Computational studies on potential new anti-Covid-19 agents with a multi-target mode of action

Ranjan K. Mohapatra, Mohammad Azam, Pranab K. Mohapatra, Ashish K. Sarangi, Mohnad Abdalla, Lina Perekhoda, Oval Yadav, Saud I. Al-Resayes, Kim Jong-Doo, Kuldeep Dhama, Azaj Ansari, Veronique Seidel, Sarika Verma, Mukesh K. Raval

Research output: Contribution to journalArticlepeer-review

16 Citations (Scopus)
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Abstract

A compound that could inhibit multiple targets associated with SARS-CoV-2 infection would prove to be a drug of choice against the virus. Human receptor-ACE2, receptor binding domain (RBD) of SARS-CoV-2 S-protein, Papain-like protein of SARS-CoV-2 (PLpro), reverse transcriptase of SARS-CoV-2 (RdRp) were chosen for in silico study. A set of previously synthesized compounds (1–5) were docked into the active sites of the targets. Based on the docking score, ligand efficiency, binding free energy, and dissociation constants for a definite conformational position of the ligand, inhibitory potentials of the compounds were measured. The stability of the protein–ligand (P-L) complex was validated in silico by using molecular dynamics simulations using the YASARA suit. Moreover, the pharmacokinetic properties, FMO and NBO analysis were performed for ranking the potentiality of the compounds as drug. The geometry optimizations and electronic structures were investigated using DFT. As per the study, compound-5 has the best binding affinity against all four targets. Moreover, compounds 1, 3 and 5 are less toxic and can be considered for oral consumption.

Original languageEnglish
Article number102086
Number of pages8
JournalJournal of King Saud University - Science
Volume34
Issue number5
Early online date13 May 2022
DOIs
Publication statusPublished - 31 Jul 2022

Keywords

  • molecular electrostatic potential
  • molecular dynamics
  • pharmacokinetics
  • natural bond orbital
  • SARS-CoV-2
  • drug-likeness prediction
  • frontiers molecular orbital
  • molecular docking

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