In silico Molecular Docking studies to investigate interactions of natural Camptothecin molecule with diabetic enzymes

Autor: Mahendran Radha, Viswanathan T, Nishandhini Marimuthu, Jeyabaskar Suganya
Rok vydání: 2017
Předmět:
Zdroj: Research Journal of Pharmacy and Technology. 10:2917
ISSN: 0974-360X
0974-3618
DOI: 10.5958/0974-360x.2017.00515.7
Popis: The third leading cause of death in the world is Diabetes. Diabetes comes under a group of metabolic disorders in which the patients possess high blood glucose level. Camptothecin is an alkaloid compound with better antioxidant activity and in china it was used as traditional drugs to cure many diseases. The early researches on camptothecin revealed that it possess better anticancer and antitumor activity when compared with current available synthetic drugs. In silico drug designing and docking studies pave a way for better understanding of inhibitory activity of the compound over the respective target proteins. Molecular docking is one of the most powerful tools for predicting and analyzing the binding interactions between receptors and small molecules at the atomic level. In the current study, an in silico binding interaction of the compound camptothecin with three important diabetic targets like Glucokinase, insulin receptor and PPAR gamma were analyzed using Arguslab. ArgusLab software is used for molecular docking, to study the interactions between small compounds and its proteins. The compound camptothecin was first tested for its drug likeliness, bioactive properties and the results predicted that the compound passed the test for being an oral drug. The docking study revealed that camptothecin showed best binding affinity towards all the three proteins. Among the three proteins, PPAR gamma protein exhibited the lowest binding energy, followed by Glucokinase and insulin receptor with the binding energy of >-8.5 kcal/mol. Thus, Camptothecin compound could be considered as a better lead molecule in the development and discovery of new anti-diabetic drugs.
Databáze: OpenAIRE