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| Credit: Unsplash/CC0 Public Domain |
According to a study published in the Proceedings of the National Academy of Sciences, Northwestern Medicine researchers used a novel high-throughput approach to solve a difficult protein design puzzle.
According to Gabriel Rocklin, Ph.D., assistant professor of pharmacology and senior author of the study, the approach could aid in the development of new therapeutics and biotechnology tools.
"The lessons from designing proteins are important for any computational protein design effort, including designing new therapeutics," said Rocklin, who is also a member of Northwestern University's Robert H. Lurie Comprehensive Cancer Center.
Protein folding is a cellular process that ensures proteins function properly and do not contribute to disease. When tested, most designed proteins are unable to fold into their designed structures, which is a major challenge in computationally designing new protein structures in a laboratory.
Rocklin's team previously discovered that, despite its simple structure, the fold is unusually difficult to design, with the best designs having only a 2% success rate. To address this issue, Rocklin's team tested thousands of new designs and examined the properties of stable and unstable designs using machine learning methods.
"Proteins have a very simple fold that resembles the letter 'M.' This structure is much simpler than most naturally occurring proteins, making it a good testing ground for understanding and improving protein design "said Rocklin.
In the current study, the researchers created over 10,000 new proteins and discovered that more than one-third of them folded into stable structures using specialized high-throughput experiments. According to Rocklin, the researchers were also able to identify the biophysical properties that stabilize proteins and compare different protein design methods.
"We increased our design success rate from 2% to 30% by making changes to our design protocol. This clarified better ways to design proteins while also assisting us in understanding what makes them stable or unstable "said Rocklin.
The current method can be applied to any computational protein design effort. According to Rocklin, proteins have the potential to be developed into therapeutics by modifying their surfaces so that they can bind to therapeutic targets.
"By connecting the two ends of the 'M' to form a loop, these proteins can become even more stable, which could be an exciting strategy for designing therapeutics," Rocklin added.
Journal information: Proceedings of the National Academy of Sciences