Binding Free Energy Landscape of Different SARS-CoV-2 Nsp1 Variants Interacting with NXF1-RRM: Insights from Molecular Dynamics Simulations and Markov State Models

Abstract The interaction of the SARS-CoV-2 Nsp1 protein with the host NXF1-RRM domain plays an important role in host gene expression, which influences virus replication and host immune surveillance. Thus, understanding the key factors that determine their binding affinity, such as mutations, is of great interest. In this work, we investigated the effects of three Nsp1 mutants─D33K/E36K/E37K/E41K, L27D/V28D, and R124E/K125E─on its binding to the NXF1-RRM domain using structural and energetic analysis. To quantify the affinity changes, we developed a computational framework for calculating the free energy landscape of protein–protein binding based on the Markov state model (MSM). Our approach is based on fluctuations of the radius of gyration and the root mean square deviation (RMSD) from a reference state. We found that the D33K/E36K/E37K/E41K mutant exerts the strongest destabilizing effect, resulting in greater structural deviation, reduced compactness, and a significant loss of intermolecular hydrogen bonds. In contrast, the L27D/V28D and R124E/K125E mutants retain structural and dynamical properties closer to those of wild-type Nsp1 (WT), indicating that these mutations have a more limited impact on the stability of the Nsp1–NXF1-RRM complex. These results are in good agreement with the previous experimental work. We also used the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method to estimate the binding free energy of these complexes, yielding results consistent with the MSM analysis and experimental data. However, the binding free energy values obtained from the MSM method are more realistic compared to the MM-PBSA method, which tends to overestimate binding free energy values or produce unphysically negative results. Therefore, the MSM approach provides a useful framework for characterizing mutation-induced changes in the structural stability and binding energetics of Nsp1–NXF1-RRM complexes.

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Journal
Journal of Chemical Information and Modeling
Published
2026-09-30
DOI
https://doi.org/10.1021/acs.jcim.6c02358
Primary Topic
Protein Structure and Dynamics
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article
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Binding Free Energy Landscape of Different SARS-CoV-2 Nsp1 Variants Interacting with NXF1-RRM: Insights from Molecular Dynamics Simulations and Markov State Models

Tri Minh Pham, Mai Suan Li, Hung Van Nguyen
Journal of Chemical Information and Modeling
Protein Structure and Dynamics
article

Binding Free Energy Landscape of Different SARS-CoV-2 Nsp1 Variants Interacting with NXF1-RRM: Insights from Molecular Dynamics Simulations and Markov State Models

Tri Minh Pham, Mai Suan Li, Hung Van Nguyen
article en

Abstract

Abstract The interaction of the SARS-CoV-2 Nsp1 protein with the host NXF1-RRM domain plays an important role in host gene expression, which influences virus replication and host immune surveillance. Thus, understanding the key factors that determine their binding affinity, such as mutations, is of great interest. In this work, we investigated the effects of three Nsp1 mutants─D33K/E36K/E37K/E41K, L27D/V28D, and R124E/K125E─on its binding to the NXF1-RRM domain using structural and energetic analysis. To quantify the affinity changes, we developed a computational framework for calculating the free energy landscape of protein–protein binding based on the Markov state model (MSM). Our approach is based on fluctuations of the radius of gyration and the root mean square deviation (RMSD) from a reference state. We found that the D33K/E36K/E37K/E41K mutant exerts the strongest destabilizing effect, resulting in greater structural deviation, reduced compactness, and a significant loss of intermolecular hydrogen bonds. In contrast, the L27D/V28D and R124E/K125E mutants retain structural and dynamical properties closer to those of wild-type Nsp1 (WT), indicating that these mutations have a more limited impact on the stability of the Nsp1–NXF1-RRM complex. These results are in good agreement with the previous experimental work. We also used the molecular mechanics Poisson–Boltzmann surface area (MM-PBSA) method to estimate the binding free energy of these complexes, yielding results consistent with the MSM analysis and experimental data. However, the binding free energy values obtained from the MSM method are more realistic compared to the MM-PBSA method, which tends to overestimate binding free energy values or produce unphysically negative results. Therefore, the MSM approach provides a useful framework for characterizing mutation-induced changes in the structural stability and binding energetics of Nsp1–NXF1-RRM complexes.

Journal of Chemical Information and Modeling
FPT University (VN), Université Paris Cité (FR), Institute of Physics (PL), Polish Academy of Sciences (PL)
Affordable and clean energy
Openalex Percentile: Top 19%
Protein Structure and Dynamics
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