New technique rapidly identifies high-performing enzymes for sustainable biomanufacturing

To make progress in the use of microbes to produce materials in a sustainable manner, it is necessary to find new molecular or enzymes tools, but this is at high intensity of labor. A team from the University of Kobe has developed a technique that can classify thousands of candidates and a workflow that can assess representatives overnight, in what can become a fundamental technology for bioprometer.

The work was published in ACS catalyst.

While oil reserves decrease and prices soar, microorganisms can produce useful chemicals and fuels from renewable resources. They can convert raw materials into products under soft conditions thanks to the use of specialized molecular tools called enzymes.

Finding appropriate enzymes, modifying and assembling them in molecular assembly lines is what bio-fabrication is. The Bioengineer of the University of Kobe, Hasunuma Tomohisa, says: “Who controls enzymes controls biomufactory. There are easily accessible databases with more than 200 million enzymatic admissions, but a large part of the information about them is speculative and which takes time and labor to confirm their function.”

To solve this problem, Hasunuma and her team have proposed a new way of automatically grouping a large number of enzymes in a way that facilitates the selection of a set of significant representatives and concentration on them.

In addition, they have developed a robotic system that can test the activity of representative enzymes on a range of raw materials in a day. Together, this would allow them to detect a wide variety of enzymes for a given function, and they decided to try it on a class of almost 7,000 enzymes involved in a process necessary to produce raw materials for fuel, plastics and flavors.

In ACS catalysis, The team reports that this approach has enabled them to identify an enzyme that has productivity up to 10 times higher than that of the current industry standard. What is just as important, however, is that the newly identified enzyme is also as versatile as this standard; In other words, it can react on a wide range of raw materials.

“Above all, this observation shows that our approach is capable of identifying the hitherto unrecognized, very active and versatile enzymes of these databases,” said Hasunuma.

The bio-engineer, however, also wishes to underline another advantage of their method, claiming: “The large amount of data on the two differences between enzymes and the differences in versatility allows us to determine the parts of the enzymes enzyme.”

Hasunuma hopes that the technology that its team has developed will be so useful that it will become a fundamental technology for the bioprometer, just like the databases themselves.

But it is already looking for the next thing, “our technology allows us to connect the enzymatic structure to the large -scale function – it is the perfect training equipment for an AI. We think of developing an AI which can then turn around and use the data in the databases to predict the function of enzymes more precisely.”