Chemo-informatics applications in the design of novel 7-keto-sempervirol derivatives as SmCB1 inhibitors with potential for treatment of Schistosomiasis

The quest for a sound treatment on the vulnerable population suffering and dying as a result of the blood flukes, S. mansoni is on the increase because both Praziquantel and Oxamniquine widely used for the treatment of Schistosomiasis for over 51 years suffer resistance and recurrence. Here-in, chemo-informatics techniques such as QSAR modeling, pharmacokinetic, docking alongside MD simulation were harnessed in designing novel 7-keto- sempevirolsempevirol derivatives that are more competent against S. mansoni. Upon QSAR screening, compound 15, which appears to be in the model's acceptability space, emerges the best with a high predicted activity. 5 new analogues with improved activity against Schistosomiasis better than the standard drug PZQ were designed from compound 15 (template 15*) on an account of the descriptors significance from the model with robust and validated parameters. Also their pharmacokinetic profiles indicates that the designed compounds have the characteristics of a good drug. Furthermore, docking evaluation fulfilled ranges from -113.121 to -100.79 kcal/mol (moldock score), with compound U1 being the best (least moldock score of -113.121 compared to PZQ and 15* (template) having a moldock score value of (-87.21 and -83.37 kcal/mol). 100-ns MD Simulation on the U1-docked complex was run using Desmond 2019-4 package. The nature and steadiness of U1 compound within the enzyme active site was further confirmed by RMSD, RMSF, RoG and H-bond assessment. Hence, we recommend compound U1 targeting the SmCB1 enzyme (6YI7) for Schistosomiasis treatment and for further medicinal evaluation and utilization.

Design and structural optimization of thiadiazole derivatives with potent GLS1 inhibitory activity

GLS1 is an attractive target not only as anticancer agents but also as candidates for various potential pharmaceutical applications such as anti-aging and anti-obesity treatments. We performed docking simulations based on the complex crystal structure of GLS1 and its inhibitor CB-839 and found that compound A bearing a thiadiazole skeleton exhibits GLS1 inhibition. Furthermore, we synthesized 27 thiadiazole derivatives in an effort to obtain a more potent GLS1 inhibitor. Among the synthesized derivatives, 4d showed more potent GLS1 inhibitory activity (IC50 of 46.7 µM) than known GLS1 inhibitor DON and A. Therefore, 4d is a very promising novel GLS1 inhibitor.

Purification and evaluation of 2, 4-di-tert butylphenol (DTBP) as a biocontrol agent against phyto-pathogenic fungi

A fungal strain, Marasmiellus sp (PUK64), isolated from the mangrove forests in Muthupet, Tamil Nadu, East coast of India, along with others were screened for the search of potent bioactive compounds. A phenolic compound, 2,4-di-tert-butylphenol (DTBP), was isolated from the most promising strain PUK64 and its chemical structure was ascertained. DTBP demonstrated remarkable antifungal activity against the phytopathogenic fungi Aspergillus oryzae, Curvularia lunata and Fusarium verticillioides. In an in-vitro experimental setup, DTBP suppressed the growth of all three fungi, among which F. verticillioides was found to be highly susceptible. This effect relates with the inhibition of spore germination and hyphal growth that we observed. DTBP showed high affinity with the F. verticillioides's β-tubulin protein (determined by ligand-protein docking) as compared to the standard fungicide carbendazim (CBZ). Molecular docking and simulation studies of DTBP with target β-tubulin further confirmed the potential of β-tubulin binding in F. verticillioides. To our knowledge, this is the first report on DTBP-mediated biocontrol of phytopathogenic fungi, produced by Marasmiellus sp. PUK64 that can be potent inhibitor of β-tubulin protein of F. verticillioides.

Multi-step structure-activity relationship screening efficiently predicts diverse PPARγ antagonists

In discovering the potential antagonist of peroxisome proliferator-activated receptor gamma (PPARγ), the structure-activity relationship (SAR) is a useful in silico method. However, it is difficult for conventional SAR approaches to predict the activities of antagonists owing to the large structural diversity of antagonistic compounds. This study provides evidence that multi-step SAR screening is applicable for predicting PPARγ antagonists by combining different complementary methodologies. We constructed three models: read-across-like SAR, docking-simulation-interpreting SAR, and deep-learning-based SAR. To provide user-customized prediction results, our multi-step SAR screening model combined the three SAR models in a stepwise manner, which subdivided them according to potential levels of the PPARγ antagonist. The read-across-like SAR, which considered specific antagonist scaffolds, revealed the highest positive predictive value (PPV). The docking-simulation-interpreting SAR, which considered the molecular surface features, revealed high statistics for the PPV and the true-positive rate (TPR). The deep-learning-based SAR showed the highest TPR at the last classification step. This multi-step SAR screening covered the antagonists of high reliability provided by a read-across-like SAR, as well as the antagonists of diverse scaffolds provided by docking-simulation-interpreting SAR and deep-learning-based SAR. Therefore, to predict PPARγ antagonists, multi-step SAR screening could be as a useful tool.