2D-QSAR, Docking Studies, andIn SilicoADMET Prediction of Polyphenolic Acetates as Substrates for Protein Acetyltransferase Function of Glutamine Synthetase ofMycobacterium tuberculosis
Author(s) -
Prija Ponnan,
Shikhar Gupta,
Madhu Chopra,
Rashmi Tandon,
A. Baghel,
Garima Gupta,
Ashok K. Prasad,
Ramesh C. Rastogi,
Mridula Bose,
Hanumantharao G. Raj
Publication year - 2013
Publication title -
isrn structural biology
Language(s) - English
Resource type - Journals
ISSN - 2314-4688
DOI - 10.1155/2013/373516
Subject(s) - antimycobacterial , docking (animal) , autodock , quantitative structure–activity relationship , mycobacterium tuberculosis , stereochemistry , in silico , chemistry , database , biochemistry , tuberculosis , medicine , nursing , pathology , gene , computer science
A novel transacetylase (TAase) function of glutamine synthetase (GS) in bacterial species such as Mycobacterium smegmatis and Mycobacterium tuberculosis H37Rv was established by us, termed as mycobacterial TAase (MTAase). Several polyphenolic acetates (PAs) were found to be substrates for MTAase by inhibiting certain receptor proteins such as glutathione S-transferase by way of acetylation. The present work describes the descriptor-based 2D-QSAR studies developed for a series of PA synthesized by us and evaluated for MTAase and antimycobacterial activity using stepwise multiple linear regression method with the kinetic constants and the minimum inhibitory constant (MIC) as the dependent variables, to address the fact that TAase activity was leading to the antimycobacterial activity. Further, blind docking methods using AutoDock were carried out to study the interaction of potent PA with the crystal structure of M. tuberculosis GS. PAs were predicted to bind M. tuberculosis GS on the protein surface away from the known active site of GS. Subsequent focussed/refined docking of potent PA with GS showed that the -amino group of Lys4 of GS formed a cation- interaction with the benzene ring of PA. Also, ADMET-related descriptors were calculated to predict the pharmacokinetic properties for the selection of the effective and bioavailable compounds.
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