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• W3215720799 abstract "Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is an emerging virus responsible for the ongoing Coronavirus Disease 19 (COVID-19) pandemic. Despite the advent of COVID-19 vaccines, pandemic fatigue is still escalating as new SARS-CoV-2 variants emerge and vaccine shortages hit globally. Hence, drug repurposing remains an alternative strategy to combat SARS-CoV-2. For centuries, plants have served as natural reservoirs of pharmacologically active compounds with minimal cytotoxicity and promising antimicrobial and antiviral activities. In this light, the present study was undertaken to virtually screen 33 phytochemicals across various cultivars against the main protease (Mpro) and Spike (S) protein of SARS-CoV-2 using ADME analysis. 31 phytochemicals obeying Lipinski’s rules were subjected to molecular docking using AutoDock Vina. Docking scores were determined by selecting the best conformation of the protein-ligand complex that exhibited the highest affinity. The study identified withanone, licoflavone A, and silibinin to interact with the S protein at the hACE2-binding site with high binding energies. Similarly, myricitrin, withanone, naringenin, licoflavone A, and silibinin exhibited high binding affinities with the substrate-binding pocket of Mpro between the domains I and II. Interestingly, licoflavone A, silibinin, and withanone interacted with both Mpro and S proteins in silico. Further, drug-likeness studies indicated withanone to be the most readily bioavailable phytochemicals among the three shortlisted ligands. Therefore, phytochemicals can be regarded as potential leads for developing inhibitors against this mysterious virus. In vitro investigations are further warranted to prove their antiviral efficacy." @default.
• W3215720799 created "2021-12-06" @default.
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• W3215720799 date "2021-12-01" @default.
• W3215720799 modified "2023-09-27" @default.
• W3215720799 title "Virtual Screening of Phytochemicals Targeting the Main Protease and Spike Protein of SARS-CoV-2: An In silico Approach" @default.
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• W3215720799 doi "https://doi.org/10.37871/jbres1357" @default.
• W3215720799 hasPublicationYear "2021" @default.
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