Researchers at Kobe University, Kobe, Hyōgo, Japan, have developed a breakthrough technique to accelerate the discovery of enzymes for sustainable biomanufacturing. Led by Hasunuma Tomohisa, the team created an automated workflow to efficiently screen thousands of enzyme candidates and evaluate their performance overnight.
“Who controls enzymes controls biomanufacturing,” said Tomohisa in a June 27 news release. “There are easily accessible databases with more than 200 million enzyme entries, but much of the information on them is speculative and it’s time consuming and labor intensive to confirm their function.”
Enzymes are crucial molecular tools that enable microorganisms to convert renewable resources into useful chemicals, fuels, plastics and flavors under mild conditions — a process essential for biomanufacturing as oil reserves diminish.
The researchers' approach combines two key elements: an automated classification system that groups large numbers of enzymes to identify meaningful representatives, and a robotic testing system that can assess enzyme activity across various raw materials within a single day. This methodology allows researchers to systematically screen enzymes for specific functions rather than relying on trial-and-error approaches.
“Our technology lets us connect enzyme structure with function on a large scale — this is perfect training material for an AI,” said Tomohisa. “We are thinking about developing an AI that can then turn around and use the data in the databases to predict the function of the enzymes more accurately.”
To validate their technique, the team tested it on nearly 7,000 enzymes involved in producing raw materials for fuels, plastics and flavors. The results, published in ACS Catalysis, identified an enzyme with productivity up to 10 times higher than current industry standards while maintaining comparable versatility across different raw materials.
Beyond discovering superior enzymes, the method generates valuable data linking enzyme structure to function. This information helps researchers understand which molecular components drive desirable traits, enabling more targeted enzyme improvements and facilitating searches for similar structures in other enzymes.
