Browse
Submit Data
Databases
API
Help

Dataset Information

35 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Pharmacophore and Virtual Screening of JAK3 inhibitors.

ABSTRACT: Janus kinase 3 (JAK3) is a non-receptor tyrosine kinases family of protein which is comprised of JAK1, JAK2, JAK3 and TYK2. It plays an important role in immune function and lymphoid development and it only resides in the hematopoietic system. Therefore, selective targeting JAK3 is a rational approach in developing new therapeutic molecule. In this study, about 116 JAK3 inhibitors were collected from the literature and were used to build four-point pharmacophore model using Phase (Schrodinger module). The statistically significant pharmacophore hypothesis of AAHR.92 with r2 value of 0.942 was used as 3D query to search against 3D database namely Zincpharmer. A total of 2, 27,483 compounds obtained as hit were subjected to high throughput virtual screening (HTVS module of Schrodinger). Among the hits, ten compounds with good G-score ranging from -12.96 to -11.18 with good binding energy to JAK3 were identified.

SUBMITTER: Rajeswari M

PROVIDER: S-EPMC3974243 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Publications

Pharmacophore and Virtual Screening of JAK3 inhibitors.

Rajeswari Murugesan M Santhi Natchimuthu N Bhuvaneswari Vembu V

Bioinformation 20140319 3

Janus kinase 3 (JAK3) is a non-receptor tyrosine kinases family of protein which is comprised of JAK1, JAK2, JAK3 and TYK2. It plays an important role in immune function and lymphoid development and it only resides in the hematopoietic system. Therefore, selective targeting JAK3 is a rational approach in developing new therapeutic molecule. In this study, about 116 JAK3 inhibitors were collected from the literature and were used to build four-point pharmacophore model using Phase (Schrodinger mo ...[more]

PMID: 24748756

Similar Datasets

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

Project description:AimTo construct a quantitative pharmacophore model of tubulin inhibitors and to discovery new leads with potent antitumor activities.MethodsLigand-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.ResultsHypo1 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.ConclusionHypo1 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

Pharmacophore Based Virtual Screening Approach to Identify Selective PDE4B Inhibitors.

Project description:Phosphodiesterase 4 (PDE4) has been established as a promising target in asthma and chronic obstructive pulmonary disease. PDE4B subtype selective inhibitors are known to reduce the dose limiting adverse effect associated with non-selective PDE4B inhibitors. This makes the development of PDE4B subtype selective inhibitors a desirable research goal. To achieve this goal, ligand based pharmacophore modeling approach is employed. Separate pharmacophore hypotheses for PDE4B and PDE4D inhibitors were generated using HypoGen algorithm and 106 PDE4 inhibitors from literature having thiopyrano [3,2-d] Pyrimidines, 2-arylpyrimidines, and triazines skeleton. Suitable training and test sets were created using the molecules as per the guidelines available for HypoGen program. Training set was used for hypothesis development while test set was used for validation purpose. Fisher validation was also used to test the significance of the developed hypothesis. The validated pharmacophore hypotheses for PDE4B and PDE4D inhibitors were used in sequential virtual screening of zinc database of drug like molecules to identify selective PDE4B inhibitors. The hits were screened for their estimated activity and fit value. The top hit was subjected to docking into the active sites of PDE4B and PDE4D to confirm its selectivity for PDE4B. The hits are proposed to be evaluated further using in-vitro assays.

| S-EPMC5610747 | biostudies-literature

Machine learning accelerates pharmacophore-based virtual screening of MAO inhibitors.

Project description:Nowadays, an efficient and robust virtual screening procedure is crucial in the drug discovery process, especially when performed on large and chemically diverse databases. Virtual screening methods, like molecular docking and classic QSAR models, are limited in their ability to handle vast numbers of compounds and to learn from scarce data, respectively. In this study, we introduce a universal methodology that uses a machine learning-based approach to predict docking scores without the need for time-consuming molecular docking procedures. The developed protocol yielded 1000 times faster binding energy predictions than classical docking-based screening. The proposed predictive model learns from docking results, allowing users to choose their preferred docking software without relying on insufficient and incoherent experimental activity data. The methodology described employs multiple types of molecular fingerprints and descriptors to construct an ensemble model that further reduces prediction errors and is capable of delivering highly precise docking score values for monoamine oxidase ligands, enabling faster identification of promising compounds. An extensive pharmacophore-constrained screening of the ZINC database resulted in a selection of 24 compounds that were synthesized and evaluated for their biological activity. A preliminary screen discovered weak inhibitors of MAO-A with a percentage efficiency index close to a known drug at the lowest tested concentration. The approach presented here can be successfully applied to other biological targets as target-specific knowledge is not incorporated at the screening phase.

| S-EPMC11369158 | biostudies-literature

Discovery of YopE Inhibitors by Pharmacophore-Based Virtual Screening and Docking.

Project description:Gram-negative bacteria Yersinia secrete virulence factors that invade eukaryotic cells via type III secretion system. One particular virulence member, Yersinia outer protein E (YopE), targets Rho family of small GTPases by mimicking regulator GAP protein activity, and its secretion mainly induces cytoskeletal disruption and depolymerization of actin stress fibers within the host cell. In this work, potent drug-like inhibitors of YopE are investigated with virtual screening approaches. More than 500,000 unique small molecules from ZINC database were screened with a five-point pharmacophore, comprising three hydrogen acceptors, one hydrogen donor, and one ring, and derived from different salicylidene acylhydrazides. Binding modes and features of these molecules were investigated with a multistep molecular docking approach using Glide software. Virtual screening hits were further analyzed based on their docking score, chemical similarity, pharmacokinetic properties, and the key Arg144 interaction along with other active site residue interactions with the receptor. As a final outcome, a diverse set of ligands with inhibitory potential were proposed.

| S-EPMC4393062 | biostudies-literature

Combinatorial Pharmacophore-Based 3D-QSAR Analysis and Virtual Screening of FGFR1 Inhibitors.

Project description:The fibroblast growth factor/fibroblast growth factor receptor (FGF/FGFR) signaling pathway plays crucial roles in cell proliferation, angiogenesis, migration, and survival. Aberration in FGFRs correlates with several malignancies and disorders. FGFRs have proved to be attractive targets for therapeutic intervention in cancer, and it is of high interest to find FGFR inhibitors with novel scaffolds. In this study, a combinatorial three-dimensional quantitative structure-activity relationship (3D-QSAR) model was developed based on previously reported FGFR1 inhibitors with diverse structural skeletons. This model was evaluated for its prediction performance on a diverse test set containing 232 FGFR inhibitors, and it yielded a SD value of 0.75 pIC50 units from measured inhibition affinities and a Pearson's correlation coefficient R2 of 0.53. This result suggests that the combinatorial 3D-QSAR model could be used to search for new FGFR1 hit structures and predict their potential activity. To further evaluate the performance of the model, a decoy set validation was used to measure the efficiency of the model by calculating EF (enrichment factor). Based on the combinatorial pharmacophore model, a virtual screening against SPECS database was performed. Nineteen novel active compounds were successfully identified, which provide new chemical starting points for further structural optimization of FGFR1 inhibitors.

| S-EPMC4490501 | biostudies-literature

Discovery of potent inhibitors for phosphodiesterase 5 by virtual screening and pharmacophore analysis.

Project description:AIM: To explore the potent inhibitor from one of the Traditional Chinese medicine (TCM), Epimedium sagittatum. METHODS: We predicted the potent compound, ES03b, de novo evolution from the four Epimedium sagittatum components were verified by molecular docking, pharmacophore analysis, and analysis of quantitative structure-activity relationship (QSAR) model, which was constructed by multiple linear regression. RESULTS: ES03b was chosen to undergo drug modification via de novo evolution. By analyzing the pharmacophore features, we found that the hydrophobic core in the binding site and the hydrogen bond generated at Asn663 played key roles in designing PDE5 inhibitors. ES03b generated 49 diversities (Evo01-49). Evo48 had high activity in prediction. Although the value of prediction was overestimated, Evo48 was suggested as the potent lead. CONCLUSION: In this study, we showed that the hydrophobic core in the binding site and hydrogen bond production on Asn663 played key roles to design PDE5 inhibitors. From several require validation analysis, Evo48 was suggested to be a potent inhibitor.

| S-EPMC4006686 | biostudies-literature

Ligand Based Pharmacophore Modeling and Virtual Screening Studies to Design Novel HDAC2 Inhibitors.

Project description:Histone deacetylases 2 (HDAC2), Class I histone deacetylase (HDAC) family, emerged as an important therapeutic target for the treatment of various cancers. A total of 48 inhibitors of two different chemotypes were used to generate pharmacophore model using 3D QSAR pharmacophore generation (HypoGen algorithm) module in Discovery Studio. The best HypoGen model consists of four pharmacophore features namely, one hydrogen bond acceptor (HBA), and one hydrogen donor (HBD), one hydrophobic (HYP) and one aromatic centres, (RA). This model was validated against 20 test set compounds and this model was utilized as a 3D query for virtual screening to validate against NCI and Maybridge database and the hits further screened by Lipinski's rule of 5, and a total of 382 hit compounds from NCI and 243 hit compounds from Maybridge were found and were subjected to molecular docking in the active site of HDAC2 (PDB: 3MAX). Finally eight hit compounds, NSC108392, NSC127064, NSC110782, and NSC748337 from NCI database and MFCD01935795, MFCD00830779, MFCD00661790, and MFCD00124221 from Maybridge database, were considered as novel potential HDAC2 inhibitors.

| S-EPMC4265523 | biostudies-literature

Structure-Based Virtual Screening of Pseudomonas aeruginosa LpxA Inhibitors Using Pharmacophore-Based Approach.

Project description:Multidrug resistance in Pseudomonas aeruginosa is a noticeable and ongoing major obstacle for inhibitor design. In P. aeruginosa, uridine diphosphate N-acetylglucosamine (UDP-GlcNAc) acetyltransferase (PaLpxA) is an essential enzyme of lipid A biosynthesis and an attractive drug target. PaLpxA is a homotrimer, and the binding pocket for its substrate, UDP-GlcNAc, is positioned between the monomer A-monomer B interface. The uracil moiety binds at one monomer A, the GlcNAc moiety binds at another monomer B, and a diphosphate form bonds with both monomers. The catalytic residues are conserved and display a similar catalytic mechanism across orthologs, but some distinctions exist between pocket sizes, residue differences, substrate positioning and specificity. The analysis of diversified pockets, volumes, and ligand positions was determined between orthologues that could aid in selective inhibitor development. Thenceforth, a complex-based pharmacophore model was generated and subjected to virtual screening to identify compounds with similar pharmacophoric properties. Docking and general Born-volume integral (GBVI) studies demonstrated 10 best lead compounds with selective inhibition properties with essential residues in the pocket. For biological access, these scaffolds complied with the Lipinski rule, no toxicity and drug likeness properties, and were considered as lead compounds. Hence, these scaffolds could be helpful for the development of potential selective PaLpxA inhibitors.

| S-EPMC7072397 | biostudies-literature

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

Identification of Novel HBV/HDV Entry Inhibitors by Pharmacophore- and QSAR-Guided Virtual Screening.

Project description:The hepatic bile acid transporter Na+/taurocholate co-transporting polypeptide (NTCP) was identified in 2012 as the high-affinity hepatic receptor for the hepatitis B and D viruses (HBV/HDV). Since then, this carrier has emerged as promising drug target for HBV/HDV virus entry inhibitors, but the synthetic peptide Hepcludex® of high molecular weight is the only approved HDV entry inhibitor so far. The present study aimed to identify small molecules as novel NTCP inhibitors with anti-viral activity. A ligand-based bioinformatic approach was used to generate and validate appropriate pharmacophore and QSAR (quantitative structure-activity relationship) models. Half-maximal inhibitory concentrations (IC50) for binding inhibition of the HBV/HDV-derived preS1 peptide (as surrogate parameter for virus binding to NTCP) were determined in NTCP-expressing HEK293 cells for 150 compounds of different chemical classes. IC50 values ranged from 2 µM up to >1000 µM. The generated pharmacophore and QSAR models were used for virtual screening of drug-like chemicals from the ZINC15 database (~11 million compounds). The 20 best-performing compounds were then experimentally tested for preS1-peptide binding inhibition in NTCP-HEK293 cells. Among them, four compounds were active and revealed experimental IC50 values for preS1-peptide binding inhibition of 9, 19, 20, and 35 µM, which were comparable to the QSAR-based predictions. All these compounds also significantly inhibited in vitro HDV infection of NTCP-HepG2 cells, without showing any cytotoxicity. The best-performing compound in all assays was ZINC000253533654. In conclusion, the present study demonstrates that virtual compound screening based on NTCP-specific pharmacophore and QSAR models can predict novel active hit compounds for the development of HBV/HDV entry inhibitors.

| S-EPMC8402622 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

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