Dataset Information

In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing.

ABSTRACT:

Background

In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than the original virus. More than half of those mutations were found in the receptor-binding domain (RBD) that directly interacts with human angiotensin-converting enzyme 2 (ACE2). This study aimed to discover potent drugs against Omicron, which were previously repurposed for coronavirus disease 2019 (COVID-19). All repurposed anti-COVID-19 drugs were compiled from previous studies and tested against the RBD of SARS-CoV-2 Omicron.

Methods

As a preliminary step, a molecular docking study was performed to investigate the potency of seventy-one compounds from four classes of inhibitors. The molecular characteristics of the best-performing five compounds were predicted by estimating the drug-likeness and drug score. Molecular dynamics simulations (MD) over 100 ns were performed to inspect the relative stability of the best compound within the Omicron receptor-binding site.

Results

The current findings point out the crucial roles of Q493R, G496S, Q498R, N501Y, and Y505H in the RBD region of SARS-CoV-2 Omicron. Raltegravir, hesperidin, pyronaridine, and difloxacin achieved the highest drug scores compared with the other compounds in the four classes, with values of 81%, 57%, 18%, and 71%, respectively. The calculated results showed that raltegravir and hesperidin had high binding affinities and stabilities to Omicron with ΔG_binding of - 75.7304 ± 0.98324 and - 42.693536 ± 0.979056 kJ/mol, respectively. Further clinical studies should be performed for the two best compounds from this study.

SUBMITTER: Mohamed EAR

PROVIDER: S-EPMC9939377 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing.

Mohamed Eslam A R EAR Abdel-Rahman Islam M IM Zaki Magdi E A MEA Al-Khdhairawi Ahmad A Abdelhamid Mahmoud M MM Alqaisi Ahmad M AM Rahim Lyana Binti Abd LBA Abu-Hussein Bilal B El-Sheikh Azza A K AAK Abdelwahab Sayed F SF Hassan Heba Ali HA

Journal of molecular modeling 20230220 3

<h4>Background</h4>In November 2021, variant B.1.1.529 of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was identified by the World Health Organization (WHO) and designated Omicron. Omicron is characterized by a high number of mutations, thirty-two in total, making it more transmissible than the original virus. More than half of those mutations were found in the receptor-binding domain (RBD) that directly interacts with human angiotensin-converting enzyme 2 (ACE2). This study aime ...[more]

PMID: 36808314

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Project description:BackgroundThe rapidly enlarging COVID-19 pandemic caused by the novel SARS-corona virus-2 is a global public health emergency of an unprecedented level. Unfortunately no treatment therapy or vaccine is yet available to counter the SARS-CoV-2 infection, which substantiates the need to expand research efforts in this direction. The indispensable function of the main protease in virus replication makes this enzyme a promising target for inhibitors screening and drug discovery to treat novel coronavirus infection. The recently concluded α-ketoamide ligand-bound X-ray crystal structure of SARS-CoV-2 Mpro (PDB ID: 6Y2F) from Zhang et al. has revealed the potential inhibitor binding mechanism and the molecular determinants responsible for substrate binding.MethodsFor the study, we have targeted the SARS-CoV-2 Mpro for the screening of FDA approved antiviral drugs and carried out molecular docking based virtual screening. Further molecular dynamic simulation studies of the top three selected drugs carried out to investigated for their binding affinity and stability in the SARS-CoV-2 Mpro active site. The phylogenetic analysis was also performed to know the relatedness between the SARS-CoV-2 genomes isolated from different countries.ResultsThe phylogenetic analysis of the SARS-CoV-2 genome reveals that the virus is closely related to the Bat-SL-CoV and does not exhibit any divergence at the genomic level. Molecular docking studies revealed that among the 77 drugs, screened top ten drugs shows good binding affinities, whereas the top three drugs: Lopinavir-Ritonavir, Tipranavir, and Raltegravir were undergone for molecular dynamics simulation studies for their conformational stability in the active site of the SARS-CoV-2 Mpro protein.ConclusionsIn the present study among the library of FDA approved antiviral drugs, the top three inhibitors Lopinavir-Ritonavir, Tipranavir, and Raltegravir show the best molecular interaction with the main protease of SARS-CoV-2. However, the in-vitro efficacy of the drug molecules screened in this study further needs to be corroborated by carrying out a biochemical and structural investigation.

Project description:SARS-CoV-2 is the virus responsible for the COVID-19 pandemic, and its effects on people worldwide continue to grow. Protein-targeted therapeutics are currently unavailable for this virus. As with other coronaviruses, the nucleocapsid (N) protein is the most conserved RNA-binding structural protein of SARS-CoV-2. The N protein is an appealing target because of its functional role in viral transcription and replication. Therefore, molecular docking method for structure-based drug design was used to investigate the binding energy and binding modes of various anti-N inhibitors in depth. The inhibitors selected were originally developed to target stress granules and other molecules involved in RNA biology, and were either FDA-approved or in the process of clinical trials for COVID-19. We aimed at targeting the N-terminal RNA binding domain (NTD) for molecular docking-based screening, on the basis of the first resolved crystal structure of SARS-CoV-2 N protein (PDB ID: 6M3M) and C-terminal domain (CTD) dimerization of the nucleocapsid phosphoprotein of SARS-COV-2 (PDB ID: 6WJI). Silmitasertib, nintedanib, ternatin, luteolin, and fedratinib were found to interact with RNA binding sites and to form a predicted protein interface with high binding energy. Similarly, silmitasertib, sirolimus-rapamycin, dovitinib, nintedanib, and fedratinib were found to interact with the SARS-CoV-2 N protein at its CTD dimerization sites, according to previous studies. In addition, we investigated an information gap regarding the relationships among the energetic landscape and stability and drug binding of the SARS-CoV-2 N NTD and CTD. Our in silico results clearly indicated that several tested drugs as potent putative inhibitors for COVID-19 therapeutics, thus indicating that they should be further validated as treatments to slow the spread of SARS-CoV-2. Supplementary Information The online version contains supplementary material available at 10.1007/s44229-022-00004-z.

Dataset Information

In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing.

Background

Methods

Results

Publications

In silico prediction of potential inhibitors for SARS-CoV-2 Omicron variant using molecular docking and dynamics simulation-based drug repurposing.

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