PULLMAN, Wash — 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.
