(707d) An Integrated Computational and Experimental Approach to Identifying Inhibitors for Sars-Cov-2 3CL Protease

The newly evolved SARS-CoV-2 has caused the COVID-19 pandemic. Although vaccines are the most efficient way to end the COVID-19 pandemic and a couple of vaccines have been authorized for emergency use to control the pandemic, safety issues for certain people, e.g., those with allergies and those with immune disorders, are still concerned. Furthermore, we have observed SARS-COV-2 variants spread (CDC, 2021). SARS-CoV-2 main protease 3CLpro, cleaving the transcript polyprotein, is essential for the rapid replication of the virus. Inhibiting this protease may open an alternative avenue towards therapeutic intervention. (Anand et al., 2003; Ratia et al., 2008). Many peptide inhibitors were designed to mimic natural viral polypeptides and covalently bind to the active site Cys₁₄₅ of 3CLpro^SARS-CoV-1. The other category of 3CLpro^SARS-CoV-1 inhibitors includes non-covalent or reversible covalent inhibitors, which have advantages regarding side effects and toxicity. In this work, we aim to identify non-covalent inhibitors against 3CLpro^SARS-CoV-2. In particular, we first implemented the molecular docking approach to quickly identify potential 3CLpro inhibitors against SARS-CoV-2 in previous research (Jin et al., 2020). To narrow down 3CLpro^SARS-CoV-2 inhibitor candidates, an integrated docking-based virtual screening and quantitative structure–activity relationship (QSAR) method was then conducted, on the basis of the aforementioned 3CLpro^SARS-CoV-1 inhibitors and half-inhibition concentration IC₅₀ data. A 3D QSAR model attempts to correlate 3D molecular structure to biological activity, often using a variety of molecular descriptors such as physicochemical, topological, electronic and steric properties (Nantasenamat et al., 2009). In particular, 3D Atomic Property Fields (APF) QSAR methods developed by ICM calculate physico-chemical properties of superimposed chemicals and utilize their half-inhibition data to weight contributions for each property through Partial-Least-Squares (PLS) regression modeling (Totrov, 2008). The developed QSAR model gave a quantitative ligand-based virtual screening approach to further evaluate the physico-chemical properties of compounds and estimate their IC₅₀ values against 3CLpro^SARS-CoV-2. Those top candidates were further evaluated in enzyme activity assay and IC₅₀ experiment.

After screening half million compounds, 288 hits in total were identified from the FDA-approved compound library and the IBScreen database. The compounds were all predicted to be bound within the active site of 3CLpro in a position similar to the crystallographic ligands. QSAR model assesses the physicochemical properties of identified compounds and estimates their inhibitory effects on 3CLpro^SARS-CoV-2. The training dataset for the QSAR model had a good quality fit (R² = 0.8967), while the testing dataset suggested the predicted IC₅₀ was still correlated to the actual IC₅₀ (R² = 0.7257). The 288 identified hits were input into the developed QASR model to estimate half-inhibition values. The predicted IC₅₀ for each compound were ranged from 0.35 µM to 46.7 µM. Top 71 compounds with predicted IC₅₀’s ranging from 0.35 µM to 19.86 µM, were selected for further evaluation in an enzyme activity assay. The top two compounds were confirmed by experiments to effectively inhibit the activity of 3CLpro^SARS-CoV-2, with IC₅₀ values of 19+/-3 µM and 38+/-3 µM, respectively. The functional groups pyrimidinetrione and quinoxaline were newly found in 3CLpro inhibitors, thus they are of high interest for lead optimization. In future studies, cellular infection and animal testing could be conducted to validate the efficacy and safety of the two newly identified compounds.

References:

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