Literature DB >> 21497958

Potential virtual lead identification in the discovery of renin inhibitors: application of ligand and structure-based pharmacophore modeling approaches.

Sundarapandian Thangapandian1, Shalini John, Sugunadevi Sakkiah, Keun Woo Lee.   

Abstract

Renin, an enzyme by cleaving angiotensinogen to angiotensin-I, controls the first and rate-limiting step of renin-angiotensin system that is associated with blood pressure. Thus Ligand and structure-based pharmacophore models were developed in this study to identify new potential leads inhibiting this rate-limiting enzyme as an efficient way to treat blood pressure. X-ray predicted binding modes of most active compounds were used in ligand-based approach whereas the 3D structural information of renin was used in structure-based approach. Pharmacophore models were validated using various methods and utilized in database searching to identify potential hits. Drug-like filters and molecular docking studies led us identifying the final hits to be employed in designing new class of future renin inhibitors.
Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Entities:  

Mesh:

Substances:

Year:  2011        PMID: 21497958     DOI: 10.1016/j.ejmech.2011.03.035

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


  17 in total

1.  Pharmacophore modeling and virtual screening studies to identify new c-Met inhibitors.

Authors:  Wenting Tai; Tao Lu; Haoliang Yuan; Fengxiao Wang; Haichun Liu; Shuai Lu; Ying Leng; Weiwei Zhang; Yulei Jiang; Yadong Chen
Journal:  J Mol Model       Date:  2011-12-28       Impact factor: 1.810

2.  Exploration of structural and physicochemical requirements and search of virtual hits for aminopeptidase N inhibitors.

Authors:  Amit K Halder; Achintya Saha; Tarun Jha
Journal:  Mol Divers       Date:  2013-01-23       Impact factor: 2.943

3.  Gene-wide identification and expression analysis of the PMEI family genes in soybean (Glycine max).

Authors:  Jingjing Wang; Lei Ling; He Cai; Changhong Guo
Journal:  3 Biotech       Date:  2020-07-06       Impact factor: 2.406

4.  Discovery of novel wee1 inhibitors via structure-based virtual screening and biological evaluation.

Authors:  Yaping Li; Yinglan Pu; Hui Liu; Li Zhang; Xingyong Liu; Yan Li; Zhili Zuo
Journal:  J Comput Aided Mol Des       Date:  2018-09-04       Impact factor: 3.686

5.  Development, evaluation and application of 3D QSAR Pharmacophore model in the discovery of potential human renin inhibitors.

Authors:  Shalini John; Sundarapandian Thangapandian; Mahreen Arooj; Jong Chan Hong; Kwang Dong Kim; Keun Woo Lee
Journal:  BMC Bioinformatics       Date:  2011-12-14       Impact factor: 3.169

6.  Novel hybrid virtual screening protocol based on molecular docking and structure-based pharmacophore for discovery of methionyl-tRNA synthetase inhibitors as antibacterial agents.

Authors:  Chi Liu; Gu He; Qinglin Jiang; Bo Han; Cheng Peng
Journal:  Int J Mol Sci       Date:  2013-07-09       Impact factor: 5.923

7.  Evaluating molecular mechanism of hypotensive peptides interactions with renin and angiotensin converting enzyme.

Authors:  Rong He; Rotimi E Aluko; Xing-Rong Ju
Journal:  PLoS One       Date:  2014-03-06       Impact factor: 3.240

8.  Exploration of virtual candidates for human HMG-CoA reductase inhibitors using pharmacophore modeling and molecular dynamics simulations.

Authors:  Minky Son; Ayoung Baek; Sugunadevi Sakkiah; Chanin Park; Shalini John; Keun Woo Lee
Journal:  PLoS One       Date:  2013-12-30       Impact factor: 3.240

9.  Discovery of novel focal adhesion kinase inhibitors using a hybrid protocol of virtual screening approach based on multicomplex-based pharmacophore and molecular docking.

Authors:  Fengbo Wu; Ting Xu; Gu He; Liang Ouyang; Bo Han; Cheng Peng; Xiangrong Song; Mingli Xiang
Journal:  Int J Mol Sci       Date:  2012-11-23       Impact factor: 5.923

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

Authors:  Jia Fei; Lu Zhou; Tao Liu; Xiang-Yang Tang
Journal:  Int J Med Sci       Date:  2013-01-23       Impact factor: 3.738

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.