Literature DB >> 23455026

Structure-based drug design using GPCR homology modeling: toward the discovery of novel selective CysLT2 antagonists.

Xiaowu Dong1, Yanmei Zhao, Xueqin Huang, Kana Lin, Jianzhong Chen, Erqing Wei, Tao Liu, Yongzhou Hu.   

Abstract

3D structure of CysLT2 receptor was constructed by using homology modeling and molecular simulations. The binding pocket of CysLT2 receptor and the proposition of the interaction mode between CysLT2 and HAMI3379 were identified. A series of dicarboxylated chalcones was then virtually evaluated through molecular docking studies. A total of six compounds 13a-f with preferable scores was further synthesized and tested for CysLT2 antagonistic activities by determination of the cytosolic free Ca(2+) levels in HEK293 cells. Compounds 13e and 13f exhibited potent and selective CysLT2 antagonistic activities with IC50 values being 7.5 and 0.25 μM, respectively.
Copyright © 2013 Elsevier Masson SAS. All rights reserved.

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Year:  2013        PMID: 23455026     DOI: 10.1016/j.ejmech.2013.01.041

Source DB:  PubMed          Journal:  Eur J Med Chem        ISSN: 0223-5234            Impact factor:   6.514


  4 in total

1.  Discovery of Highly Potent Dual CysLT1 and CysLT2 Antagonist.

Authors:  Satoshi Itadani; Shinya Takahashi; Masaki Ima; Tetsuya Sekiguchi; Manabu Fujita; Yoshisuke Nakayama; Jun Takeuchi
Journal:  ACS Med Chem Lett       Date:  2014-10-06       Impact factor: 4.345

2.  Integrating docking scores and key interaction profiles to improve the accuracy of molecular docking: towards novel B-RafV600E inhibitors.

Authors:  Chun-Qi Hu; Kang Li; Ting-Ting Yao; Yong-Zhou Hu; Hua-Zhou Ying; Xiao-Wu Dong
Journal:  Medchemcomm       Date:  2017-07-24       Impact factor: 3.597

Review 3.  Molecular mechanisms of target recognition by lipid GPCRs: relevance for cancer.

Authors:  M T M van Jaarsveld; J M Houthuijzen; E E Voest
Journal:  Oncogene       Date:  2015-12-07       Impact factor: 9.867

4.  Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Authors:  Hongzhao Wang; Zijian Qin; Aixia Yan
Journal:  Mol Divers       Date:  2021-02-03       Impact factor: 3.364

  4 in total

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