Application of established computational techniques to identify potential SARS-CoV-2 Nsp14-MTase inhibitors in low data regimes†

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has led to significant global morbidity and mortality. A crucial viral protein, the non-structural protein 14 (nsp14), catalyzes the methylation of viral RNA and plays a critical role in viral genome replication and transcription. Due to the low mutation rate in the nsp region among various SARS-CoV-2 variants, nsp14 has emerged as a promising therapeutic target. However, discovering potential inhibitors remains a challenge. In this work, we introduce a computational pipeline for the rapid and efficient identification of potential nsp14 inhibitors by leveraging virtual screening and the NCI open compound collection, which contains 250 000 freely available molecules for researchers worldwide. The introduced pipeline provides a cost-effective and efficient approach for early-stage drug discovery by allowing researchers to evaluate promising molecules without incurring synthesis expenses. Our pipeline successfully identified seven candidates that inhibit the MTase activity of nsp14. Among these, one compound, NSC62033, demonstrated strong binding affinity to nsp14, exhibiting a dissociation constant of 427 ± 84 nM. In addition, we gained new insights into the structure and function of this protein through molecular dynamics simulations. Furthermore, our molecular dynamics simulations suggest potential new conformational states of the protein, with residues Phe367, Tyr368, and Gln354 in the binding pocket potentially playing a role in stabilizing interactions with novel ligands, though further validation is required. Our findings also indicate that metal coordination complexes may be important for the function of the binding pocket. Lastly, we present the solved crystal structure of the nsp14-MTase complexed with SS148 (PDB:8BWU), a potent inhibitor of methyltransferase activity at the nanomolar level (IC₅₀ value of 70 ± 6 nM).