Exploration of the structural requirements of Aurora Kinase B inhibitors by a combined QSAR, modelling and molecular simulation approach

Sesame oil ameliorates valproic acid-induced hepatotoxicity in mice: integrated in vivo-in silico study

Sesame oil (SO) has been exhibited to have anti-inflammatory and antioxidant influences. The goal of this experiment was to look into SO's hepato-protective properties and underlying processes in valproic acid (VPA)-induced hepatotoxicity. Molecular docking was carried out to clarify the functional and structural underlying mechanism of SO ameliorative effect. Mice were given 8 mL/kg/day of SO (orally) and 100 mg/kg/day of VPA (i.p.) for 21 days. The results revealed that VPA caused a considerable increase in hepatic malondialdehyde levels while decreasing the activity of glutathione peroxidase (GPx) enzyme. There was also a significant rise in serum levels of interleukins 1β and 6 (IL-1β and IL-6) and a significant decrease in hepatic (PXR) gene expression level. SO co-administration with VPA significantly normalized the antioxidant and anti-inflammatory status and upregulated the gene expression level of PXR. In silico docking analysis results confirmed these results. This study concluded that supplementation of SO attenuated VPA-induced oxidative stress and inflammation. Hence, it was recommended as a dietary supplement for protection against VPA-induced hepatotoxicity.Communicated by Ramaswamy H. Sarma.

Identification of novel inhibitors for SARS-CoV-2 as therapeutic options using machine learning-based virtual screening, molecular docking and MD simulation

The new coronavirus SARS-COV-2, which emerged in late 2019 from Wuhan city of China was regarded as causing agent of the COVID-19 pandemic. The primary protease which is also known by various synonymous i.e., main protease, 3-Chymotrypsin-like protease (3CL^PRO) has a vital role in the replication of the virus, which can be used as a potential drug target. The current study aimed to identify novel phytochemical therapeutics for 3CL^PRO by machine learning-based virtual screening. A total of 4,000 phytochemicals were collected from deep literature surveys and various other sources. The 2D structures of these phytochemicals were retrieved from the PubChem database, and with the use of a molecular operating environment, 2D descriptors were calculated. Machine learning-based virtual screening was performed to predict the active phytochemicals against the SARS-CoV-2 3CL^PRO. Random forest achieved 98% accuracy on the train and test set among the different machine learning algorithms. Random forest model was used to screen 4,000 phytochemicals which leads to the identification of 26 inhibitors against the 3CL^PRO. These hits were then docked into the active site of 3CL^PRO. Based on docking scores and protein-ligand interactions, MD simulations have been performed using 100 ns for the top 5 novel inhibitors, ivermectin, and the APO state of 3CL^PRO. The post-dynamic analysis i.e,. Root means square deviation (RMSD), Root mean square fluctuation analysis (RMSF), and MM-GBSA analysis reveal that our newly identified phytochemicals form significant interactions in the binding pocket of 3CL^PRO and form stable complexes, indicating that these phytochemicals could be used as potential antagonists for SARS-COV-2.

Pharmacophore Synergism in Diverse Scaffold Clinches in Aurora Kinase B

Aurora kinase B (AKB) is a crucial signaling kinase with an important role in cell division. Therefore, inhibition of AKB is an attractive approach to the treatment of cancer. In the present work, extensive quantitative structure-activity relationships (QSAR) analysis has been performed using a set of 561 structurally diverse aurora kinase B inhibitors. The Organization for Economic Cooperation and Development (OECD) guidelines were used to develop a QSAR model that has high statistical performance (R²_tr = 0.815, Q²_LMO = 0.808, R²_ex = 0.814, CCC_ex = 0.899). The seven-variable-based newly developed QSAR model has an excellent balance of external predictive ability (Predictive QSAR) and mechanistic interpretation (Mechanistic QSAR). The QSAR analysis successfully identifies not only the visible pharmacophoric features but also the hidden features. The analysis indicates that the lipophilic and polar groups-especially the H-bond capable groups-must be present at a specific distance from each other. Moreover, the ring nitrogen and ring carbon atoms play important roles in determining the inhibitory activity for AKB. The analysis effectively captures reported as well as unreported pharmacophoric features. The results of the present analysis are also supported by the reported crystal structures of inhibitors bound to AKB.

A Deep Learning-Based Quantitative Structure-Activity Relationship System Construct Prediction Model of Agonist and Antagonist with High Performance

Molecular design and evaluation for drug development and chemical safety assessment have been advanced by quantitative structure–activity relationship (QSAR) using artificial intelligence techniques, such as deep learning (DL). Previously, we have reported the high performance of prediction models molecular initiation events (MIEs) on the adverse toxicological outcome using a DL-based QSAR method, called DeepSnap-DL. This method can extract feature values from images generated on a three-dimensional (3D)-chemical structure as a novel QSAR analytical system. However, there is room for improvement of this system’s time-consumption. Therefore, in this study, we constructed an improved DeepSnap-DL system by combining the processes of generating an image from a 3D-chemical structure, DL using the image as input data, and statistical calculation of prediction-performance. Consequently, we obtained that the three prediction models of agonists or antagonists of MIEs achieved high prediction-performance by optimizing the parameters of DeepSnap, such as the angle used in the depiction of the image of a 3D-chemical structure, data-split, and hyperparameters in DL. The improved DeepSnap-DL system will be a powerful tool for computer-aided molecular design as a novel QSAR system.

Identification of polypharmacological anticancerous molecules against Aurora kinase family of proteins

The Human Aurora Kinase (AURK) protein family is the key player of cell cycle events including spindle assembly, kinetochore formation, chromosomal segregation, centrosome separation, microtubule dynamics, and cytokinesis. Their aberrant expression has been extensively linked with chromosomal instability in addition to derangement of multiple tumor suppressors and oncoprotein regulated pathways. Therefore, the AURK family of kinases is a promising target for the treatment of various types of cancer. Over the past few decades, several potential inhibitors of AURK proteins have been identified and have reached various phases of clinical trials. But very few molecules have currently crossed the safety criteria due to their various toxic side effects. In the present study, we have adopted a computational polypharmacological strategy and identified four novel molecules that can target all three AURKs. These molecules were further investigated for their binding stabilities at the ATP binding pocket using molecular dynamics based simulation studies. The molecules selected adopting a multipronged computational approach can be considered as potential AURKs inhibitors for cancer therapeutics.