Virtual Screening Procedure Research Articles

Receptor-based virtual screening evaluation for the identification of estrogen receptor β ligands

In this paper, a receptor-based virtual screening study for the identification of estrogen receptor β (ERβ) ligands was developed. Starting from a commercial database of 400 000 molecules, only six compounds resulted to be potential active ligands of ERβ. Interestingly, all the six molecules possess scaffolds that had already been reported in known ERβ ligands. Therefore, the results obtained herein confirm the reliability of our virtual screening procedure, thus encouraging the application of this protocol to larger commercial databases in order to identify new ERβ ligands.

Further Insights Into the Pharmacology of the Human Trace Amine‐Associated Receptors: Discovery of Novel Ligands for TAAR1 by a Virtual Screening Approach

Trace Amine-Associated Receptor 1 (TAAR1) is a G protein-coupled receptor that is expressed in brain and periphery and responds to a class of compounds called trace amines, such as β-phenylethylamine (β-PEA), tyramine, tryptamine, octopamine. The receptor is known to have a very rich pharmacology and could be also activated by different classes of compounds, including dopaminergic, adrenergic and serotonergic ligands. It is expected that targeting hTAAR1 could provide a novel pharmacological approach for several human disorders, such as schizophrenia, depression, attention deficit hyperactivity disorder, Parkinson's disease and metabolic diseases. Only recently, a small number of selective hTAAR1 agonists (among which RO5166017 and T1 AM) and antagonist (EPPTB), have been reported in literature. With the aim to identify new molecular entities able to act as ligands for this target, we used an homology model for the hTAAR1 and performed a virtual screening procedure on an in-house database of compounds. A number of interesting molecules were selected and by testing them in an in vitro assay we found several agonists and one antagonist, with activities in the low micromolar range. These compounds could represent the starting point for the development of more potent and selective TAAR1 ligands.

3D Pharmacophore modeling in the molecules of 5,7-dimethyl-6-phenylazo-3H-thiazolo[4,5-b]pyridine-2-one derivatives

Flexible molecular docking studies for 5,7-dimethyl-6-phenylazo-3H-thiazolo[4,5-b]pyridin-2-ones have been performed with the purpose to reveal their potency as enzymes involved in the arachidonic acid (AA) cascade inhibitors: both cyclooxygenase isoforms (COX-1 and COX-2), and microsomal prostaglandin E synthase-1 (mPGES-1). The protein-ligand interaction fingerprint (PLIF) tool implemented in MOE software has been used for summarizing the interactions between ligands and the abovementioned enzymes. Receptor interaction fingerprints have been generated from the docked poses of the virtual screening hits with COX-1,2 and mPGES-1 active sites coordinates. 3D pharmacophore models containing two and three points queries as the combination of their structures steric and electronic parameters have been generated and it provides the affinity and inhibitory activity of the novel compounds towards multiply receptors. The analysis of the pharmacophore models obtained indicates the functionality of fused bicyclic thiazolopyridine scaffold which provides the steric placement of at least one of these heterocycles atoms in the respective pharmacophore centres. The fused thiazolo[4,5-b]pyridine-2-one system may be considered as a promosing scaffold for creating diverse combinatorial libraries of potential biologically active substances. The final conclusion has been confirmed by the results of the virtual screening procedures and pharmacophore centres modeling in molecules of novel thiazolo [4,5-b]pyridine-2-one.

Cover Picture: Insight into the Structural Requirements of Protoporphyrinogen Oxidase Inhibitors: Molecular Docking and CoMFA of Diphenyl Ether, Isoxazole Phenyl, and Pyrazole Phenyl Ether (Chin. J. Chem. 9/2013)

The cover picture shows that a multistep framework is developed with the ultimate goal of identifying novel herbicides. Protoporphyrinogen oxidase (PPO, EC 1.3.3.4) is one of the most significant targets for herbicide design. Using the crystal structure of tobacco mitochondrial PPO (mtPPO) as template, inhibitors were docked into the active site. The docking pose of each compound was subsequently used in a receptor-based alignment, leading to the development of a significant CoMFA model with r2 value of 0.98 and q2 (cross validation r2) value of 0.63. This novel multistep framework gives insight into the structural characteristics for the binding of inhibitors. In addition, the simplicity of the proposed approach may be particularly applicable in virtual screening procedures. More details are discussed in the article by Yang et al. on page 1153–1158.

Insight into the Structural Requirements of Protoporphyrinogen Oxidase Inhibitors: Molecular Docking and CoMFA of Diphenyl Ether, Isoxazole Phenyl, and Pyrazole Phenyl Ether

AbstractProtoporphyrinogen oxidase (PPO, EC 1.3.3.4) is one of the most significant targets for a large family of inhibitors that may be used as herbicide, bactericide, fungicide, or photosensitizing activator to treat cancer through photodynamic therapy (PDT). Molecular docking and CoMFA were combined in a multistep framework with the ultimate goal of identifying important factor contributing to the activity of PPO inhibitors. As a continuation of the previous research work on the development of new PPO inhibitors, the bioassay results indicated that good PPO inhibitors were discovered in all of the three chemical series with IC50 values ranging from 0.010 to 0.061 µmol·L−1. Using the crystal structure of tobacco mitochondrial PPO (mtPPO) as template, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor‐based alignment, leading to the development of a significant CoMFA model with r2 value of 0.98 and q2 (cross validation r2) value of 0.63. This novel multistep framework gives insight into the structural characteristics for the binding of inhibitors, and it can be extended to other classes of PPO inhibitors. In addition, the simplicity of the proposed approach may be particularly applicable in virtual screening procedures.

Rational Design of Novel Potential p38α MAPK Inhibitors with Drug-like Properties Using Pharmacophore and Similarity-based Virtual Screening Procedures

p38α MAPK, initially discovered as a target for inhibition of proinflammatory cytokines, has been linked to several CNS disorders in preclinical animal models of CNS diseases, including Alzheimer’s disease, Amyotrophic Lateral Sclerosis (ALS), cerebral ischemia and neuropathic pain. Here we describe pharmacophore and similarity-based virtual screenings of more than 18.3 million compounds based on correlation between the specific binding mode and activity of different types of p38α MAPK inhibitors and present novel potential p38α MAPK inhibitors.

Identification of PPARgamma Partial Agonists of Natural Origin (II): In Silico Prediction in Natural Extracts with Known Antidiabetic Activity

BackgroundNatural extracts have played an important role in the prevention and treatment of diseases and are important sources for drug discovery. However, to be effectively used in these processes, natural extracts must be characterized through the identification of their active compounds and their modes of action.Methodology/Principal FindingsFrom an initial set of 29,779 natural products that are annotated with their natural source and using a previously developed virtual screening procedure (carefully validated experimentally), we have predicted as potential peroxisome proliferators-activated receptor gamma (PPARγ) partial agonists 12 molecules from 11 extracts known to have antidiabetic activity. Six of these molecules are similar to molecules with described antidiabetic activity but whose mechanism of action is unknown. Therefore, it is plausible that these 12 molecules could be the bioactive molecules responsible, at least in part, for the antidiabetic activity of the extracts containing them. In addition, we have also identified as potential PPARγ partial agonists 10 molecules from 16 plants with undescribed antidiabetic activity but that are related (i.e., they are from the same genus) to plants with known antidiabetic properties. None of the 22 molecules that we predict as PPARγ partial agonists show chemical similarity with a group of 211 known PPARγ partial agonists obtained from the literature.Conclusions/SignificanceOur results provide a new hypothesis about the active molecules of natural extracts with antidiabetic properties and their mode of action. We also suggest plants with undescribed antidiabetic activity that may contain PPARγ partial agonists. These plants represent a new source of potential antidiabetic extracts. Consequently, our work opens the door to the discovery of new antidiabetic extracts and molecules that can be of use, for instance, in the design of new antidiabetic drugs or functional foods focused towards the prevention/treatment of type 2 Diabetes Mellitus.

Identification and Characterization of New DNA G-Quadruplex Binders Selected by a Combination of Ligand and Structure-Based Virtual Screening Approaches

Nowadays, it has been demonstrated that DNA G-quadruplex arrangements are involved in cellular aging and cancer, thus boosting the discovery of selective binders for these DNA secondary structures. By taking advantage of available structural and biological information on these structures, we performed a high throughput in silico screening of commercially available molecules databases by merging ligand- and structure-based approaches by means of docking experiments. Compounds selected by the virtual screening procedure were then tested for their ability to interact with the human telomeric G-quadruplex folding by circular dichroism, fluorescence spectroscopy, and photodynamic techniques. Interestingly, our screening succeeded in retrieving a new promising scaffold for G-quadruplex binders characterized by a psoralen moiety.

Modelling the possible bioactivity of ellagitannin-derived metabolites. In silico tools to evaluate their potential xenoestrogenic behavior

Estrogen Receptors (ERs) are ligand-dependent intracellular transcriptional factors involved in many diseases. ERs mediate many of the effects of estrogens, but also exogenous compounds are able to interfere with ER, disrupting endocrine signaling. Among these xenobiotics, compounds present in food and feed make ERs a relevant target in the context of dietary modulation of health. In this sense, urolithins, gut microbiota derived metabolites of plant polyphenolic ellagitannins, may represent good candidates to act as phytoestrogens that are able to modulate the activity of ERs. An in silico method to qualitatively evaluate the potential xenoestrogenic agonistic behavior of ellagitannin-derived metabolites is proposed. The "full-dry" in silico approach involved structure based virtual screening (SBVS), docking simulations and re-scoring procedures. Results provided valuable insights about the phase II conjugations (glucuronidation, sulfation, and methylation, occurring in vivo) affecting the estrogenicity of these compounds on α- and β-ER isoforms. Hydroxylation patterns also revealed a significant role in the agonistic behavior of urolithin derivatives. On the whole, ellagitannin-derived metabolites exerted different predicted xenoestrogenic activity depending on chemical structures, and the applied in silico approach may represent a successful and easy choice to analyze enormous datasets of food-related compounds in order to understand their potential biological features.

Identification of PPARgamma Partial Agonists of Natural Origin (I): Development of a Virtual Screening Procedure and In Vitro Validation

BackgroundAlthough there are successful examples of the discovery of new PPARγ agonists, it has recently been of great interest to identify new PPARγ partial agonists that do not present the adverse side effects caused by PPARγ full agonists. Consequently, the goal of this work was to design, apply and validate a virtual screening workflow to identify novel PPARγ partial agonists among natural products.Methodology/Principal FindingsWe have developed a virtual screening procedure based on structure-based pharmacophore construction, protein-ligand docking and electrostatic/shape similarity to discover novel scaffolds of PPARγ partial agonists. From an initial set of 89,165 natural products and natural product derivatives, 135 compounds were identified as potential PPARγ partial agonists with good ADME properties. Ten compounds that represent ten new chemical scaffolds for PPARγ partial agonists were selected for in vitro biological testing, but two of them were not assayed due to solubility problems. Five out of the remaining eight compounds were confirmed as PPARγ partial agonists: they bind to PPARγ, do not or only moderately stimulate the transactivation activity of PPARγ, do not induce adipogenesis of preadipocyte cells and stimulate the insulin-induced glucose uptake of adipocytes.Conclusions/SignificanceWe have demonstrated that our virtual screening protocol was successful in identifying novel scaffolds for PPARγ partial agonists.

Identification of novel human dipeptidyl peptidase-IV inhibitors of natural origin (Part II): in silico prediction in antidiabetic extracts.

BackgroundNatural extracts play an important role in traditional medicines for the treatment of diabetes mellitus and are also an essential resource for new drug discovery. Dipeptidyl peptidase IV (DPP-IV) inhibitors are potential candidates for the treatment of type 2 diabetes mellitus, and the effectiveness of certain antidiabetic extracts of natural origin could be, at least partially, explained by the inhibition of DPP-IV.Methodology/Principal FindingsUsing an initial set of 29,779 natural products that are annotated with their natural source and an experimentally validated virtual screening procedure previously developed in our lab (Guasch et al.; 2012) [1], we have predicted 12 potential DPP-IV inhibitors from 12 different plant extracts that are known to have antidiabetic activity. Seven of these molecules are identical or similar to molecules with described antidiabetic activity (although their role as DPP-IV inhibitors has not been suggested as an explanation for their bioactivity). Therefore, it is plausible that these 12 molecules could be responsible, at least in part, for the antidiabetic activity of these extracts through their inhibitory effect on DPP-IV. In addition, we also identified as potential DPP-IV inhibitors 6 molecules from 6 different plants with no described antidiabetic activity but that share the same genus as plants with known antidiabetic properties. Moreover, none of the 18 molecules that we predicted as DPP-IV inhibitors exhibits chemical similarity with a group of 2,342 known DPP-IV inhibitors.Conclusions/SignificanceOur study identified 18 potential DPP-IV inhibitors in 18 different plant extracts (12 of these plants have known antidiabetic properties, whereas, for the remaining 6, antidiabetic activity has been reported for other plant species from the same genus). Moreover, none of the 18 molecules exhibits chemical similarity with a large group of known DPP-IV inhibitors.

Computational studies on the substrate interactions of influenza A virus PB2 subunit.

Influenza virus, which spreads around the world in seasonal epidemics and leads to large numbers of deaths every year, has several ribonucleoproteins in the central core of the viral particle. These viral ribonucleoproteins can specifically bind the conserved 3′ and 5′ caps of the viral RNAs with responsibility for replication and transcription of the viral RNA in the nucleus of infected cells. A fundamental question of most importance is that how the cap-binding proteins in the influenza virus discriminates between capped RNAs and non-capped ones. To get an answer, we performed molecular dynamics simulations and free energy calculations on the influenza A virus PB2 subunit, an important component of the RNP complexes, with a cap analog m7GTP. Our calculations showed that some key residues in the active site, such as Arg355, His357, Glu361 as well as Gln406, could offer significant hydrogen bonding and hydrophobic interactions with the guanine ring of the cap analog m7GTP to form an aromatic sandwich mechanism for the cap recognition and positioning in the active site. Subsequently, we applied this idea to a virtual screening procedure and identified 5 potential candidates that might be inhibitors against the PB2 subunit. Interestingly, 2 candidates Cpd1 and Cpd2 have been already reported to have inhibitory activities to the influenza virus cap-binding proteins. Further calculation also showed that they had comparatively higher binding affinities to the PB2 subunit than that of m7GTP. We believed that our findings could give an atomic insight into the deeper understanding of the cap recognition and binding mechanism, providing useful information for searching or designing novel drugs against influenza viruses.

In Silico Identification of Potential Cholestasis-Inducing Agents via Modeling of Na+-Dependent Taurocholate Cotransporting Polypeptide Substrate Specificity

Na(+)-dependent taurocholate cotransporting polypeptide (NTCP, SLC10A1) is the main transporter facilitating the hepatic uptake of bile acids from the circulation. Consequently, the interaction of xenobiotics, including therapeutic drugs, with the bile acid binding pocket of NTCP could lead to impairment of hepatic bile acid uptake. We pursued a 3D-pharmacophore approach to model the NTCP substrate and inhibitor specificity and investigated whether it is possible to identify compounds with intrinsic NTCP inhibitory properties. Based on known endogenous NTCP substrates, a 3D-pharmacophore model was built, which was subsequently used to screen two virtual libraries together containing the structures of 10 million compounds. Studies with Chinese hamster ovary cells overexpressing human NTCP, human hepatocytes, ex vivo perfused rat livers, and bile duct-cannulated rats were conducted to validate the activity of the virtual screening hits. Modeling yielded a 3D-pharmacophore, consisting of two hydrogen bond acceptors and three hydrophobic features. Six out of 10 structurally diverse compounds selected in the first virtual screening procedure significantly inhibited taurocholate uptake in the NTCP overexpressing cells. For the most potent inhibitor identified, an anthraquinone derivative, this finding was confirmed in human hepatocytes and perfused rat livers. Subsequent structure and activity relationship studies with analogs of this derivative indicated that an appropriate distance between hydrogen bond acceptor features and presence of one or two negative charges appear critical for a successful NTCP interaction. In conclusion, pharmacophore modeling was successfully used to identify compounds that inhibit NTCP. Our approach represents an important first step toward the in silico flagging of potential cholestasis-inducing molecules.

Pyrazole[3,4-e][1,4]thiazepin-7-one derivatives as a novel class of Farnesoid X Receptor (FXR) agonists

A virtual screening procedure was applied to the discovery of structurally diverse non-steroidal Farnesoid X Receptor (FXR) agonists. From 117 compounds selected by virtual screening, a total of 47 compounds were found to be FXR agonists, with 34 of them showing activity below a concentration of 20μM. 1H-Pyrazole[3,4-e][1,4]thiazepin-7-one-based hit compound 7 was chosen for hit-to-lead optimization. A large number of 1H-pyrazole[3,4-e][1,4]thiazepin-7-one derivatives was designed, synthesized, and evaluated by a cell-based luciferase transactivation assay for their agonistic activity against FXR. Most of them exhibited low micromolar range of potency and very high efficacy.

Molecular Modeling on Pyrimidine-Urea Inhibitors of TNF-α Production: An Integrated Approach Using a Combination of Molecular Docking, Classification Techniques, and 3D-QSAR CoMSIA

Molecular docking, classification techniques, and 3D-QSAR CoMSIA were combined in a multistep framework with the ultimate goal of identifying potent pyrimidine-urea inhibitors of TNF-α production. Using the crystal structure of p38α, all the compounds were docked into the enzyme active site. The docking pose of each compound was subsequently used in a receptor-based alignment for the generation of the CoMSIA fields. "Active" and "inactive" compounds were used to build a Random Tree classification model using the docking score and the CoMSIA fields as input parameters. Domain of applicability indicated the compounds for which activity estimations can be accepted with confidence. For the active compounds, a 3D-QSAR CoMSIA model was subsequently built to accurately estimate the IC(50) values. This novel multistep framework gives insight into the structural characteristics that affect the binding and the inhibitory activity of these analogues on p38α MAP kinase, and it can be extended to other classes of small-molecule inhibitors. In addition, the simplicity of the proposed approach provides expansion to its applicability such as in virtual screening procedures.

Freely Accessible Databases of Commercial Compounds For High- Throughput Virtual Screenings

In the last decades computer-aided drug design techniques have been successfully used to guide the selection of new hit compounds with biological activity. These methods, that include a broad range of chemoinformatic and computational chemistry algorithms, are still disciplines in full bloom. In particular, virtual screening procedures have celebrated a great popularity for the rapid and cost-effective assessment of large chemical libraries of commercial compounds. While the usage of in silico techniques promises an effective speed-up at the early-stage of the development of new active compounds, computational projects starting from scratch with raw chemical data are often associated with resource- and time-consuming preparation protocols, almost blunting the advantages of using these techniques. In order to help facing these difficulties, in the last years several chemoinformatic projects and tools have emerged in literature and have been useful in preparing curated databases of chemical compounds for high-throughput virtual screening purposes. The review will focus on the detailed analysis of free databases of commercial chemical compounds that are currently employed in virtual screening campaigns for drug design. The scope of this review is to compare such databases and suggest the reader on how and in which conditions the usage of these databases could be recommended.

Discovery of α7-Nicotinic Receptor Ligands by Virtual Screening of the Chemical Universe Database GDB-13

The chemical universe database GDB-13 enumerates 977 million organic molecules up to 13 atoms of C, N, O, Cl, and S that are virtually possible following simple rules for chemical stability and synthetic feasibility. Analogs of nicotine were identified in GDB-13 using the city-block distance in MQN-space (CBD(MQN)) as a similarity measure, combined with a restriction eliminating problematic structural elements. The search was carried out with a Web browser available at www.gdb.unibe.ch . This virtual screening procedure selected 31 504 analogs of nicotine from GDB-13, from which 48 were known nicotinic ligands reported in Chembl. An additional 60 virtual screening hits were purchased and tested for modulation of the acetylcholine signal at the human α7 nAChR expressed in Xenopus oocytes, which led to the identification of three previously unknown inhibitors. These experiments demonstrate for the first time the use of GDB-13 for ligand discovery.

Combining 2D and 3D in silico methods for rapid selection of potential PDE5 inhibitors from multimillion compounds’ repositories: biological evaluation

Rapid in silico selection of target focused libraries from commercial repositories is an attractive and cost-effective approach when starting new drug discovery projects. If structures of active compounds are available rapid 2D similarity search can be performed on multimillion compounds' databases. This in silico approach can be combined with physico-chemical parameter filtering based on the property space of the active compounds and 3D virtual screening if the structure of the target protein is available. A multi-step virtual screening procedure was developed and applied to select potential phosphodiesterase 5 (PDE5) inhibitors in real time. The combined 2D/3D in silico method resulted in the identification of 14 novel PDE5 inhibitors with <1 μMIC(50) values and the hit rate in the second in silico selection and in vitro screening round exceeded the 20%.

A Comparative Study of Nonlinear Machine Learning for the "In Silico" Depiction of Tyrosinase Inhibitory Activity from Molecular Structure.

In the preset report, for the first time, support vector machine (SVM), artificial neural network (ANN), Bayesian networks (BNs), k-nearest neighbor (k-NN) are applied and compared on two "in-house" datasets to describe the tyrosinase inhibitory activity from the molecular structure. The data set Data I is used for the identification of tyrosinase inhibitors (TIs) including 701 active and 728 inactive compounds. Data II consists of active chemicals for potency estimation of TIs. The 2D TOMOCOMD-CARDD atom-based quadratic indices are used as molecular descriptors. The derived models show rather encouraging results with the areas under the Receiver Operating Characteristic (AURC) curve in the test set above 0.943 and 0.846 for the Data I and Data II, respectively. Multiple comparison tests are carried out to compare the performance of the models and reveal the improvement of machine learning (ML) techniques with respect to statistical ones (see Chemometr. Intell. Lab. Syst. 2010, 104, 249). In some cases, these ameliorations are statistically significant. The tests also demostrate that k-NN, despite being a rather simple approach, presents the best behavior in both data. The obtained results suggest that the ML-based models could help to improve the virtual screening procedures and the confluence of these different techniques can increase the practicality of data mining procedures of chemical databases for the discovery of novel TIs as possible depigmenting agents.

Discovery of Novel Inhibitors of the NorA Multidrug Transporter of Staphylococcus aureus

Four novel inhibitors of the NorA efflux pump of Staphylococcus aureus, discovered through a virtual screening process, are reported. The four compounds belong to different chemical classes and were tested for their in vitro ability to block the efflux of a well-known NorA substrate, as well as for their ability to potentiate the effect of ciprofloxacin (CPX) on several strains of S. aureus, including a NorA overexpressing strain. Additionally, the MIC values of each of the compounds individually are reported. A structure-activity relationship study was also performed on these novel chemotypes, revealing three new compounds that are also potent NorA inhibitors. The virtual screening procedure employed FLAP, a new methodology based on GRID force field descriptors.