Unknown

Dataset Information

0

Evaluation of potential anti-RNA-dependent RNA polymerase (RdRP) drugs against the newly emerged model of COVID-19 RdRP using computational methods.


ABSTRACT:

Introduction

Despite all the efforts to treat COVID-19, no particular cure has been found for this virus. Since developing antiviral drugs is a time-consuming process, the most effective approach is to evaluate the approved and under investigation drugs using in silico methods. Among the different targets within the virus structure, as a vital component in the life cycle of coronaviruses, RNA-dependent RNA polymerase (RdRP) can be a critical target for antiviral drugs. The impact of the existence of RNA in the enzyme structure on the binding affinity of anti-RdRP drugs has not been investigated so far.

Methods

In this study, the potential anti-RdRP effects of a variety of drugs from two databases (Zinc database and DrugBank) were evaluated using molecular docking. For this purpose, the newly emerged model of COVID-19 (RdRP) post-translocated catalytic complex (PDB ID: 7BZF) that consists of RNA was chosen as the target.

Results

The results indicated that idarubicin (IDR), a member of the anthracycline antibiotic family, and fenoterol (FNT), a known beta-2 adrenergic agonist drug, tightly bind to the target enzyme and could be used as potential anti-RdRP inhibitors of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These outcomes revealed that due to the ligand-protein interactions, the presence of RNA in this structure could remarkably affect the binding affinity of inhibitor compounds.

Conclusion

In silico approaches, such as molecular docking, could effectively address the problem of finding appropriate treatment for COVID-19. Our results showed that IDR and FNT have a significant affinity to the RdRP of SARS-CoV-2; therefore, these drugs are remarkable inhibitors of coronaviruses.

SUBMITTER: Poustforoosh A 

PROVIDER: S-EPMC7895701 | biostudies-literature |

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC7544925 | biostudies-literature
| S-EPMC10118056 | biostudies-literature
| S-EPMC8011644 | biostudies-literature
| S-EPMC8840377 | biostudies-literature
| S-EPMC7934700 | biostudies-literature
| S-EPMC7441777 | biostudies-literature
| S-EPMC8520695 | biostudies-literature
| S-EPMC9038185 | biostudies-literature
| S-EPMC7102646 | biostudies-literature
| S-EPMC7502160 | biostudies-literature