A team of bioengineers and synthetic biologists has developed a machine-learning guided platform to design and test thousands of new enzymes quickly and efficiently. The platform, described in a paper titled “Accelerated enzyme engineering by machine-learning guided cell-free expression,” uses machine learning to predict the behavior of enzymes and test their performance in various chemical reactions. This approach allows for rapid iteration and optimization of enzyme design, bypassing traditional methods that require manual modification of DNA and testing in living cells. The platform was used to synthesize a small-molecule pharmaceutical at 90% yield, a significant improvement over previous attempts. The researchers also demonstrated the ability to generate multiple specialized enzymes in parallel to produce eight additional therapeutics. The potential applications of this technology are vast, including the development of sustainable and efficient processes in industries such as pharmaceuticals, food production, and environmental remediation. While there are still challenges to overcome, including lack of high-quality data, the researchers believe that machine learning can revolutionize the field of enzyme engineering and accelerate the development of new technologies.
AI-Powered Enzyme Optimization Accelerates Discovery
