A team of researchers at Brown University has developed a way of using machine learning to rapidly predict multiple protein configurations to advance understanding of protein dynamics and functions. Monteiro da Silva uses computational methods to model protein dynamics and looks for ways to improve methods or find new methods that work best for different situations. For this study, he partnered with Brenda Rubenstein, an associate professor of chemistry and physics, and other Brown researchers to experiment with an existing A.I.-powered computational method called AlphaFold 2.

 

The accuracy of AlphaFold 2 has revolutionized protein structure prediction, the method has limitations: It allows scientists to model proteins only in a static state at a specific point in time. During most cellular processes, proteins will change shape dynamically. In order to match protein targets to drugs to treat cancer and other diseases, researchers need a more accurate understanding of these physiological changes. They need to go beyond 3D shapes to understanding 4D shapes, with the fourth dimension being time. In this study, the researchers were able to manipulate the evolutionary signals from the protein to use AlphaFold 2 to rapidly predict multiple protein conformations, as well as how often those structures are populated. If you understand the multiple snapshots that make up the dynamics of what's going on with the protein, then you can find multiple different ways of targeting the proteins with drugs and treating diseases.

 

The protein on which the team focused in this study was one that had different drugs developed for it. Yet for many years, no one could understand why some of the drugs succeeded or failed. It all came down to the fact that these specific proteins have multiple conformations, as well as to understanding how the drugs bind to the different conformations, instead of to the one static structure that these techniques previously predicted; knowing the set of conformations was incredibly important to understanding how these drugs actually functioned in the body. As for next steps, the research team is refining their machine learning approach, making it more accurate as well as generalizable, and more useful for a range of applications.