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Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB
Repurposing has gained momentum globally and become an alternative avenue for drug discovery because of its better success rate, and reduced cost, time and issues related to safety than the conventional drug discovery process. Several drugs have already been successfully repurposed for other clinica...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Biomedical Informatics
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5312000/ https://www.ncbi.nlm.nih.gov/pubmed/28275291 http://dx.doi.org/10.6026/97320630012368 |
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author | Brindha, Sridharan Sundaramurthi, Jagadish Chandrabose Velmurugan, Devadasan Vincent, Savariar Gnanadoss, John Joel |
author_facet | Brindha, Sridharan Sundaramurthi, Jagadish Chandrabose Velmurugan, Devadasan Vincent, Savariar Gnanadoss, John Joel |
author_sort | Brindha, Sridharan |
collection | PubMed |
description | Repurposing has gained momentum globally and become an alternative avenue for drug discovery because of its better success rate, and reduced cost, time and issues related to safety than the conventional drug discovery process. Several drugs have already been successfully repurposed for other clinical conditions including drug resistant tuberculosis (DR-TB). Though TB can be cured completely with the use of currently available anti-tubercular drugs, emergence of drug resistant strains of Mycobacterium tuberculosis and the huge death toll globally, together necessitate urgently newer and effective drugs for TB. Therefore, we performed virtual screening of 1554 FDA approved drugs against murE, which is essential for peptidoglycan biosynthesis of M. tuberculosis. We used Glide and AutoDock Vina for virtual screening and applied rigid docking algorithm followed by induced fit docking algorithm in order to enhance the quality of the docking prediction and to prioritize drugs for repurposing. We found 17 drugs binding strongly with murE and three of them, namely, lymecycline, acarbose and desmopressin were consistently present within top 10 ranks by both Glide and AutoDock Vina in the induced fit docking algorithm, which strongly indicates that these three drugs are potential candidates for further studies towards repurposing for TB. |
format | Online Article Text |
id | pubmed-5312000 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | Biomedical Informatics |
record_format | MEDLINE/PubMed |
spelling | pubmed-53120002017-03-08 Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB Brindha, Sridharan Sundaramurthi, Jagadish Chandrabose Velmurugan, Devadasan Vincent, Savariar Gnanadoss, John Joel Bioinformation Hypothesis Repurposing has gained momentum globally and become an alternative avenue for drug discovery because of its better success rate, and reduced cost, time and issues related to safety than the conventional drug discovery process. Several drugs have already been successfully repurposed for other clinical conditions including drug resistant tuberculosis (DR-TB). Though TB can be cured completely with the use of currently available anti-tubercular drugs, emergence of drug resistant strains of Mycobacterium tuberculosis and the huge death toll globally, together necessitate urgently newer and effective drugs for TB. Therefore, we performed virtual screening of 1554 FDA approved drugs against murE, which is essential for peptidoglycan biosynthesis of M. tuberculosis. We used Glide and AutoDock Vina for virtual screening and applied rigid docking algorithm followed by induced fit docking algorithm in order to enhance the quality of the docking prediction and to prioritize drugs for repurposing. We found 17 drugs binding strongly with murE and three of them, namely, lymecycline, acarbose and desmopressin were consistently present within top 10 ranks by both Glide and AutoDock Vina in the induced fit docking algorithm, which strongly indicates that these three drugs are potential candidates for further studies towards repurposing for TB. Biomedical Informatics 2016-11-22 /pmc/articles/PMC5312000/ /pubmed/28275291 http://dx.doi.org/10.6026/97320630012368 Text en © 2016 Biomedical Informatics http://creativecommons.org/licenses/by/3.0/ 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. |
spellingShingle | Hypothesis Brindha, Sridharan Sundaramurthi, Jagadish Chandrabose Velmurugan, Devadasan Vincent, Savariar Gnanadoss, John Joel Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB |
title | Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB |
title_full | Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB |
title_fullStr | Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB |
title_full_unstemmed | Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB |
title_short | Docking-based virtual screening of known drugs against murE of Mycobacterium tuberculosis towards repurposing for TB |
title_sort | docking-based virtual screening of known drugs against mure of mycobacterium tuberculosis towards repurposing for tb |
topic | Hypothesis |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5312000/ https://www.ncbi.nlm.nih.gov/pubmed/28275291 http://dx.doi.org/10.6026/97320630012368 |
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