Predicting the impact of protein mutations with simple maths

Researchers from the Centre for Genomic Regulation (CRG) in Spain and the Wellcome Sanger Institute in the UK have discovered that mutations affect protein stability following remarkably simple rules. Their discovery, published in the journal Nature, could help accelerate the development of new treatments for diseases, as well as the design of new proteins with industrial applications.

Proteins — the building blocks of life — are chains made up of 20 different types of smaller units called amino acids. A single mutation swaps one amino acid for another, changing the protein’s shape. This can mark the difference between health and disease. Many diseases, including cancer and neurodegenerative disorders, are caused by more than one mutation in a protein.

Predicting how mutations alter a protein’s shape is critical for understanding their contribution to disease. However, with so many amino acids in a protein, there are an astronomical number of ways mutations can combine — and as proteins get longer, the different number of combinations rises exponentially. For a protein 100 amino acids long, there are more possible combinations than there are atoms in the entire universe. The vast majority of known proteins, especially those contributing to human disease, are much longer.

“There are 17 billion different combinations of a protein that is 34 amino acids in length with only a single change allowed at each position,” said study co-author Aina Martí Aranda, who began the project at the CRG and is currently a PhD student at the Wellcome Sanger Institute. “If it took just one second to test a single combination, we’d need a total of 539 years to try them all. It’s not a feasible experiment.”

For years, there has been an underlying assumption that two mutations might interact with each other in unexpected ways, enhancing or suppressing each other’s effects. As noted by Martí Aranda, “The fear that two mutations interacting can unpredictably affect the whole structure made us use incredibly complex models.”

Now, a team led by CRG’s Dr André Faure and Professor Ben Lehner, with dual affiliation between the CRG and the Wellcome Sanger Institute, has discovered that the impact of mutations on protein stability is more predictable than previously thought. Their study found that while mutations do interact, it is a relatively rare occurrence, and the vast majority affect a protein independently of each other.

“Our discovery turns an old understanding on its head, showing that the endless possibilities of protein mutations boil down to straightforward rules,” Lehner said. “We don’t need supercomputers to predict a protein’s behaviour — just good measurements and simple maths will do.”

The researchers made the discovery by generating thousands of protein variants, each with different combinations of mutations that could produce functional proteins. They then tested the stability of the proteins, generating a vast amount of data on how each mutation and combination of mutations affect proteins. The experimental outcomes closely matched models which assume that the total effect of multiple mutations can be calculated by simply adding up the effects of each individual mutation.

The team’s findings can help better understand and target genetic diseases. For example, some genetic disorders are caused by many mutations in one protein and patients may have different combinations of mutations, making it challenging to predict disease severity and response to treatments. With the new understanding that most mutations act independently, clinicians can find new ways of predicting how various mutation combinations affect a protein’s stability and function, leading to more accurate prognoses and personalised treatment plans.

The study can also lead to more efficient drug development. Some drugs correct misfolded proteins, such as in Alzheimer’s disease, where the changing shape of amyloid-beta proteins forms plaques in the brain. Researchers can now better predict which mutations are most destabilising and design molecules that specifically stabilise these regions.

The study also has implications for biotechnologists using protein design to tackle different types of problems. For example, some enzymes have the ability to break down plastics in the environment. Researchers could design new enzymes with enhanced activity and stability by adding beneficial mutations together.

That said, the new study does have some limitations. For example, the researchers did not capture more complex interactions involving three or more mutations. In some proteins, these higher-order interactions could significantly impact stability and are not predicted by simply adding up individual effects. Also, while the findings can dramatically reduce the number of experiments needed, some level of experimental validation is still necessary to confirm predictions, especially for critical applications like drug development where there may be unforeseen effects or rare interactions that the models do not capture.

Image caption: GRB2-SH3 is a protein with 34 amino acids, meaning there are 17 billion different combinations if only a single change allowed at each position. Image credit: Aina Martí Aranda.