In-silico investigation of some recent natural compounds for their potential use against SARS-CoV-2: a DFT, molecular docking and molecular dynamics study

Abstract

Since its first appearance in December 2019, SARS-CoV-2 has infected many people all over the world, causing serious health problems in many people and causing many deaths, but no specific drug has been developed yet. SARS-CoV-2 main protease (SARS-CoV-2 M^pro) has an important role in viral replication and transcription, so inhibition of this enzyme is proposed to be an attractive route for the treatment of COVID-19. Natural compounds have been used in the treatment of many diseases throughout the history. In this study, it was aimed to investigate SARS-CoV-2 M^pro inhibition abilities, thus the therapeutic potentials of some novel phytochemicals which have recently been entered the literature. For this purpose, eleven novel phytochemicals obtained from various natural resources have been investigated for their potential antiviral activity against SARS-CoV-2 with the use of in silico methods. In the first part of the study, DFT (density functional theory) calculations were performed on the investigated compounds. In this part, geometry optimizations, vibrational analyses, and MEP (molecular electrostatic potential) map calculations were performed. In the second part, molecular docking calculations and then molecular dynamics (MD) simulations were performed to investigate how these natural compounds interact with SARS-CoV-2 M^pro which is a promising target for COVID-19 treatments. In this part, MM-PBSA (molecular mechanics with Poisson-Boltzmann surface area) calculations were also performed to determine the binding free energies of the investigated compounds. Results showed that most of the investigated compounds interacted with SARS-CoV-2 M^pro effectively and can be promising structures for drug development studies for COVID-19.

Communicated by Ramaswamy H. Sarma

Keywords:

• COVID-19
• SARS-CoV-2 main protease
• ADME
• MM-PBSA
• druglikeness

Disclosure statement

The authors declare that there is no conflict of interest.

Additional information

Funding

The computational studies reported in this paper were partially performed at TUBITAK ULAKBIM, High Performance and Grid Computing Center (TRUBA resources) and partially at Kocaeli University.