Abstract

Objectives: The comprehensive in silico study aims to figure out the most effective aromatic phytochemical ligands among a number from a library, considering their pharmacokinetic effi¬cacies in blocking “angiotensin-converting enzyme 2 (ACE2) receptor–severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S protein” complex formation as part of a target-specific drug designing.
Materials and Methods: A library of 57 aromatic pharmacophore phytochemical ligands was prepared from where the top five ligands depending on Absorption, Distribution, Metabolism, Excretion, and Toxicity (ADMET) and quantitative structure-activity relationship (QSAR)-based pharmacokinetic properties were considered. The selected ligands were optimized for commenc¬ing molecular docking and dynamic simulation as a complex with the ACE2 receptor to compare their blocking efficacy with the control drug. The ligand–receptor complexes’ accuracy in prevent¬ing the Spike (S) protein of SARS-CoV-2 penetration inside the host cells has been analyzed through hydrogen–hydrophobic bond interactions, principal component analysis (PCA), root mean square deviation (RMSD), root mean square fluctuation (RMSF), and B-Factor. Advanced in silico program¬ming language and bioanalytical software were used for high throughput and authentic results.
Results: ADMET and QSAR revealed Rhamnetin, Lactupicrin, Rhinacanthin D, Flemiflavanone D, and Exiguaflavanone A as the ligands of our interest to be compared with the control Cassiarin D. According to the molecular docking binding affinity to block ACE2 receptor, the efficiency mount¬ings were Rhinacanthin D > Flemiflavanone D > Lactupicrin > Exiguaflavanone A > Rhamnetin. The binding affinity of the Cassiarin D–ACE2 complex was (−10.2 KJ/mol) found inferior to the Rhinacanthin D–ACE2 complex (−10.8 KJ/mol), referring to Rhinacanthin D as a more stable candi¬date to use as drugs. The RMSD values of protein–ligand complexes evaluated according to their structural conformation and stable binding pose ranged between 0.1~2.1 Å. The B-factor showed that very few loops were present in the protein structure. The RMSF peak fluctuation regions ranged 5–250, predicting efficient ligand–receptor interactions.
Conclusion: The experiment sequentially measures all the parameters required in referring to any pharmacophore as a drug, considering which all aromatic components analyzed in the study can strongly be predicted as target-specific medication against the novel coronavirus 2019 infection.

Key words: Antiviral efficacy; molecular docking; dynamic simulation; principal component analysis; targeted drug designing