A systematic drug repurposing approach to identify promising inhibitors from FDA-approved drugs against Nsp4 protein of SARS-CoV-2.

COVID-19 is caused by SARS-CoV-2 and responsible for the ongoing global pandemic in the world. After more than a year, we are still in lurch to combat and control the situation. Therefore, new therapeutic options to control the ongoing COVID-19 are urgently in need. In our study, we found that nonstructural protein 4 (Nsp4) of SARS-CoV-2 could be a potential target for drug repurposing. Due to availability of only the crystal structure of C-terminal domain of Nsp4 (Ct-Nsp4) and its crucial participation in viral RNA synthesis, we have chosen Ct-Nsp4 as a target for screening the 1600 FDA-approved drugs using molecular docking. Top 102 drugs were found to have the binding energy equal or less than -7.0 kcal/mol. Eribulin and Suvorexant were identified as the two most promising drug molecules based on the docking score. The dynamics of Ct-Nsp4-drug binding was monitored using 100 ns molecular dynamics simulations. From binding free energy calculation over the simulation, both the drugs were found to have considerable binding energy. The present study has identified Eribulin and Suvorexant as promising drug candidates. This finding will be helpful to accelerate the drug discovery process against COVID-19 disease.Communicated by Ramaswamy H. Sarma.

Hybrid in Silico Drug Discovery Study toward the Development of Oral Antivirals for COVID-19

Hybrid in silico drug discovery was performed by combining large-scale quantum molecular dynamics (QMD) simulations with the conventional in silico drug discovery, focusing on developing covalent inhibitors against the main protease (M-pro) of SARS-CoV-2, the virus responsible for ongoing COVID-19 pandemic. The crystal structures and instantaneous structures obtained from the large-scale QMD simulations for M-pro were used as receptors in ensemble docking to estimate the binding affinities of the four ligands: the natural substrate recognized by M-pro, that recognized by the other enzyme of SARS-CoV-2, approved covalent inhibitor (PF-07321332), and the new candidate compound X determined from virtual screening. The present result shows that the binding affinity of X was comparable to that of PF-07321332, demonstrating the potency of our drug discovery.

In-silico drug repurposing and molecular dynamics puzzled out potential SARS-CoV-2 main protease inhibitors.

Herein, the DrugBank database which contains 10,036 approved and investigational drugs was explored deeply for potential drugs that target SARS-CoV-2 main protease (Mpro). Filtration process of the database was conducted using three levels of accuracy for molecular docking calculations. The top 35 drugs with docking scores > -11.0 kcal/mol were then subjected to 10 ns molecular dynamics (MD) simulations followed by molecular mechanics-generalized Born surface area (MM-GBSA) binding energy calculations. The results showed that DB02388 and Cobicistat (DB09065) exhibited potential binding affinities towards Mpro over 100 ns MD simulations, with binding energy values of -49.67 and -46.60 kcal/mol, respectively. Binding energy and structural analyses demonstrated the higher stability of DB02388 over Cobicistat. The potency of DB02388 and Cobicistat is attributed to their abilities to form several hydrogen bonds with the essential amino acids inside the active site of Mpro. Compared to DB02388 and Cobicistat, Darunavir showed a much lower binding affinity of -34.83 kcal/mol. The present study highlights the potentiality of DB02388 and Cobicistat as anti-COVID-19 drugs for clinical trials. Communicated by Ramaswamy H. Sarma.

Application of Tensor Decomposition to Gene Expression of Infection of Mouse Hepatitis Virus Can Identify Critical Human Genes and Efffective Drugs for SARS-CoV-2 Infection.

To better understand the genes with altered expression caused by infection with the novel coronavirus strain SARS-CoV-2 causing COVID-19 infectious disease, a tensor decomposition (TD)-based unsupervised feature extraction (FE) approach was applied to a gene expression profile dataset of the mouse liver and spleen with experimental infection of mouse hepatitis virus, which is regarded as a suitable model of human coronavirus infection. TD-based unsupervised FE selected 134 altered genes, which were enriched in protein-protein interactions with orf1ab, polyprotein, and 3C-like protease that are well known to play critical roles in coronavirus infection, suggesting that these 134 genes can represent the coronavirus infectious process. We then selected compounds targeting the expression of the 134 selected genes based on a public domain database. The identified drug compounds were mainly related to known antiviral drugs, several of which were also included in those previously screened with an in silico method to identify candidate drugs for treating COVID-19.

Computational-based drug repurposing methods in COVID-19.

COVID-19, as a newly emerging disease, has disrupted human's different activities. Hence, it is essential to develop drugs or vaccines in order to control COVID-19. Since there is not a medication or vaccine for treating the disease and drug development project is a time and cost consuming process, drug repurposing approaches may yield to proper curing plans. However, there are some limitations in this field, which make the process a challenging one. This letter aims to introduce drug repurposing methods and the existing challenges to detect candidate drugs which may be helpful in controlling COVID-19.