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We’ve talked before about how predictive models for protein folding alone aren’t enough; researchers need to take things several steps further, understanding where proteins move within our cells and how they interact with other molecules. The more complete that picture of protein behavior becomes, the greater the impact will actually be.
What happened: Researchers at MIT recently made a significant stride in fleshing out that picture with the launch of a biological AI model called FragFold.
The model, which is built on the back of Google DeepMind’s AlphaFold, is able to predict how protein fragments bind to and inhibit full-length proteins in E. Coli. It has the potential, according to the researchers, to act as a “generalizable approach” to finding molecules that are likely to inhibit targeted protein functions.
Importantly, the model is able to apply this level of behavioral prediction to “proteins without known functions, without known interactions, without even known structures,” meaning researchers can better understand molecular behavior without understanding the details of disordered proteins that are historically difficult to study.
Why it matters: Unsurprisingly, this might have significant implications for biological research, specifically, the study of disease and design of therapeutics. Gene-Wei Li, an associate professor of biology at MIT, said that “we can imagine delivering functionalized fragments that can modify native proteins, change their subcellular localization, and even reprogram them to create new tools for studying cell biology and treating diseases.”
