Researchers have unveiled that the influence of mutations on protein stability follows surprisingly simple guidelines. This finding could significantly expedite the creation of new treatments for various diseases and aid in the crafting of novel proteins for industrial uses.
A study released today in the journal Nature, led by scientists from the Centre for Genomic Regulation (CRG) and the Wellcome Sanger Institute, has revealed that mutations affect protein stability based on remarkably straightforward principles. This discovery could have significant implications for speeding up the development of new disease treatments and designing proteins for industrial purposes.
Proteins consist of chains made from twenty distinct types of components called amino acids. A single mutation replaces one amino acid with another, leading to a change in the protein’s structure. This alteration can be the key factor between health and illness. Many diseases, like cancer and neurodegenerative disorders, stem from multiple mutations within a protein.
Understanding how mutations change a protein’s shape is essential to grasp their role in disease. However, given the numerous amino acids in a protein, the potential combinations of mutations are staggering. Testing every possible combination experimentally to see how they affect a protein is essentially unfeasible.
“There are 17 billion potential combinations for a protein composed of 34 amino acids if we allow only one change in each position. If we spent just one second testing each combination, completing all tests would take 539 years. This is simply not practical,” explains Aina Martí Aranda, a co-author on the study, who began the project at the CRG and is currently pursuing her PhD at the Wellcome Sanger Institute in the UK.
As the length of proteins increases, the combinations grow exponentially. A protein that is one hundred amino acids long has more possible variations than the total atoms present in the entire universe. Most known proteins, particularly those implicated in human diseases, are significantly longer.
Despite this complex web of possibilities, the research conducted by Dr. André Faure at the Centre for Genomic Regulation in Barcelona, and ICREA Research Professor Ben Lehner, who has a dual affiliation with CRG and the Wellcome Sanger Institute, has led to the revelation that the effects of mutations on protein stability are actually more predictable than previously believed.
For years, it was often assumed that two mutations could interact in unexpected manners, either enhancing or diminishing each other’s effects. “The concern that interacting mutations may unpredictably alter the overall structure prompted us to resort to extremely complicated models,” says Martí Aranda.
The study discovered that although mutations can interact, such instances are relatively infrequent, with the majority affecting a protein’s stability independently. “Our findings challenge traditional views, demonstrating that the vast array of protein mutations can be boiled down to simple rules. We don’t require supercomputers for predicting a protein’s behavior—just accurate measurements and straightforward mathematics will suffice,” states Dr. Lehner.
The research team made this breakthrough by creating thousands of protein variants, each having different combinations of mutations capable of forming functional proteins. They then assessed the stability of these proteins, compiling extensive data on how individual mutations and their combinations influence protein stability. The results aligned closely with models suggesting that the overall impact of multiple mutations can be calculated by merely summing the effects of each single mutation.
This knowledge can enhance understanding and targeting of genetic disorders. For instance, certain genetic conditions arise from multiple mutations in a single protein. Patients may carry various mutation combinations, complicating predictions regarding disease severity and treatment responses.
With the realization that most mutations act independently, healthcare professionals can devise new approaches to foresee how different mutation combinations influence a protein’s stability and functionality. This advancement could enable more precise prognosis and tailored treatment strategies, ultimately improving patient outcomes.
The findings could also streamline drug development efforts. Some medications target misfolded proteins, as seen in Alzheimer’s disease, where the altered shape of amyloid-beta proteins leads to plaque formation in the brain. Researchers can now more accurately identify which mutations lead to instability and develop molecules aimed at stabilizing these regions specifically.
This study holds promise for biotechnologists engaged in protein design to address various challenges. For example, certain enzymes can decompose plastics in the environment. By combining advantageous mutations, researchers might engineer new enzymes with improved activity and stability.
While this discovery signifies a substantial leap forward, the researchers acknowledge some limitations in their study. For instance, interactions involving three or more mutations were not fully explored. In some instances, these higher-order interactions might significantly influence stability and cannot be predicted by merely aggregating individual effects.
Moreover, although their findings could dramatically reduce the number of necessary experiments, a degree of experimental validation remains essential to confirm predictions, particularly for critical applications like drug development, where unforeseen effects or rare interactions may not be captured by the models.
