Official Project Description
Nipah virus (NiV) is a highly dangerous pathogen first identified in the late 1990s that poses a significant threat to global health.
Naturally carried by fruit bats, the virus can spread to humans directly, through intermediate hosts like pigs, or via person-to-person contact.
Infections are often fatal, with mortality rates between 40% and 80%, typically resulting from severe respiratory issues or acute brain inflammation (encephalitis).
The World Health Organization has designated it a priority disease, signaling an urgent global need for accelerated research into effective medical countermeasures to prevent a potential pandemic. This project uses a data-driven approach to engineer "decoy" proteins that neutralize the Nipah Virus (NiV) by mimicking EFNB2, the virus's natural receptor for human cells.
While our machine learning models successfully identify effective variants 70% of the time, the physical reasons behind their success remain unclear.
To bridge this gap, we use molecular dynamics simulations to examine these engineered proteins at the atomic level.
By performing alchemical calculations, we can measure the binding free energy between the decoy and the virus, thereby quantifying how much each structural change contributes to viral neutralization.
This detailed view helps us determine whether a decoy’s improved performance stems from a better physical fit, like puzzle pieces locking together, or from new electrostatic attractions that stabilize the bond.
Ultimately, these simulations move us beyond predictive modeling and into a deeper biophysical understanding, enabling the design of more precise and effective antiviral therapies.