Identification of novel human renin inhibitors through a combined approach of pharmacophore modelling, molecular DFT analysis and in silico screening. (August 2017)

• Record Type: Journal Article
• Authors: Gogoi, Dhrubajyoti; Baruah, Vishwa Jyoti; Chaliha, Amrita Kashyap; Kakoti, Bibhuti Bhushan; Sarma, Diganta; Buragohain, Alak Kumar
• Is Part Of: Computational biology and chemistry. Volume 69(2017)
• Page Start: 28
• Page End: 40
• Publication Date: 2017-08
• Subjects: Pharmacophore modelling -- Renin -- ADMET -- Docking -- DFT; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2017.04.005
• Languages: English
• ISSNs: 1476-9271

Abstract

Graphical abstract: Highlights: A ligand-based 3D pharmacophore model was developed for identification of novel human renin inhibitors. Pharmacophore validation and evaluation were performed based on established validation techniques. Pharmacophore-based virtual screening was applied to retrieve potent human renin inhibitors. Molecular interactions and binding orientation study were performed by molecular docking analysis. DFT-based analysis of best hits identified through molecular docking was conducted for their further validation. Abstract: Renin is an aspartyl protease of the renin–angiotensin system (RAS) and the first enzyme of the biochemical pathway for the generation of angiotensin II – a potent vasoconstrictor involved in the maintenance of cardiovascular homeostasis and the regulation of blood pressure. High enzymatic specificity of renin and its involvement in the catalysis of the rate-limiting step of the RAS hormone system qualify it as a good target for inhibition of hypertension and other associated diseases. Ligand-based pharmacophore model (Hypo1) was generated from a training set of 24 compounds with renin inhibitory activity. The best hypothesis consisted of one Hydrogen Bond Acceptor (HBA), three Hydrophobic Aliphatic (HY-Al) and one Ring Aromatic (AR) features. This well-validated pharmacophore hypothesis (correlation coefficient 0.95) was further utilized as a 3D query to screen database compounds, which included structures from two natural product repositories. These screened compounds were further analyzed for drug-likeness and ADMET studies. The compounds which satisfied the qualifying criteria were then subjected to molecular docking and Density Functional Theory (DFT) analysis in order to discern their atomic level interactions at the active site of the 3D structure of rennin. The pharmacophore-based modelling that has been used to generate the novel findings of the present study would be an avant-garde approach towards the development of potent inhibitors of renin.