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Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina

The Mur E enzyme of Mur pathway of Mycobacterium tuberculosis is an attractive drug target as it is unique to bacteria and is absent in mammalian cells. The virtual screening of large libraries of drug like molecules against a protein target is a common strategy used to identify novel inhibitors. Ho...

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Detalles Bibliográficos
Autores principales: Singh, Shilpi, Bajpai, Urmi, Lynn, Andrew Michael
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Biomedical Informatics 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4261115/
https://www.ncbi.nlm.nih.gov/pubmed/25512687
http://dx.doi.org/10.6026/97320630010697
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author Singh, Shilpi
Bajpai, Urmi
Lynn, Andrew Michael
author_facet Singh, Shilpi
Bajpai, Urmi
Lynn, Andrew Michael
author_sort Singh, Shilpi
collection PubMed
description The Mur E enzyme of Mur pathway of Mycobacterium tuberculosis is an attractive drug target as it is unique to bacteria and is absent in mammalian cells. The virtual screening of large libraries of drug like molecules against a protein target is a common strategy used to identify novel inhibitors. However, the method has a large number of pitfalls, with large variations in accuracy caused in part by inaccurate protocols, use of improper standards and libraries, and system dependencies such as the potential for nonspecific docking from large active-site cavities. The screening of drug-like small molecules from diversity sets can, however, be used to short-list potential fragments as building blocks to generate leads with improved specificity. We describe a protocol to implement this strategy, which involves an analysis of the active site and known inhibitors to identify orthospecific determinants, virtual screening of a drug-like diversity library to identify potential drug primitives, and inspection of the potential docked fragments for both binding potential and toxicity. The protocol is implemented on the M.tb Mur E protein which has a large active site with poor enrichment of known positives and a set of drug-like molecules that meets this criteria is presented for further analysis. ABBREVIATIONS: MTB - Mycobacterium tuberculosis, NCI - National Cancer Institute, PDB - Protein Databank.
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spelling pubmed-42611152014-12-15 Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina Singh, Shilpi Bajpai, Urmi Lynn, Andrew Michael Bioinformation Hypothesis The Mur E enzyme of Mur pathway of Mycobacterium tuberculosis is an attractive drug target as it is unique to bacteria and is absent in mammalian cells. The virtual screening of large libraries of drug like molecules against a protein target is a common strategy used to identify novel inhibitors. However, the method has a large number of pitfalls, with large variations in accuracy caused in part by inaccurate protocols, use of improper standards and libraries, and system dependencies such as the potential for nonspecific docking from large active-site cavities. The screening of drug-like small molecules from diversity sets can, however, be used to short-list potential fragments as building blocks to generate leads with improved specificity. We describe a protocol to implement this strategy, which involves an analysis of the active site and known inhibitors to identify orthospecific determinants, virtual screening of a drug-like diversity library to identify potential drug primitives, and inspection of the potential docked fragments for both binding potential and toxicity. The protocol is implemented on the M.tb Mur E protein which has a large active site with poor enrichment of known positives and a set of drug-like molecules that meets this criteria is presented for further analysis. ABBREVIATIONS: MTB - Mycobacterium tuberculosis, NCI - National Cancer Institute, PDB - Protein Databank. Biomedical Informatics 2014-11-27 /pmc/articles/PMC4261115/ /pubmed/25512687 http://dx.doi.org/10.6026/97320630010697 Text en © 2014 Biomedical Informatics This is an open-access article, which permits unrestricted use, distribution, and reproduction in any medium, for non-commercial purposes, provided the original author and source are credited.
spellingShingle Hypothesis
Singh, Shilpi
Bajpai, Urmi
Lynn, Andrew Michael
Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina
title Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina
title_full Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina
title_fullStr Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina
title_full_unstemmed Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina
title_short Structure based virtual screening to identify inhibitors against MurE Enzyme of Mycobacterium tuberculosis using AutoDock Vina
title_sort structure based virtual screening to identify inhibitors against mure enzyme of mycobacterium tuberculosis using autodock vina
topic Hypothesis
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4261115/
https://www.ncbi.nlm.nih.gov/pubmed/25512687
http://dx.doi.org/10.6026/97320630010697
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