Abstract
The spike glycoprotein (S) of the SARS-CoV-2 virus surface plays a key role in receptor binding and virus entry. The S protein uses the angiotensin converting enzyme (ACE2) for entry into the host cell and binding to ACE2 occurs at the receptor binding domain (RBD) of the S protein. Therefore, the protein-protein interactions (PPIs) between the SARS-CoV-2 RBD and human ACE2, could be attractive therapeutic targets for drug discovery approaches designed to inhibit the entry of SARS-CoV-2 into the host cells. Herein, with the support of machine learning approaches, we report structure-based virtual screening as an effective strategy to discover PPIs inhibitors from ZINC database. The proposed computational protocol led to the identification of a promising scaffold which was selected for subsequent binding mode analysis and that can represent a useful starting point for the development of new treatments of the SARS-CoV-2 infection.
Keywords: COVID-19; PPI focused library; QSAR; Virtual screening; docking.
【저자키워드】 COVID-19, Virtual screening, docking, QSAR, PPI focused library, 【초록키워드】 Treatment, Zinc, ACE2, Drug discovery, protocol, S protein, SARS-COV-2 infection, spike glycoprotein, Infection, SARS-CoV-2 virus, Virtual screening, docking, angiotensin converting enzyme, ZINC database, Receptor binding domain, human ACE2, RBD, virus entry, inhibitor, protein-protein interaction, binding, Analysis, PPI, angiotensin, Receptor binding, therapeutic target, SARS-CoV-2 RBD, host cells, host cell, starting point, Support, enzyme, machine learning approaches, approach, effective, subsequent, inhibit, occur, the S protein, entry of SARS-CoV-2, the SARS-CoV-2, the SARS-CoV-2 virus, was selected, 【제목키워드】 SARS-CoV-2, spike, targeting,