Identification of lead compounds from large natural product library targeting 3C-like protease of SARS-CoV-2 using E-pharmacophore modelling, QSAR and molecular dynamics simulation

Anti-diabetic, anti-pancreatic lipase, and anti-protein glycation potential of Irvingia gabonensis stem bark extracts: in vitro and in silico studies

Diabetes mellitus is a chronic metabolic disorder that affects glucose, lipid, and protein metabolism. Targeting these metabolic derangements can optimize the therapeutic strategies for this disease. Utilizing in vitro and in silico models, this study investigated the ability of aqueous and ethanol extracts of Irvingia gabonensis to inhibit α-amylase, α-glucosidase, pancreatic lipase, and protein glycation. High-performance liquid chromatography (HPLC) was used to identify the compounds found in the stem bark of I. gabonensis. In silico analysis determined the binding mode and mechanism of interactions between the enzymes and phytochemicals. With an IC50 value of 11.47 µg/ml, the aqueous extract demonstrated higher inhibitory efficacy against α-amylase compared to the ethanol extract (IC50 19.88 µg/ml). However, the ethanol extract had stronger inhibitory activities against α-glucosidase, pancreatic lipase, and protein glycation compared to the aqueous extract (IC50 values of 3.05, 32.85, 0.0014 versus 25.72, 332.42, 0.018 µg/ml respectively). Quercetin ranked highest in binding energy with α-amylase (-6.6 kcal/mol), α-glucosidase (-6.6 kcal/mol), and pancreatic lipase (-5.6 kcal/mol). This was followed by rhamnetin (6.5, 6.5, and 6.1 kcal/mol respectively). Hydrogen bonding, hydrophobic interactions, and pi-pi stacking are forces responsible for the binding of quercetin and rhamnetin to these enzymes. Molecular dynamics simulation showed that the lead phytochemicals formed stable and energetically stabilized complexes with the target proteins. This study showed that the extracts of I. gabonensis stem bark had significant in vitro anti-diabetic, anti-pancreatic lipase, and anti-protein glycation activities. The strong binding affinities of some of the identified compounds could be responsible for the inhibitory potential of the extracts. I. gabonensis stem bark could be further explored as a natural remedy for the treatment of diabetes mellitus and its complications.

High-throughput screening and in vitro evaluation of CSB-0914; a novel small molecule NF-κB inhibitor attenuating inflammatory responses through NF-κB, Nrf2 and HO-1 cross-talk

Unpleasant side effects of standard inflammatory drugs urges search for novel therapeutic candidates. This study aims in identifying novel anti-inflammatory NF-κB inhibitor by high-throughput computational and in-vitro pre-clinical approaches. Lead candidate selection was conducted by the use of computational docking molecular-dynamic simulations. The RBL-2H3 cell line, derived from rat basophils, was used to evaluate the release of cytokines and degranulation. The study focused on the study of neutrophil elastase and its role in cellular motility. Flow cytometry was utilized to evaluate the activation of basophils and the expression of critical signaling proteins. High throughput screening identified CSB-0914 to stably bind NF-κB-p50 subunit. Dose based loss in T NF-α and IL-2 release were observed in RBL-2H3 cells in addition to degranulation inhibition by CSB-0914. The compound demonstrated significant efficacy in reducing basophil activation assay induced by FcεRI receptors, with an IC50 value of 98.41 nM.. A dose dependent decrease in neutrophil migration and elastase were observed when treated with CSB- 0914. The compound was effective in decreasing. Upon stimulation, RBL-2H3 cells exhibited phosphorylation of NF-κB p-65 as well as upregulation of the Nrf2 and HO-1 signaling pathways. Collectively, our study has successfully identified a novel inhibitor called CSB-0914 that effectively regulates inflammatory responses. These reactions are primarily mediated by the interplay between NF-κB, Nrf2, and HO-1. The findings of this study provide support for the need to conduct more research on CSB-0914 with the aim of its development as a pharmaceutical agent for anti-inflammatory purposes.Communicated by Ramaswamy H. Sarma.

Study on the molecular mechanism of anti-liver cancer effect of Evodiae fructus by network pharmacology and QSAR model

Introduction: Evodiae Fructus (EF) is the dried, near ripe fruit of Euodia rutaecarpa (Juss.) Benth in Rutaceae. Numerous studies have demonstrated its anti-liver cancer properties. However, the molecular mechanism of Evodiae fructus against liver cancer and its structure-activity connection still require clarification. Methods: We utilized network pharmacology and a QSAR (2- and 3-dimensional) model to study the anti-liver cancer effect of Evodiae fructus. First, by using network pharmacology to screen the active substances and targets of Evodiae fructus, we investigated the signaling pathways involved in the anti-liver cancer actions of Evodiae fructus. The 2D-QSAR pharmacophore model was then used to predict the pIC50 values of compounds. The hiphop method was used to create an ideal 3D-QSAR pharmacophore model for the prediction of Evodiae fructus compounds. Finally, molecular docking was used to validate the rationality of the pharmacophore, and molecular dynamics was used to disclose the stability of the compounds by assessing the trajectories in 10 ns using RMSD, RMSF, Rg, and hydrogen bonding metrics. Results: In total, 27 compounds were acquired from the TCMSP and TCM-ID databases, and 45 intersection targets were compiled using Venn diagrams. Network integration analysis was used in this study to identify SRC as a primary target. Key pathways were discovered by KEGG pathway analysis, including PD-L1 expression and PD-1 checkpoint pathway, EGFR tyrosine kinase inhibitor resistance, and ErbB signaling pathway. Using a 2D-QSAR pharmacophore model and the MLR approach to predict chemical activity, ten highly active compounds were found. Two hydrophobic features and one hydrogen bond acceptor feature in the 3D-QSAR pharmacophore model were validated by training set chemicals. The results of molecular docking revealed that 10 active compounds had better docking scores with SRC and were linked to residues via hydrogen and hydrophobic bonds. Molecular dynamics was used to show the structural stability of obacunone, beta-sitosterol, and sitosterol. Conclusion:Pharmacophore 01 has high selectivity and the ability to distinguish active and inactive compounds, which is the optimal model for this study. Obacunone has the optimal binding ability with SRC. The pharmacophore model proposed in this study provides theoretical support for further screening effective anti-cancer Chinese herbal compounds and optimizing the compound structure.

The Development of Pharmacophore Models for the Search of New Natural Inhibitors of SARS-CoV-2 Spike RBD-ACE2 Binding Interface

To date, some succeeding variants of SARS-CoV-2 have become more contagious. This virus is known to enter human cells by binding the receptor-binding domain (RBD) of spike protein with the angiotensin-converting enzyme 2 (ACE2), the latter being a membrane protein that regulates the renin-angiotensin system. Since the host cell receptor plays a critical role in viral entry, inhibition of the RBD-ACE2 complex is a promising strategy for preventing COVID-19 infection. In the present communication, we propose and utilize an approach based on the generation of a complex of pharmacophore models and subsequent Induced Fit Docking (IFD) to identify potential inhibitors of the main binding sites of the Omicron SARS-CoV-2 RBD(S1)-ACE2 complex (PDB ID: 7T9L) among a number of natural products of various types and origins. Several natural compounds have been found to provide a high affinity for the receptor of interest. It is expected that the present results will stimulate further research aimed at the development of specialized drugs against this virus.

A review on computational approaches that support the researches on traditional Chinese medicines (TCM) against COVID-19

BACKGROUND

COVID-19 highly caused contagious infections and massive deaths worldwide as well as unprecedentedly disrupting global economies and societies, and the urgent development of new antiviral medications are required. Medicinal herbs are promising resources for the discovery of prophylactic candidate against COVID-19. Considerable amounts of experimental efforts have been made on vaccines and direct-acting antiviral agents (DAAs), but neither of them was fast and fully developed.

PURPOSE

This study examined the computational approaches that have played a significant role in drug discovery and development against COVID-19, and these computational methods and tools will be helpful for the discovery of lead compounds from phytochemicals and understanding the molecular mechanism of action of TCM in the prevention and control of the other diseases.

METHODS

A search conducting in scientific databases (PubMed, Science Direct, ResearchGate, Google Scholar, and Web of Science) found a total of 2172 articles, which were retrieved via web interface of the following websites. After applying some inclusion and exclusion criteria and full-text screening, only 292 articles were collected as eligible articles.

RESULTS

In this review, we highlight three main categories of computational approaches including structure-based, knowledge-mining (artificial intelligence) and network-based approaches. The most commonly used database, molecular docking tool, and MD simulation software include TCMSP, AutoDock Vina, and GROMACS, respectively. Network-based approaches were mainly provided to help readers understanding the complex mechanisms of multiple TCM ingredients, targets, diseases, and networks.

CONCLUSION

Computational approaches have been broadly applied to the research of phytochemicals and TCM against COVID-19, and played a significant role in drug discovery and development in terms of the financial and time saving.

Methodology-Centered Review of Molecular Modeling, Simulation, and Prediction of SARS-CoV-2

Despite tremendous efforts in the past two years, our understanding of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), virus-host interactions, immune response, virulence, transmission, and evolution is still very limited. This limitation calls for further in-depth investigation. Computational studies have become an indispensable component in combating coronavirus disease 2019 (COVID-19) due to their low cost, their efficiency, and the fact that they are free from safety and ethical constraints. Additionally, the mechanism that governs the global evolution and transmission of SARS-CoV-2 cannot be revealed from individual experiments and was discovered by integrating genotyping of massive viral sequences, biophysical modeling of protein-protein interactions, deep mutational data, deep learning, and advanced mathematics. There exists a tsunami of literature on the molecular modeling, simulations, and predictions of SARS-CoV-2 and related developments of drugs, vaccines, antibodies, and diagnostics. To provide readers with a quick update about this literature, we present a comprehensive and systematic methodology-centered review. Aspects such as molecular biophysics, bioinformatics, cheminformatics, machine learning, and mathematics are discussed. This review will be beneficial to researchers who are looking for ways to contribute to SARS-CoV-2 studies and those who are interested in the status of the field.

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.