Faizul Azam1. 1. Department of Pharmaceutical Chemistry & Pharmacognosy, Unaizah College of Pharmacy, Qassim University, Unaizah 51911, Saudi Arabia.
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.
Teicoplanin is a n class="Chemical">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 nucleocapsidprotein 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-nucleocapsidprotein 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-19patients.
Authors: Arif Khan; Mohammed A Alsahli; Mohammad A Aljasir; Hamzah Maswadeh; Mugahid A Mobark; Faizul Azam; Khaled S Allemailem; Faris Alrumaihi; Fahad A Alhumaydhi; Ahmad A Almatroudi; Naif AlSuhaymi; Masood A Khan Journal: J Inflamm Res Date: 2022-04-08
Authors: Faris Alrumaihi; Masood Alam Khan; Ali Yousif Babiker; Mohammed Alsaweed; Faizul Azam; Khaled S Allemailem; Ahmad A Almatroudi; Syed Rizwan Ahamad; Naif AlSuhaymi; Mahdi H Alsugoor; Ahmed N Algefary; Arif Khan Journal: Pharmaceutics Date: 2022-01-20 Impact factor: 6.321
Authors: Faris Alrumaihi; Masood Alam Khan; Ali Yousif Babiker; Mohammed Alsaweed; Faizul Azam; Khaled S Allemailem; Ahmad A Almatroudi; Syed Rizwan Ahamad; Mahdi H Alsugoor; Khloud Nawaf Alharbi; Nahlah Makki Almansour; Arif Khan Journal: Molecules Date: 2022-03-28 Impact factor: 4.411
Authors: Arif Khan; Mohammed A Alsahli; Mohammad A Aljasir; Hamzah Maswadeh; Mugahid A Mobark; Faizul Azam; Khaled S Allemailem; Faris Alrumaihi; Fahad A Alhumaydhi; Ameen S S Alwashmi; Ahmed A Almatroudi; Mahdi H Alsugoor; Masood A Khan Journal: Pharmaceutics Date: 2022-01-09 Impact factor: 6.321