Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis
Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037021002725 |
| _version_ | 1818871972313956352 |
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| author | Alexander D.H. Kingdon Luke J. Alderwick |
| author_facet | Alexander D.H. Kingdon Luke J. Alderwick |
| author_sort | Alexander D.H. Kingdon |
| collection | DOAJ |
| description | Mycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed. |
| first_indexed | 2024-12-19T12:31:24Z |
| format | Article |
| id | doaj.art-a9abc7b6090c439bbd4635f3da077fc1 |
| institution | Directory Open Access Journal |
| issn | 2001-0370 |
| language | English |
| last_indexed | 2024-12-19T12:31:24Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computational and Structural Biotechnology Journal |
| spelling | doaj.art-a9abc7b6090c439bbd4635f3da077fc12022-12-21T20:21:23ZengElsevierComputational and Structural Biotechnology Journal2001-03702021-01-011937083719Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosisAlexander D.H. Kingdon0Luke J. Alderwick1Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United KingdomCorresponding author.; Institute of Microbiology and Infection, School of Biosciences, University of Birmingham, Edgbaston, Birmingham B15 2TT, United KingdomMycobacterium tuberculosis is the causative agent of TB and was estimated to cause 1.4 million death in 2019, alongside 10 million new infections. Drug resistance is a growing issue, with multi-drug resistant infections representing 3.3% of all new infections, hence novel antimycobacterial drugs are urgently required to combat this growing health emergency. Alongside this, increased knowledge of gene essentiality in the pathogenic organism and larger compound databases can aid in the discovery of new drug compounds. The number of protein structures, X-ray based and modelled, is increasing and now accounts for greater than > 80% of all predicted M. tuberculosis proteins; allowing novel targets to be investigated. This review will focus on structure-based in silico approaches for drug discovery, covering a range of complexities and computational demands, with associated antimycobacterial examples. This includes molecular docking, molecular dynamic simulations, ensemble docking and free energy calculations. Applications of machine learning onto each of these approaches will be discussed. The need for experimental validation of computational hits is an essential component, which is unfortunately missing from many current studies. The future outlooks of these approaches will also be discussed.http://www.sciencedirect.com/science/article/pii/S2001037021002725Drug discoveryMycobacterium tuberculosisIn silicoDockingMachine learning |
| spellingShingle | Alexander D.H. Kingdon Luke J. Alderwick Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis Computational and Structural Biotechnology Journal Drug discovery Mycobacterium tuberculosis In silico Docking Machine learning |
| title | Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis |
| title_full | Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis |
| title_fullStr | Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis |
| title_full_unstemmed | Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis |
| title_short | Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis |
| title_sort | structure based in silico approaches for drug discovery against mycobacterium tuberculosis |
| topic | Drug discovery Mycobacterium tuberculosis In silico Docking Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2001037021002725 |
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