An integrated metabolomic and proteomic approach for the identification of covalent inhibitors of the main protease (M) of SARS-COV-2 from crude natural extracts.

M represents one of the most promising drug targets for SARS-Cov-2, as it plays a crucial role in the maturation of viral polyproteins into functional proteins. HTS methods are currently used to screen M inhibitors, and rely on searching chemical databases and compound libraries, meaning that they only consider previously structurally clarified and isolated molecules. A great advancement in the hit identification strategy would be to set-up an approach aimed at exploring un-deconvoluted mixtures of compounds such as plant extracts. Hence, the aim of the present study is to set-up an analytical platform able to fish-out bioactive molecules from complex natural matrices even where there is no knowledge on the constituents. The proposed approach begins with a metabolomic step aimed at annotating the MW of the matrix constituents. A further metabolomic step is based on identifying those natural electrophilic compounds able to form a Michael adduct with thiols, a peculiar chemical feature of many M inhibitors that covalently bind the catalytic Cys145 in the active site, thus stabilizing the complex. A final step consists of incubating recombinant M with natural extracts and identifying compounds adducted to the residues within the M active site by bottom-up proteomic analysis (nano-LC-HRMS). Data analysis is based on two complementary strategies: (i) a targeted search applied by setting the adducted moieties identified as Michael acceptors of Cys as variable modifications; (ii) an untargeted approach aimed at identifying the whole range of adducted peptides containing Cys145 on the basis of the characteristic b and y fragment ions independent of the adduct. The method was set-up and then successfully tested to fish-out bioactive compounds from the crude extract of Scutellaria baicalensis, a Chinese plant containing the catechol-like flavonoid baicalin and its corresponding aglycone baicalein which are well-established inhibitors of M. Molecular dynamics (MD) simulations were carried out in order to explore the binding mode of baicalin and baicalein, within the SARS-CoV-2 M active site, allowing a better understanding of the role of the nucleophilic residues (i.e. His41, Cys145, His163 and His164) in the protein-ligand recognition process.