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bioRxiv · 10.1101/2025.07.28.667129

Predicting interactions between the SARS-CoV-2 spike glycoprotein and the human proteome using AphaFold and molecular dynamics simulations

Liu, Q.·Henderson, M.
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Abstract

AbstractSince the COVID-19 pandemic began, the SARS-CoV-2 virus has caused over 775 million cases and more than 7 million deaths worldwide. Despite progress in treatments and vaccines, we still need better ways to prevent and treat the disease. To do this, a clearer understanding of how SARS-CoV-2 interacts with human proteins is needed. We developed a new computational tool to predict and study these protein-protein interactions. Our method uses two stages of AlphaFold, an AI tool, to predict how SARS-CoV-2 proteins bind to human proteins, followed by molecular dynamics simulation to refine these predictions. We tested this method in a small study focusing on the S1 subunit of the SARS-CoV-2 spike protein interacting with human cell junction and synaptic proteins, and in a larger study screening the spike S1 N- terminal domain against the entire human proteome. We validated the method using experimental virus-human protein interactions. The results provide valuable insights into how SARS-CoV-2 interacts with human proteins, guiding future experiments to better understand COVID-19s short- and long-term effects. This developed method could be utilized to study protein-protein interactions in other biological systems.

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Liu, Q., Henderson, M.. 2025-07-28. Predicting interactions between the SARS-CoV-2 spike glycoprotein and the human proteome using AphaFold and molecular dynamics simulations. https://doi.org/10.1101/2025.07.28.667129

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