Pharmacophore Model Refinement for 11β-Hydroxysteroid Dehydrogenase Inhibitors: Search for Modulators of Intracellular Glucocorticoid Concentrations

11β-Hydroxysteroid dehydrogenases (11β-HSD) control the intracellular concentrations of glucocorticoids: 11β-HSD1 converts the inactive cortisone to the active cortisol, and 11β-HSD2 is responsible for the opposite reaction. Inhibition of 11β-HSD1 is beneficial in the treatment of metabolic syndrome, whereas 11β-HSD2 inhibition leads to hypertension. Therefore, 11β-HSD1 inhibitors should be selective over 11β-HSD2. To support drug discovery and toxicological studies, we have previously reported pharmacophore models for 11β-HSD1 and 2 inhibition. These models represent the common chemical features of 11β-HSD inhibitors, which were used as virtual screening filter. Since new inhibitors are constantly discovered, the quality of the pharmacophore models has to be evaluated in order to maintain a good predictive power. In this study, we report a systematic evaluation and refinement of our pharmacophore model collection. We employed our models for virtual screening, especially focusing on the 11β-HSD2 inhibition. In total, 42 compounds were biologically evaluated and among these we discovered 17 11β-HSD inhibitors that decreased the residual enzyme activity to 50% or less at the concentration of 20 µM. The experimental 11β-HSD1 and 2 readouts from these compounds were used for further model refinement. Evaluation metrics were applied for a quantitative comparison of the old and newly generated models which resulted in a set of improved pharmacophore models offering reliable in silico tools for the identification of novel and selective 11β-HSD inhibitors.

Identification of putative estrogen receptor-mediated endocrine disrupting chemicals using QSAR- and structure-based virtual screening approaches

Identification of endocrine disrupting chemicals is one of the important goals of environmental chemical hazard screening. We report on the development of validated in silico predictors of chemicals likely to cause estrogen receptor (ER)-mediated endocrine disruption to facilitate their prioritization for future screening. A database of relative binding affinity of a large number of ERα and/or ERβ ligands was assembled (546 for ERα and 137 for ERβ). Both single-task learning (STL) and multi-task learning (MTL) continuous quantitative structure-activity relationship (QSAR) models were developed for predicting ligand binding affinity to ERα or ERβ. High predictive accuracy was achieved for ERα binding affinity (MTL R(2)=0.71, STL R(2)=0.73). For ERβ binding affinity, MTL models were significantly more predictive (R(2)=0.53, p<0.05) than STL models. In addition, docking studies were performed on a set of ER agonists/antagonists (67 agonists and 39 antagonists for ERα, 48 agonists and 32 antagonists for ERβ, supplemented by putative decoys/non-binders) using the following ER structures (in complexes with respective ligands) retrieved from the Protein Data Bank: ERα agonist (PDB ID: 1L2I), ERα antagonist (PDB ID: 3DT3), ERβ agonist (PDB ID: 2NV7), and ERβ antagonist (PDB ID: 1L2J). We found that all four ER conformations discriminated their corresponding ligands from presumed non-binders. Finally, both QSAR models and ER structures were employed in parallel to virtually screen several large libraries of environmental chemicals to derive a ligand- and structure-based prioritized list of putative estrogenic compounds to be used for in vitro and in vivo experimental validation.

Structure-based identification of novel PPAR gamma ligands

Peroxisome proliferator-activated receptor γ (PPARγ) is a nuclear receptor with an important role in the glucose metabolism and a target for type 2 diabetes mellitus therapy. The recent findings relating the use of the receptor full agonist rosiglitazone and the incidence of myocardial infarction raised concerns regarding whether receptor activation can actually be useful for diabetes management. The discovery of MRL-24 and GQ-16, ligands that can partially activate PPARγ and prevent weight gain and fluid retention, showed that a submaximal receptor activation can be a goal in the development of new ligands for PPARγ. Additionally, two previously described receptor antagonists, SR-202 and BADGE, were also shown to improve insulin sensitivity and decrease TNF-α level, revealing that receptor antagonism may also be an approach to pursue. Here, we used a structure-based approach to screen the subset 'Drugs-Now' of ZINC database. Fifteen ligands were selected after visual inspection and tested for their ability to bind to PPARγ. A benzoimidazol acetate, a bromobenzyl-thio-tetrazol benzoate and a [[2-[(1,3-dioxoinden-2-ylidene)methyl]phenoxy]methyl]benzoate were identified as PPARγ ligands, with IC50 values smaller than 10μM. Molecular dynamic simulations showed that the residues H323, H449, Y327, Y473, K367 and S289 are key structural elements for the molecular recognition of these ligands and the polar arm of PPARγ binding pocket.

Exploring the effect of PARP-1 flexibility in docking studies

Poly(ADP-ribose)polymerase-1 (PARP-1) is an enzyme belonging to the ADP-ribosyltransferase family. A large body of works has validated PARP-1 as an attractive drug target for different therapeutic areas, including cancers and ischemia. Accordingly, sampling the conformational space of the enzyme is pivotal to understand its functions and improve structure-based drug discovery approaches. In the first part of this study we apply replica exchange molecular dynamic (REMD) simulations to sample the conformational space of the catalytic domain of PARP-1 in the ligand-bound and unbound forms. In the second part, we assess how and to what extend the emerging enzyme flexibility affects the performance of docking experiments of a library of PARP-1 inhibitors. This study pinpoints a putative key role of conformational shifts of Leu324, Tyr325 and Lys242 in opening an additional binding site pocket that affects the binding of ligands to the catalytic cleft of PARP-1. Furthermore, it highlights the improvement of the enrichment factor of active ligands obtained in docking experiments when using conformations generated with REMD simulations of ligand-bound PARP-1.

In silico methods in the discovery of endocrine disrupting chemicals

The prevalence of sex hormone-dependent cancers, reproductive problems, obesity, and cardiovascular complications has risen especially in the Western world. It has been suggested, that the exposure to various endocrine disrupting chemicals (EDCs) contributes to the development and progression of these diseases. EDCs can interfere with various proteins: nuclear steroid hormone receptors, such as estrogen-, androgen-, glucocorticoid- and mineralocorticoid receptors (ER, AR, GR, MR), and enzymes that are involved in steroid hormone synthesis and metabolism, for example hydroxysteroid dehydrogenases (HSDs). Numerous chemicals are known as endocrine disruptors. However, the mechanism of action for most of these EDCs is still unknown. It is exhaustive and time consuming to test in vitro all chemicals - potential EDCs - used in industry, agriculture or as food preservatives against their effects on the endocrine system. Computational methods, such as virtual screening, quantitative structure activity relationships and docking, are already well recognized and used in drug development. The same methods could also aid the research on EDCs. So far, the computational methods in the search of EDCs have been retrospective. There are, however, some prospective studies reporting the use of in silico methods: five studies reporting the identification of previously unknown 17β-HSD3 inhibitors, MR agonists, and ER antagonists/agonists. This review provides an overview of case studies and in silico methods that are used in the search of EDCs. This article is part of a Special Issue entitled 'CSR 2013'.

Inferences from the ADMET analysis of predicted inhibitors to Follicle Stimulating Hormone in the context of infertility

Follicle stimulating hormone (FSH) is a glycoprotein secreted by gonadotrophs of the anterior pituitary gland that regulates reproduction in mammals. FSH targets its receptor (FSHR) expressed only on grannulosa cells and induce the maturation of ovarian follicles in females. The levels of both FSH and FSHR rise until the middle of estrus cycle and then falls on level at the time of ovulation. It is associated with stimulated sertoli cell proliferation in testes and supports spermatogenesis in males. The interaction between the polypeptide FSH hormone and its corresponding receptor is highly selective. Therefore, it is of interest to inhibit FSH in the context of infertility. The structure of FSH (PDB ID: 1XWD) is screened using molecular docking techniques against the ZINC database (a database of 2.7 million compounds) with reference to known standard compounds. This exercise identifies compounds with better binding and ADMET (Absorption, Digestion, Metabolism, Excretion and Toxicity) properties compared to known standard compounds. These observations find application for the consideration of such compounds for further validation towards inhibiting the FSH.

Structure-based discovery of antagonists for GluN3-containing N-methyl-D-aspartate receptors

NMDA receptors are ligand-gated ion channels that assemble into tetrameric receptor complexes composed of glycine-binding GluN1 and GluN3 subunits (GluN3A-B) and glutamate-binding GluN2 subunits (GluN2A-D). NMDA receptors can assemble as GluN1/N2 receptors and as GluN3-containing NMDA receptors, which are either glutamate/glycine-activated triheteromeric GluN1/N2/N3 receptors or glycine-activated diheteromeric GluN1/N3 receptors. The glycine-binding GluN1 and GluN3 subunits display strikingly different pharmacological selectivity profiles. However, the pharmacological characterization of GluN3-containing receptors has been hampered by the lack of methods and pharmacological tools to study GluN3 subunit pharmacology in isolation. Here, we have developed a method to study the pharmacology of GluN3 subunits in recombinant diheteromeric GluN1/N3 receptors by mutating the orthosteric ligand-binding pocket in GluN1. This method is suitable for performing compound screening and characterization of structure-activity relationship studies on GluN3 ligands. We have performed a virtual screen of the orthosteric binding site of GluN3A in the search for antagonists with selectivity for GluN3 subunits. In the subsequent pharmacological evaluation of 99 selected compounds, we identified 6-hydroxy-[1,2,5]oxadiazolo[3,4-b]pyrazin-5(4H)-one (TK80) a novel competitive antagonist with preference for the GluN3B subunit. Serendipitously, we also identified [2-hydroxy-5-((4-(pyridin-3-yl)thiazol-2-yl)amino]benzoic acid (TK13) and 4-(2,4-dichlorobenzoyl)-1H-pyrrole-2-carboxylic acid (TK30), two novel non-competitive GluN3 antagonists. These findings demonstrate that structural differences between the orthosteric binding site of GluN3 and GluN1 can be exploited to generate selective ligands.

Computer-aided identification of novel protein targets of bisphenol A

The xenoestrogen bisphenol A (2,2-bis-(p-hydroxyphenyl)-2-propane, BPA) is a known endocrine-disrupting chemical used in the fabrication of plastics, resins and flame retardants, that can be found throughout the environment and in numerous every day products. Human exposure to this chemical is extensive and generally occurs via oral route because it leaches from the food and beverage containers that contain it. Although most of the effects related to BPA exposure have been linked to the activation of the estrogen receptor (ER), the mechanisms of the interaction of BPA with protein targets different from ER are still unknown. Therefore, the objective of this work was to use a bioinformatics approach to identify possible new targets for BPA. Docking studies were performed between the optimized structure of BPA and 271 proteins related to different biochemical processes, as selected by text-mining. Refinement docking experiments and conformational analyses were carried out using LigandScout 3.0 for the proteins selected through the affinity ranking (lower than -8.0kcal/mol). Several proteins including ERR gamma (-9.9kcal/mol), and dual specificity protein kinases CLK-4 (-9.5kcal/mol), CLK-1 (-9.1kcal/mol) and CLK-2 (-9.0kcal/mol) presented great in silico binding affinities for BPA. The interactions between those proteins and BPA were mostly hydrophobic with the presence of some hydrogen bonds formed by leucine and asparagine residues. Therefore, this study suggests that this endocrine disruptor may have other targets different from the ER.

New scaffolds of natural origin as Integrase-LEDGF/p75 interaction inhibitors: virtual screening and activity assays

The disruption of crucial interactions between HIV-1 Integrase and cellular cofactor LEDGF/p75 represents an emerging approach for the design and development of new antiretroviral agents. In this study we report the successful application of a structure-based virtual screening strategy for the discovery of natural hit structures able to inhibit Integrase-LEDGF/p75 interaction. The application of sequential filters (drug-likeness, 3D-pharmacophore mapping, docking, molecular dynamics simulations) yielded a hit list of compounds, out of which 9 were tested in the in vitro AlphaScreen assays and 8 exhibited a detectable inhibition of the interaction between the two proteins. The best inhibitors belong to different chemical classes and could be represent a good starting point for further optimization and structure-activity relationship studies.

Targeting the Dbl and dock-family RhoGEFs: a yeast-based assay to identify cell-active inhibitors of Rho-controlled pathways

The Ras-like superfamily of low molecular weight GTPases is made of five major families (Arf/Sar, Rab, Ran, Ras, and Rho), highly conserved across evolution. This is in keeping with their roles in basic cellular functions (endo/exocytosis, vesicular trafficking, nucleocytoplasmic trafficking, cell signaling, proliferation and apoptosis, gene regulation, F-actin dynamics), whose alterations are associated with various types of diseases, in particular cancer, neurodegenerative, cardiovascular, and infectious diseases. For these reasons, Ras-like pathways are of great potential in therapeutics and identifying inhibitors that decrease signaling activity is under intense research. Along this line, guanine exchange factors (GEFs) represent attractive targets. GEFs are proteins that promote the active GTP-bound state of GTPases and represent the major entry points whereby extracellular cues are converted into Ras-like signaling. We previously developed the yeast exchange assay (YEA), an experimental setup in the yeast in which activity of a mammalian GEF can be monitored by auxotrophy and color reporter genes. This assay was further engineered for medium-throughput screening of GEF inhibitors, which can readily select for cell-active and specific compounds. We report here on the successful identification of inhibitors against Dbl and CZH/DOCK-family members, GEFs for Rho GTPases, and on the experimental setup to screen for inhibitors of GEFs of the Arf family. We also discuss on inhibitors developed using virtual screening (VS), which target the GEF/GTPase interface with high efficacy and specificity. We propose that using VS and YEA in combination may represent a method of choice for identifying specific and cell-active GEF inhibitors.

An in silico evaluation of the ADMET profile of the StreptomeDB database

Background

Computer-aided drug design (CADD) often involves virtual screening (VS) of large compound datasets and the availability of such is vital for drug discovery protocols. This paper presents an assessment of the “drug-likeness” and pharmacokinetic profile of > 2,400 compounds of natural origin, currently available in the recently published StreptomeDB database.

Methods

The evaluation of “drug-likeness” was performed on the basis of Lipinski’s “Rule of Five”, while 46 computed physicochemical properties or molecular descriptors were used to predict the absorption, distribution, metabolism, elimination and toxicity (ADMET) of the compounds.

Results

This survey demonstrated that, of the computed molecular descriptors, about 28% of the compounds within the StreptomeDB database were compliant, having properties which fell within the range of ADMET properties of 95% of currently known drugs, while about 44% of the compounds had ≤ 2 violations. Moreover, about 50% of the compounds within the corresponding “drug-like” subset showed compliance, while >83% of the “drug-like” compounds had ≤ 2 violations.

Conclusions

In addition to the previously verified range of measured biological activities, the compounds in the StreptomeDB database show interesting DMPK profiles and hence could represent an important starting point for hit/lead discovery from natural sources. The generated data are available and could be highly useful for natural product lead generation programs.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-1801-2-353) contains supplementary material, which is available to authorized users.

Identification of quinazolinyloxy biaryl urea as a new class of SUMO activating enzyme 1 inhibitors

SUMO activating enzyme 1 (SUMO E1) is the first enzyme in sumoylation pathway and an important cancer drug target. However, only a few inhibitors were reported up to now that includes three natural products, semi-synthetic protein inhibitors and one AMP mimic. Here, we report the identification of quinazolinyloxy biaryl urea as a new class of SUMO E1 inhibitors. The most active compound of this class inhibited the in vitro sumoylation with an IC50 of 13.4 μM. This compound inhibits sumoylation by blocking the formation of SUMOE1-SUMO thioester intermediate. The biological activity of the most active compound is comparable to previously reported inhibitors with properties suitable for medicinal chemistry optimization for potency and druggability.

Structure based design towards the identification of novel binding sites and inhibitors for the chikungunya virus envelope proteins

Chikungunya virus is an emerging arbovirus that is widespread in tropical regions and is spreading quickly to temperate climates with recent epidemics in Africa, Asia, Europe and the Americas. It is having an increasingly major impact on humans with potentially life-threatening and debilitating arthritis. Thus far, neither vaccines nor medications are available to treat or control the virus and therefore, the development of medicinal chemistry is a vital and immediate issue that needs to be addressed. The viral envelope proteins play a major role during infection through mediation of binding and fusion with the infected cell surfaces. The possible binding target sites of the chikungunya virus envelope proteins have not previously been investigated; we describe here for the first time the identification of novel sites for potential binding on the chikungunya glycoprotein complexes and the identification of possible antagonists for these sites through virtual screening using two successive docking scores; FRED docking for fast precise screening, with the top hits then subjected to a ranking scoring using the AUTODOCK algorithm. Both the immature and the mature forms of the chikungunya envelope proteins were included in the study to increase the probability of finding positive and reliable hits. Some small molecules have been identified as good in silico chikungunya virus envelope proteins inhibitors and these could be good templates for drug design targeting this virus.

Quantification of free ligand conformational preferences by NMR and their relationship to the bioactive conformation

Accurate unbound solution 3D-structures of ligands provide unique opportunities for medicinal chemistry and, in particular, a context to understand binding thermodynamics and kinetics. Previous methods of deriving these 3D-structures have had neither the accuracy nor resolution needed for drug design and have not yet realized their potential. Here, we describe and apply a NMR methodology to the aminoglycoside streptomycin that can accurately quantify accessible 3D-space and rank the occupancy of observed conformers to a resolution that enables medicinal chemistry understanding and design. Importantly, it is based upon conventional small molecule NMR techniques and can be performed in physiologically-relevant solvents. The methodology uses multiple datasets, an order of magnitude more experimental data than previous NMR approaches and a dynamic model during refinement, is independent of computational chemistry and avoids the problem of virtual conformations. The refined set of solution 3D-shapes for streptomycin can be grouped into two major families, of which the most populated is almost identical to the 30S ribosomal subunit bioactive shape. We therefore propose that accurate unbound ligand solution conformations may, in some cases, provide a subsidiary route to bioactive shape without crystallography. This experimental technique opens up new opportunities for drug design and more so when complemented with protein co-crystal structures, SAR data and pharmacophore modeling.

Pharmacophore Alignment Search Tool (PhAST): Significance Assessment of Chemical Similarity

Previously, we proposed a ligand-based virtual screening technique (PhAST) based on global alignment of linearized interaction patterns. Here, we applied techniques developed for similarity assessment in local sequence alignments to our method resulting in p-values for chemical similarity. We compared two sampling strategies, a simple sampling strategy and a Markov Chain Monte Carlo (MCMC) method, and investigated the similarity of sampled distributions to Gaussian, Gumbel, modified Gumbel, and Gamma distributions. The Gumbel distribution with a Gaussian correction term was identified as the most similar to the observed empirical distributions. These techniques were applied in retrospective screenings on a drug-like dataset. Obtained p-values were adjusted to the size of the screening library with four different methods. Evaluation of E-value thresholds corroborated the Bonferroni correction as a preferred means to identify significant chemical similarity with PhAST. An online version of PhAST with significance estimation is available at http://modlab-cadd.ethz.ch/.

Similarity-based virtual screening for microtubule stabilizers reveals novel antimitotic scaffold

Microtubules are among the most studied and best characterized cancer targets identified to date. Many microtubule stabilizers have been introduced so far that work by disrupting the dynamic instability of microtubules causing mitotic block and apoptosis. However, most of these molecules, especially taxol and epothilone, suffer absorption, toxicity and/or resistance problems. Here we employ a novel similarity-based virtual screening approach in the hope of finding other microtubule stabilizers that perform better and have lower toxicity and resistance. Epothilones, discodermolide, eleutherobin and sarcodictyin A have been found to compete with taxanes for the β-tubulin binding site, which suggests common chemical features qualifying for that. Our approach was based on similarity screening against all these compounds and other microtubule stabilizers, followed by virtual screening against the taxol binding site. Some novel hits were found, together with a novel highly rigid molecular scaffold. After visual manipulations, redocking and rescoring of this novel scaffold, its affinity dramatically increased in a promising trend, which qualifies for biological testing.

Exploring the Molecular Basis of Qo bc1 Complex Inhibitors Activity to Find Novel Antimalarials Hits

Cytochrome bc1 complex is a crucial element in the mitochondrial respiratory chain, being indispensable for the survival of several species of Plasmodia that cause malaria and, therefore, it is a promising target for antimalarial drug development. We report a molecular docking study building on the most recently obtained X-ray structure of the Saccharomyces cerevisiae bc1 complex (PDB code: 3CX5) using several reported inhibitors with experimentally determined IC50 values against the Plasmodium falciparum bc1 complex. We produced a molecular docking model that correlated the calculated binding free energy with the experimental inhibitory activity of each compound. This Qo model was used to search the drug-like database included in the MOE package for novel potential bc1 complex inhibitors. Twenty three compounds were chosen to be tested for their antimalarial activity and four of these compounds demonstrated activity against the chloroquine-resistant W2 strain of P. falciparum. The most active compounds were also active against the atovaquone-resistant P. falciparum FCR3 strain and S. cerevisiae. Our study suggests the validity of the yeast bc1 complex structure as a model for the discovery of new antimalarial hits.

The emerging role of cloud computing in molecular modelling

There is a growing recognition of the importance of cloud computing for large-scale and data-intensive applications. The distinguishing features of cloud computing and their relationship to other distributed computing paradigms are described, as are the strengths and weaknesses of the approach. We review the use made to date of cloud computing for molecular modelling projects and the availability of front ends for molecular modelling applications. Although the use of cloud computing technologies for molecular modelling is still in its infancy, we demonstrate its potential by presenting several case studies. Rapid growth can be expected as more applications become available and costs continue to fall; cloud computing can make a major contribution not just in terms of the availability of on-demand computing power, but could also spur innovation in the development of novel approaches that utilize that capacity in more effective ways.

PyPLIF: Python-based Protein-Ligand Interaction Fingerprinting

Structure-based virtual screening (SBVS) methods often rely on docking score. The docking score is an over-simplification of the actual ligand-target binding. Its capability to model and predict the actual binding reality is limited. Recently, interaction fingerprinting (IFP) has come and offered us an alternative way to model reality. IFP provides us an alternate way to examine protein-ligand interactions. The docking score indicates the approximate affinity and IFP shows the interaction specificity. IFP is a method to convert three dimensional (3D) protein-ligand interactions into one dimensional (1D) bitstrings. The bitstrings are subsequently employed to compare the protein-ligand interaction predicted by the docking tool against the reference ligand. These comparisons produce scores that can be used to enhance the quality of SBVS campaigns. However, some IFP tools are either proprietary or using a proprietary library, which limits the access to the tools and the development of customized IFP algorithm. Therefore, we have developed PyPLIF, a Python-based open source tool to analyze IFP. In this article, we describe PyPLIF and its application to enhance the quality of SBVS in order to identify antagonists for estrogen α receptor (ERα).

Virtual screening for oseltamivir-resistant a (H5N1) influenza neuraminidase from traditional Chinese medicine database: a combined molecular docking with molecular dynamics approach

The neuraminidase (NA) of the influenza virus is the target of antiviral drug, oseltamivir. Recently, cases are reported that Influenza virus becoming resistant to oseltamivir, necessitating the development of new long-acting antiviral compounds. Most importantly, H274Y mutation in neuraminidase exhibits high levels of resistance to oseltamivir. In this report, a novel class of lead molecule with potential NA inhibitory activity was found from the traditional Chinese medicine database (TCMD) using virtual screening approach. Initially ADME properties of the lead compounds were analyzed with respect to the Lipinski rule of five. Subsequently, the data reduction was carried out by employing molecular docking study. Final validation was done by means of molecular dynamic simulations. The toxicity profiles for the screened compound were also analyzed. The result indicates that neoglucobrassicin (a compound derived from TCMD) become a promising lead compound and be effective in treating oseltamivir-resistant influenza virus strains.

Fusing similarity rankings in ligand-based virtual screening

Data fusion is the name given to a range of methods for combining multiple sources of evidence. This mini-review summarizes the use of one such class of methods for combining the rankings obtained when similarity searching is used for ligand-based virtual screening. Two main approaches are described: similarity fusion involves combining rankings from single searches based on multiple similarity measures; and group fusion involves combining rankings from multiple searches based on a single similarity measure. The review then focuses on the rules that are available for combining similarity rankings, and on the evidence that exists for the superiority of fusion-based methods over conventional similarity searching.

A Strategy Combining Differential Low-Throughput Screening and Virtual Screening (DLS-VS) Accelerating the Discovery of new Modulators for the Orphan GPR34 Receptor

The DLS-VS strategy was developed as an integrated method for identifying chemical modulators for orphan GPCRs. It combines differential low-throughput screening (DLS) and virtual screening (VS). The two cascaded techniques offer complementary advantages and allow the experimental testing of a minimal number of compounds. First, DLS identifies modulators specific for the considered receptor among a set of receptors, through the screening of a small library with diverse chemical compounds. Then, an active molecular model of the receptor is built by homology to a validated template, and it is progressively refined by rotamers modification for key side-chains, by VS of the already screened library, and by iterative selection of the model generating the best enrichment. The refined active model is finally used for the VS of a large chemical library and the selection of a small set of compounds for experimental testing. Applied to the orphan receptor GPR34, the DLS-VS strategy combined the experimental screening of 20 000 compounds and the virtual screening of 1 250 000 compounds. It identified one agonist and eight inverse agonists, showing a high chemical diversity. We describe the method. The strategy can be applied to other GPCRs.

Molecular modeling, dynamics studies and virtual screening of Fructose 1, 6 biphosphate aldolase-II in community acquired- methicillin resistant Staphylococcus aureus (CA-MRSA)

Community-acquired methicillin-resistant Staphylococcus aureus (CA-MRSA) has recently emerged as a nosocomial pathogen to the community which commonly causes skin and soft-tissue infections (SSTIs). This strain (MW2) has now become resistant to the most of the beta-lactam antibiotics; therefore it is the urgent need to identify the novel drug targets. Recently fructose 1,6 biphosphate aldolase-II (FBA) has been identified as potential drug target in CA-MRSA. The FBA catalyses the retro-ketolic cleavage of fructose-1,6-bisphosphate (FBP) to yield dihydroxyacetone phosphate (DHAP) and glyceraldehyde-3-phosphate (G3P) in glycolytic pathway. In the present research work the 3D structure of FBA was predicted using the homology modeling method followed by validation. The molecular dynamics simulation (MDS) of the predicted model was carried out using the 2000 ps time scale and 1000000 steps. The MDS results suggest that the modeled structure is stable. The predicted model of FBA was used for virtual screening against the NCI diversity subset-II ligand databases which contain 1364 compounds. Based on the docking energy scores, it was found that top four ligands i.e. ZINC01690699, ZINC13154304, ZINC29590257 and ZINC29590259 were having lower energy scores which reveal higher binding affinity towards the active site of FBA. These ligands might act as potent inhibitors for the FBA so that the menace of antimicrobial resistance in CA-MRSA can be conquered. However, pharmacological studies are required to confirm the inhibitory activity of these ligands against the FBA in CA-MRSA.

Wine Compounds as a Source for HTS Screening Collections. A Feasibility Study

High throughput screening (HTS) is extensively used to identify hit and lead compounds in drug discovery programmes. Designing quality screening libraries is a challenge in terms of water solubility, stability and potential oral bioavailability of the compounds. Wines are widely consumed and wine compounds are inherently water soluble, stable and relatively non-toxic. Furthermore, many wine compounds have been proved health-beneficial. To evaluate the feasibility to use wine compounds 3317 were collected from the literature. Their physiochemical properties were evaluated with main stream molecular descriptors. According to the results ∼25 % of the compounds are lead-like; nearly 80 % lie within drug-like chemical space and finally 90 % conform to known drug space (KDS). The rotatable bonds descriptor was the most effective defining lead-like space. The results suggest that many of the wine compounds are interesting and suitable candidates for screening libraries after suitable filtering.

Structure-based design of eugenol analogs as potential estrogen receptor antagonists

Eugenol is an essential oil mainly found in the buds and leaves of clove (Syzygium aromaticum (L.) Merrill and Perry), which has been reported to have activity on inhibition of cell proliferation and apoptosis induction in human MCF-7 breast cancer cells. This biological activity is correlated to its activity as an estrogen receptor antagonist. In this article, we present the construction and validation of structure-based virtual screening (SBVS) protocols to identify the potent estrogen receptor α (ER) antagonists. The selected protocol, which gave acceptable enrichment factors as a virtual screening protocol, subsequently used to virtually screen eugenol, its analogs and their dimers. Based on the virtual screening results, dimer eugenol of 4-[4-hydroxy-3-(prop-2-en-1- yl)phenyl]-2-(prop-2-en-1-yl)phenol is recommended to be developed further in order to discover novel and potent ER antagonists.

Modeling of mammalian olfactory receptors and docking of odorants

Olfactory receptors (ORs) belong to the superfamily of G protein-coupled receptors (GPCRs), the second largest class of genes after those related to immunity, and account for about 3 % of mammalian genomes. ORs are present in all multicellular organisms and represent more than half the GPCRs in mammalian species (e.g., the mouse OR repertoire contains >1,000 functional genes). ORs are mainly expressed in the olfactory epithelium where they detect odorant molecules, but they are also expressed in a number of other cells, such as sperm cells, although their functions in these cells remain mostly unknown. It has recently been reported that ORs are present in tumoral tissues where they are expressed at different levels than in healthy tissues. A specific OR is over-expressed in prostate cancer cells, and activation of this OR has been shown to inhibit the proliferation of these cells. Odorant stimulation of some of these receptors results in inhibition of cell proliferation. Even though their biological role has not yet been elucidated, these receptors might constitute new targets for diagnosis and therapeutics. It is important to understand the activation mechanism of these receptors at the molecular level, in particular to be able to predict which ligands are likely to activate a particular receptor ('deorphanization') or to design antagonists for a given receptor. In this review, we describe the in silico methodologies used to model the three-dimensional (3D) structure of ORs (in the more general framework of GPCR modeling) and to dock ligands into these 3D structures.

Identifications small molecules inhibitor of p53-mortalin complex for cancer drug using virtual screening

Mortalin was over expressed in tumor cells and bind to p53 protein. This interaction was suggested to promote sequestration of p53 in the cytoplasm, thereby inhibiting its nuclear activity. The p53 is a tumor suppressor that is essential for the prevention of cancer development and loss of p53 function is one of the early events in immortalization of human cells. Therefore, abrogation p53-mortalin interaction using small molecule is guaranteed stop cancer cell grow. However study interaction of p53-mortalin, and its inhibition using small molecule is still challenging because specific site of mortalin that bind to p53, vice versa, is still debatable. This study has aims to analyze the p53-binding site of mortalin using molecular docking and to screen drug-like compounds that have potential as inhibitors of p53-mortalin interaction using virtual screening. The result showed that the lowest energy binding of p53-mortalin complex is -31.89 kcal/mol, and p53 protein bind to substrate binding domain of mortalin (THR433; VAL435; LEU436; LEU437; PRO442; ILE558; LYS555). Furthermore, the p53-binding domain of mortalin was used as receptor to screen 9000 drug-like compounds from ZINC database using molecular docking program Auto Dock Vina in PyRx 0.8 (Virtual Screening Tools). Here, we have identified three drug-like compounds that are ZINC01019934, ZINC00624418 and ZINC00664532 adequate to interrupt stability of p53-mortalin complex that warrant for anticancer agent.

Inhibitors of RNA editing as potential chemotherapeutics against trypanosomatid pathogens

The related trypanosomatid pathogens, Trypanosoma brucei spp., Trypanosoma cruzi and Leishmania spp. cause devastating diseases in humans and animals and continue to pose a major challenge in drug development. Mitochondrial RNA editing, catalyzed by multi-protein complexes known as editosomes, has provided an opportunity for development of efficient and specific chemotherapeutic targets against trypanosomatid pathogens. This review will discuss both methods for discovery of RNA editing inhibitors, as well as inhibitors against the T. brucei editosome that were recently discovered through creative virtual and high throughput screening methods. In addition, the use of these inhibitors as agents that can block or perturb one or more steps of the RNA editing process will be discussed. These inhibitors can potentially be used to study the dynamic processing and assembly of the editosome proteins. A thorough understanding of the mechanisms and specificities of these new inhibitors is needed in order to contribute to both the functional studies of an essential gene expression mechanism and to the possibility of future drug development against the trypanosomatid pathogens.

Optimization of Structure Based Virtual Screening Protocols Against Thymidine Monophosphate Kinase Inhibitors as Antitubercular Agents

Thymidine monophosphate kinase from Mycobacterium tuberculosis (TMPKMtub ) is an established drug target against tuberculosis. The enzyme TMPKMtub is responsible for the survival of bacterium MTB and required to synthesize an essential building block of the bacterial DNA which is thymidine triphosphate (TTP). There are several potent inhibitors available against the target enzyme but the majority are substrate analogues. Recently, three dimensional structures of the enzyme TMPKMtub inhibitor complexes were resolved using X-ray crystallography. These available crystal structures were the basis of initiating a structure based lead identification campaign against TMPKMtub . The available information was utilized to perform structure-based virtual screening against TMPKMtub with the hope to diversify the structures of the current inhibitors. In order to setup the protocol, 10 000 out of 45 000 drug-like molecules were randomly selected from National Cancer Institute's (NCI) database. Additionally 105 known inhibitors along with 11 natural substrates were mixed with the 10 000 selected compounds. For the current study, a rigid based docking algorithm, i.e., FRED has been utilized to set up an efficient docking and scoring protocol. The methods including enrichment curves, consensus scoring and ROC curves are providing useful insights into the setting up of a suitable structure-based docking protocol against TMPKMtub . As a result, an optimum docking and scoring function has been identified for future large scale virtual screening. In the present work, we have demonstrated a rational choice of protocol for structure based virtual screening of chemical libraries and help to understand the influence of receptor flexibility by using multiple geometries.

Self-Organizing Maps for In Silico Screening and Data Visualization

Self-organizing maps, which are unsupervised artificial neural networks, have become a very useful tool in a wide area of disciplines, including medicinal chemistry. Here, we will focus on two applications of self-organizing maps: the use of self-organizing maps for in silico screening and for clustering and visualisation of large datasets. Additionally, the importance of parameter selection is discussed and some modifications to the original algorithm are summarised.

A comprehensive molecular interaction map for Hepatitis B virus and drug designing of a novel inhibitor for Hepatitis B X protein

Hepatitis B virus (HBV) infection is a leading source of liver diseases such as hepatitis, cirrhosis and hepatocellular carcinoma. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Hepatitis B virus (HBV). Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by HBV. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. We find that some nodes in the network that prove to be topologically important, in particular HBx is also known to be important target protein used for the treatment of HBV. Therefore, HBx protein is the preferential choice for inhibition to stop the proteolytic processing. Hence, the 3D structure of HBx protein was downloaded from PDB. Ligands for the active site were designed using LIGBUILDER. The HBx protein's active site was explored to find out the critical interactions pattern for inhibitor binding using molecular docking methodology using AUTODOCK Vina. It should be noted that these predicted data should be validated using suitable assays for further consideration.

Targeting PKC-β II and PKB Connection: Design of Dual Inhibitors

Protein kinase C (PKC) has been the center of many cell signaling pathways. PKC isoforms, specifically PKC-β II is linked to both diabetic complications as well as in promotion of angiogenesis and regulation of cancers. PKC-β II activates the PKB/Akt pathway. Enzastaurin, a selective PKC-β II inhibitor has been found to inhibit PKB/Akt by suppressing the regulation of various cancerous pathways. In the present work, we carried out an in depth study on the binding mode of inhibitors of PKC-β II, enzastaurin and ruboxistaurin with the active site residues of PKB and PKC-β II. A ligand based approach has been further used to determine the pharmacophoric features and spatial arrangement of molecules, having common properties necessary for appropriate binding to the active site of both targets. Virtual screening of the respective pharmacophores of both proteins led to identification of hits which may be useful for treatment of diabetic complications and cancer. The study has highlighted important features that may be considered in the future for designing novel inhibitors.

Design of a novel nucleoside analog as potent inhibitor of the NAD dependent deacetylase, SIRT2

Sirtuins (class III histone deacetylase) are evolutionarily conserved NAD(+)-dependent enzymes that catalyze the deacetylation of acetyl-lysine residues of histones and other target proteins. Because of their associations in various pathophysiological conditions, the identification of small molecule modulators has been of significant interest. In the present study, virtual screening was carried out with NCI Diversity Set II using crystal structure of hSIRT2 (PDB ID: 1J8F) as a model for the docking procedure to find potential compounds, which were then subjected to experimental tests for their in vitro SIRT2 inhibitory activity. One of the 40 compounds tested, NSC671136 (IUPAC name: 6-Acetyl-4-oxo-1,3-diphenyl-2-thioxo-1,2,3,4-tetrahydrothieno[2,3-d]pyrimidin-5-yl 2,4-dichlorobenzoate) has structurally unique scaffold, showed strong inhibitory activity towards SIRT2 with IC(50) of ~8.7 μM and to a lesser extent on SIRT1 activity. The reported compound is substantially potent compared to the published SIRT2 inhibitors and serves as an excellent base for future lead development.

Identifying Novel Type ZBGs and Nonhydroxamate HDAC Inhibitors Through a SVM Based Virtual Screening Approach

Histone deacetylase inhibitors (HDACi) have been successfully used for the treatment of cancers and other diseases. Search for novel type ZBGs and development of non-hydroxamate HDACi has become a focus in current research. To complement this, it is desirable to explore a virtual screening (VS) tool capable of identifying different types of potential inhibitors from large compound libraries with high yields and low false-hit rates similar to HTS. This work explored the use of support vector machines (SVM) combined with our newly developed putative non-inhibitor generation method as such a tool. SVM trained by 702 pre-2008 hydroxamate HDACi and 64334 putative non-HDACi showed good yields and low false-hit rates in cross-validation test and independent test using 220 diverse types of HDACi reported since 2008. The SVM hit rates in scanning 13.56 M PubChem and 168K MDDR compounds are comparable to HTS rates. Further structural analysis of SVM virtual hits suggests its potential for identification of non-hydroxamate HDACi. From this analysis, a series of novel ZBG and cap groups were proposed for HDACi design.

Prioritizing Hits with Appropriate Trade-Offs Between HIV-1 Reverse Transcriptase Inhibitory Efficacy and MT4 Blood Cells Toxicity Through Desirability-Based Multiobjective Optimization and Ranking

Nonnucleoside reverse transcriptase (RT) inhibitors (NNRTIs) constitute a promising therapeutic option for AIDS. However, the emergence of virus-NNRTIs resistance was found to be a major problem in the field. Toward that goal, a "knock-out" strategy stands out between the several options to circumvent the problem. However the high drug or drug-drug concentrations often used generate additional safety concerns. The need for approaches able to early integrate drug- or lead-likeness, toxicity and bioavailability criteria in the drug discovery phase is an emergent issue. Given that, we propose a combined strategy based on desirability-based multiobjective optimization (MOOP) and ranking for the prioritization of HIV-1 NNRTIs hits with appropriate trade-offs between inhibitory efficacy over the HIV-1 RT and toxic effects over MT4 blood cells. Through the MOOP process, the theoretical levels of the predictive variables required to reach a desirable RT inhibitor candidate with the best possible compromise between efficacy and safety were found. This information is used as a pattern to rank the library of compounds according to a similarity-based structural criterion, providing a ranking quality of 64 %/71 %/73 % in training/validation/test set. A comparative study between the sequential, parallel and multiobjective virtual screening revealed that the multiobjective approach can outperform the other approaches. These results suggest that the identification of NNRTIs hits with appropriate trade-offs between potency and safety, rather than fully optimized hits solely based on potency, can facilitate the hit to lead transition and increase the likelihood of the candidate to evolve into a successful antiretroviral drug.

Finding Inspiration in the Protein Data Bank to Chemically Antagonize Readers of the Histone Code

Members of the Royal family of proteins are readers of the histone code that contain aromatic cages capable of recognizing specific sequences and lysine methylation states on histone tails. These binding modules play a key role in epigenetic signalling, and are part of a larger group of epigenetic targets that are becoming increasingly attractive for drug discovery. In the current study, pharmacophore representations of the aromatic cages forming the methyl-lysine (Me-Lys) recognition site were used to search the Protein Data Bank (PDB) for ligand binding pockets possessing similar chemical and geometrical features in unrelated proteins. The small molecules bound to these sites were then extracted from the PDB, and clustered based on fragments binding to the aromatic cages. The compounds collected are numerous and structurally diverse, but point to a limited set of preferred chemotypes; these include quaternary ammonium, sulfonium, and primary, secondary and tertiary amine moieties, as well as aromatic, aliphatic or orthogonal rings, and bicyclic systems. The chemical tool-kit identified can be used to design antagonists of the Royal family and related proteins.

Screening Outside the Catalytic Site: Inhibition of Macromolecular Inter-actions Through Structure-Based Virtual Ligand Screening Experiments

During these last 15 years, drug discovery strategies have essentially focused on identifying small molecules able to inhibit catalytic sites. However, other mechanisms could be targeted. Protein-protein interactions play crucial roles in a number of biological processes, and, as such, their disruption or stabilization is becoming an area of intense activity. Along the same line, inhibition of protein-membrane could be of major importance in several disease indications. Despite the many challenges associated with the development of such classes of interaction modulators, there has been considerable success in the recent years. Importantly, through the existence of protein hot-spots and the presence of druggable pockets at the macromolecular interfaces or in their vicinities, it has been possible to find small molecule effectors using a variety of screening techniques, including combined virtual ligand-in vitro screening strategy. Indeed such in silico-in vitro protocols emerge as the method of choice to facilitate our quest of novel drug-like compounds or of mechanistic probes aiming at facilitating the understanding of molecular reactions involved in the Health and Disease process. In this review, we comment recent successes of combined in silico-in vitro screening methods applied to modulating macromolecular interactions with a special emphasis on protein-membrane interactions.