In silico evaluation of the inhibitory potential of nucleocapsid inhibitors of SARS-CoV-2: a binding and energetic perspective

Abstract

The COVID-19 outbreak brought on by the SARS-CoV-2 virus continued to infect a sizable population worldwide. The SARS-CoV-2 nucleocapsid (N) protein is the most conserved RNA-binding structural protein and is a desirable target because of its involvement in viral transcription and replication. Based on this aspect, this study focused to repurpose antiviral compounds approved or in development for treating COVID-19. The inhibitors chosen are either FDA-approved or are currently being studied in clinical trials against COVID-19. Initially, they were designed to target stress granules and other RNA biology. We have utilized structure-based molecular docking and all-atom molecular dynamics (MD) simulation approach to investigate in detail the binding energy and binding modes of the different anti-N inhibitors to N protein. The result showed that five drugs including Silmitasterib, Ninetanidinb, Ternatin, Luteolin, Fedratinib, PJ34, and Zotatafin were found interacting with RNA binding sites as well as to predicted protein interface with higher binding energy. Overall, drug binding increases the stability of the complex with maximum stability found in the order, Silmitasertib > PJ34 > Zotatatafin. In addition, the frustration changes due to drug binding brings a decrease in local frustration and this decrease is mainly observed in α-helix, β3, β5, and β6 strands and are important for drug binding. Our in-silico data suggest that an effective interaction occurs for some of the tested drugs and prompt their further validation to reduce the rapid outspreading of SARS-CoV-2.

Communicated by Ramaswamy H. Sarma

Keywords:

• SARS-CoV-2
• Nucleocapsid protein
• N-terminal domain
• molecular docking
• local frustration
• drug-protein interaction

Author contributions

V.K., S.H., and A.P., conceived and designed the study, S.H., P.K., and D.M.M., performed the experiments and calculations, and prepared figures of the results, F.B., N.A.J, P.K, A.P., and V.K. wrote the main text. All authors contributed to the article and approved the submitted version.

Acknowledgments

We sincerely thank DST-SERB for providing the GPU computational Facility.

Disclosure statement

No potential conflict of interest was reported by the authors.

Funding

The author(s) reported there is no funding associated with the work featured in this article.