Computational prediction of the molecular mechanism of statin group of drugs against SARS-CoV-2 pathogenesis

Abstract

Abstract Recently published clinical data from COVID-19 patients indicated that statin therapy is associated with a better clinical outcome and a significant reduction in the risk of mortality. In this study by computational analysis, we have aimed to predict the possible mechanism of the statin group of drugs by which they can inhibit SARS-CoV-2 pathogenesis. Blind docking of the critical structural and functional proteins of SARS-CoV-2 like RNA-dependent RNA polymerase, M-protease of 3-CL-Pro, Helicase, and the Spike proteins ( wild type and mutants from different VOCs) were performed using the Schrodinger docking tool. We observed that fluvastatin and pitavastatin showed fair, binding affinities to RNA polymerase and 3-CL-Pro, whereas fluvastatin showed the strongest binding affinity to the helicase. Fluvastatin also showed the highest affinity for the Spike Delta and a fair docking score for other spike variants. Additionally, molecular dynamics simulation confirmed the formation of a stable drug-protein complex between Fluvastatin and target proteins. Thus our study shows that of all the statins, fluvastatin can bind to multiple target proteins of SARS-CoV-2, including the spike-mutant proteins. This property might contribute to the potent antiviral efficacy of this drug.