Novel toll-like receptor 4 (TLR4) antagonists identified by structure- and ligand-based virtual screening

Toll-like receptor 4 (TLR4) in complex with its accessory protein MD-2 represents an emerging target for the treatment of severe sepsis and neuropathic pain. We performed structure-based and ligand-based virtual screening targeting the TLR4-MD-2 interface. Three in silico hit compounds showed promising TLR4 antagonistic activities with micromolar IC50 values. These compounds also suppressed cytokine secretion by human peripheral blood mononuclear cells. The specific affinity of the most potent hit was confirmed by surface plasmon resonance direct-binding experiments. The results of our study represent a very promising starting point for the development of potent small-molecule antagonists of TLR4.

Design checkpoint kinase 2 inhibitors by pharmacophore modeling and virtual screening techniques

Damage to DNA is caused by ionizing radiation, genotoxic chemicals or collapsed replication forks. When DNA is damaged or cells fail to respond, a mutation that is associated with breast or ovarian cancer may occur. Mammalian cells control and stabilize the genome using a cell cycle checkpoint to prevent damage to DNA or to repair damaged DNA. Checkpoint kinase 2 (Chk2) is one of the important kinases, which strongly affects DNA-damage and plays an important role in the response to the breakage of DNA double-strands and related lesions. Therefore, this study concerns Chk2. Its purpose is to find potential inhibitors using the pharmacophore hypotheses (PhModels) and virtual screening techniques. PhModels can identify inhibitors with high biological activities and virtual screening techniques are used to screen the database of the National Cancer Institute (NCI) to retrieve compounds that exhibit all of the pharmacophoric features of potential inhibitors with high interaction energy. Ten PhModels were generated using the HypoGen best algorithm. The established PhModel, Hypo01, was evaluated by performing a cost function analysis of its correlation coefficient (r), root mean square deviation (RMSD), cost difference, and configuration cost, with the values 0.955, 1.28, 192.51, and 16.07, respectively. The result of Fischer's cross-validation test for the Hypo01 model yielded a 95% confidence level, and the correlation coefficient of the testing set (rtest) had a best value of 0.81. The potential inhibitors were then chosen from the NCI database by Hypo01 model screening and molecular docking using the cdocker docking program. Finally, the selected compounds exhibited the identified pharmacophoric features and had a high interaction energy between the ligand and the receptor. Eighty-three potential inhibitors for Chk2 are retrieved for further study.

Physicochemical vs. Vibrational Descriptors for Prediction of Odor Receptor Responses

Responses of olfactory receptors (ORs) can be predicted by applying machine learning methods on a multivariate encoding of an odorant's chemical structure. Physicochemical descriptors that encode features of the molecular graph are a popular choice for such an encoding. Here, we explore the EVA descriptor set, which encodes features derived from the vibrational spectrum of a molecule. We assessed the performance of Support Vector Regression (SVR) and Random Forest Regression (RFR) to predict the gradual response of Drosophila ORs. We compared a 27-dimensional variant of the EVA descriptor against a set of 1467 descriptors provided by the eDragon software package, and against a 32-dimensional subset thereof that has been proposed as the basis for an odor metric consisting of 32 descriptors (HADDAD). The best prediction performance was reproducibly achieved using SVR on the highest-dimensional feature set. The low-dimensional EVA and HADDAD feature sets predicted odor-OR interactions with similar accuracy. Adding charge and polarizability information to the EVA descriptor did not improve the results but rather decreased predictive power. Post-hoc in vivo measurements confirmed these results. Our findings indicate that EVA provides a meaningful low-dimensional representation of odor space, although EVA hardly outperformed "classical" descriptor sets.

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.