An integrated computational approach towards novel drugs discovery against polyketide synthase 13 thioesterase domain of Mycobacterium tuberculosis

Altharawi, Ali; Alossaimi, Manal A.; Alanazi, Mohammed M.; Alqahatani, Safar M; Qamar, Muhammad Tahir ul

doi:10.1038/s41598-023-34222-8

Cited by 6 publications

(3 citation statements)

References 65 publications

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“…Additionally, based on the Lipinski's rule of five, the molecular weight of a drug compound should be ≤ 500 daltons to allow for permeation through tissues and maximum absorption (Altharawi, et al, 2023). Therefore, it can be concluded that edible millipedes contain drug-like compounds that exhibit antimicrobial activity and can potentially inhibit the extended-spectrum beta-lactamase-secreting bacteria as revealed by this study.…”

Section: Discussion Recommendations and Conclusionmentioning

confidence: 74%

In-silico identification of extended-spectrum beta-lactamase potential inhibitors in edible millipedes

Nkaiwuatei

2024

Preprint

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Antimicrobial resistance is currently one of the leading causes of death globally, and one of the heaviest health burdens in the African region. Despite this challenge, research and development of novel antimicrobial drugs is still quite slow due to the long process and cost involved. Some of the serious pathogens attributable to antimicrobial-resistant infections are the Extended-spectrum beta-lactamase (ESBL) producing pathogens which mainly include the gram-negative bacteria. The goal of this study was to identify potential ESBL inhibitors from edible millipedes. Millipedes contain a variety of bioactive compounds including alkaloids (p-benzoquinone) and phenols (p-cresol), and they have been used as sources of food for humans. Studies have shown that the Bobo people of Burkina Faso consume three types of millipede species, namely; Tymbodesmus falcatus, Sphenodesmus sheribongensis and Spirostreptidae as food sources. Based on the literature, these edible millipede species contain a variety of compounds in their grandular defensive secretions which include; p-cresol, p-benzoquinone, hydrogen cyanide, toluquinone, 2, 3-dimethoxybenzoquinone, 2-methoxy-3-methylbenzoquinone, toluhydroquinone and mandelonitrile. The aim of this study was therefore to investigate the activity of these compounds secreted by the edible millipede species on ESBL in comparison with 2 positive and 2 negative controls. This work was entirely performed computationally using various bioinformatics tools and softwares and was achieved by performing molecular docking of the ligands (the millipede compounds) against the target protein (ESBL) which served as the receptor. First, the 3D structures of all the ligands were downloaded from the PubChem chemical structures database while that of the target protein (ESBL) was obtained from the RCS Protein Databank (PDB). The structures of both the ligands and the target protein were prepared using UCSF Chimera tool as well as Autodock4 tool and subsequently converted into executable files. Then molecular docking was performed using Autodock vina and the scripts were run on the Windows Command Prompt to determine the binding affinities of the ligands on the target protein, the binding residues, and the physicochemical properties of the ligands as well as the best beta-lactamase enzyme inhibitor. The drug-likeness of the ligands, ADME properties and toxicity were tested using Lipinski’s rule of five, the Swiss Institute of Bioinformatics (Swiss-ADME) software and admetSAR web server, respectively. Out of the eight ligands tested, it was found that 2, 3-dimethoxybenzoquinone with a binding energy of -7.2 is a potential ESBL inhibitor.

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Section: Discussion Recommendations and Conclusionmentioning

confidence: 74%

In-silico identification of extended-spectrum beta-lactamase potential inhibitors in edible millipedes

Nkaiwuatei

2024

Preprint

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“…70 Further, Pks13 performs an acyltransferase activity and transfers the mycolate precursors to trehalose, forming TMM, a common precursor of the mycolate-containing compounds. 70–72 Isoxyl and thioacetazone showed antimycobacterial activity by inhibiting the dehydratase HadAB of the type II FAS. 73…”

Section: Anti-tubercular Drug Targetsmentioning

confidence: 99%

TargetingMycobacterium tuberculosisiron-scavenging tools: a recent update on siderophores inhibitors

Kumar,

Adhikrao

2023

RSC Med. Chem.

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“…Drug development has been significantly impacted by artificial intelligence, which has completely changed how we find and create new medication [1]. Drug development using conventional procedures is frequently labour-intensive, expensive, and ineffective [2]. Nevertheless, AI provides a variety of tools and methodologies that help accelerate several phases of the drug discovery process, resulting in more effective and precise medicines.…”

Section: Introductionmentioning

confidence: 99%

Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine

Unogwu,

Ike,

Joktan

2023

MJAIH

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Artificial intelligence (AI) plays a vital role in the development of pharmaceuticals. The whole drug development process, from target identification and lead optimization to clinical trials and post-marketing monitoring, has the potential to be revolutionized by AI. Data analysis is a crucial area where AI thrives. To find trends, correlations, and prospective targets for therapeutic intervention, AI systems can analyse enormous volumes of data, including electronic health records, molecular databases, academic literature, and clinical trial data. This saves time and resources by enabling researchers to decide which compounds or pathways to investigate. Lead optimization is another area where AI is essential since algorithms can screen and forecast the efficacy of new drug candidates. To improve efficacy, safety, and pharmacokinetics, the most promising leads are prioritized, and their attributes are optimized. By examining past data to pinpoint patient groupings that are most likely to respond to a certain medication, AI can also improve clinical trial design and patient selection. This advances success rates aids in the development of personalized medication and can support post-marketing surveillance by continuously scanning real-world data for adverse events or unexpected drug-drug interactions after a medicine has been licensed. This permits the quicker detection of potential safety issues and contributes to maintaining the continuous safety of licensed medications. However, some obstacles must be overcome to completely utilize AI in medication development. These include the requirement for high-quality data, assuring AI model transparency and interpretability, taking into account ethical issues, and regulatory frameworks for using AI in drug development.

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An integrated computational approach towards novel drugs discovery against polyketide synthase 13 thioesterase domain of Mycobacterium tuberculosis

Cited by 6 publications

References 65 publications

In-silico identification of extended-spectrum beta-lactamase potential inhibitors in edible millipedes

In-silico identification of extended-spectrum beta-lactamase potential inhibitors in edible millipedes

TargetingMycobacterium tuberculosisiron-scavenging tools: a recent update on siderophores inhibitors

Employing Artificial Intelligence Methods in Drug Development: A New Era in Medicine

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