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Published on: December 9, 2022
Deep mutational scanning of SARS-CoV-2 nucleocapsid reveals regional stability patterns
Alasdair D Keith1, Meredith M Keen1, Filipp Frank1
1Department of Biochemistry, Emory University School of Medicine, Atlanta, Georgia, USA.
Summary
Point mutations in SARS-CoV-2 nucleocapsid protein (NP) affect its stability and viral pathogenicity. Deep mutational scanning revealed specific regions and mutations, like R203K, impacting NP folding fitness and potentially driving viral evolution.
Area of Science:
- Virology
- Molecular Biology
- Protein Engineering
Background:
- Point mutations in SARS-CoV-2 nucleocapsid protein (NP) can alter viral detection, life cycle, and pathogenicity.
- Understanding NP mutation effects is crucial for comprehending viral evolution and developing diagnostics.
Purpose of the Study:
- To comprehensively map the impact of all possible point mutations on SARS-CoV-2 NP stability using deep mutational scanning (DMS).
- To identify specific protein regions and mutations influencing NP folding fitness and to evaluate computational prediction methods.
Main Methods:
- Deep mutational scanning (DMS) was employed to assess the effect of 7876 (98.9%) of all possible point mutations on SARS-CoV-2 NP surface expression, termed surface-expression-based folding fitness (SEBFF).
- Computational methods (machine learning and physics-based) were evaluated for their ability to predict DMS data.
Main Results:
- NP susceptibility to stability changes varied by region, with the RNA-binding domain (RBD) and dimerization domain (DD) being more sensitive.
- The R203K mutation in the linker region was found to increase NP folding fitness, suggesting a role in the evolution of variants like Alpha and Omicron.
- Per-site mutation predictions were accurate, with machine learning models outperforming physics-based approaches.
Conclusions:
- DMS provides a high-resolution map of SARS-CoV-2 NP mutational landscape, revealing critical regions and specific mutations impacting protein stability.
- Computational tools, particularly machine learning, show promise for predicting the functional impact of protein mutations.
- Findings offer insights into SARS-CoV-2 evolution and potential targets for therapeutic or diagnostic development.
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