Browse
Submit Data
Databases
API
Help

Dataset Information

21 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Discovery of new liver X receptor agonists by pharmacophore modeling and shape-based virtual screening.

ABSTRACT: Agonists of liver X receptors (LXR) ? and ? are important regulators of cholesterol metabolism, but agonism of the LXR? subtype appears to cause hepatic lipogenesis, suggesting LXR?-selective activators are attractive new lipid lowering drugs. In this work, pharmacophore modeling and shape-based virtual screening were combined to predict new LXR?-selective ligands. Out of the 10 predicted compounds, three displayed significant LXR activity. Two activated both LXR subtypes. The third compound activated LXR? 1.8-fold over LXR?.

SUBMITTER: Temml V

PROVIDER: S-EPMC3934620 | biostudies-literature | 2014 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Discovery of new liver X receptor agonists by pharmacophore modeling and shape-based virtual screening.

Temml Veronika V Voss Constance V CV Dirsch Verena M VM Schuster Daniela D

Journal of chemical information and modeling 20140206 2

Agonists of liver X receptors (LXR) α and β are important regulators of cholesterol metabolism, but agonism of the LXRα subtype appears to cause hepatic lipogenesis, suggesting LXRβ-selective activators are attractive new lipid lowering drugs. In this work, pharmacophore modeling and shape-based virtual screening were combined to predict new LXRβ-selective ligands. Out of the 10 predicted compounds, three displayed significant LXR activity. Two activated both LXR subtypes. The third compound act ...[more]

PMID: 24502802

Similar Datasets

Pharmacophore modeling and virtual screening for the discovery of new type 4 cAMP phosphodiesterase (PDE4) inhibitors.

Project description:Type 4 cAMP phosphodiesterase (PDE4) inhibitors show a broad spectrum of anti-inflammatory effects in almost all kinds of inflamed cells, by an increase in cAMP levels which is a pivotal second messenger responsible for various biological processes. These inhibitors are now considered as the potential drugs for treatment of chronic inflammatory diseases. However, some recently marketed inhibitors e.g., roflumilast, have shown adverse effects such as nausea and emesis, thus restricting its use. In order to identify novel PDE4 inhibitors with improved therapeutic indexes, a highly correlating (r = 0.963930) pharmacophore model (Hypo1) was established on the basis of known PDE4 inhibitors. Validated Hypo1 was used in database screening to identify chemical with required pharmacophoric features. These compounds are further screened by using the rule of five, ADMET and molecular docking. Finally, twelve hits which showed good results with respect to following properties such as estimated activity, calculated drug-like properties and scores were proposed as potential leads to inhibit the PDE4 activity. Therefore, this study will not only assist in the development of new potent hits for PDE4 inhibitors, but also give a better understanding of their interaction with PDE4. On a wider scope, this will be helpful for the rational design of novel potent enzyme inhibitors.

| S-EPMC3858292 | biostudies-literature

Dual-target virtual screening by pharmacophore elucidation and molecular shape filtering.

Project description:Dual-target inhibitors gained increased attention in the past years. A novel in silico approach was employed for the discovery of dual 5-lipoxygenase/soluble epoxide hydrolase inhibitors. The ligand-based approach uses excessive pharmacophore elucidation and pharmacophore alignment in conjunction with shape-based scoring. The virtual screening results were verified in vitro, leading to nine novel inhibitors including a dual-target compound.

| S-EPMC4025812 | biostudies-other

Pharmacophore modeling, virtual screening, and molecular docking studies for discovery of novel Akt2 inhibitors.

Project description:Akt2 is considered as a potential target for cancer therapy. In order to find novel Akt2 inhibitors which have different scaffolds, structure-based pharmacophore model and 3D-QSAR pharmacophore model were built and validated by different methods. Then, they were used for chemical databases virtual screening. The selected compounds were further analyzed and refined using drug-like filters and ADMET analysis. Finally, seven hits with different scaffolds were picked out for docking studies. These seven hits were predicted to have high inhibitory activity and good ADMET properties, they may act as novel leads for Akt2 inhibitors designing.

| S-EPMC3558715 | biostudies-literature

Pharmacophore modeling improves virtual screening for novel peroxisome proliferator-activated receptor-gamma ligands.

Project description:Peroxisome proliferator-activated receptor-gamma (PPAR?) is a nuclear hormone receptor involved in regulating various metabolic and immune processes. The PPAR family of receptors possesses a large binding cavity that imparts promiscuity of ligand binding not common to other nuclear receptors. This feature increases the challenge of using computational methods to identify PPAR ligands that will dock favorably into a structural model. Utilizing both ligand- and structure-based pharmacophore methods, we sought to improve agonist prediction by grouping ligands according to pharmacophore features, and pairing models derived from these features with receptor structures for docking. For 22 of the 33 receptor structures evaluated we observed an increase in true positive rate (TPR) when screening was restricted to compounds sharing molecular features found in rosiglitazone. A combination of structure models used for docking resulted in a higher TPR (40 %) when compared to docking with a single structure model (<20 %). Prediction was also improved when specific protein-ligand interactions between the docked ligands and structure models were given greater weight than the calculated free energy of binding. A large-scale screen of compounds using a marketed drug database verified the predictive ability of the selected structure models. This study highlights the steps necessary to improve screening for PPAR? ligands using multiple structure models, ligand-based pharmacophore data, evaluation of protein-ligand interactions, and comparison of docking datasets. The unique combination of methods presented here holds potential for more efficient screening of compounds with unknown affinity for PPAR? that could serve as candidates for therapeutic development.

| S-EPMC4395532 | biostudies-literature

Tubulin inhibitors: pharmacophore modeling, virtual screening and molecular docking.

Project description:AIM: To construct a quantitative pharmacophore model of tubulin inhibitors and to discovery new leads with potent antitumor activities. METHODS: Ligand-based pharmacophore modeling was used to identify the chemical features responsible for inhibiting tubulin polymerization. A set of 26 training compounds was used to generate hypothetical pharmacophores using the HypoGen algorithm. The structures were further validated using the test set, Fischer randomization method, leave-one-out method and a decoy set, and the best model was chosen to screen the Specs database. Hit compounds were subjected to molecular docking study using a Molecular Operating Environment (MOE) software and to biological evaluation in vitro. RESULTS: Hypo1 was demonstrated to be the best pharmacophore model that exhibited the highest correlation coefficient (0.9582), largest cost difference (70.905) and lowest RMSD value (0.6977). Hypo1 consisted of one hydrogen-bond acceptor, a hydrogen-bond donor, a hydrophobic feature, a ring aromatic feature and three excluded volumes. Hypo1 was validated with four different methods and had a goodness-of-hit score of 0.81. When Hypo1 was used in virtual screening of the Specs database, 952 drug-like compounds were revealed. After docking into the colchicine-binding site of tubulin, 5 drug-like compounds with the required interaction with the critical amino acid residues and the binding free energies < -4 kcal/mol were selected as representative leads. Compounds 1 and 3 exhibited inhibitory activity against MCF-7 human breast cancer cells in vitro. CONCLUSION: Hypo1 is a quantitative pharmacophore model for tubulin inhibitors, which not only provides a better understanding of their interaction with tubulin, but also assists in discovering new potential leads with antitumor activities.

| S-EPMC4088285 | biostudies-literature

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

Project description:17?-Hydroxysteroid dehydrogenase 2 (17?-HSD2) catalyzes the inactivation of estradiol into estrone. This enzyme is expressed only in a few tissues, and therefore its inhibition is considered as a treatment option for osteoporosis to ameliorate estrogen deficiency. In this study, ligand-based pharmacophore models for 17?-HSD2 inhibitors were constructed and employed for virtual screening. From the virtual screening hits, 29 substances were evaluated in vitro for 17?-HSD2 inhibition. Seven compounds inhibited 17?-HSD2 with low micromolar IC50 values. To investigate structure-activity relationships (SAR), 30 more derivatives of the original hits were tested. The three most potent hits, 12, 22, and 15, had IC50 values of 240 nM, 1 ?M, and 1.5 ?M, respectively. All but 1 of the 13 identified inhibitors were selective over 17?-HSD1, the enzyme catalyzing conversion of estrone into estradiol. Three of the new, small, synthetic 17?-HSD2 inhibitors showed acceptable selectivity over other related HSDs, and six of them did not affect other HSDs.

| S-EPMC4111740 | biostudies-literature

The discovery of potential acetylcholinesterase inhibitors: a combination of pharmacophore modeling, virtual screening, and molecular docking studies.

Project description:BACKGROUND: Alzheimer's disease (AD) is the most common cause of dementia characterized by progressive cognitive impairment in the elderly people. The most dramatic abnormalities are those of the cholinergic system. Acetylcholinesterase (AChE) plays a key role in the regulation of the cholinergic system, and hence, inhibition of AChE has emerged as one of the most promising strategies for the treatment of AD. METHODS: In this study, we suggest a workflow for the identification and prioritization of potential compounds targeted against AChE. In order to elucidate the essential structural features for AChE, three-dimensional pharmacophore models were constructed using Discovery Studio 2.5.5 (DS 2.5.5) program based on a set of known AChE inhibitors. RESULTS: The best five-features pharmacophore model, which includes one hydrogen bond donor and four hydrophobic features, was generated from a training set of 62 compounds that yielded a correlation coefficient of R = 0.851 and a high prediction of fit values for a set of 26 test molecules with a correlation of R² = 0.830. Our pharmacophore model also has a high Güner-Henry score and enrichment factor. Virtual screening performed on the NCI database obtained new inhibitors which have the potential to inhibit AChE and to protect neurons from A? toxicity. The hit compounds were subsequently subjected to molecular docking and evaluated by consensus scoring function, which resulted in 9 compounds with high pharmacophore fit values and predicted biological activity scores. These compounds showed interactions with important residues at the active site. CONCLUSIONS: The information gained from this study may assist in the discovery of potential AChE inhibitors that are highly selective for its dual binding sites.

| S-EPMC3036604 | biostudies-literature

Ligand-based Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Discovery of Potential Topoisomerase I Inhibitors.

Project description:Camptothecin (CPT), a natural product and its synthetic derivatives exert potent anticancer activity by selectively targeting DNA Topoisomerase I (Top1) enzyme. CPT and its clinically approved derivatives are used as Top1 poisons for cancer therapy suffer from many limitations related to stability and toxicity. In order to envisage structurally diverse novel chemical entity as Top1 poison with better efficacy, Ligand-based-pharmacophore model was developed using 3D QSAR pharmacophore generation (HypoGen algorithm) methodology in Discovery studio 4.1 clients. The chemical features of 29 CPT derivatives were taken as the training set. The selected pharmacophore model Hypo1 was further validated by 33 test set molecules and used as a query model for further screening of 1,087,724 drug-like molecules from ZINC databases. These molecules were subjected to several assessments such as Lipinski rule of 5, SMART filtration and activity filtration. The molecule obtained after filtration was further scrutinized by molecular docking analysis on the active site of Top1 crystal structure (PDB ID: 1T8I). Six potential inhibitory molecules have been selected by analyzing the binding interaction and Ligand-Pharmacophore mapping with the validated pharmacophore model. Toxicity assessment TOPKAT program provided three potential inhibitory 'hit molecules' ZINC68997780, ZINC15018994 and ZINC38550809. MD simulation of these three molecules proved that the ligand binding into the protein-DNA cleavage complex is stable and the protein-ligands conformation remains unchanged. These three hit molecules can be utilized for designing future class of potential topoisomerase I inhibitor.

| S-EPMC6396084 | biostudies-literature

Discovery of potential M2 channel inhibitors based on the amantadine scaffold via virtual screening and pharmacophore modeling.

Project description:The M2 channel protein on the influenza A virus membrane has become the main target of the anti-flu drugs amantadine and rimantadine. The structure of the M2 channel proteins of the H3N2 (PDB code 2RLF) and 2009-H1N1 (Genbank accession number GQ385383) viruses may help researchers to solve the drug-resistant problem of these two adamantane-based drugs and develop more powerful new drugs against influenza A virus. In the present study, we searched for new M2 channel inhibitors through a combination of different computational methodologies, including virtual screening with docking and pharmacophore modeling. Virtual screening was performed to calculate the free energies of binding between receptor M2 channel proteins and 200 new designed ligands. After that, pharmacophore analysis was used to identify the important M2 protein-inhibitor interactions and common features of top binding compounds with M2 channel proteins. Finally, the two most potential compounds were determined as novel leads to inhibit M2 channel proteins in both H3N2 and 2009-H1N1 influenza A virus.

| S-EPMC6264534 | biostudies-literature

Fragment-based Shape Signatures: a new tool for virtual screening and drug discovery.

Project description:Since its introduction in 2003, the Shape Signatures method has been successfully applied in a number of drug design projects. Because it uses a ray-tracing approach to directly measure molecular shape and properties (as opposed to relying on chemical structure), it excels at scaffold hopping, and is extraordinarily easy to use. Despite its advantages, a significant drawback of the method has hampered its application to certain classes of problems; namely, when the chemical structures considered are large and contain heterogeneous ring-systems, the method produces descriptors that tend to merely measure the overall size of the molecule, and begin to lose selective power. To remedy this, the approach has been reformulated to automatically decompose compounds into fragments using ring systems as anchors, and to likewise partition the ray-trace in accordance with the fragment assignments. Subsequently, descriptors are generated that are fragment-based, and query and target molecules are compared by mapping query fragments onto target fragments in all ways consistent with the underlying chemical connectivity. This has proven to greatly extend the selective power of the method, while maintaining the ease of use and scaffold-hopping capabilities that characterized the original implementation. In this work, we provide a full conceptual description of the next generation Shape Signatures, and we underline the advantages of the method by discussing its practical applications to ligand-based virtual screening. The new approach can also be applied in receptor-based mode, where protein-binding sites (partitioned into subsites) can be matched against the new fragment-based Shape Signatures descriptors of library compounds.

| S-EPMC3880490 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data