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- W3123572784 abstract "Abstract The unprecedented global pandemic of COVID-19 has created a daunting scenario urging an immediate generation of therapeutic strategy. Interventions to curb the spread of viral infection primarily include setting targets against the virus. Here in this study we target S protein to obstruct the viral attachment and entry and also the M pro to prevent the viral replication. For this purpose, the interaction of S protein and M pro with phytocompounds, sanguinarine and eugenol, and their derivatives were studied using computational tools. It is evident from the docking studies that 8-Hydroxydihydrosanguinarine, a derivative of sanguinarine, exhibits maximum binding affinity with both the targets. The binding energies of the ligand with S protein and M pro scored to be ΔGb-9.4 Kcal/mol and ΔGb-10.3 Kcal/mol respectively. MD simulation studies depict that the phytocompound could effectively cause structural perturbations in the targets which would affect their functions. 8-Hydroxydihydrosanguinarine distorts the α-helix in the secondary structure of M pro and RBD site of S protein. Protein-protein interaction study in presence of 8-hydroxydihydrosanguinarine (8-HDS) also corroborate the above findings which indicate that this polyphenol interfere in the coupling of S Protein and ACE2. The alterations in protonation of M pro suggest that the protein structure undergoes significant structural changes at neutral pH. ADME (Physicochemical, Lipophilicity, Water Solubility, Pharmacokinetics, Drug-likeness) property of 8- hydroxydihydrosanguinarine suggests that this could be a potential drug. This makes the phyto-alkaloid a possible therapeutic molecule for anti COVID-19 drug design." @default.
- W3123572784 created "2021-02-01" @default.
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- W3123572784 date "2021-01-27" @default.
- W3123572784 modified "2023-09-26" @default.
- W3123572784 title "8-Hydroxydihydrosanguinarine (8-HDS), a pyridone containing analogue of sanguinarine, can be a potential inhibitor of S protein and M protease of SARS CoV2: Insights from computational studies" @default.
- W3123572784 doi "https://doi.org/10.21203/rs.3.rs-153786/v1" @default.
- W3123572784 hasPublicationYear "2021" @default.
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