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Title

Molecular docking analysis of candidate compounds derived from medicinal plants with type 2 diabetes mellitus targets

 

Authors

Pratistha Singh1, Vinay Kumar Singh2, Anil Kumar Singh1

 

Affiliation

1Department of Dravyaguna, Faculty of Ayurveda, Institute of Medical Sciences, Banaras Hindu University, Varanasi, India; 2Centre for Bioinformatics, School of Biotechnology, Institute of Science, Banaras Hindu University, Varanasi, India;

 

Email

Pratistha Singh - E-mail: psingh30.bhu@gmail.com; *Corresponding author

 

Article Type

Research Article

 

Date

Received January 16, 2019; Revised February 13, 2019; Accepted February 15, 2019; Published March 15, 2019

 

Abstract

Herbal drugs are used for the treatment of diseases and disorders with its less side effects, easy availability and low cost. Several bioactive compounds have been isolated from medicinal plants such as Ficus benghelensis, Ficus racemosa, Ficus religiosa, Thespesia populena and Ficus lacur bouch were taken for screening. This study aimed to evaluate molecular interactions of selected diabetes mellitus (DM) targets with bioactive compounds isolated from Ficus benghelensis, Ficus racemosa, Ficus religiosa, Thespesia populena and Ficus lacur bouch. In this article, screening of the best substances as bioactive compounds is achieved by molecular docking analysis with 3 best selected DM target proteins i.e., aldose reductase (AR), Insulin Receptor (IR) and Mono-ADP ribosyltransferase-sirtuin-6 (SIRT6). In this analysis six potential bioactive compounds (gossypetin, herbacetin, kaempferol, leucoperalgonidin, leucodelphinidin and sorbifolin) were successfully identified on the basis of binding energy (>8.0 kcal/mol) and dissociation constant using YASARA. Out of six compounds, herbacetin and sorbifolin were observed as most suitable ligands for management of diabetes mellitus.

 

Keywords

Diabetes mellitus; in silico docking; aldose reductase; insulin receptor; SIRT-6; medicinal plants.

 

Citation

Singh et al. Bioinformation 15(3): 179-188 (2019)

 

Edited by

P Kangueane

 

ISSN

0973-2063

 

Publisher

Biomedical Informatics

 

License

This is an Open Access article which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. This is distributed under the terms of the Creative Commons Attribution License.