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 comparing their conformational dynamics with those of the wild-type protein, we investigate how engineered mutations reshape the conformational landscape and alter structural flexibility.
These simulations allow us to identify mutation-induced conformational changes and characterize the molecular interactions that stabilize functionally relevant states, providing mechanistic insight into the enhanced performance of the engineered variants.