In silico screening of natural compounds to inhibit interaction of human ACE2 receptor and spike protein of SARS-CoV-2 for the prevention of COVID-19

Identification of Novel PI3Kα Inhibitor Against Gastric Cancer: QSAR-, Molecular Docking-, and Molecular Dynamics Simulation-Based Analysis

Gastric cancer (GC) is a malignant tumor with global incidence and death ranking fifth and fourth, respectively. GC patients nevertheless have a poor prognosis despite the effectiveness of more advanced chemotherapy and surgical treatment options. The second most frequently mutated gene in GC is PI3Kalpha, a confirmed oncogene that results in abnormal PI3K/AKT/mTOR signaling, causing enhanced translation, proliferation, and survival, and is mutated in 7-25% of GC patients. The protein PI3Kalpha was targeted in the present study by utilizing machine learning (ML), molecular docking, and simulation. A total of 9214 molecules from the DrugBank database were chosen for the first screening. A training set for 6770 compounds tested against PI3Kalpha was assessed to create a quantitative structure-activity relationship-based machine learning model using five different classification algorithms: random forest, random tree, J48 pruned tree, decision stump, and REPTree. Furthermore, consideration was given to the random forest classifier for screening based on its performance index (Kappa statistics, ROC, and MCC). Overall, 1539 of the 9214 drug bank compounds were predicted to be active. Thereafter, three pharmacological filters, Lipinski's rule, Ghose filter, and Veber rule, were applied to test the drug-like properties of the screened compounds. Twenty-six of 1593 compounds showed excellent drug-like properties and were further considered for molecular docking. Thereafter, two compounds were screened as hits because they possessed the molecular docked position with the lowest binding energy and an excellent bonding profile. The binding stability of the selected compounds was further assessed through molecular dynamics simulations for up to 100 ns. Furthermore, compound 1-(3-(2,4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea was selected as a potential hit in the final screening by analyzing a number of parameters, including the Rg, RMSD, RMSF, H bonding, and SASA profile. Therefore, we conclude that compound 1-(3-(2, 4-dimethylthiazol-5-YL)-4-oxo-2,4-dihydroindeno[1,2-C]pyrazol-5-YL)-3-(4-methylpiperazin-1-YL) urea has efficient inhibitory potential against PI3Kalpha protein and could be utilized for the development of effective drugs against GC.

Repurposing FIASMAs against Acid Sphingomyelinase for COVID-19: A Computational Molecular Docking and Dynamic Simulation Approach

Over the past few years, COVID-19 has caused widespread suffering worldwide. There is great research potential in this domain and it is also necessary. The main objective of this study was to identify potential inhibitors against acid sphingomyelinase (ASM) in order to prevent coronavirus infection. Experimental studies revealed that SARS-CoV-2 causes activation of the acid sphingomyelinase/ceramide pathway, which in turn facilitates the viral entry into the cells. The objective was to inhibit acid sphingomyelinase activity in order to prevent the cells from SARS-CoV-2 infection. Previous studies have reported functional inhibitors against ASM (FIASMAs). These inhibitors can be exploited to block the entry of SARS-CoV-2 into the cells. To achieve our objective, a drug library containing 257 functional inhibitors of ASM was constructed. Computational molecular docking was applied to dock the library against the target protein (PDB: 5I81). The potential binding site of the target protein was identified through structural alignment with the known binding pocket of a protein with a similar function. AutoDock Vina was used to carry out the docking steps. The docking results were analyzed and the inhibitors were screened based on their binding affinity scores and ADME properties. Among the 257 functional inhibitors, Dutasteride, Cepharanthine, and Zafirlukast presented the lowest binding affinity scores of −9.7, −9.6, and −9.5 kcal/mol, respectively. Furthermore, computational ADME analysis of these results revealed Cepharanthine and Zafirlukast to have non-toxic properties. To further validate these findings, the top two inhibitors in complex with the target protein were subjected to molecular dynamic simulations at 100 ns. The molecular interactions and stability of these compounds revealed that these inhibitors could be a promising tool for inhibiting SARS-CoV-2 infection.

Discovery of adapalene and dihydrotachysterol as antiviral agents for the Omicron variant of SARS-CoV-2 through computational drug repurposing

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has been significantly paralyzing the societies, economies and health care systems around the globe. The mutations on the genome of SARS-CoV-2 led to the emergence of new variants, some of which are classified as "variant of concern" due to their increased transmissibility and better viral fitness. The Omicron variant, as the latest variant of concern, dominated the current COVID-19 cases all around the world. Unlike the previous variants of concern, the Omicron variant has 15 mutations on the receptor-binding domain of spike protein and the changes in the key amino acid residues of S protein can enhance the binding ability of the virus to hACE2, resulting in a significant increase in the infectivity of the Omicron variant. Therefore, there is still an urgent need for treatment and prevention of variants of concern, particularly for the Omicron variant. In this study, an in silico drug repurposing was conducted through the molecular docking of 2890 FDA-approved drugs against the mutant S protein of SARS-CoV-2 for Omicron variant. We discovered promising drug candidates for the inhibition of alarming Omicron variant such as quinestrol, adapalene, tamibarotene, and dihydrotachysterol. The stability of ligands complexed with the mutant S protein was confirmed using MD simulations. The lead compounds were further evaluated for their potential use and side effects based on the current literature. Particularly, adapalene, dihydrotachysterol, levocabastine and bexarotene came into prominence due to their non-interference with the normal physiological processes. Therefore, this study suggests that these approved drugs can be considered as drug candidates for further in vitro and in vivo studies to develop new treatment options for the Omicron variant of SARS-CoV-2.

Virtual screening and molecular dynamics simulations provide insight into repurposing drugs against SARS-CoV-2 variants Spike protein/ACE2 interface

After over two years of living with Covid-19 and hundreds of million cases worldwide there is still an unmet need to find proper treatments for the novel coronavirus, due also to the rapid mutation of its genome. In this context, a drug repositioning study has been performed, using in silico tools targeting Delta Spike protein/ACE2 interface. To this aim, it has been virtually screened a library composed by 4388 approved drugs through a deep learning-based QSAR model to identify protein-protein interactions modulators for molecular docking against Spike receptor binding domain (RBD). Binding energies of predicted complexes were calculated by Molecular Mechanics/Generalized Born Surface Area from docking and molecular dynamics simulations. Four out of the top twenty ranking compounds showed stable binding modes on Delta Spike RBD and were evaluated also for their effectiveness against Omicron. Among them an antihistaminic drug, fexofenadine, revealed very low binding energy, stable complex, and interesting interactions with Delta Spike RBD. Several antihistaminic drugs were found to exhibit direct antiviral activity against SARS-CoV-2 in vitro, and their mechanisms of action is still debated. This study not only highlights the potential of our computational methodology for a rapid screening of variant-specific drugs, but also represents a further tool for investigating properties and mechanisms of selected drugs.

Mulberrofuran G, a Mulberry Component, Prevents SARS-CoV-2 Infection by Blocking the Interaction between SARS-CoV-2 Spike Protein S1 Receptor-Binding Domain and Human Angiotensin-Converting Enzyme 2 Receptor

Despite the recent development of RNA replication-targeted COVID-19 drugs by global pharmaceutical companies, their prescription in clinical practice is limited by certain factors, including drug interaction, reproductive toxicity, and drug resistance. COVID-19 drugs with multiple targets for the SARS-CoV-2 life cycle may lead to a successful reduction in drug resistance as well as enhanced therapeutic efficacy, and natural products are a potential source of molecules with therapeutic effects against COVID-19. In this study, we investigated the inhibitory efficacy of mulberrofuran G (MG), a component of Morus alba L., also known as mulberry, which has been used as food and traditional medicine, on the binding of the spike S1 receptor-binding domain (RBD) protein to the angiotensin-converting enzyme 2 (ACE2) receptor, which is the initial stage of the SARS-CoV-2 infection. In competitive enzyme-linked immunosorbent assays, MG effectively blocked the spike S1 RBD: ACE2 receptor molecular binding, and investigations using the BLItz system and in silico modeling revealed that MG has high affinity for both proteins. Finally, we confirmed that MG inhibits the entry of SARS-CoV-2 spike pseudotyped virus and a clinical isolate of SARS-CoV-2 into cells, suggesting that MG might be a promising therapeutic candidate for preventing SARS-CoV-2 binding to the cell surface during early infection.