Computational Prediction of the Potential Target of SARS-CoV-2 Inhibitor Plitidepsin via Molecular Docking, Dynamic Simulations and MM-PBSA Calculations.
Chem Biodivers
; 19(2): e202100719, 2022 Feb.
Article
in English
| MEDLINE | ID: covidwho-1527422
ABSTRACT
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) replication depends on the interaction between the viral proteins and the human translation machinery. The cytotoxic peptide plitidepsin was found to inhibit CoV-2 up to 90 % at a concentration of 0.88â
nM. In vitro studies suggest that this activity may be attributed to the inhibition of the eukaryotic translation elongation factor 1A (eEF1A). However, recent reports raised the potential for other cellular targets which plitidepsin may use to exert its potent antiviral activity. The lack of data about these potential targets represents a major limitation for its structural optimization. This work describes the use of a molecular modeling approach to rationalize the inâ
vitro antiviral activity of plitidepsin and to identify potential cellular targets. The developed protocol involves an initial molecular docking step followed by molecular dynamics and binding free energy calculations. The results reveal the potential for plitidepsin to bind to the active site of the key enzyme SARS-CoV-2 RdRp. The results also highlight the importance of van der Waals interactions for proper binding with the enzyme. We believe that the results presented in this study could provide the grounds for the optimization of plitidepsin analogs as SARS-CoV-2 inhibitors.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
Antiviral Agents
/
Peptides, Cyclic
/
Depsipeptides
/
SARS-CoV-2
Type of study:
Prognostic study
Limits:
Humans
Language:
English
Journal:
Chem Biodivers
Journal subject:
Biochemistry
/
Chemistry
Year:
2022
Document Type:
Article
Affiliation country:
Cbdv.202100719
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