tetano
Editor, Senior Moderator
Sci Rep
. 2026 Jul 2.
doi: 10.1038/s41598-026-60085-w. Online ahead of print.
Computer-assisted design of arylethylbenzamides as predicted nanomolar inhibitors of papain-like cysteine protease of SARS-CoV-2
Lukas Kerti[SUP] 1 [/SUP], Vladimir Frecer[SUP] 2 [/SUP]
Affiliations
The pandemic of the new coronavirus SARS-CoV-2, which causes the severe acute respiratory syndrome COVID-19, represents a long-term threat to the health of the human population. Therefore, the continuous development of new small-molecule antivirals remains essential to effectively address current infections and ensure preparedness for future pandemic threats. The objective of the study was to computationally design, optimize, and prioritize (R)-arylethylbenzamide (AREB) analogs as potential inhibitors of SARS-CoV-2 papain-like protease (PL[SUP]pro[/SUP]), a conserved antiviral target involved in viral polyprotein cleavage and host immune evasion signaling. We hypothesize that receptor-structure-guided expansion of the AREB scaffold in four distinct regions - reaching towards the BL2-groove, the Cys111 catalytic site, the electrostatic Glu167/Asp164 region, and the Val70[SUP]Ub[/SUP] pocket adjacent to ubiquitin - can produce candidates with predicted low-nanomolar inhibitory potency while maintaining the drug-like properties of the new analogs. Multiple published PL[SUP]pro[/SUP]-inhibitor crystal structures[SUP]1-3[/SUP] were used as templates after molecular mechanics refinement. A QSAR model was built from 51 published[SUP]1,3[/SUP] AREB inhibitors by correlating the calculated relative enzyme-inhibitor interaction energies (ΔΔE[SUB]int,MM[/SUB]) with the experimentally determined pIC[SUB]50[/SUB] values using linear regression and extensive 5-fold cross-validation. Four virtual combinatorial libraries were designed and enumerated for substitution sites (R[SUB]1[/SUB] - R[SUB]4[/SUB]) of a common scaffold and screened by extra-precision docking followed by MM-GB/SA rescoring. The predicted IC[SUB]50[/SUB][SUP]pre[/SUP] values were used for the potency ranking of the new AREB analogs. Predicted ADME-related descriptors guided iterative virtual library focusing and filtering. For selected leads, QM/MM interaction energies (ΔΔE[SUB]int,QM/MM,aq[/SUB]) and TIP4P explicit-solvent 200 ns MD simulations were used to assess the stability of the binding mode of promising new PL[SUP]pro[/SUP] inhibitors. The QSAR model demonstrated strong internal validity and predictability, enabling the prioritization of designed analogs. After ADME-based filtering, three final drug candidates with predicted IC[SUB]50[/SUB][SUP]pre[/SUP] = 5.2-5.3 nM and an estimated 18-fold increase in potency compared to the most potent reference inhibitor considered (IC[SUB]50[/SUB][SUP]exp[/SUP] = 94 nM) were prioritized. The top candidates preserved the hallmark BL2-loop closure binding mode while extending stabilizing interaction networks to additional subsites targeted by the individual R-group design strategy. MD trajectories supported stable pocket occupancy over a 200 ns simulation and sustained key hydrogen bonds and hydrophobic contacts. An integrated QSAR and structure-guided computational workflow prioritized synthetically available drug-like AREB analogs as putative SARS-CoV-2 PL[SUP]pro[/SUP] inhibitors with predicted low nanomolar potency. Although the presented findings are mostly computational and require experimental validation, the identified lead compounds represent promising candidates for further antiviral agent development.
Keywords: Computer-aided drug design; Molecular dynamics; Papain-like protease of SARS-CoV-2; QM/MM calculations; QSAR.
. 2026 Jul 2.
doi: 10.1038/s41598-026-60085-w. Online ahead of print.
Computer-assisted design of arylethylbenzamides as predicted nanomolar inhibitors of papain-like cysteine protease of SARS-CoV-2
Lukas Kerti[SUP] 1 [/SUP], Vladimir Frecer[SUP] 2 [/SUP]
Affiliations
- PMID: 42393217
- DOI: 10.1038/s41598-026-60085-w
The pandemic of the new coronavirus SARS-CoV-2, which causes the severe acute respiratory syndrome COVID-19, represents a long-term threat to the health of the human population. Therefore, the continuous development of new small-molecule antivirals remains essential to effectively address current infections and ensure preparedness for future pandemic threats. The objective of the study was to computationally design, optimize, and prioritize (R)-arylethylbenzamide (AREB) analogs as potential inhibitors of SARS-CoV-2 papain-like protease (PL[SUP]pro[/SUP]), a conserved antiviral target involved in viral polyprotein cleavage and host immune evasion signaling. We hypothesize that receptor-structure-guided expansion of the AREB scaffold in four distinct regions - reaching towards the BL2-groove, the Cys111 catalytic site, the electrostatic Glu167/Asp164 region, and the Val70[SUP]Ub[/SUP] pocket adjacent to ubiquitin - can produce candidates with predicted low-nanomolar inhibitory potency while maintaining the drug-like properties of the new analogs. Multiple published PL[SUP]pro[/SUP]-inhibitor crystal structures[SUP]1-3[/SUP] were used as templates after molecular mechanics refinement. A QSAR model was built from 51 published[SUP]1,3[/SUP] AREB inhibitors by correlating the calculated relative enzyme-inhibitor interaction energies (ΔΔE[SUB]int,MM[/SUB]) with the experimentally determined pIC[SUB]50[/SUB] values using linear regression and extensive 5-fold cross-validation. Four virtual combinatorial libraries were designed and enumerated for substitution sites (R[SUB]1[/SUB] - R[SUB]4[/SUB]) of a common scaffold and screened by extra-precision docking followed by MM-GB/SA rescoring. The predicted IC[SUB]50[/SUB][SUP]pre[/SUP] values were used for the potency ranking of the new AREB analogs. Predicted ADME-related descriptors guided iterative virtual library focusing and filtering. For selected leads, QM/MM interaction energies (ΔΔE[SUB]int,QM/MM,aq[/SUB]) and TIP4P explicit-solvent 200 ns MD simulations were used to assess the stability of the binding mode of promising new PL[SUP]pro[/SUP] inhibitors. The QSAR model demonstrated strong internal validity and predictability, enabling the prioritization of designed analogs. After ADME-based filtering, three final drug candidates with predicted IC[SUB]50[/SUB][SUP]pre[/SUP] = 5.2-5.3 nM and an estimated 18-fold increase in potency compared to the most potent reference inhibitor considered (IC[SUB]50[/SUB][SUP]exp[/SUP] = 94 nM) were prioritized. The top candidates preserved the hallmark BL2-loop closure binding mode while extending stabilizing interaction networks to additional subsites targeted by the individual R-group design strategy. MD trajectories supported stable pocket occupancy over a 200 ns simulation and sustained key hydrogen bonds and hydrophobic contacts. An integrated QSAR and structure-guided computational workflow prioritized synthetically available drug-like AREB analogs as putative SARS-CoV-2 PL[SUP]pro[/SUP] inhibitors with predicted low nanomolar potency. Although the presented findings are mostly computational and require experimental validation, the identified lead compounds represent promising candidates for further antiviral agent development.
Keywords: Computer-aided drug design; Molecular dynamics; Papain-like protease of SARS-CoV-2; QM/MM calculations; QSAR.