Flavan-based phytoconstituents inhibit Mpro, a SARS-COV-2 molecular target, in silico

Abstract

A well-validated in-silico approach can provide promising drug candidates for the treatment of the ongoing CoVID19 pandemic. In this study, we have screened 32 phytochemical constituents (PCCs) with Mpro binding site (PDB:6W63) based on which we identified three possible candidates that are likely to be effective against CoVID19—viz., licoleafol (binding energy: −8.1 kcal/mol), epicatechin gallate (–8.5 kcal/mol) and silibinin (–8.4 kcal/mol) that result in higher binding affinity than the known inhibitor, X77 (–7.7 kcal/mol). Molecular dynamics (MD) simulations of PCCs-Mpro complex confirmed molecular docking results with high structural and dynamical stability. The selected compounds were found to exhibit low mean squared displacements (licoleafol: 2.25 ± 0.43 Å, epicatechin gallate: 1.93 ± 0.35 Å, and silibinin: 1.39 ± 0.19 Å) and overall low fluctuations of the binding complexes (root mean squared fluctuations below 2 Å). Visualization of the MD trajectories and structural analyses revealed that they remain confined to the initial binding region, with mean fluctuations lower than 3 Å. To access the collective motion of the atoms, we performed principal component analysis demonstrating that the first 10 principal components are the major contributors (approximate contribution of 80%) and are responsible for the overall PCCs motion. Considering that the three selected PCCs share the same flavan backbone and exhibit antiviral activity against hepatitis C, we opine that licoleafol, epi-catechin gallate, and silibinin can be promising anti-CoVID19 drug candidates.

Communicated by Ramaswamy H. Sarma

Keywords:

• Mpro
• licoleafol
• epicatechin gallate
• silibinin
• molecular docking
• redocking
• molecular dynamics simulations

Acknowledgments

SM and DS contributed equally to the study. GK designed the study. SM and DS prepared and analyzed all the figures except Figure 2 (prepared by SJ and GK). AKS and DS prepared Tables 1 and 2. NS, DS and SM prepared Supplementary table 1. SB contributed to MD simulations data generation, data analysis, figures preparation and text writing. GK edited the first draft and finalized the manuscript. The authors thank valuable suggestions provided by Rubin Abagyan, University of California San Diego, and Student, Debarghya Ghosal for initial help with supplementary table 1.

Disclosure statement

The authors declare no conflicts of interest.

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.

Additional information

Funding

This work was supported by Microsoft under the project MCB200120 in COVID-19 HPC Consortium.