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?:abstract
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The coronavirus disease 2019 (COVID-19) pandemic, caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), presents an unprecedented challenge to global public health with researchers striving to find a possible therapeutic candidate that could limit the spread of the virus. In this context, the present study employed an in silico molecular interaction-based approach to estimate the inhibitory potential of the phytochemicals from ethnomedicinally relevant Indian plants including Justicia adhatoda, Ocimum sanctum and Swertia chirata, with reported antiviral activities against crucial SARS-CoV-2 proteins. SARS-CoV-2 proteins associated with host attachment and viral replication namely, spike protein, main protease enzyme M(pro) and RNA-dependent RNA polymerase (RdRp) are promising druggable targets for COVID-19 therapeutic research. Extensive molecular docking of the phytocompounds at the binding pockets of the viral proteins revealed their promising inhibitory potential. Subsequent assessment of physicochemical features and potential toxicity of the compounds followed by robust molecular dynamics simulations and analysis of MM-PBSA energy scoring function revealed anisotine against SARS-CoV-2 spike and M(pro) proteins and amarogentin against SARS-CoV-2 RdRp as potential inhibitors. It was interesting to note that these compounds displayed significantly higher binding energy scores against the respective SARS-CoV-2 proteins compared to the relevant drugs that are currently being targeted against them. Present research findings confer scopes to explore further the potential of these compounds in vitro and in vivo towards deployment as efficient SARS-CoV-2 inhibitors and development of novel effective therapeutics. Communicated by Ramaswamy H. Sarma
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?:doi
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10.1080/07391102.2020.1860133
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Journal_of_biomolecular_structure_&_dynamics
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document_parses/pdf_json/13916d8360b170427cb943ef3c67c2797b28a363.json
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document_parses/pmc_json/PMC7808002.xml.json
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?:title
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Anisotine and amarogentin as promising inhibitory candidates against SARS-CoV-2 proteins: a computational investigation
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