AI identifies single protein interaction that blocks virus entry into cells before infection

AI found a way to stop a virus before it enters cells

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.

Researchers block virus entry by targeting key protein interaction

Washington State UniversityDec 15 2025 Washington State University researchers have found a way to modulate a common virus protein to prevent viruses from entering cells where it can cause illness, a discovery that could someday lead to new antiviral treatments. In the fundamental research, reported in the journal Nanoscale, the researchers in the School of Mechanical and Materials Engineering and the Department of Veterinary Microbiology and Pathology were able to find and block an important interaction at the molecular level that allows the herpes virus to enter cells. "Viruses are very smart," said Jin Liu, corresponding author on the paper 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." In their work, the researchers worked with a "fusion" protein that is used by herpes viruses to fuse with and enter cells to cause many illnesses. Researchers have a poor understanding of how exactly the complex protein opens up and invades cells, which is part of the reason that there aren't vaccines for these common types of viruses. Using artificial intelligence and simulations at the molecular scale, Professors Prashanta Dutta and Jin Liu from the School of Mechanical and Materials Engineering, sifted through thousands of possible interactions to find an important amino acid that plays a key role in allowing the harmful viruses to enter cells. They developed an algorithm to exam thousands of interactions among the amino acids, which are the building blocks of the protein. They then developed a machine learning method to differentiate the interactions and identify the most important ones. Led by Anthony Nicola, in the Department of Veterinary Microbiology and Pathology, the researchers then made a mutation to one of the important amino acids and found that it significantly blocked the virus' fusion success. The herpes virus was unable to enter cells. The simulations and machine learning were critical in tackling the experiments because the experiments to test just one interaction could take several months, said Liu. "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." While the researchers know the interaction is important, they still don't have a complete picture of just how the structure of the larger protein changes with an introduced mutation. They hope to further enlist simulations and machine learning to get a bigger picture of the entire protein's behavior. "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." In addition to Liu, Dutta and Nicola, the project was conducted by PhD students Ryan Odstrcil, Albina Makio, and McKenna Hull. The work was funded by the National Institutes of Health. Washington State University Journal reference: DOI: 10.1039/D5NR03235K

AI Helped Researchers Block a Virus Before Infection Began - Decrypt

Scientists said the approach could help guide future antiviral and disease research, though it remains early-stage. Most antiviral drugs target viruses after they have already slipped inside human cells. Researchers at Washington State University said they found a way to intervene earlier, identifying a single molecular interaction that viruses rely on to enter cells in the first place. The research, published in November in the journal Nanoscale, focused on viral entry, one of the least understood and most difficult stages of infection to disrupt, using artificial intelligence and molecular simulations to identify a critical interaction within a fusion protein that, when altered in laboratory experiments, prevented the virus from entering new cells. "Viruses attack cells through thousands of interactions," Professor Jin Liu, a mechanical and materials engineering professor at Washington State University, told Decrypt. "Our research is to identify the most important one, and once we identify that interaction, we can figure out a way to prevent the virus from getting into the cell and stop the spread of disease." The study grew out of work that began more than two years ago, shortly after the COVID-19 pandemic, and was led by Veterinary Microbiology and Pathology Professor Anthony Nicola, with funding from the National Institutes of Health. In the study, researchers examined herpes viruses as a test case. These viruses rely on a surface fusion protein, glycoprotein B (gB), which is essential for driving membrane fusion during entry. Scientists have long known that gB is central to infection, but its large size, complex architecture, and coordination with other viral entry proteins have made it difficult to pinpoint which of its many internal interactions are functionally critical. Liu said the value of artificial intelligence in the project was not that it uncovered something unknowable to human researchers, but that it made the search far more efficient. Instead of relying on trial and error, the team used simulations and machine learning to analyze thousands of possible molecular interactions simultaneously and rank which ones were most important. "In biological experiments, you usually start with a hypothesis. You think this region may be important, but in that region there are hundreds of interactions," Liu said. "You test one, maybe it's not important, then another. That takes a lot of time and a lot of money. With simulations, the cost can be neglected, and our method is able to identify the real important interactions that can then be tested in experiments." AI is increasingly being used in medical research to identify disease patterns that are difficult to detect through traditional methods. Recent studies have applied machine learning to predict Alzheimer's years before symptoms appear, flag subtle signs of disease in MRI scans, and forecast long-term risk for hundreds of conditions using large health record datasets. The U.S. government has also begun investing in the approach, including a $50 million National Institutes of Health initiative to apply AI to childhood cancer research. Beyond virology, Liu said the same computational framework could be applied to diseases driven by altered protein interactions, including neurodegenerative disorders such as Alzheimer's disease. "The most important thing is knowing which interaction to target," Liu said. "Once we can provide that target, people can look at ways to weaken it, strengthen it, or block it. That's really the significance of this work."