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?:abstract
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A novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) has emerged as the causative agent behind the coronavirus disease 2019 (COVID-19) pandemic. Treatment efforts have been severely impeded due to the lack of specific effective antiviral drugs for the treatment of COVID-associated pathologies. In the present research endeavour the inhibitory prospects of cyanobacterial metabolites were assessed at the active binding pockets of the two vital SARS-CoV-2 proteases namely, main protease (M(pro)) and the papain-like protease (PL(pro)) that proteolytically process viral polyproteins and facilitate viral replication, employing an in silico molecular interaction-based approach. It was evident from our analysis based on the binding energy scores that the metabolites cylindrospermopsin, deoxycylindrospermopsin, carrageenan, cryptophycin 52, eucapsitrione, tjipanazole, tolyporphin and apratoxin A exhibited promising inhibitory potential against the SARS-CoV-2 M(pro). The compounds cryptophycin 1, cryptophycin 52 and deoxycylindrospermopsin were observed to display encouraging binding energy scores with the PL(pro) of SARS-CoV-2. Subsequent estimation of physicochemical properties and potential toxicity of the metabolites followed by robust molecular dynamics simulations and analysis of MM-PBSA energy scoring function established deoxycylindrospermopsin as the most promising inhibitory candidate against both SARS-CoV-2 proteases. Present research findings bestow ample scopes to further exploit the potential of deoxycylindrospermopsin as a successful inhibitor of SARS-CoV-2 in vitro and in vivo and pave the foundation for the development of novel effective therapeutics against COVID-19. Communicated by Ramaswamy H. Sarma
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?:doi
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10.1080/07391102.2020.1794972
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?:journal
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Journal_of_Biomolecular_Structure_&_Dynamics
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document_parses/pdf_json/13af54e4c21aeada56bd0df80d61e87203941dd0.json
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document_parses/pmc_json/PMC7441779.xml.json
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?:title
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Cyanobacterial metabolites as promising drug leads against the M(pro) and PL(pro) of SARS-CoV-2: an in silico analysis
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