Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors

Bharatham, Nagakumar; Bharatham, Kavitha; Lee, Keun Woo

doi:10.1016/j.jmgm.2006.08.002

Cited by 47 publications

(18 citation statements)

References 33 publications

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“…A significant hypothesis must possess the large difference between null and fixed cost values. 25 In this study, the null cost value of the top 10 hypotheses is 170.484, and the fixed cost value is 109.033. Configuration cost value is 7.…”

Section: Resultsmentioning

confidence: 93%

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H₁Receptor

Thangapandian¹,

Krishnamoorthy²,

Strouboulis³

et al. 2010

Bulletin of the Korean Chemical Society

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Human histamine H1 receptor (HHR1) is a G protein-coupled receptor and a primary target for antiallergic therapy. Here, the ligand-based three-dimensional pharmacophore models were built from a set of known HHR1 inverse agonists using HypoGen module of CATALYST software. All ten generated pharmacophore models consist of five essential features: hydrogen bond acceptor, ring aromatic, positive ionizable and two hydrophobic functions. Best model had a correlation coefficient of 0.854 for training set compounds and it was validated with an external test set with a high correlation value of 0.925. Using this model Maybridge database containing 60,000 compounds was screened for potential leads. A rigorous screening for drug-like compounds unveiled RH01692 and SPB00834, two novel molecules for HHR1 with good CATALYST fit and estimated activity values. The new lead molecules were docked into the active site of constructed HHR1 homology model based on recently crystallized squid rhodopsin as template. Both the hit compounds were found to have critical interactions with Glu177, Phe432 and other important amino acids. The interpretations of this study may effectively be deployed in designing of novel HHR1 inverse agonists.

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Section: Resultsmentioning

confidence: 93%

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H₁Receptor

Thangapandian¹,

Krishnamoorthy²,

Strouboulis³

et al. 2010

Bulletin of the Korean Chemical Society

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“…All 10 hypotheses are validated by the test set method and the CatScramble method. 35 The 862 molecules were taken as test set from the in-house database by the same rule and way of selecting training set, and were screened by Hypo7. The statistical validation of Hypo7 was performed by Fischer's randomization test, using CatScramble.…”

mentioning

confidence: 99%

Pharmacophore Identification for Peroxisome Proliferator-Activated Receptor Gamma Agonists

Sohn¹,

Lee²,

Park³

et al. 2011

Bulletin of the Korean Chemical Society

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Peroxisome proliferator-activated receptors (PPARs) are members of nuclear receptors and their activation induces regulation of fatty acid storage and glucose metabolism. Therefore, the PPARγ is a major target for the treatment of type 2 diabetes mellitus. In order to generate pharmacophore model, 1080 known agonists database was constructed and a training set was selected. The Hypo7, selected from 10 hypotheses, contains four features: three hydrogen-bond acceptors (HBA) and one general hydrophobic (HY). This pharmacophore model was validated by using 862 test set compounds with a correlation coefficient of 0.903 between actual and estimated activity. Secondly, CatScramble method was used to verify the model. Hence, the validated Hypo7 was utilized for searching new lead compounds over 238,819 and 54,620 chemical structures in NCI and Maybridge database, respectively. Then the leads were selected by screening based on the pharmacophore model, predictive activity, and Lipinski's rules. Candidates were obtained and subsequently the binding affinities to PPARγ were investigated by the molecular docking simulations. Finally the best two compounds were presented and would be useful to treat type 2 diabetes.

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“…The Lipinski's filter was applied on these 4893 states, leaving 2852 compounds [33,34] . As different conformers fit the pharmacophore and 3D-QSAR model differently [35] , we generated conformations for all 2852 molecules using the ConfGen application of Schrödinger using the OPLS-2005 force-field and distance-dependent electrostatic treatment with a dielectric constant of 4.0. The search mode was rapid and an energy window of 100 kJ mol-1 was used for saving conformations.…”

Section: Computational Detailsmentioning

confidence: 99%

Discovery of Some Potent PDHK Inhibitors Using Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies

Azad

Bhandari

Kakkar

2016

Journal of Biochemistry and Molecular Biology Research

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Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors

Cited by 47 publications

References 33 publications

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H₁Receptor

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H₁Receptor

Pharmacophore Identification for Peroxisome Proliferator-Activated Receptor Gamma Agonists

Discovery of Some Potent PDHK Inhibitors Using Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies

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Pharmacophore identification and virtual screening for methionyl-tRNA synthetase inhibitors

Cited by 47 publications

References 33 publications

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H1Receptor

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H1Receptor

Pharmacophore Identification for Peroxisome Proliferator-Activated Receptor Gamma Agonists

Discovery of Some Potent PDHK Inhibitors Using Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies

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Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H₁Receptor

Pharmacophore Modeling, Virtual Screening and Molecular Docking Studies for Identification of New Inverse Agonists of Human Histamine H₁Receptor