Washington State University scientists have identified a way to interfere with a key viral protein, stopping viruses from entering cells where they can trigger disease. The finding points to a potential new direction for antiviral therapies in the future.
The study, published in the journal Nanoscale, focused on uncovering and blocking a specific molecular interaction that herpes viruses rely on to gain access to cells. The work brought together researchers from the School of Mechanical and Materials Engineering and the Department of Veterinary Microbiology and Pathology.
"Viruses are very smart," said Jin Liu, corresponding author of the study and a professor in the School of Mechanical and Materials Engineering. "The whole process of invading cells is very complex, and there are a lot of interactions. Not all of the interactions are equally important -- most of them may just be background noise, but there are some critical interactions."
Understanding the Viral Fusion Process
The team examined a viral "fusion" protein that herpes viruses use to merge with and enter cells, a process responsible for many infections. Scientists still have limited insight into how this large and complex protein changes shape to make cell entry possible, which helps explain why vaccines for these widespread viruses have been difficult to develop.
To tackle this challenge, researchers turned to artificial intelligence and detailed molecular simulations. Professors Prashanta Dutta and Jin Liu analyzed thousands of potential interactions within the protein to identify a single amino acid that plays an essential role in viral entry. They created an algorithm to examine interactions among amino acids, the basic components of proteins, and then applied machine learning to sort through them and pinpoint the most influential ones.
Using AI to Pinpoint a Critical Weak Spot
After identifying the key amino acid, the research team moved to laboratory experiments led by Anthony Nicola from the Department of Veterinary Microbiology and Pathology. By introducing a targeted mutation to this amino acid, they found that the virus could no longer successfully fuse with cells. As a result, the herpes virus was blocked from entering the cells altogether.
According to Liu, the use of simulations and machine learning was essential because experimentally testing even a single interaction can take months. Narrowing down the most important interaction ahead of time made the experimental work far more efficient.
"It was just a single interaction from thousands of interactions. If we don't do the simulation and instead did this work by trial and error, it could have taken years to find," said Liu. "The combination of theoretical computational work with the experiments is so efficient and can accelerate the discovery of these important biological interactions."
What Researchers Still Need to Learn
Although the team confirmed the importance of this specific interaction, many questions remain about how the mutation changes the structure of the full fusion protein. The researchers plan to continue using simulations and machine learning to better understand how small molecular changes ripple through the entire protein.
"There is a gap between what the experimentalists see and what we can see in the simulation," said Liu. "The next step is how this small interaction affects the structural change at larger scales. That is also very challenging for us."
The research was carried out by Liu, Dutta, and Nicola along with PhD students Ryan Odstrcil, Albina Makio, and McKenna Hull. Funding for the project was provided by the National Institutes of Health.
