To make advances in using microbes to sustainably produce materials, it is necessary to find new molecular tools, or enzymes—but this is labor intensive. A Kobe University team has developed a technique that can classify thousands of candidates and a workflow that can evaluate representatives overnight, in what may become a fundamental technology for biomanufacturing.

The work has been published in ACS Catalysis.

As oil reserves dwindle and prices soar, microorganisms can produce useful chemicals and fuels from renewable resources. They can convert raw materials into products under mild conditions through the use of specialized molecular tools called enzymes.

Finding appropriate enzymes, modifying them and putting them together into molecular assembly lines is what biomanufacturing is all about. Kobe University bioengineer Hasunuma Tomohisa says, “Who controls enzymes controls biomanufacturing. 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.”

To solve this issue, Hasunuma and his team came up with a new way of automatically grouping large numbers of enzymes in a way that makes it easy to select a set of meaningful representatives and focus research on those.

In addition, they developed a robotic system that can test the activity of the representative enzymes on a range of raw materials within one day. Together, this would allow them to screen a large variety of enzymes for a given function, and they decided to try it on a class of almost 7,000 enzymes that are involved in a process needed to produce the raw materials for fuels, plastics and flavors.

In ACS Catalysis, the team reports that this approach allowed them to identify an enzyme that has productivity up to 10 times higher than that of the current industry standard. What’s equally important, though, is that the newly identified enzyme is also as versatile as that standard; that is, it can perform the reaction on a broad range of raw materials.

“Most of all, this finding demonstrates that our approach is able to identify hitherto unrecognized, highly active and versatile enzymes from these databases,” Hasunuma said.

The bioengineer, however, is also keen to point out another benefit of their method, saying, “The large amount of data on both the differences between the enzymes and the differences in their versatility allows us to pinpoint which parts of the enzyme are probably responsible for a given desirable trait. This not only helps us to clarify the action of an enzyme and improve that function in a more targeted way, but also lets us search for that structure in yet other enzymes.”

Hasunuma hopes that the technology his team developed will be so useful that it becomes a fundamental technology for biomanufacturing, just like the databases themselves.

But he is already looking for the next thing, “Our technology lets us connect enzyme structure with function on a large scale—this is the perfect training material for an AI. 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.”