A multilevel approach for screening natural compounds as an antiviral agent for COVID-19. (June 2022)

• Record Type: Journal Article
• Authors: Vasighi, Mahdi; Romanova, Julia; Nedyalkova, Miroslava
• Is Part Of: Computational biology and chemistry. Volume 98(2022)
• Publication Date: 2022-06
• Subjects: Computer-aided drug design -- Docking -- DFT -- Cluster analyses -- Principal component analysis; Chemistry -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; Biochemistry -- Data processing; Biology -- Data processing; Molecular biology -- Data processing; Periodicals; Electronic journals; 542.85
• DOI: 10.1016/j.compbiolchem.2022.107694
• Languages: English
• ISSNs: 1476-9271

Abstract

Abstract: The COVID-19 has a worldwide spread, which has prompted concerted efforts to find successful drug treatments. Drug design focused on finding antiviral therapeutic agents from plant-derived compounds which may disrupt the attachment of SARS-CoV-2 to host cells is with a pivotal need and role in the last year. Herein, we provide an approach based on drug design methods combined with machine learning approaches to classify and discover inhibitors for COVID-19 from natural products. The spike receptor-binding domain (RBD) was docked with database of 125 ligands. The docking protocol based on several steps was performed within Autodock Vina to identify the high-affinity binding mode and to reveal more insights into interaction between the phytochemicals and the RBD domain. A protein-ligand interaction analyzer has been developed. The drug-likeness properties of explored inhibitors are analyzed in the frame of exploratory data analyses. The developed computational protocol yielded a comprehensive pipeline for predicting the inhibitors to prevent the entry RBD region. Graphical Abstract: ga1 Highlights: A potent screening approach for natural remedial molecules was proposed. The binding affinity with the RBD of the S-protein of SARS-CoV2 was investigated. The exploratory data analysis is based on molecular docking and machine learning.