In silico Prediction of Mozenavir as potential drug for SARS-CoV-2 infection via Binding Multiple Drug Targets
Estari Mamidalaa, Rakesh Davella, Swapna Gurrapu, Munipally Praveen, Kumar, Abhiav

TL;DR
This study uses molecular docking and dynamics simulations to identify mozenavir as a promising drug candidate against SARS-CoV-2 by targeting key viral proteins, especially furin, suggesting potential for repurposing in COVID-19 treatment.
Contribution
It demonstrates the potential of mozenavir as a SARS-CoV-2 inhibitor through in silico methods, highlighting furin as a key target for antiviral development.
Findings
Mozenavir shows strong binding affinity to SARS-CoV-2 proteins.
Furin has the highest binding affinity among targets, indicating its potential as a drug target.
Molecular dynamics confirm the stability of mozenavir-protein complexes.
Abstract
Since the epidemic began in November 2019, no viable medicine against SARS-CoV-2 has been discovered. The typical medication discovery strategy requires several years of rigorous research and development as well as a significant financial commitment, which is not feasible in the face of the current epidemic. Through molecular docking and dynamic simulation studies, we used the FDA-approved drug mezonavir against the most important viral targets, including spike (S) glycoprotein, Transmembrane serine protease 2 (TMPRSS2), RNA-dependent RNA polymerase (RdRp), Main protease (Mpro), human angiotensin-converting enzyme 2 (ACE-2), and furin. These targets are critical for viral replication and infection propagation because they play a key role in replication/transcription and host cell recognition. Molecular docking revealed that the antiviral medication mozenavir showed a stronger affinity…
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Taxonomy
TopicsSARS-CoV-2 and COVID-19 Research · Computational Drug Discovery Methods · vaccines and immunoinformatics approaches
