Computer-aided drug design for the double-membrane vesicle pore complex inhibitors against SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the etiological agent of the ongoing global pandemic, has constituted the worst global health disaster in recent years. However, no antiviral drugs have proved clinically efficacious to combat SARS-CoV-2 infection. The SARS-CoV-2 double-membrane vesicle (DMV) pore complex, particularly for its positively charged residues R1613, R1614, R303, R305, and R306, which are highly conserved across beta-coronaviruses and play a critical role in mediating RNA transport and virus replication, has been validated as an effective drug target. Here, we employed computer-aided drug design (CADD) techniques for the first time to screen the antiviral compounds against SARS-CoV-2 by targeting the crystal structure of the SARS-CoV-2 DMV nsp3-4 pore complex. A total of 486,387 drug compounds were subjected to virtual screening, such as toxicity prediction, ADMET prediction, molecular docking, and target analysis. The six compounds (three for each binding site) were selected based on their lowest binding energies. Notably, Compound 391 demonstrated the strongest binding affinity to the critical positively charged residues R1613 and R1614 at binding site 1, meanwhile, Compound 5,157 exhibited the most stable interactions with the essential positively charged residues R303, R305, and R306 at binding site 2. These results demonstrate that Compound 391 and Compound 5,157 exhibit greater potential antiviral effects, which provide a theoretical basis for further confirmation against SARS-CoV-2 in vitro and in vivo studies.