[Disclosure: AgFunderNews’ parent company AgFunder is an investor in Scindo]
- Scindo—a UK-based startup building an AI-powered enzyme discovery and design platform—has raised a £4 million ($5.4 million) seed round.
- The round was co-led by Kadmos Capital and Clay Capital, with participation from PINC, the venture arm of food and beverage company Paulig, and existing investors Synbioven, AgFunder, SOSV, Farvatn Venture and Savantus Ventures.
- Scindo develops enzymes—nature’s tiny biological catalysts—that can transform a wide range of feedstocks into ingredients that have historically been sourced from petrochemicals.
Founded in 2020 by Dr. Gustaf Hemberg, Dr. Ben Davis, and Juliet Sword, Scindo combines AI models with proprietary data to accelerate enzyme discovery and optimization.
The firm, which has established partnerships with leading specialty chemical manufacturers, develops enzymes for several industry verticals including food and flavorings, cosmetics, and specialty chemicals.
With the new funding, it will expand its platform, scale wet-lab capabilities and strengthen its team.
“The specialty chemicals industry has long sought to move away from petrochemical-derived ingredients, but existing approaches have struggled with complex natural feedstocks,” said Ali Morrow, partner at lead investor Clay Capital.
“Scindo’s approach creates molecular craftsmen: enzymes designed for specific industrial jobs that offer cost-competitive natural alternatives and unlock previously inaccessible feedstocks, creating significant opportunities globally to end the industry’s reliance on crude oil.”
Designer enzymes
Scindo CEO Gustaf Hemberg told AgFunderNews: “When we started, we were looking at enzymes with quite rare functionalities [such as] CH activations [breaking or modifying carbon-hydrogen bonds; enzymes that can do this are rare in nature]. We were looking at taking molecules apart through carbon bond cleavages [thereby opening up routes for degrading stubborn molecules such as plastics].
“By discovering more of these quite unknown [naturally occurring] enzymes, we were able to use that data and feed it into machine learning and AI for new discovery of unknown functionalities but also to use that in a generative way to design novel enzymes to do new things.
“At that’s at the core of what we do. Essentially, by gathering new examples and building a proprietary data set of enzymes with specific functionalities and selectivities [how specifically the enzyme performs a task], you can essentially allow the machine learning to understand what part of the sequence or part of the structure drives the key selectivity you’re looking for.”
He added: “And that’s really been the problem with public data, which is concentrated around specific enzyme families. It’s very hard to use public data to design [enzymes for] specific fields that are of high interest to us.”
Once Scindo has identified or designed promising candidate enzymes with the functionality it is looking for, it secures DNA sequences [from third parties] enabling it to produce the enzymes in a microbial host such as yeast or fungus and can then start testing them, said Hemberg.
“We have quite a big chemistry screening platform, so we are able to test the enzymes in the lab, characterize them and then feed that data back into the machine learning. Closing the loop between real life results and machine learning has been really critical for us.”
Once it has tested some candidates, it can do further work to rank them based on viability for scaling up in a microbial expression system and then work with an enzyme manufacturer to scale up production, said Hemberg.
Cell-free biomanufacturing
Scindo is working on enzymes that can perform multiple functions, from breaking down plastics into high-value ingredients, to creating lower-cost flavor & fragrance ingredients that can be made via cell-free biomanufacturing, said Hemberg.
In the case of the flavor & fragrance ingredients, he said, “The feedstock would be some sort of agricultural fatty acid. We’ve designed different enzyme systems that can selectively convert that feedstock into flavor molecules. We haven’t announced it yet but we have a partnership where we’re starting pilot scaling.
“Some of those flavor ingredients could be produced with precision fermentation [by engineering microbes to express them in costly steel fermentation tanks], but that is much more expensive [than using a cell-free approach just using enzymes], the titers are quite low, and you generate a lot of waste metabolites.”
By using a cell-free approach that utilizes the internal machinery of microbial cells (such as enzymes) to convert feedstocks into the target flavor molecules, Scindo can significantly reduce production costs, he claimed.
Operating without the constraints of a cell also allows you to operate across a wider range of conditions (pH, temperature) and enables you to produce the target ingredients more rapidly, he claimed.
“We’re hoping to target a market launch in the next 12 months or so for our first two products.”
Proprietary data sets
Stepping back, he said, the world’s largest enzyme companies tend to be “tend to focus on very specific enzyme families for very specific traditional applications, particularly in the food space. We’re focusing more on applications that have traditionally been much more difficult to target with enzymes.
“The key thing is that we have collected proprietary data for these very specific enzyme families that have very broad applicability to handling carbon chains. And I think that’s really what separates us. We have data that is not publicly available.”
The post Scindo raises $5.4m seed round for AI-powered enzyme discovery platform appeared first on AgFunderNews.