Assay development and screening of inhibitors targeting the SARS-CoV-2 2'-O-methyltransferase NSP16

Abstract

The coronavirus disease-2019 (COVID-19) pandemic, etiologically caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has profoundly impacted the global health. While vaccines have been developed, they have shown limited efficacy in treating patients already under infection or preventing infection with emerging SARS-CoV-2 variants. The nonstructural protein 16 (NSP16), with the assistance of the nonstructural protein 10 (NSP10), is responsible for forming the Cap-1 structure, which is critical for viral replication and immune evasion through the 5^'-capping of viral mRNA. As a result, NSP16/NSP10 has emerged as a promising target for antiviral treatment of coronaviruses. In this study, we aimed to discover small molecule inhibitors of NSP16/NSP10 by leveraging recent structural insights and combined tools of virtual and experimental screenings. We designed a simple scintillation proximity assay to enable biochemical testing for NSP16/NSP10 enzymatic activity and applied it to screen inhibitors from candidate hit compounds that are derived from molecular docking-based virtual screenings. We identified potential hits that inhibit the NSP16 activity with cellular efficacy. Together with structural analysis and chemotype categorization, this study lays the groundwork for novel antiviral therapeutics development against SARS-CoV-2 and related coronaviruses.

Keywords

COVID-19 / SARS-CoV-2 / NSP16 / NSP10 / Assay design / Inhibitor screening / Drug discovery

CRediT authorship contribution statement

Mengtong Cao: Writing - review & editing, Writing - original draft, Investigation, Formal analysis, Data curation, Conceptualization. Carl W. Trieshmann: Writing - review & editing, Writing - original draft, Visualization, Methodology, Investigation, Data curation. Subodh Kumar Samrat: Writing - review & editing, Data curation. Hongmin Li: Writing - review & editing, Supervision, Investigation, Funding acquisition, Data curation. Yifei Wu: Writing - review & editing, Investigation, Data curation. Steven P. Maher: Writing - review & editing, Writing - original draft, Methodology, Investigation, Data curation. Angela A. Bae: Writing - review & editing, Writing - original draft, Investigation, Data curation. Zhong-Ru Xie: Writing - review & editing, Data curation. Robert J. Hogan: Writing - review & editing, Methodology, Funding acquisition. Y. George Zheng: Writing - review & editing, Supervision, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization.

Data availability

All data generated or analyzed during this study are included in this article and its supplementary information files.

Ethics approval

Not applicable.

Declaration of generative AI in scientific writing

Not applicable.

Funding information

This study was supported by grants AI158176 to Y.G.Z. and AI175435 and AI177149 to H.L. from the National Institute of Allergy and Infectious Diseases (NIAID), the National Institutes of Health (NIH). Y.G.Z. was additionally supported by a National Institute of General Medical Sciences grant GM149230. H.L. was also supported by the NIH grants: AI161845 and AI131669.

Conflict of interest statement

The authors declare that they have no financial or non-financial competing interests related to this work.

Acknowledgments

The authors would like to acknowledge the use of several specialized software tools in this study. Molecular docking was performed using Schrödinger Maestro, and molecular dynamics simulations were conducted using Desmond developed by D. E. Shaw Research. Molecular descriptor calculations and SAR/QSAR analyses utilized DataWarrior (1D descriptors), Crescent Silico Chem Master (1D and 2D descriptors), and Cresset Flare (3D descriptors). Pharmacophore modeling was carried out using LigandScout. These resources were essential in supporting the computational aspects of the research.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pscia.2025.100076.

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