETrME of CpomPBP1 were determined as well. These findings were significant to providing insights for the future searching and optimization of active semiochemicals.

Pharmacophore-based screening of differentially-expressed PGF, DDIT4, COMP and CHI3L1 from hMSC cell lines reveals five novel therapeutic compounds for primary osteoporosis

As many societies age, primary osteoporosis (PO) is increasingly a major health problem. Current drug treatments such as alendronate and risedronate have known side effects. We took an agnostic empirical approach to find PO therapeutic compounds. We examined 13,548,960 probe data-points from mesenchymal stromal cell (hMSC) lines and found that PGF, DDIT4, and COMP to be up-regulated, and CHI3L1, down-regulated. We then identified their druggable domains. For the up-regulated differentially-expressed genes, we used protein-protein interactions to find residue clusters as binding surfaces. We then employed pharmacophore models to screen 15,407,096 conformations of 22,723,923 compounds, which identified (6R,9R)-6-(2-furyl)-9-(1H-indol-3-yl)-2-(trifluoromethyl)-5,6,7, 9-tetrahydro-4H[1,2,4]triazolo[5,1],(2S)-N1-[2-[2-(methylamino)-2-oxo-ethyl]phenyl]-N2-phenylpyrrolidine-1,2-dicarboxamide, and 2-furyl-(1H-indol-3-yl)-methyl-BLAHone as candidate compounds. For the down-regulated CH13L1, we relied on genome-wide disease signatures to identify (11alpha)-9-fluoro-11,17,21-trihydroxypregn-4-ene-3,20-dione and Genistein as candidate compounds. Our approach differs from previous research as we did not confine our drug targets to hypothesized compounds in the existing literature. Instead, we allowed the full expression profile of PO cell lines to reveal the most desirable targets. Second, our differential gene analysis revealed both up- and down-regulated genes, in contrast to the literature, which has focused on inhibiting only up-regulated genes. Third, our virtual screening universe of 22,723,923 compounds was more than 100 times larger than those in the known literature.

Type 2 17-β hydroxysteroid dehydrogenase as a novel target for the treatment of osteoporosis

Low estradiol level in postmenopausal women is implicated in osteoporosis, which occurs because of the high bone resorption rate. Estrogen formation is controlled by 17-β hydroxysteroid dehydrogenase 17-β HSD enzymes, where 17-β HSD type 1 contributes in the formation of estradiol, while type 2 catalyzes its catabolism. Inhibiting 17-β HSD2 can help in increasing estradiol concentration. Several promising 17-β HSD2 inhibitors that can act at low nanomolar range have been identified. However, there are some specific challenges associated with the application of these compounds. Our review provides an up-to-date summary of the current status and recent progress in the production of 17-β HSD2 inhibitors as well as the future challenges in their clinical application.

Computationally Guided Identification of Novel Mycobacterium tuberculosis GlmU Inhibitory Leads, Their Optimization, and in Vitro Validation

Mycobacterium tuberculosis (Mtb) infections are causing serious health concerns worldwide. Antituberculosis drug resistance threatens the current therapies and causes further need to develop effective antituberculosis therapy. GlmU represents an interesting target for developing novel Mtb drug candidates. It is a bifunctional acetyltransferase/uridyltransferase enzyme that catalyzes the biosynthesis of UDP-N-acetyl-glucosamine (UDP-GlcNAc) from glucosamine-1-phosphate (GlcN-1-P). UDP-GlcNAc is a substrate for the biosynthesis of lipopolysaccharide and peptidoglycan that are constituents of the bacterial cell wall. In the current study, structure and ligand based computational models were developed and rationally applied to screen a drug-like compound repository of 20,000 compounds procured from ChemBridge DIVERSet database for the identification of probable inhibitors of Mtb GlmU. The in vitro evaluation of the in silico identified inhibitor candidates resulted in the identification of 15 inhibitory leads of this target. Literature search of these leads through SciFinder and their similarity analysis with the PubChem training data set (AID 1376) revealed the structural novelty of these hits with respect to Mtb GlmU. IC50 of the most potent identified inhibitory lead (5810599) was found to be 9.018 ± 0.04 μM. Molecular dynamics (MD) simulation of this inhibitory lead (5810599) in complex with protein affirms the stability of the lead within the binding pocket and also emphasizes on the key interactive residues for further designing. Binding site analysis of the acetyltransferase pocket with respect to the identified structural moieties provides a thorough analysis for carrying out the lead optimization studies.

Pharmacophore Models and Pharmacophore-Based Virtual Screening: Concepts and Applications Exemplified on Hydroxysteroid Dehydrogenases

Computational methods are well-established tools in the drug discovery process and can be employed for a variety of tasks. Common applications include lead identification and scaffold hopping, as well as lead optimization by structure-activity relationship analysis and selectivity profiling. In addition, compound-target interactions associated with potentially harmful effects can be identified and investigated. This review focuses on pharmacophore-based virtual screening campaigns specifically addressing the target class of hydroxysteroid dehydrogenases. Many members of this enzyme family are associated with specific pathological conditions, and pharmacological modulation of their activity may represent promising therapeutic strategies. On the other hand, unintended interference with their biological functions, e.g., upon inhibition by xenobiotics, can disrupt steroid hormone-mediated effects, thereby contributing to the development and progression of major diseases. Besides a general introduction to pharmacophore modeling and pharmacophore-based virtual screening, exemplary case studies from the field of short-chain dehydrogenase/reductase (SDR) research are presented. These success stories highlight the suitability of pharmacophore modeling for the various application fields and suggest its application also in futures studies.

Discovery of new Mycobacterium tuberculosis proteasome inhibitors using a knowledge-based computational screening approach

Mycobacterium tuberculosis bacteria cause deadly infections in patients [Corrected]. The rise of multidrug resistance associated with tuberculosis further makes the situation worse in treating the disease. M. tuberculosis proteasome is necessary for the pathogenesis of the bacterium validated as an anti-tubercular target, thus making it an attractive enzyme for designing Mtb inhibitors. In this study, a computational screening approach was applied to identify new proteasome inhibitor candidates from a library of 50,000 compounds. This chemical library was procured from the ChemBridge (20,000 compounds) and the ChemDiv (30,000 compounds) databases. After a detailed analysis of the computational screening results, 50 in silico hits were retrieved and tested in vitro finding 15 compounds with IC₅₀ values ranging from 35.32 to 64.15 μM on lysate. A structural analysis of these hits revealed that 14 of these compounds probably have non-covalent mode of binding to the target and have not reported for anti-tubercular or anti-proteasome activity. The binding interactions of all the 14 protein-inhibitor complexes were analyzed using molecular docking studies. Further, molecular dynamics simulations of the protein in complex with the two most promising hits were carried out so as to identify the key interactions and validate the structural stability.

Enrichment assessment of multiple virtual screening strategies for Toll-like receptor 8 agonists based on a maximal unbiased benchmarking data set

Toll-like receptor 8 agonists, which activate adaptive immune responses by inducing robust production of T-helper 1-polarizing cytokines, are promising candidates for vaccine adjuvants. As the binding site of toll-like receptor 8 is large and highly flexible, virtual screening by individual method has inevitable limitations; thus, a comprehensive comparison of different methods may provide insights into seeking effective strategy for the discovery of novel toll-like receptor 8 agonists. In this study, the performance of knowledge-based pharmacophore, shape-based 3D screening, and combined strategies was assessed against a maximum unbiased benchmarking data set containing 13 actives and 1302 decoys specialized for toll-like receptor 8 agonists. Prior structure-activity relationship knowledge was involved in knowledge-based pharmacophore generation, and a set of antagonists was innovatively used to verify the selectivity of the selected knowledge-based pharmacophore. The benchmarking data set was generated from our recently developed 'mubd-decoymaker' protocol. The enrichment assessment demonstrated a considerable performance through our selected three-layer virtual screening strategy: knowledge-based pharmacophore (Phar1) screening, shape-based 3D similarity search (Q4_combo), and then a Gold docking screening. This virtual screening strategy could be further employed to perform large-scale database screening and to discover novel toll-like receptor 8 agonists.

Identification of protein kinase CK2 inhibitors using solvent dipole ordering virtual screening

Novel protein kinase CK2 inhibitors were identified using the solvent dipole ordering virtual screening method. A total of 26 compounds categorized in 15 distinct scaffold classes inhibited greater than 50% of enzyme activity at 50 μM, and eight exhibited IC50 values less than 10 μM. Most of the identified compounds are lead-like and dissimilar to known inhibitors. The crystal structures of two of the CK2 complexes revealed the high accuracy of the predicted binding modes.

An Ab Initio Method for Designing Multi-Target Specific Pharmacophores using Complementary Interaction Field of Aspartic Proteases

For past few decades, key objectives of rational drug discovery have been the designing of specific and selective ligands for target proteins. Infectious diseases like malaria are continuously becoming resistant to traditional medicines, which inculcates need for new approaches to design inhibitors for antimalarial targets. A novel method for ab initio designing of multi target specific pharmacophores using the interaction field maps of active sites of multiple proteins has been developed to design 'specificity' pharmacophores for aspartic proteases. The molecular interaction field grid maps of active sites of aspartic proteases (plasmepsin II & IV from Plasmodium falciparum, plasmepsin from Plasmodium vivax, pepsin & cathepsin D from human) are calculated and common pharmacophoric features for favourable binding spots in active sites are extracted in the form of cliques of graphs using inductive logic programming (ILP). The two pharmacophore ensembles are constructed from largest common cliques by imposing size of receptor active site (L) and domain-specific receptor-ligand information (S). The overlap of chemical space between two ensembles and the results of virtual screening of inhibitor database with known activities show that this method can design efficient pharmacophores with no prior ligand information.

PAINS in the assay: chemical mechanisms of assay interference and promiscuous enzymatic inhibition observed during a sulfhydryl-scavenging HTS

Significant resources in early drug discovery are spent unknowingly pursuing artifacts and promiscuous bioactive compounds, while understanding the chemical basis for these adverse behaviors often goes unexplored in pursuit of lead compounds. Nearly all the hits from our recent sulfhydryl-scavenging high-throughput screen (HTS) targeting the histone acetyltransferase Rtt109 were such compounds. Herein, we characterize the chemical basis for assay interference and promiscuous enzymatic inhibition for several prominent chemotypes identified by this HTS, including some pan-assay interference compounds (PAINS). Protein mass spectrometry and ALARM NMR confirmed these compounds react covalently with cysteines on multiple proteins. Unfortunately, compounds containing these chemotypes have been published as screening actives in reputable journals and even touted as chemical probes or preclinical candidates. Our detailed characterization and identification of such thiol-reactive chemotypes should accelerate triage of nuisance compounds, guide screening library design, and prevent follow-up on undesirable chemical matter.

Methods for generating and applying pharmacophore models as virtual screening filters and for bioactivity profiling

Biological effects of small molecules in an organism result from favorable interactions between the molecules and their target proteins. These interactions depend on chemical functionalities, bonds, and their 3D-orientations towards each other. These 3D-arrangements of chemical functionalities that make a small molecule active towards its target can be described by pharmacophore models. In these models, chemical functionalities are represented as so-called features. Commonly, they are obtained either from a set of active compounds or directly from the observed protein-ligand interactions as present in X-ray crystal structures, NMR structures, or docking poses. In this review, we explain the basics of pharmacophore modeling including dataset generation, 3D-representations and conformational analysis of small molecules, pharmacophore model construction, model validation, and its benefits to virtual screening and other applications.