In silico design of novel quinazoline-based compounds as potential Mycobacterium tuberculosis PknB inhibitors through 2D and 3D-QSAR, molecular dynamics simulations combined with pharmacokinetic predictions

Comprehensive coverage on anti-mycobacterial endeavour reported during 2022

TB being one of the deadliest diseases and second most common infectious cause of deaths, poses the severe threat to global health. The extended duration of therapy owing to resistance and its upsurge in immune-compromised patients have been the driving force for the development of novel of anti-TB scaffolds. Recently, we have compiled the account of anti-mycobacterial scaffolds published during 2015-2020 and updated them in 2021. The present work involves the insights on the anti-mycobacterial scaffolds reported in 2022 with their mechanism of action, structure activity relationships, along with the key perceptions for the design of newer anti-TB agents for the broader interests of medicinal chemists.

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

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.

Construction of an MLR-QSAR Model Based on Dietary Flavonoids and Screening of Natural α-Glucosidase Inhibitors

Postprandial hyperglycemia can be reduced by inhibiting α-glucosidase activity. Common α-glucosidase inhibitors such as acarbose may have various side effects. Therefore, it is important to find natural products that are non-toxic and have high α-glucosidase-inhibitory activity. In the present study, a comprehensive computational analysis of 27 dietary flavonoid compounds with α-glucosidase-inhibitory activity was performed. These included flavonoids, flavanones, isoflavonoids, dihydrochalcone, flavan-3-ols, and anthocyanins. Firstly, molecular fingerprint similarity clustering analysis was performed on the target molecules. Secondly, multiple linear regression quantitative structure-activity relationship (MLR-QSAR) models of dietary flavonoids (2D descriptors and 3D descriptors optimized), with R² of 0.927 and 0.934, respectively, were constructed using genetic algorithms. Finally, the MolNatSim tool based on the COCONUT database was used to match the similarity of each flavonoid in this study, and to sequentially perform molecular enrichment, similarity screening, and QSAR prediction. After screening, five kinds of natural product molecule (2-(3,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, norartocarpetin, 2-(2,5-dihydroxyphenyl)-5,7-dihydroxy-4H-chromen-4-one, 2-(3,4-dihydroxyphenyl)-5-hydroxy-4H-chromen-4-one, and morelosin) were finally obtained. Their IC_50pre values were 8.977, 31.949, 78.566, 87.87, and 94.136 µM, respectively. Pharmacokinetic predictions evaluated the properties of the new natural products, such as bioavailability and toxicity. Molecular docking analysis revealed the interaction of candidate novel natural flavonoid compounds with the amino acid residues of α-glucosidase. Molecular dynamics (MD) simulations and molecular mechanics/generalized Born surface area (MMGBSA) further validated the stability of these novel natural compounds bound to α-glucosidase. The present findings may provide new directions in the search for novel natural α-glucosidase inhibitors.

The Role of Phosphorylation and Acylation in the Regulation of Drug Resistance in Mycobacterium tuberculosis

Tuberculosis is a chronic and lethal infectious disease caused by Mycobacterium tuberculosis. In previous decades, most studies in this area focused on the pathogenesis and drug targets for disease treatments. However, the emergence of drug-resistant strains has increased the difficulty of clinical trials over time. Now, more post-translational modified proteins in Mycobacterium tuberculosis have been discovered. Evidence suggests that these proteins have the ability to influence tuberculosis drug resistance. Hence, this paper systematically summarizes updated research on the impacts of protein acylation and phosphorylation on the acquisition of drug resistance in Mycobacterium tuberculosis through acylation and phosphorylation protein regulating processes. This provides us with a better understanding of the mechanism of antituberculosis drugs and may contribute to a reduction the harm that tuberculosis brings to society, as well as aiding in the discovery of new drug targets and therapeutic regimen adjustments in the future.