Multi-Targeting Approach in Selection of Potential Molecule for COVID-19 Treatment

Abstract

The coronavirus disease (COVID-19) is a pandemic that started in the City of Wuhan, Hubei Province, China, caused by the spread of coronavirus (SARS-CoV-2). Drug discovery teams around the globe are in a race to develop a medicine for its management. It takes time for a novel molecule to enter the market, and the ideal way is to exploit the already approved drugs and repurpose them therapeutically. We have attempted to screen selected molecules with an affinity towards multiple protein targets in COVID-19 using the Schrödinger suit for in silico predictions. The proteins selected were angiotensin-converting enzyme-2 (ACE2), main protease (MPro), and spike protein. The molecular docking, prime MM-GBSA, induced-fit docking (IFD), and molecular dynamics (MD) simulations were used to identify the most suitable molecule that forms a stable interaction with the selected viral proteins. The ligand-binding stability for the proteins PDB-IDs 1ZV8 (spike protein), 5R82 (Mpro), and 6M1D (ACE2), was in the order of nintedanib > quercetin, nintedanib > darunavir, nintedanib > baricitinib, respectively. The MM-GBSA, IFD, and MD simulation studies imply that the drug nintedanib has the highest binding stability among the shortlisted. Nintedanib, primarily used for idiopathic pulmonary fibrosis, can be considered for repurposing for us against COVID-19.

Keywords

• SARS-CoV-2
• molecular docking
• prime MM-GBSA
• induced-fit docking
• molecular dynamics simulation