Case Study — Machine Learning-Guided Enzyme Engineering: Improving CO2-Fixation rate of Glycolyl-CoA Carboxylase
Guiding Leading Synthetic Biologists 2.8x Beyond Nobel Prize Winning Method
The Challenge
Maximizing enzyme optimization, minimizing resource use
The research group has spent years attempting to improve the speed at which this complex enzyme can catalyse CO2. They used rational design and directed evolution, a method that earned Frances Arnold the Nobel prize just four years earlier. These industry-standard approaches can take years of hands-on research and trial and error to deliver results. From the 8,000 variants, the group tested in their final round of directed evolution, only 20% turned out to be functional proteins. Of those, just one variant displayed enhanced properties.
Our client explains,
The Solution
Discovering a 2.8x more effective variant in under 5 minutes
Our AI-based algorithm created a list of novel protein variants to take to the wet lab for the client’s research team. The calculation took less than five minutes but replaced weeks of manual modeling.
The team experimentally tested the 10 variants our AI-tool produced. 90% of those tested were active. Two of the 10 variants tested displayed enhanced properties:
- One showed a 2.8-fold increase in catalytic rate
- Another consumed less ATP, making it 50% more energy-efficient
The Results
“We were amazed by the results”
Before partnering with us, the client had nearly given up on further optimizing the carboxylase. With Exazyme, they achieved a rapid and major breakthrough. The 2.8x improvement in protein catalysis speed surpassed even the best results achieved during years of using traditional methods.
The head of this research group says,
The Future
Pushing the boundaries of protein engineering
Today, our partnership with this client continues on their upcoming research projects. As the team continues to improve the carboxylase catalysis rate and more, we offer customized tools that make designing proteins as easy as using an app.
Ingmar says, “We look forward to continuing to push the boundaries of what's possible with AI for protein engineering." We can’t wait to see what we’ll discover.