Identification of broad-spectrum inhibitors for the cryptic binding site of SARS-CoV-2 NSP1 across multiple variants using molecular dynamics simulation

Non-structural protein 1 (NSP1) is a critical viral virulence factor expressed early during SARS-CoV-2 infection that facilitates host immune evasion by supressing host gene expression through its interaction with the 40S ribosomal subunit. NSP1 occludes the mRNA entry channel, thereby inhibiting translation of host transcripts while selectively permitting viral mRNA translation. Recent advances in structural biology have revealed previously unrecognized cryptic binding pocket within NSP1, expanding the repertoire of viable drug targets. In this study, we systematically investigated an allosteric pocket of NSP1 as a novel intervention strategy to disrupt NSP1-mRNA interactions. Virtual screening of FDA-approved drugs identified sorafenib as a promising allosteric inhibitor. For the broad-spectrum efficacy evaluation, sorafenib inhibitory potential against a variety of clinically relevant NSP1 mutants was tested. Detailed molecular dynamics simulations of the wild-type and mutant systems showed that sorafenib binding leads to different conformational and dynamic changes, that result in protein stabilization. Residue-level fluctuation analyses indicated that sorafenib had relatively stable interactions within the allosteric pocket even in the presence of destabilizing mutations, suggesting resilience against mutation-driven resistance. We have identified allosteric targeting as a viable approach to blocking the functional role of NSP1 in host translation shutdown. Sorafenib emerges as a promising broad-spectrum NSP1 inhibitor for additional experimental testing and development of NSP1-directed antiviral therapeutics.

All FDA-approved drugs were first optimized using the semi-empirical PM6 method. The optimized structures were subsequently subjected to virtual screening against the cryptic binding site of NSP1 using AutoDock. MD simulations of the selected protein-ligand complexes were carried out using GROMACSv2023. Binding free energies were estimated using the MM/PBSA method and steered molecular dynamics simulations were performed to evaluate ligand dissociation. Post-MD trajectory analyses were performed using various built-in GROMACS tools and PyMol.