In San Diego, scientists at ag biotech startup Cellibre are looking to replace traditional agriculture for producing high-value ingredients. One of their first targets is the highly valuable cannabinoid market, which they believe can be disrupted by producing natural, high-quality, medical-grade cannabinoids via fermentation, eliminating the need for agriculture completely.
To achieve this goal, they are abandoning the economically inefficient model of growing plants, to harvest key extracted and isolated compounds like CBD. But knowing that cellular manufacturing for cannabinoids will be superior to plant-based products is one thing; accomplishing it is quite another.
Cellibre scientists teamed up with the Telesis Bio service group, allowing them to identify a novel enzyme for cannabinoid production and hone its performance using targeted libraries from Telesis Bio — doing so much faster and more cost-effectively than any other method would have allowed.
“Targeted libraries allowed us to make very complex designs that we normally would not be able to test, because each design would have to be built individually, by de novo synthesis, costing far too much, or as huge pooled libraries containing mostly variants we aren’t interested in,” says Russell Komor, director of biochemistry at Cellibre. “The Telesis Bio approach gives us whatever mutations we want, and the data we get are so much more powerful and more predictive.”
Komor’s team found that the optimal candidate was one they couldn’t have designed any other way. It included three mutations that separately were not all highly beneficial, but together had excellent performance.
“In the past, our recombination designs would need to be modified to fit the molecular biology tools needed to build them,” Komor says. “Now, we’re making any designs we want, and we don’t have to worry about the molecular biology. This really allows protein engineers to focus on engineering.”
To learn more about this project and see data about enzyme performance and more, check out the full case study.