We report the discovery of 31 SARS-CoV-2 inhibitors identified across two computational-to-experimental screening campaigns, with average cell-based antiviral potency satisfying 〈EC50〉 ≤ CC50/3 in A549-hACE2 cells. These compounds were selected from 60 successfully tested molecules evaluated using nanoluciferase reporter assay (nLuc), cytopathic effect (CPE), and host-cell cytotoxicity (CC50) assays. We used a funnel-like computational framework where candidate compounds were progressively prioritized through different scores, improving robustness against the limitations of individual metrics. However, none of the docking-based scoring metrics, including MM/GBSA and Glide scores, showed statistically significant correlation with experimental activity, illustrating the limitations of docking-only approaches and supporting a major contribution of the machine-learning predictions to the high hit-identification rate. Although direct target validation is still lacking, the machine-learning (ML) workflow was grounded in experimental nsp13 inhibition labels, and molecular dynamics (MD) simulations suggest that these compounds can act by stabilizing apo-like open conformations of nsp13, with differential interactions involving the 1B domain contributing to potency differences. Among the compounds evaluated, the phenoxypropanol (PP) and bipiperidine (BPP) series stood out as the most promising SARS-CoV-2 antivirals. Integrated analysis of structure–activity relationships, physicochemical parameters, metabolic stability, and interdomain dynamics delineated structural optimization paths for both series.
Alma C. Castañeda-Leautaud, Thomas D. Bannister, Eunjung Kim et al.· Chemical Science· 0 citations
The SARS-CoV-2 main protease (MPro) is an essential enzyme for viral replication and a primary target for antiviral drug development. Despite extensive structural and biochemical characterization, the allosteric mechanisms by which dimerization informs conformational changes at active site lack an explicit comparison across the different states that identify key residues that connect substrate binding, dimerization, and catalytic activation. Here, we integrate microsecond time scale all-atom molecular dynamics (MD) simulations with dynamical network analysis to characterize how ligand binding and dimerization modulate the allosteric communication landscape of MPro. We performed triplicate 1-μs simulations of MPro in the monomer and dimer states. For each of these states, we simulated MPro in the apo state, as well as bound to a natural peptide substrate (nsp 15/16), the covalent inhibitor nirmatrelvir (Paxlovid) and the noncovalent inhibitor ensitrelvir (Xocova). Dynamical network analyses from the resulting simulations reveal that dimerization redirects the highest correlated motions from the interdomain loop towards the domain II and III interface. At the dimer interface, we identified N-terminal and domain II β-hairpin residues that act as central communication hubs creating networks that connect both chains in the dimer. Small molecule binding to the active site further modulates these networks in distinct ways: nirmatrelvir and peptide substrate binding results in the formation of allosteric networks within the oxyanion loop, while ensitrelvir-bound monomeric MPro results in a dimer-like network, suggesting an inhibitory "allosteric switch" mechanism that may hinder dimerization upon binding. Across all systems, domain III emerges as an allosteric "pivot", providing a platform that allows the most relevant networks to connect inter-chain communication to the active site upon dimerization. Together, these findings define how correlated motion networks couple active-site dynamics to dimerization and ligand binding, providing molecular insight into the principles governing allosteric regulation in MPro. This framework highlights potential avenues for developing antivirals that target not only the catalytic site but also the communication pathways sustaining dimer stability and enzymatic function.
Javier O. Sanlley Hernandez, Carla Calvó-Tusell, Fiona L. Kearns et al.· Biophysical Journal· 0 citations
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