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article · Advances in Basic and Applied Sciences

Molecular docking, Molecular dynamics simulation and MMPBSA studies on potential lead compounds fighting NSP6 of SARS-CoV-2

Abstract

Background: The non-structural protein 6 (NSP6) of SARS-CoV-2 is one of the most fascinating NSPs for drug targeting, due to its crucial role in the viral replication inside the host cells. This study aimed to predict the tertiary structure of the protein and to find a vaccine candidate to fight NSP6. Methods: The AlphaFold server was used to predict the tertiary structure of the protein, then the model was refined using the DeepRefiner server, and finally, the quality of the refined model was assessed using the SAVES server. A full library of the available chemical ligands was downloaded from the ZINC20 database, and then these ligands were docked against the NSP6 protein. The physicochemical and drug likeness and the toxicity of the picked-up ligands were tested using SwissParam, Swiss-ADME, ProTox III and ADMET-AI servers. The ligand complex with the NSP6 was subjected to Molecular dynamics simulation to evaluate its interactions with the protein through RMSD, RMSF, SASA, Rg, H-bonds, and free energy studies. The MD simulations were run at 100 ns time function to study the changes of the trajectories and the parameters of the complexes compared to the NSP6-apo. Results: AlphaFold server produced a high-quality model, and after refinement, SAVES server indicated that the structure had a quality percent of 99.94%. The docking process selected two ligands ZINC0117742510 and ZINC1500127684, that are suitable as vaccine candidates. The MDS analysis revealed that the NSP6-ZINC1500127684 and the NSP6-ZINC0117742510 proved minimal deviations and suitable stability compared to the NSP6-apo. The MMPBSA analysis indicated that NSP6-ZINC1500127684 complex had a lower binding energy than NSP6-ZINC0117742510. Conclusion: Therefore, ZINC0117742510 and ZINC1500127684 ligands were proven as potential vaccine candidates against the SARS-CoV-2 NSP6 protein.

Research topics

  • Computational Drug Discovery Methods

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DOI: 10.21608/abas.2025.410458.1072

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