Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Kingdon, Alexander D. H.; Alderwick, Luke J.

doi:10.1016/j.csbj.2021.06.034

Cited by 11 publications

(6 citation statements)

References 152 publications

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“…The PDB [19] contains more than 2500 enzymes specifically on TB [206], and a good guess is that there are likely thousands of ligand possibilities. The protein and ligand wealth of data are immediately accessible on the Internet with a click.…”

Section: Advantages and Drawbacks Of Computational Methodsmentioning

confidence: 99%

“…For instance, the NarL enzyme is necessary for anaerobic survival throughout infection, while BioA is crucial for biotin synthesis during the latency phase of Mycobacterium TB infection [203,204]. Furthermore, the EthR protein functions in developing ethionamide resistance and, consequently, survives potentials after drug treatment [205,206]. UCSF Chimera CDD-823953; GSK-735826A PyrG and PanK (siosynthesis of DNA and RNA) [192] Many studies of the different targeted enzymes indicate that many are engaged in either intermediate metabolism or lipid metabolism in Mtb.…”

Section: Molecular Docking and Density Functional Theory Applied To Mtbmentioning

confidence: 99%

“…As a result, the focus of these early drug discovery initiatives continues to be on totally new druglike chemical discoveries. The apparent lack of further experimental evidence (in vitro or in vivo) showing compound bioactivity in many of these investigations (Table 8) is an evident issue that precludes these anticipated compounds from being carried onward [206].…”

Section: Molecular Docking and Density Functional Theory Applied To Mtbmentioning

confidence: 99%

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Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Ejalonibu

Ogundare

El-Rashedy

et al. 2021

IJMS

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Developing new, more effective antibiotics against resistant Mycobacterium tuberculosis that inhibit its essential proteins is an appealing strategy for combating the global tuberculosis (TB) epidemic. Finding a compound that can target a particular cavity in a protein and interrupt its enzymatic activity is the crucial objective of drug design and discovery. Such a compound is then subjected to different tests, including clinical trials, to study its effectiveness against the pathogen in the host. In recent times, new techniques, which involve computational and analytical methods, enhanced the chances of drug development, as opposed to traditional drug design methods, which are laborious and time-consuming. The computational techniques in drug design have been improved with a new generation of software used to develop and optimize active compounds that can be used in future chemotherapeutic development to combat global tuberculosis resistance. This review provides an overview of the evolution of tuberculosis resistance, existing drug management, and the design of new anti-tuberculosis drugs developed based on the contributions of computational techniques. Also, we show an appraisal of available software and databases on computational drug design with an insight into the application of this software and databases in the development of anti-tubercular drugs. The review features a perspective involving machine learning, artificial intelligence, quantum computing, and CRISPR combination with available computational techniques as a prospective pathway to design new anti-tubercular drugs to combat resistant tuberculosis.

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Section: Advantages and Drawbacks Of Computational Methodsmentioning

confidence: 99%

Section: Molecular Docking and Density Functional Theory Applied To Mtbmentioning

confidence: 99%

Section: Molecular Docking and Density Functional Theory Applied To Mtbmentioning

confidence: 99%

See 1 more Smart Citation

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Ejalonibu

Ogundare

El-Rashedy

et al. 2021

IJMS

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“…A recent review article discusses in detail about the different targets which have been used for ligand screening against TB along with providing a list of websites and repositories from which novel ligands can be retrieved ( Ejalonibu et al, 2021 ). In silico tools which can be used for target identification like, homology modelling, virtual screening, molecular docking, and molecular dynamics simulation to identify druggable targets and lead natural metabolites to fight against M. tuberculosis have also been reviewed in other informative articles ( Okombo and Chibale, 2017 ; Rudraraju et al, 2022 ( Kingdon & Alderwick, 2021 ; Swain and Hussain, 2022)). In one study, M. tuberculosis genes were choosen druggable target to prepare specific compound library from ZINC database, ChEMBL database, and Enamine REAL database for virtual screening.…”

Section: Introductionmentioning

confidence: 99%

Advances in computational frameworks in the fight against TB: The way forward

Naidu

Nayak

et al. 2023

Front. Pharmacol.

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Around 1.6 million people lost their life to Tuberculosis in 2021 according to WHO estimates. Although an intensive treatment plan exists against the causal agent, Mycobacterium Tuberculosis, evolution of multi-drug resistant strains of the pathogen puts a large number of global populations at risk. Vaccine which can induce long-term protection is still in the making with many candidates currently in different phases of clinical trials. The COVID-19 pandemic has further aggravated the adversities by affecting early TB diagnosis and treatment. Yet, WHO remains adamant on its “End TB” strategy and aims to substantially reduce TB incidence and deaths by the year 2035. Such an ambitious goal would require a multi-sectoral approach which would greatly benefit from the latest computational advancements. To highlight the progress of these tools against TB, through this review, we summarize recent studies which have used advanced computational tools and algorithms for—early TB diagnosis, anti-mycobacterium drug discovery and in the designing of the next-generation of TB vaccines. At the end, we give an insight on other computational tools and Machine Learning approaches which have successfully been applied in biomedical research and discuss their prospects and applications against TB.

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“…Although a target-based approach is time-consuming, expensive Thus, the first step of a target-based approach is the selection and validation of a drug target (76). Drug target selection mainly comprises of a single gene, gene product or molecular mechanism required for bacterial survival, virulence or pathogenesis, and are commonly essential genes (72,77). Essential targets are targets that are indispensable for the bacterial growth and/or survival.…”

Section: I27 Target-based Drug Discoverymentioning

confidence: 99%

Investigating Mtr and MshC as potential targets in the fight against Mycobacterium abscessus infections

Piller

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Structure-based in silico approaches for drug discovery against Mycobacterium tuberculosis

Abstract: Graphical abstract

Cited by 11 publications

References 152 publications

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Drug Discovery for Mycobacterium tuberculosis Using Structure-Based Computer-Aided Drug Design Approach

Advances in computational frameworks in the fight against TB: The way forward

Investigating Mtr and MshC as potential targets in the fight against Mycobacterium abscessus infections

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