Molecular Analysis of Potential Inhibitors Against SARS-CoV-2: Computational Approach

Document Type : Regular Article

Authors

1 Department of Pure and Industrial Chemistry, Faculty of Physical Sciences, University of Nigeria, Nsukka 410001, Enugu State, Nigeria. Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban 4041, South Africa

2 Baruch S. Blumberg Institute, Doylestown, United States

3 Catalysis and Peptide Research Unit, School of Health Sciences, University of KwaZulu-Natal, Durban 4041, South Africa

4 Department of Physiology, School of Health and Health Technology, Federal University of Technology Akure, Ondo State, Nigeria

10.22036/pcr.2022.353391.2153

Abstract

SARS-CoV-2 has ravaged numerous in the world and it is at present a subject of research. In this study, we present two potential antiviral compounds for the treatment of this disease compared with the current reference drug. The objective of this work is geared at investigating two antiviral compounds as a promising therapy for the treatment of COVID-19. Molecular dynamics simulation and quantum theory of atoms in molecules (QTAIM) were employed to study the host-guest interaction and dynamics between the nCov protein and inhibitors. Results obtained after a triplicate run of 100 ns for each compound revealed that compound B2 showed better inhibitory potential with MMGBSA binding energy of -40.05 kcal/mol compared to the referenced drug. It is fascinating to note that B1 had a comparable binding potential with B2 suggesting some similarity in their inhibitory features. The QTAIM results showed that Laplacian 𝛻2ρ(r) and ellipticity (ε) are positive indicating a stable protein-ligand interaction. The order of stability agrees with the MM-GBSA energies trend obtained. The results show that B1 and B2 might arise as a hopeful therapeutic for the cure of Covid-19. Though, a crucial trial can be showed to authenticate this conclusion.

Graphical Abstract

Molecular Analysis of Potential Inhibitors Against SARS-CoV-2: Computational Approach

Keywords