Abstract
Tuberculosis (TB) is known to mankind as one of the most pervasive and persistent of diseases since the early days of civilization. The growing resistance of the causative pathogen Mycobacterium tuberculosis to the standard drug regimen for TB poses further difficulty in its treatment and control. Screening of novel plant-derived compounds with promising anti-tubercular activity has been cited as a prospective route for new anti-tubercular drug discovery and design. Justicia adhatoda L. is a perennial evergreen shrub which is widely mentioned in scientific literature on account of its potent anti-mycobacterial properties. In the present study, we have employed a series of computational methodologies to reveal the probable molecular interactions of vasicine, the principal alkaloid of Justicia adhatoda L., and two of its close natural derivatives- vasicinone and deoxyvasicine, with certain biological targets in M. tuberculosis. Targets were identified from literature and through a reverse Pharmacophore-based approach. Subsequent comparative molecular docking to identify the best ligand-target interactions revealed Antigen 85C of M. tuberculosis as the most potent biological target of vasicine on the basis of optimum molecular docking values. A chemogenomics approach was also employed to validate the molecular interactions between the same class of chemical compounds as vasicine and Antigen 85C. Further, a library of structural analogs of vasicine was created by bioiosterism-based drug design to identify structural analogs with better inhibitory potential against Antigen 85C.
Keywords: Tuberculosis, Justicia adhatoda, vasicine, reverse pharmacophore mapping, molecular docking, Antigen 85C, bioisosterism.
Combinatorial Chemistry & High Throughput Screening
Title:An In Silico Approach for Identification of Potential Anti-Mycobacterial Targets of Vasicine and Related Chemical Compounds
Volume: 19 Issue: 1
Author(s): Amrita Kashyap Chaliha, Dhrubajyoti Gogoi, Pankaj Chetia, Diganta Sarma and Alak Kumar Buragohain
Affiliation:
Keywords: Tuberculosis, Justicia adhatoda, vasicine, reverse pharmacophore mapping, molecular docking, Antigen 85C, bioisosterism.
Abstract: Tuberculosis (TB) is known to mankind as one of the most pervasive and persistent of diseases since the early days of civilization. The growing resistance of the causative pathogen Mycobacterium tuberculosis to the standard drug regimen for TB poses further difficulty in its treatment and control. Screening of novel plant-derived compounds with promising anti-tubercular activity has been cited as a prospective route for new anti-tubercular drug discovery and design. Justicia adhatoda L. is a perennial evergreen shrub which is widely mentioned in scientific literature on account of its potent anti-mycobacterial properties. In the present study, we have employed a series of computational methodologies to reveal the probable molecular interactions of vasicine, the principal alkaloid of Justicia adhatoda L., and two of its close natural derivatives- vasicinone and deoxyvasicine, with certain biological targets in M. tuberculosis. Targets were identified from literature and through a reverse Pharmacophore-based approach. Subsequent comparative molecular docking to identify the best ligand-target interactions revealed Antigen 85C of M. tuberculosis as the most potent biological target of vasicine on the basis of optimum molecular docking values. A chemogenomics approach was also employed to validate the molecular interactions between the same class of chemical compounds as vasicine and Antigen 85C. Further, a library of structural analogs of vasicine was created by bioiosterism-based drug design to identify structural analogs with better inhibitory potential against Antigen 85C.
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Chaliha Kashyap Amrita, Gogoi Dhrubajyoti, Chetia Pankaj, Sarma Diganta and Buragohain Kumar Alak, An In Silico Approach for Identification of Potential Anti-Mycobacterial Targets of Vasicine and Related Chemical Compounds, Combinatorial Chemistry & High Throughput Screening 2016; 19 (1) . https://dx.doi.org/10.2174/1386207319666151203001708
DOI https://dx.doi.org/10.2174/1386207319666151203001708 |
Print ISSN 1386-2073 |
Publisher Name Bentham Science Publisher |
Online ISSN 1875-5402 |
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