December 3, 2024

Decoding protein interactions: A step toward personalized medicine

Investigating how proteins interact is key to understand how cells work and communicate. In a new study — the first of its kind to examine the effects of all possible mutations in a protein across interactions with its partners, FMI researchers have provided key insights into how protein interactions are governed and how mutations influence cellular functions.

Proteins are the molecular machines of life, performing tasks ranging from driving chemical reactions to orchestrating cell communication. For these tasks, proteins must bind to the right partners with precision, avoiding mispairings that could disrupt cellular processes and lead to disease.

Scientists have long been curious about how changes in the sequence of amino acids — the building blocks of proteins — can alter a protein’s binding capabilities. To investigate this question, researchers in the Diss lab analyzed the effects of all possible mutations in a single protein across its interactions with an entire family of partner proteins. They focused on a protein called JUN, which plays a key role in DNA binding and cellular communication.

The researchers mutated every single amino acid in JUN and measured the impact of each mutation on JUN’s interactions with 54 different partner proteins. The mutations influenced the interactions of JUN in two ways: some affected the protein’s ability to bind with all its partners, while others determined its selectivity for individual partners. These two factors often operate in a delicate balance, says study senior author Guillaume Diss. “If a mutation increases the specificity for one partner, it will tend to decrease the affinity for all other partners,” he says.

The findings provide new insights into how proteins evolve and adapt their functions over time. The study also marks a first: previously, protein interaction studies focused on a few partners, but this work examined JUN’s interactions across an entire protein family, Diss says.

By decoding the rules that govern protein interactions, Diss and his team hope to develop predictive models to better understand how mutations contribute to disease. For example, he says, such models could advance personalized medicine by assessing how a person’s genetic makeup influences their likelihood to develop conditions such as type 2 diabetes and Alzheimer’s disease.