Washington State University researchers have discovered a way to prevent herpes viruses from entering human cells by targeting a critical molecular interaction essential for viral infection. The breakthrough, detailed in the journal Nanoscale, opens a promising new path for developing broad-spectrum antiviral therapies. The study focused on the viral fusion protein used by herpes viruses to merge with and invade host cells—a key step in initiating infection. Despite the widespread impact of herpes viruses, developing effective vaccines has proven difficult, partly due to limited understanding of how this complex protein functions during cell entry. To unravel the process, scientists combined artificial intelligence with advanced molecular simulations. Jin Liu, a professor in the School of Mechanical and Materials Engineering and the study’s corresponding author, led the computational effort alongside Prashanta Dutta. Using machine learning, they analyzed thousands of potential amino acid interactions within the fusion protein to identify one that was uniquely vital for viral entry. Amino acids are the building blocks of proteins, and their precise arrangement dictates protein shape and function. The team developed an algorithm to sift through these interactions and pinpoint the single amino acid that played a decisive role in enabling the virus to fuse with cells. Once identified, the researchers collaborated with Anthony Nicola from the Department of Veterinary Microbiology and Pathology to conduct laboratory experiments. By introducing a precise mutation to that critical amino acid, they successfully disrupted the fusion process—rendering the virus unable to enter cells. “This was just one interaction among thousands,” Liu explained. “Without AI-driven simulations, finding it through trial and error could have taken years. The synergy between computational modeling and experimental validation dramatically speeds up discovery.” The findings demonstrate how computational tools can efficiently identify high-impact biological targets that would otherwise remain hidden in the complexity of protein dynamics. Still, significant questions remain. While the mutation blocked viral entry, researchers don’t yet fully understand how it alters the larger structure of the fusion protein. The team plans to continue using simulations and machine learning to explore how small changes at the molecular level influence the protein’s overall shape and function across larger scales. “There’s a gap between what we observe in experiments and what simulations reveal,” Liu noted. “Understanding how this single change triggers broader structural shifts is the next major challenge.” The research was conducted by Liu, Dutta, and Nicola, along with PhD students Ryan Odstrcil, Albina Makio, and McKenna Hull. Funding was provided by the National Institutes of Health.