Lead Discovery of SARS-CoV-2 Main Protease Inhibitors through Covalent Docking-Based Virtual Screening

Abstract

During almost all 2020, coronavirus disease 2019 (COVID-19) pandemic has constituted the major risk for the worldwide health and economy, propelling unprecedented efforts to discover drugs for its prevention and cure. At the end of the year, these efforts have culminated with the approval of vaccines by the American Food and Drug Administration (FDA) and the European Medicines Agency (EMA) giving new hope for the future. On the other hand, clinical data underscore the urgent need for effective drugs to treat COVID-19 patients. In this work, we embarked on a virtual screening campaign against the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) M pro chymotrypsin-like cysteine protease employing our in-house database of peptide and non-peptide ligands characterized by different types of warheads acting as Michael acceptors. To this end, we employed the AutoDock4 docking software customized to predict the formation of a covalent adduct with the target protein. In vitro verification of the inhibition properties of the most promising candidates allowed us to identify two new lead inhibitors that will deserve further optimization. From the computational point of view, this work demonstrates the predictive power of AutoDock4 and suggests its application for the in silico screening of large chemical libraries of potential covalent binders against the SARS-CoV-2 M pro enzyme.

A Comparison between Enrichment Optimization Algorithm (EOA)-Based and Docking-Based Virtual Screening

“…Still, our EOA results are significantly higher at 0.92 ± 0.02 and 30.2 ± 8.1 for the AUC and EF 1% , respectively. Finally, we note that while a consensus approach may benefit docking-based VS, in many projects, single structure-based VS is still the method of choice, either because of limited computational resources or because of lack of multiple crystal structures for the target of interest [ 34 , 35 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 ].…”

In silico studies of selected xanthophylls as potential candidates against SARS-CoV-2 targeting main protease (Mpro) and papain-like protease (PLpro)

“…In silico studies allow for fast prediction of activity and toxicity of natural compounds. SARS-CoV-2 computational analyses are important in searching for ac-tive drugs or designing vaccines [30][31][32][33][34]. The main protease (Mpro) and the papain-like protease (PLpro) can be targets for anti-SARS-CoV-2 drugs.…”

Computationally driven discovery of SARS-CoV-2 Mproinhibitors: from design to experimental validation

“…Many studies have been devoted to the design of new M pro inhibitors 3,5,[15][16][17][18][19][20][21][22][23][24][25] through joint computational and experimental approaches. In particular, a recent study by the Jorgensen group highlighted the usefulness of relative binding free energy (RBFE) computations as part of the drug design process.…”