Abstract
Teicoplanin is a glycopeptide antibiotic effective against several bacterial infections, has exhibited promising therapeutic efficiency against COVID-19 in vitro, and the rationale for its use in COVID-19 is yet to be recognized. Hence, in this study a number of molecular modeling techniques were employed to decrypt the mechanistic insight of teicoplanin interaction with several COVID-19 drug targets. Initially, molecular docking was employed to study the teicoplanin interaction with twenty-five SARS-CoV-2 structural and non-structural proteins which was followed by molecular mechanics/generalized Born surface area (MM/GBSA) computation for binding energy predictions of top ten models from each target. Amongst all macromolecular targets, the N-terminal domain of the nucleocapsid protein displayed the strongest affinity with teicoplanin showing binding energies of −7.4 and −102.13 kcal/mol, in docking and Prime MM/GBSA, respectively. Thermodynamic stability of the teicoplanin-nucleocapsid protein was further probed by molecular dynamics simulations of protein–ligand complex as well as unbounded protein in 100 ns trajectories. Post-simulation MM-GBSA computation of 50 frames extracted from simulated trajectories estimated an average binding energy of −62.52 ± 12.22 kcal/mol. In addition, conformational state of protein in complex with docked teicoplanin displayed stable root-mean-square deviation/fluctuation. In conclusion, computational investigation of the potential targets of COVID-19 and their interaction mechanism with teicoplanin can guide the design of novel therapeutic armamentarium for the treatment of SARS-CoV-2 infection. However, additional studies are warranted to establish the clinical use or relapses, if any, of teicoplanin in the therapeutic management of COVID-19 patients.
Highlights
Accepted: 12 May 2021The outbreak of COVID-19 pandemic in China, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) primarily spreads through close contact amongst people by sneezing, coughing, or by communicating verbally
Several computational techniques such as molecular docking, molecular mechanics/generalized Born surface area (MM-GBSA), and molecular dynamics were exploited toBorn inspect interactions between docking, simulation molecular mechanics/generalized surfacethe areabinding (MM-GBSA), and molecular dynamics and simulation weredrug exploited to inspect the binding between teicoplanin potential targets associated withinteractions
AutoDock Vina used in this study was deemed reliable for studying teicoplanin interaction with potential SARS-CoV-2 targets
Summary
The outbreak of COVID-19 pandemic in China, caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) primarily spreads through close contact amongst people by sneezing, coughing, or by communicating verbally. A widely available FDA-approved antibiotic, teicoplanin (Figure 1), is among the molecule of interest as probable COVID-19 medicine. It belongs to the glycopeptide class of antibiotic having low toxicity profile in humans and routinely used in clinical practice for the treatment of bacterial infections. It has demonstrated antiviral efficacy against several kinds of viruses such as Published: 15 July 2021. Antibiotics 2021, 10, 856 humans and routinely used in clinical practice for the treatment of bacterial infections It has demonstrated antiviral efficacy against several kinds of viruses.
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