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This article is part of the supplement: 22nd International Conference on Genome Informatics: Bioinformatics

Open Access Proceedings

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

Shalini John, Sundarapandian Thangapandian, Mahreen Arooj, Jong Chan Hong, Kwang Dong Kim and Keun Woo Lee*

Author Affiliations

Division of Applied Life Science (BK21 Program), Systems and Synthetic Agrobiotech Center (SSAC), Research Institute of Natural Science(RINS), Plant Molecular Biology and Biotechnology Research Center (PMBBRC), Gyeongsang National University (GNU), 501 Jinju-daero, Gazha-dong, Jinju 660-701, Republic of Korea

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BMC Bioinformatics 2011, 12(Suppl 14):S4  doi:10.1186/1471-2105-12-S14-S4

Published: 14 December 2011

Abstract

Background

Renin has become an attractive target in controlling hypertension because of the high specificity towards its only substrate, angiotensinogen. The conversion of angiotensinogen to angiotensin I is the first and rate-limiting step of renin-angiotensin system and thus designing inhibitors to block this step is focused in this study.

Methods

Ligand-based quantitative pharmacophore modeling methodology was used in identifying the important molecular chemical features present in the set of already known active compounds and the missing features from the set of inactive compounds. A training set containing 18 compounds including active and inactive compounds with a substantial degree of diversity was used in developing the pharmacophore models. A test set containing 93 compounds, Fischer randomization, and leave-one-out methods were used in the validation of the pharmacophore model. Database screening was performed using the best pharmacophore model as a 3D structural query. Molecular docking and density functional theory calculations were used to select the hit compounds with strong molecular interactions and favorable electronic features.

Results

The best quantitative pharmacophore model selected was made of one hydrophobic, one hydrogen bond donor, and two hydrogen bond acceptor features with high a correlation value of 0.944. Upon validation using an external test set of 93 compounds, Fischer randomization, and leave-one-out methods, this model was used in database screening to identify chemical compounds containing the identified pharmacophoric features. Molecular docking and density functional theory studies have confirmed that the identified hits possess the essential binding characteristics and electronic properties of potent inhibitors.

Conclusion

A quantitative pharmacophore model of predictive ability was developed with essential molecular features of a potent renin inhibitor. Using this pharmacophore model, two potential inhibitory leads were identified to be used in designing novel and future renin inhibitors as antihypertensive drugs.