Broad-spectrum coronavirus 3C-like protease peptidomimetic inhibitors effectively block SARS-CoV-2 replication in cells: Design, synthesis, biological evaluation, and X-ray structure determination.

Despite the approval of vaccines, monoclonal antibodies and restrictions during the pandemic, the demand for new efficacious and safe antivirals is compelling to boost the therapeutic arsenal against the COVID-19. The viral 3-chymotrypsin-like protease (3CLpro) is an essential enzyme for replication with high homology in the active site across CoVs and variants showing an almost unique specificity for Leu-Gln as P2-P1 residues, allowing the development of broad-spectrum inhibitors. The design, synthesis, biological activity, and cocrystal structural information of newly conceived peptidomimetic covalent reversible inhibitors are herein described. The inhibitors display an aldehyde warhead, a Gln mimetic at P1 and modified P2-P3 residues. Particularly, functionalized proline residues were inserted at P2 to stabilize the ß-turn like bioactive conformation, modulating the affinity. The most potent compounds displayed low/sub-nM potency against the 3CLpro of SARS-CoV-2 and MERS-CoV and inhibited viral replication of three human CoVs, i.e. SARS-CoV-2, MERS-CoV, and HCoV 229 in different cell lines. Particularly, derivative 12 exhibited nM-low µM antiviral activity depending on the virus, and the highest selectivity index. Some compounds were co-crystallized with SARS-CoV-2 3CLpro validating our design. Altogether, these results foster future work toward broad-spectrum 3CLpro inhibitors to challenge CoVs related pandemics.

Discovery of novel SARS-CoV-2 3CL protease covalent inhibitors using deep learning-based screen.

SARS-CoV-2 3CL protease is one of the key targets for drug development against COVID-19. Most known SARS-CoV-2 3CL protease inhibitors act by covalently binding to the active site cysteine. Yet, computational screens against this enzyme were mainly focused on non-covalent inhibitor discovery. Here, we developed a deep learning-based stepwise strategy for selective covalent inhibitor screen. We used a deep learning framework that integrated a directed message passing neural network with a feed-forward neural network to construct two different classifiers for either covalent or non-covalent inhibition activity prediction. These two classifiers were trained on the covalent and non-covalent 3CL protease inhibitors dataset, respectively, which achieved high prediction accuracy. We then successively applied the covalent inhibitor model and the non-covalent inhibitor model to screen a chemical library containing compounds with covalent warheads of cysteine. We experimentally tested the inhibition activity of 32 top-ranking compounds and 12 of them were active, among which 6 showed IC50 values less than 12 µM and the strongest one inhibited SARS-CoV-2 3CL protease with an IC50 of 1.4 µM. Further investigation demonstrated that 5 of the 6 active compounds showed typical covalent inhibition behavior with time-dependent activity. These new covalent inhibitors provide novel scaffolds for developing highly active SARS-CoV-2 3CL covalent inhibitors.