Viruses possess a formidable ability to enter our cells to multiply there, thus triggering diseases. However, a team from Washington State University has just shown that by targeting a single molecular interaction among thousands, it is possible to completely block this entry process. This approach opens the door to new methods for countering infections.
This work, published in the journal
Nanoscale, brings together engineers and microbiologists. They focused their efforts on a specific viral protein, essential to viruses. This so-called fusion protein acts like a key allowing the virus to attach to the cell and then fuse with it to penetrate (explanation at the end of the article).
Illustration image Pixabay
Faced with the detailed architecture of this large protein, the researchers turned to artificial intelligence. Molecular-scale simulations allowed them to analyze thousands of possible interactions between the amino acids that compose it. An algorithm and machine learning techniques then isolated the most important connection for the success of the infection.
The next step was to verify this prediction in the laboratory. By genetically modifying the virus to alter this single identified amino acid, experiments confirmed that the pathogen became incapable of fusing with cell membranes. The infection was thus stopped dead, validating the central role of this interaction.
This method combining simulations and experimentation represents a considerable time-saving. As explained by Jin Liu, professor and corresponding author, testing each interaction in the laboratory would take months, even years. The computational work effectively guides research towards the most promising targets.
Although this discovery is encouraging, it also raises new questions. Scientists are now seeking to understand how such a localized change influences the overall structure of the large fusion protein. They are continuing their simulations to clarify these mechanisms on a larger scale.
Fusion proteins: the viruses' entry key
Viruses cannot reproduce on their own. They absolutely must enter a host cell to hijack its machinery and produce new copies of themselves. To do this, many viruses, such as those causing herpes, influenza, or HIV, use special proteins on their surface called fusion proteins.
These proteins act like elaborate recognition and opening mechanisms. First, they bind to specific receptors located on the membrane of the target cell. This binding then triggers a major change in the shape of the viral protein.
This conformational change is the decisive step. It allows the membrane of the virus and that of the cell to come extremely close together, then to fuse. Once this fusion is accomplished, the genetic material of the virus can be injected inside the cell, initiating the infection.
Therefore, understanding the precise structure and function of these proteins is a major challenge. Blocking their action, either by preventing the initial binding or by disrupting the shape change, constitutes a powerful antiviral strategy to neutralize the virus before it even penetrates.
Artificial intelligence at the service of biology
Modern biology generates astronomical amounts of data, particularly on the structure of molecules like proteins. Manually analyzing all the possible interactions between the thousands of atoms that compose them is a virtually impossible task. This is where artificial intelligence (AI) and machine learning come in.
These computing technologies can be trained to recognize patterns and make predictions. In the case of this study, the researchers first created a detailed computer model of the viral protein. Algorithms then examined all the forces and bonds between its different amino acids.
Machine learning made it possible to process this mass of data to identify which interactions were the most stable or the most decisive for the protein's function. It was thus able to 'learn' to distinguish the 'background noise' from the connections truly essential to the infection process.
This approach transforms research. Instead of proceeding through long and costly trial and error in the laboratory, scientists can now use AI to quickly target the most promising elements for experimental testing, thus considerably accelerating the pace of discoveries.