Insights into structures of imidazo oxazines as potent polyketide synthase XIII inhibitors using molecular modeling techniques

Discovery of new oxadiazolo pyridine derivatives as potent ghrelin O-acyltransferase inhibitors using molecular modeling techniques

Diabesity is a major global health concern, and ghrelin O-acyltransferase (GOAT) acts as an important target for the development of new inhibitors of this disease. The present work highlights a detailed QSAR study using QSARINS software, which provides an excellent model equation using descriptors. Here, the best model equation developed has two variables, namely MLFER_E and XlogP, with statistical parameters R2 = 0.8433, LOF = 0.0793, CCCtr = 0.915, Q2LOO = 0.8303, Q2LMO = 0.8275, CCCcv = 0.9081, R2ext = 0.7712, and CCCext = 0.8668. A higher correlation of the key structural fragments with activity is validated by the developed QSAR model. Furthermore, molecular docking helped us identify the binding interactions. Thirty four new molecules with better predicted biological activity (pIC50) were designed. The binding energy of four compounds have shown higher binding activity into the membrane protein (PDB Id: 6BUG). Molecular dynamics simulation has established the stability of the protein-ligand complex over 100 ns. DFT and ADME-toxicity analyses also confirmed their drug-like properties. Based on our findings, we report that these new oxadiazolo pyridine derivatives lead to the development of potent candidates for further development.

Graphical abstract

METTL3-mediated HOTAIRM1 promotes vasculogenic mimicry in glioma via regulating IGFBP2 expression. METTL3 expression is high in glioma cells and tissues that stabilize and enhance HOTAIRM1 expression. This HOTAIRM1 then interacts with IGFBP2 which in turn promotes glioma cell malignancy and vasculogenic mimicry (VM) formation, thus providing a new direction for glioma therapy.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40203-023-00167-z.

Targeting polyketide synthase 13 for the treatment of tuberculosis

Tuberculosis (TB) is one of the most threatening diseases for humans, however, the drug treatment strategy for TB has been stagnant and inadequate, which could not meet current treatment needs. TB is caused by Mycobacterial tuberculosis, which has a unique cell wall that plays a crucial role in its growth, virulence, and drug resistance. Polyketide synthase 13 (Pks13) is an essential enzyme that catalyzes the biosynthesis of the cell wall and its critical role is only found in Mycobacteria. Therefore, Pks13 is a promising target for developing novel anti-TB drugs. In this review, we first introduced the mechanism of targeting Pks13 for TB treatment. Subsequently, we focused on summarizing the recent advance of Pks13 inhibitors, including the challenges encountered during their discovery and the rational design strategies employed to overcome these obstacles, which could be helpful for the development of novel Pks13 inhibitors in the future.

Quantitative structure activity relationship (QSAR) modeling study of some novel thiazolidine 4-one derivatives as potent anti-tubercular agents

This study aims to develop a QSAR model for Antitubercular activity. The quantitative structure-activity relationship (QSAR) approach predicted the thiazolidine-4-ones derivative's Antitubercular activity. For the QSAR study, 53 molecules with Antitubercular activity on H37Rv were collected from the literature. Compound structures were drawn by ACD/Labs ChemSketch. The energy minimization of the 2D structure was done using the MM2 force field in Chem3D pro. PaDEL Descriptor software was used to construct the molecular descriptors. QSARINS software was used in this work to develop the 2D QSAR model. A series of thiazolidine 4-one with MIC data were taken from the literature to develop the QSAR model. These compounds were split into a training set (43 compounds) and a test set (10 compounds). The PaDEL software calculated 2300 descriptors for this series of thiazolidine 4-one derivatives. The best predictive Model 4, which has R2 of 0.9092, R2adj of 0.8950 and LOF parameter of 0.0289 identify a preferred fit. The QSAR study resulted in a stable, predictive, and robust model representing the original dataset. In the QSAR equation, the molecular descriptor of MLFER_S, GATSe2, Shal, and EstateVSA 6 positively correlated with Antitubercular activity. While the SpMAD_Dzs 6 is negatively correlated with Antitubercular activity. The high polarizability, Electronegativities, Surface area contributions and number of Halogen atoms in the thiazolidine 4-one derivatives will increase the Antitubercular activity.

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.