Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) emergence has resulted in a global health crisis. As a consequence, discovering an effective therapy that saves lives and slows the spread of the pandemic is a global concern currently.
In silico
drug repurposing is highly regarded as a precise computational method for obtaining fast and reliable results. Transmembrane serine-type 2 (TMPRSS2) is a SARS CoV-2 enzyme that is essential for viral fusion with the host cell. Inhibition of TMPRSS2 may block or lessen the severity of SARS-CoV-2 infection. In this study, we aimed to perform an
in silico
drug repurposing to identify drugs that can effectively inhibit SARS-CoV-2 TMPRSS2. As there is no 3D structure of TMPRSS2 available, homology modeling was performed to build the 3D structure of human TMPRSS2. 3848 world-approved drugs were screened against the target. Based on docking scores and visual outcomes, the best-fit drugs were chosen. Molecular dynamics (MD) and density functional theory (DFT) studies were also conducted. Five potential drugs (Amikacin, isepamicin, butikacin, lividomycin, paromomycin) exhibited promising binding affinities. In conclusion, these findings empower purposing these agents.
The current novel corona virus illness (COVID-19) is a developing viral disease that was discovered in 2019. There is currently no viable therapeutic strategy for this illness management. Because traditional medication development and discovery has lagged behind the threat of emerging and re-emerging illnesses like Ebola, MERS-CoV, and, more recently, SARS-CoV-2. Drug developers began to consider drug repurposing (or repositioning) as a viable option to the more traditional drug development method. The goal of drug repurposing is to uncover new uses for an approved or investigational medicine that aren't related to its original use. The main benefits of this strategy are that there is less developmental risk and that it takes less time because the safety and pharmacologic requirements are met. The main protease (Mpro) of corona viruses is one of the well-studied and appealing therapeutic targets. As a result, the current research examines the molecular docking of Mpro (PDB ID:
5R81
) conjugated repurposed drugs. 12,432 approved drugs were collected from ChEMBL and drugbank libraries, and docked separately into the receptor grid created on 5R81, using the three phases of molecular docking including high throughput virtual screening (HTVS), standard precision (SP), and extra precision (XP). Based on docking scores and MM-GBSA binding free energy calculation, top three drugs (kanamycin, sulfinalol and carvedilol) were chosen for further analyses for molecular dynamic simulations.
Breast cancer (BC) is one of the main types of cancer that endangers women’s lives. The characteristics of triple-negative breast cancer (TNBC) include a high rate of recurrence and the capacity for metastasis; therefore, new therapies are urgently needed to combat TNBC. Dual targeting HDAC6 and Hsp90 has shown good synergistic effects in treating metastatic TNBC. The goal of this study was to find potential HDAC6 and Hsp90 dual inhibitors. Therefore, several in silico approaches have been used. An e-pharmacophore model generation based on the HDAC6-ligand complex and subsequently a pharmacophore-based virtual screening on 270,450 natural compounds from the ZINC were performed, which resulted in 12,663 compounds that corresponded to the obtained pharmacophoric hypothesis. These compounds were docked into HDAC6 and Hsp90. This resulted in the identification of three compounds with good docking scores and favorable free binding energy against the two targets. The top three compounds, namely ZINC000096116556, ZINC000020761262, and ZINC000217668954, were further subjected to ADME prediction and molecular dynamic simulations, which showed promising results in terms of pharmacokinetic properties and stability. As a result, these three compounds can be considered potential HDAC6 and Hsp90 dual inhibitors and are recommended for experimental evaluation.
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