An In silico Investigation to Identify Promising Inhibitors for SARS-CoV-2 Mpro Target

V Alagarsamy; P Shyam Sundar; B Narendhar; M T Sulthana; Vishaka S Kulkarni; A Dharshini Aishwarya; V Raja Solomon; S Murugesan; S Jubie; K Rohitha; Sangeeta Dhanwar

doi:10.2174/1573406419666230413112802

An In silico Investigation to Identify Promising Inhibitors for SARS-CoV-2 M^pro Target

Med Chem. 2023;19(9):925-938. doi: 10.2174/1573406419666230413112802.

Authors

Affiliations

¹ Medicinal Chemistry Research Laboratory, MNR College of Pharmacy, Sangareddy, 502 294, Gr. Hyderabad, India.
² Department of Pharmacy, Pilani, Birla Institute of Technology & Sciences Pilani Campus, Pilani-333031, India.
³ Department of Pharmaceutical Chemistry, JSS College of Pharmacy, Udhagamandalam, 643001, India.

PMID: 37069723
DOI: 10.2174/1573406419666230413112802

Abstract

Background: A limited number of small molecules against SARS-CoV-2 has been discovered since the epidemic commenced in November 2019. The conventional medicinal chemistry approach demands more than a decade of the year of laborious research and development and a substantial financial commitment, which is not achievable in the face of the current epidemic.

Objective: This study aims to discover and recognize the most effective and promising small molecules by interacting SARS-CoV-2 M^pro target through computational screening of 39 phytochemicals from five different Ayurvedic medicinal plants.

Methods: The phytochemicals were downloaded from Research Collaboratory for Structural Bioinformatics (RCSB) Protein Data Bank (PDB) PubChem, and the SARS-CoV-2 protein (PDB ID: 6LU7; M^pro) was taken from the PDB. The molecular interactions, binding energy, and ADMET properties were analyzed.

Results: The binding affinities were studied using a structure-based drug design of molecular docking, divulging 21 molecules possessing greater to equal affinity towards the target than the reference standard. Molecular docking analysis identified 13 phytochemicals, sennoside-B (-9.5 kcal/mol), isotrilobine (-9.4 kcal/mol), trilobine (-9.0 kcal/mol), serratagenic acid (-8.1 kcal/mol), fistulin (-8.0 kcal/mol), friedelin (-7.9 kcal/mol), oleanolic acid (-7.9 kcal/mol), uncinatone (-7.8 kcal/mol), 3,4-di- O-caffeoylquinic acid (-7.4 kcal/mol), clemaphenol A (-7.3 kcal/mol), pectolinarigenin (-7.2 kcal/mol), leucocyanidin (-7.2 kcal/mol), and 28-acetyl botulin (-7.2 kcal/mol) from ayurvedic medicinal plants phytochemicals possess greater affinity than the reference standard Molnupiravir (-7.0 kcal/mol) against SARS-CoV-2-M^pro.

Conclusion: Two molecules, namely sennoside-B, and isotrilobine with low binding energies, were predicted as most promising. Furthermore, we carried out molecular dynamics simulations for the sennoside-B protein complexes based on the docking score. ADMET properties prediction confirmed that the selected docked phytochemicals were optimal. These compounds can be investigated further and utilized as a parent core molecule to create novel lead molecules for preventing COVID-19.

Keywords: ADMET; COVID-19; MD simulation; SARS-CoV-2; medicinal plants; molecular docking.

MeSH terms

COVID-19*
Chemistry, Pharmaceutical
Humans
Molecular Docking Simulation
Molecular Dynamics Simulation
Protease Inhibitors
SARS-CoV-2*
Sennosides

Substances

Sennosides
Protease Inhibitors