Introduction

This article is a reprint of an interview originally published on our company’s note page in September 2024. To make it accessible to a wider audience, we are sharing it here on our official tech blog. Please note that the content reflects the context and information available at the time of the original publication.

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— This marks the very first interview in our series! Thank you for joining us, Nakamura-san.
To begin, could you tell us how you joined the company? …Although, come to think of it, you’re actually one of the founding members, aren’t you?

Yes, that’s right.
I co-founded digzyme Inc. while I was still a student at Tokyo Institute of Technology, together with Mr. Torai¹, who is now our CEO, and Professor Yamada², who serves as our CSO and is an associate professor at Tokyo Tech.

The inspiration for starting the company came from research we were conducting in the Yamada Lab³, which later became the foundation of digzyme. We saw strong potential for turning that research into a viable business.

— The research that became the foundation of the company… we’d love to hear more about it. What aspects made you see its potential as a business?

It was research that eventually evolved into what is now digzyme Moonlight™, focused on enzyme discovery.

At the time, Professor Yamada and Nagase & Co., Ltd. were conducting a joint research project. I joined them and contributed by performing additional computational analyses.

Specifically, Nagase approached us with a request:
"We’re looking for an enzyme with these specific properties."
Our task was to search for suitable enzymes from a database based on that request. The process was going well—we were consistently identifying promising candidates. So, the initial plan was to publish the results as a research paper.

From there, I started thinking: What if this discovery technology could be applied to other domains as well?
That line of thinking gradually led us to explore broader possibilities—and that was the starting point for digzyme’s business model.

In terms of its broader applications, what I specifically had in mind was this:
Could we identify enzymes capable of catalyzing synthetic reactions for compounds where no such enzymes have been discovered yet?

That’s exactly what we achieved through our collaboration with Nagase.

The project focused on enzymes from a particular plant that produces a unique compound. But extracting the enzyme by physically grinding the plant each time? That’s obviously not scalable.

So we thought: What if we could reproduce this biosynthesis using microbial enzymes instead?
We began screening for enzymes that might be able to catalyze the same reaction—and we found quite a few.

In fact, we identified a number of enzymes that could potentially synthesize previously unknown or commercially unavailable compounds. That was really exciting.

— That’s incredible!

Yes, and from there, we began to discuss in the paper the possibility that these enzymes might allow us to synthesize compounds that were previously considered extremely difficult to produce—with much more ease than expected.

When I reported this to Professor Yamada, he pointed out that there must be many people out there who need exactly these kinds of enzymes. That led to the idea that this could be really interesting as a business.

And the timing couldn’t have been better—around that same time, Mr. Torai was actively exploring the idea of launching a startup.
So with that momentum, the three of us—Professor Yamada, Mr. Torai, and myself—decided to start working together.

Once we did, we immediately began receiving inquiries from people saying,
"If there’s a technology that can identify enzymes like that, we’d love to use it."
That strong interest convinced us: there’s real demand out there—so we decided to found the company.

ーー Indeed, conventional enzyme development has required an enormous amount of trial and error to identify genes encoding enzymes suited to specific purposes.
Because of this, there has been a heavy reliance on serendipitous discoveries, which brings uncertainty and results in enormous development costs. Considering these challenges, it’s clear there is substantial demand for better methods.
After founding the company, what kinds of work have you been involved in?

Right after the company was founded, I worked on developing the core technology — the enzyme discovery software. At the same time, we began exploring how to further apply this technology to develop areas that would become key strengths for the company.

We already had several research and development themes internally, so I was also involved in those development projects.

However, I was still a student for the first six months after founding the company. After graduation, I worked at a pharmaceutical company for nearly two years.
During that period, I participated in discussions around technology development while balancing this work alongside my role at digzyme.

— After that, you transitioned to committing full-time to digzyme, correct?

Yes. Around the time I fully committed to digzyme, a project funded by NEDO⁴ (New Energy and Industrial Technology Development Organization) started.

In this project, we worked on designing spotlight⁵ (digzyme Spotlight™, our enzyme function improvement platform). Together with Mr. Torai and the team, we discussed ideas such as:
“If we build a machine learning model like this, it should work well.”

Once the budget was secured, we allocated resources to the researchers accordingly, directing who should develop which parts, and proceeded with the development in that manner.

— Naturally, you gradually took on the role of leader for each project. You also became responsible for recruitment and training as CTO, correct?

Yes. As the team gradually grew, I also began training new employees in dry lab research techniques.
The very first was Mr. Isozaki⁶, who joined us as a part-time staff member just after the company was founded. Since he originally came from a wet lab background rather than dry lab, Mr. Torai and I taught him the dry lab techniques.

— I see. So how do you actually go about training people in dry lab techniques?

Basically, I believe the best way is to work through real examples.
When people study programming in general, they often start with simple exercises like “what happens if you program 1 + 1 = 2?” But…
that kind of practice doesn’t really stick, and it often leaves people wondering, “How does this actually apply in real life?”

— That makes sense.

If you don’t tackle real-world problems, it’s often not very engaging and doesn’t translate well into practical skills. So I think assigning realistic tasks is crucial for effective learning.
For example, the training materials I create include problems I personally worked on in the past. Trainees solve these problems and go through the entire process as a way to learn.
More recently, we often use actual challenges from our clients as test cases, and have trainees work through them together with Mr. Takayama⁷ and Mr. Isozaki.

— Thank you for the detailed explanation. How have your recent responsibilities been?ですか？

My main responsibilities have been managing individual projects and overseeing research resource allocation.

— How do you feel about taking on management responsibilities?

For me, management has never really been something I struggled with. Even when I was a student working part-time, I often took on roles like team leader, managing members and coordinating tasks. I have this feeling that I can’t be satisfied unless I’m involved in the core aspects of things (laughs).

When I worked at the pharmaceutical company, I wasn’t the type to be content just doing experiments or research at the ground level—I needed to dive into the detailed discussions and the essence of the projects to feel satisfied.
Of course, I enjoy the research itself and want to keep doing it, but I’m not someone who can just work blindly without fully understanding what’s going on…
In that sense, I guess naturally I ended up taking on management roles.

That said, I’ve been gradually handing over these management responsibilities to Mr. Isozaki and Mr. Takayama, and I’m now returning more to my core work—leading new technology development and building foundational technologies.
Of course, I’m always thinking about what kind of new technologies our dry and wet lab teams should have, and brainstorming ideas to further strengthen digzyme’s competitive advantages.

— I’m very interested in the ideas for further strengthening digzyme’s competitive advantages.

Absolutely. Regarding how we can grow going forward, I want us to thoroughly discuss and develop the necessary software and wet lab technologies in the business areas that the divisions have identified as targets for expansion.

At the same time, I personally focus on ensuring that the foundational tools and platforms we build within each project are effectively utilized to drive the progress of those projects.

— Nakamura-san, what aspects of working at digzyme do you find most rewarding?

I find it very rewarding to apply new technologies to real-world challenges, developing and updating the missing pieces as we go.

For example, in developing digzyme Spotlight™, we were pioneering the use of AI and machine learning to improve enzymes at a time when almost no one else in the world was doing this.

Before that, it was common to study enzyme structures and conduct research like,
"If we change the part of the protein that interacts with the substrate, the substrate might be affected as well, so let’s mutate it to improve activity."
However, AI and machine learning methods were still not widely adopted.

At the same time, there were many requests to improve enzyme performance—it was a challenge across the entire enzyme industry.
I enjoy facing challenges and solving problems, so I found it very rewarding to create and develop programs while discussing ideas like,
"With digzyme, we should be able to predict and design mutants that increase activity using AI."

But honestly, I enjoy tackling problems of any size, whether big or small.
The Spotlight project was somewhat large scale, but even fixing small, everyday annoyances—things like "this is kind of a hassle"—and watching the system run more smoothly is something I really like.
No matter the scale, I find great satisfaction in new technology development and problem-solving—the process of continuous improvement.

— I see. It sounds like digzyme is really supported by you, Nakamura-san, who finds fulfillment in solving challenges big and small! Since we’re on this topic, I’d love to hear more about the uniqueness of Spotlight.
I’ve heard that the platform was developed by a team that fully leveraged each member’s unique background. Could you please tell us more about this aspect?

Spotlight is a program that uses machine learning algorithms to predict
“If we do it this way, we should be able to identify which parts of the enzyme to modify.”

I had been studying machine learning throughout my student days and also while working at a pharmaceutical company, so I was able to apply that knowledge to create the platform.

Regarding members who are experts in sequence analysis, there’s Mr. Torai and Mr. Hikoyuu⁸ (Informatics Specialist Mr. Hikoyuu Suzuki). They have been studying genome-level gene and protein sequence analysis extensively in the lab.

Then there’s Mr. Tamura⁹ (Informatics Specialist Mr. Koichi Tamura), who is highly knowledgeable about three-dimensional structural data.

So the three of them—Torai, Hikoyuu, and Tamura—worked on determining which features the model should learn from, combining sequence and structural data expertise. Meanwhile, I focused on conceptualizing the machine learning models and approaches.

Finally, Mr. Isozaki implemented the system, and that’s how Spotlight was completed.

— It’s truly moving to hear how you’ve brought together such collective wisdom. May I ask about the challenges you’ve faced at work, and what helped you overcome them?

Rather than challenges per se, I’d say that recruitment has been quite tough.
It’s a significant matter both for the company and for the candidates whose lives are deeply impacted, so I recognize it as a serious responsibility.

In that context, I struggled a lot with how to make the right decisions when hiring people who will shape digzyme’s future.
After several rounds of recruitment, I feel like I’ve finally gotten the hang of it.
Mr. Torai has a very good sense of how to conduct interviews and ask questions, so I’ve learned a lot by following his example.

As for what kind of people we specifically look for, it definitely comes down to those who don’t give off any sense of incongruity during conversations.
We want candidates who not only respond within expected parameters but can also go beyond that in their answers—those are the people we want to hire. On the other hand, if their answers seem stuck one or two steps behind what we expect, it’s a bit difficult to move forward with them.

It’s also important that candidates are good at troubleshooting. Wet lab research especially comes with its share of failures.
In dry lab work, if something goes wrong, you can usually retry quickly—and that’s a field where I tend to come up with ideas easily. But with wet experiments, if you want to redo something, you might lose a whole week, causing significant schedule shifts.

Honestly, I’m not that well-versed in wet lab work myself, so when something goes wrong, it’s important for me to have someone who knows more than I do and can think and act independently during the problem-solving phase.
Troubleshooting experimental issues happens quite often, so we try to hire people who can handle these situations well.
We ask candidates how they’ve handled failures in the past to assess their troubleshooting skills and make sure we bring in capable individuals.

— I see. Since you mentioned WET lab, it makes sense that digzyme’s WET capabilities are so impressive given the team you’ve built.

Exactly. I believe one of digzyme’s strengths in WET lab is that our team can handle a surprisingly wide range of tasks.
For example, when we want to evaluate a certain enzyme, we read relevant papers, develop protocols, try experiments, express proteins, and perform the evaluation. Of course, this requires researchers who can actually conduct solid scientific work.
It’s not something anyone can just casually do by saying, “Hey, read this paper and try replicating the experiment!” (laughs)
Being able to handle that naturally is actually a very high-level skill.

Conversely, it’s very rare that our dry lab analysis gets stalled because of issues on the wet lab side. I say this casually, but it’s actually a remarkable achievement.

That said, while our technical skills are very high, we’re not particularly strong in terms of resources. Compared to many companies and academic labs, we don’t possess special microbial strains or proprietary genetic engineering techniques. We primarily use publicly available materials.
So, honestly, we don’t have an edge in terms of resources, but I take pride in the strong abilities of our research staff.

— I see. That’s reassuring to hear.

Yes. By the way, in DRY lab work, we often don’t know the exact causal relationships. There are many uncertainties about which is the cause and which is the effect.
So when we analyze enzymes, we proceed while considering the possibility of false positives.
We narrow down candidates to very promising ones, but after that, we rely heavily on the high level of WET lab expertise.

For example, even when using E. coli, they don’t just use one strain—they prepare multiple strains, as well as various other organisms—and skillfully conduct experiments to overcome challenges. That expertise is invaluable.

— Looking ahead, what kinds of challenges would you like to take on?

Basically, I hope the projects we’re working on progress through their stages and eventually get launched as products that genuinely make a difference.
I’m excited about the enzymes we’re currently developing becoming actual products — it would be great to say, “This enzyme is actually in that product!” someday.

From a technical standpoint, as projects advance, new challenges unique to those stages will arise, and I want to tackle those.

For example, when you want to convert a certain compound into another, the numerical goals like “the amount of enzyme required” or “the efficiency needed” will become more concrete than they are now.
Achieving—or not achieving—those targets will be a crucial issue in the near future.

The next stage will be “mass production.” We will need to set production performance goals such as “to meet this product price, the culture medium must be this volume,” or “we need to produce this much enzyme.”
Since these goals directly impact business continuity, we must resolve them properly.

Also, I’d love to try developing completely artificial enzymes.

By “completely,” I mean—it’s a tough challenge (laughs). Usually, we base enzyme design on natural enzymes found in microbes or improve upon them. But now, with AI technology, it’s becoming possible to design enzymes from scratch, purely from data.
This means you could design enzymes on a computer that aren’t really based on any natural microbial enzyme, though they might bear some resemblance.

Of course, whether people would want to eat food containing such artificially designed enzymes is another question (laughs).

Currently, we’re bound by natural enzymes as the base, but this approach allows us to break free and create entirely original enzymes.
Even if practical use is uncertain, it’s exciting because it feels truly novel.

I’m also interested in developing systems that don’t rely on microbes, like cell-free systems.

We’re already exploring and discussing cell-free approaches internally, but basically, wet lab processes still mostly involve genetically modifying microbes to express proteins. Often, “protein expression fails,” which is a big hurdle.
Cell-free systems can sometimes reduce that problem—though expression failure can also happen there—so I want to try that.
In any case, I hope to eliminate the unique uncertainties of bio processes.

— When you say "the unique uncertainties of bio processes," what do you mean exactly?

In biological experiments, there are often cases where things just don’t work well, and nobody really knows why.
I think it would be amazing if that “I don’t really understand why it’s failing” part could be eliminated.

I’m not saying we already have a way to guarantee success—this is really just a dream at this point (laughs).

For example, take just the “culture conditions.”
There’s no theoretical way to know exactly which culture conditions are best. When culturing a microbe, you repeatedly try different combinations of ingredients in the medium and experimentally find which one works best.
It’s not like you can say, “This is the best condition” based purely on theory—it’s more like, “I don’t really know why, but this works better.”

Sometimes the culture grows well, and sometimes it doesn’t. Protein production is similar—it’s not consistent; sometimes you get a lot, other times less. There’s quite a bit of variation.

Biological experiments inherently have these fluctuations.

“Failure” is an extreme example of this uncertainty, but even when things are going well, there are times when it’s “especially good” and times when it’s “just okay,” so there’s a lot of variability and error.

Even amid these uncertainties, one major challenge remains “poor expression depending on the host organism.”
To improve this, we often think, “It would be great if there was a system that could express any enzyme from any organism.”
If such a system exists, I feel that cell-free systems might be the answer.

— Expression of anything—that’s quite a dream. Thank you for the detailed explanation. Lastly, do you have a message for future team members considering applying to digzyme?

“Let’s tackle the ambiguous challenges of biology together with cutting-edge technology and innovative ideas!”

— Thank you very much, Mr. Nakamura.

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¹ Nao Torai – CEO and co-founder of digzyme Inc.
² Dr. Takuji Yamada – Associate Professor at Tokyo Institute of Technology and CSO of digzyme Inc.
³ Yamada Lab – Laboratory for Life Science and Technology at Tokyo Institute of Technology.
⁴ NEDO: Japan’s national agency for promoting research and development of new energy and industrial technologies.
⁵ spotlight: digzyme’s platform for enzyme function improvement through machine learning.
⁶ Principal Investigator Mr. Tatsuhiro Isozaki
⁷ Principal Investigator Mr. Yuki Takayama
⁸ Informatics Specialist Mr. Hikoyuu Suzuki
⁹ Informatics Specialist Mr. Koichi Tamura

Closing Remarks

▼ Original article is available here (note)
https://note.com/digzyme/n/n4cb24197110b

Introduction

From May 21 (Wed) to May 23 (Fri), 2025, ifia JAPAN 2025 was held over three days.
As with last year, our CEO, Dr. Watarai, gave an exhibitor presentation at the event.
The full presentation is now available on YouTube—please feel free to take a look.

In this exhibitor presentation, we introduced the newly launched “digzyme Custom Enzyme Lab,” unveiled on May 21, 2025.
The session covered two key technological approaches: DRY (bioinformatics-based analysis) and WET (experimental validation), and provided an overview of the entire platform.

This article takes a deeper dive into two potential real-world applications of the digzyme Custom Enzyme Lab, which were briefly mentioned during the presentation.
Through a Q&A format and from the perspective of our CEO Dr. Watarai, we explore the technical breakthroughs behind each case, as well as the in silico design strategies employed.

While the presentation offered a high-level overview, this article aims to give you a more concrete understanding of the capabilities and potential of the digzyme Custom Enzyme Lab.

We invite you to read on and explore the details—beginning with the first case study.

Expected Application Case 1 of the digzyme Custom Enzyme Lab

Q: What do you consider the most significant value of this result?
A: The physical properties of carbohydrates vary depending on the linkage patterns between constituent monosaccharides.
This case is particularly valuable because it represents a rare example—even in academic contexts—where in silico techniques successfully identified an enzyme capable of constructing a specific glycan structure.
Moreover, the target enzyme was discovered with just 10 experimental validations, which highlights the efficiency and precision of the approach.

Q: What was innovative about this approach compared to conventional methods?
A:In this case, our proprietary, detailed analytical techniques ultimately proved effective when applied to the deep learning (DL)-based structural prediction technologies of the time, such as AlphaFold2. Traditional homology-based models had difficulty predicting subtle structural differences in proteins that lead to variations in glycan structures. However, the AI technologies available at the time enabled us to capture some of these critical features to a certain extent.
(Note: As there is still a gap between these earlier AI technologies and today's cutting-edge generative models, we use the term "AI" here for convenience.)

Q: What team efforts or contributions led to this success?
A: The lead researcher deeply investigated the client’s specific needs and successfully translated them into tailored screening criteria for enzyme selection.
By working closely with our core development team, a customized analysis pipeline was developed, which was crucial to achieving this outcome.
We believe one of our key strengths is the ability to flexibly build new tools and solutions beyond our existing platforms to meet unique and complex challenges.

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Next, let us introduce the second case study, which was conducted in collaboration with Mitsubishi Chemical Corporation.

Expected Application Case 2 of the digzyme Custom Enzyme Lab

Q: What do you consider the most significant value of this result?
A: PVC (polyvinyl chloride) is a synthetic compound whose mass production began in the 20th century and does not exist in nature.
Assuming that natural microorganisms have not evolved degradation mechanisms for such materials, it would be highly unlikely to discover well-optimized degrading enzymes from natural sources.
However, living organisms are known to retain a wide variety of “non-optimized” or dormant genes within their genomes, which may later contribute to adaptation under environmental pressure.
This case can be seen as an attempt to identify such latent enzymatic functions through in silico screening—making it a particularly challenging theme.

Q: How long would it have taken to discover such an enzyme using conventional methods?
A: In recent years, there have been several studies that identify artificial plastic-degrading enzymes using methods akin to enrichment culturing. For example, researchers may submerge a particular type of plastic resin in the seabed for an extended period, then retrieve and observe its degradation, or isolate and culture microbes from biofilms formed on the plastic.
When successful, these efforts can uncover microorganisms with plastic-degrading enzymes, allowing identification through genomic analysis or BAC library construction. However, due to the inherently slow degradation process, such approaches often require years to yield results.
Moreover, it is common for degradation not to occur at all, resulting in unsuccessful attempts. In contrast, in silico discovery can typically be completed within about six months, making it a relatively efficient method even for targets that would otherwise require long-term experimental work.

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Conclusion

Reflecting on the presentation, Dr. Watarai shared the following comment:

“With digzyme Custom Enzyme Lab, we are able to prepare in silico libraries in advance—similar to what we did in these collaborative cases. It’s a service we recommend to customers seeking to test purified enzymes from high-precision candidate libraries.”

As this statement illustrates, a bioinformatics-based approach to enzyme design has the potential to dramatically accelerate practical enzyme development, even under resource-constrained conditions.
As applications continue to expand across diverse domains, digzyme Custom Enzyme Lab is expected to play a pivotal role as a core technological foundation.

Introduction

My name is Murase from the Food Business Division.
Our company exhibited at "ifia JAPAN 2025 – The 30th International Food Ingredients & Additives Exhibition and Conference", held at Tokyo Big Sight from Wednesday, May 21 to Friday, May 23, 2025, following our participation last year.

During the exhibition, we had the valuable opportunity to engage directly with many visitors who showed strong interest in our technologies.
At our booth, we introduced our latest initiatives to these attendees. One of the main highlights was the launch of our new solution, “digzyme Custom Enzyme Lab”
(For more details, please refer to our press release:https://prtimes.jp/main/html/rd/p/000000018.000050097.html）

The launch received an overwhelmingly positive response, far exceeding our expectations. Our booth was filled with lively discussions throughout the exhibition, as we received numerous specific questions and inquiries from many visitors each day.

In this special edition of our tech blog, commemorating the launch of “digzyme Custom Enzyme Lab”, we’ve selected some of the most frequently asked questions from the exhibition and provided detailed answers in a Q&A format.

This post is not only for those interested in our new solution, but also for anyone curious about enzyme-based development who may be wondering where to start.
We hope you’ll find useful insights—please read on to the end!

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Q: For what types of product development can “digzyme Custom Enzyme Lab” be applied?

A：“digzyme Custom Enzyme Lab” is a flexible solution that can be applied to a wide range of development themes—from specific goals such as improving the efficiency of existing enzyme-based manufacturing processes to broader, more exploratory themes like developing novel food ingredients using enzymes.

By repeatedly exchanging purified enzyme samples and receiving feedback from your in-house evaluations, the development direction can be adjusted flexibly at each stage.

Q: What kind of information is provided with the purified enzyme samples?

A：We perform preliminary testing to confirm enzyme activity and provide a profile including optimal temperature, optimal pH, thermal stability, and pH stability. These data are provided alongside the purified enzyme samples.
Verification in your specific application or evaluation system can be conducted by your team.

Q：What is the quantity of purified enzyme included in the sample?

A：The quantity depends on the development theme and is determined through consultation. As a general guideline, samples are typically provided in volumes of several milliliters of enzyme solution, equivalent to several milligrams of protein.

Q：How do you define or set the initial development timeline?

A：Following a prior evaluation of the requested development theme, we assess the feasibility and propose an initial development timeline.
In most cases, the initial phase—covering in silico enzyme design through to the first delivery of a purified enzyme sample—is completed within 2 to 6 months.

Q：Is non-GMO enzyme development an option?

A：Yes, it is possible. For more details, please refer to the “digzyme Express” introduction page:https://www.digzyme.com/cms/wp-content/uploads/digzyme_Express_ol.pdf

Q：Is “digzyme Custom Enzyme Lab” a solution exclusively for the food industry?

A：“digzyme Custom Enzyme Lab” is a versatile solution available for use not only in the food industry but also in other sectors, including the chemical industry.

Q：If a suitable enzyme is found among the provided purified enzyme samples, what happens next?

A：Enzymes developed via “digzyme Custom Enzyme Lab” can smoothly transition into manufacturing development. digzyme provides comprehensive support throughout the entire process, including manufacturing technology development and regulatory approvals, accompanying you until your project is fully commercialized.

Q：How is intellectual property handled for the developed enzyme library?

A：If you find a promising enzyme among those developed via “digzyme Custom Enzyme Lab” and decide to pursue its commercialization, we are prepared to accommodate your needs flexibly.

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This concludes our responses regarding the services provided through “digzyme Custom Enzyme Lab”.
Please feel free to contact us anytime, as we remain flexible and ready to accommodate your specific needs during the actual development process.

Thank you very much for reading through this Q&A.

If you have any questions or require further clarification, please do not hesitate to reach out to us via the contact form below.

[▼ Contact Form]
https://www.digzyme.com/contact/

Introduction

I am Isozaki from the Business Development Department. Our company conducts explorations of artificial synthetic routes from "raw materials" to "target products" using enzymatic reactions. By simply inputting the compound structure data of the "target products" and "raw materials", we can output potential synthetic route candidates for producing the target product from the starting compound. In this blog, I will introduce a specific example where we predict a route to synthesize 4-amino-cinnamic acid, a which is used in the production of high-strength polymers  for high-strength polymers, from glucose and the enzymes involved in the reactions.

Materials Used for Synthetic Pathway Exploration

In Tateyama et al. (2016), 4-amino-cinnamic acid is used as a which is used in the production of high-strength polymers for producing high-strength polymers. The pathway used to synthesize this 4-amino-cinnamic acid is shown in Figure 1. Glucose serves as the raw material, and 4-amino-phenylalanine is produced using Escherichia coli engineered with Aminodeoxychorismate synthase (PapA) derived from Streptomyces venezuelae and Aminodeoxychorismate synthase (PapBC) derived from S. pristinaespiralis. Furthermore, this 4-amino-phenylalanine is used as a raw material, along with E. coli engineered with Phenylalanine ammonia-lyase (RgPAL) derived from Rhodotorula glutinis, to produce 4-amino-cinnamic acid.

Results

1. Biosynthetic Pathway Exploration

By inputting glucose as the Starting compound and 4-amino cinnamic acid as the product, an artificial synthesis pathway, as shown in Figure 1, was output. The output pathway was identical to the known synthesis pathway of chorismate from glucose, leading to the synthesis of 4-amino cinnamic acid via 4-amino phenyl alanine.

2. Similar Reaction Exploration

Among the artificial synthesis pathways identified in Result 1, the similar reaction from 4-amino phenyl alanine to 4-amino cinnamic acid was explored.

Through the exploration of similar reactions, a reaction that removes an amino group and generates a double bond was identified. Some of the similar reactions with a high degree of similarity to the target reaction and their rankings are shown in Figure 2. Similar reactions were extracted, including those that match the target reaction exactly.

3. Exploration of Corresponding Enzymes for Similar Reactions

In Result 2, similar reactions for the target reaction were extracted. The enzyme sequences responsible for these similar reactions were extracted by taxon. The filtered sequences were then compared with the enzymes used in the paper. Sequences were extracted at three levels: Rhodotorula genus, Eukaryota domain, and all taxa (Table 1). The extracted sequences included those that exhibited over 90% sequence homology with the sequences used in the paper.

Conclusion

In this blog, we demonstrated the exploration of artificial synthetic pathways. We explored an artificial route to synthesize the compound 4-amino cinnamic acid, which serves as a raw material for high-strength polymers, from glucose. We aimed to determine whether we could find enzymes that synthesize 4-amino cinnamic acid from 4-amino phenyl alanine using similar reaction enzyme exploration techniques. For the above reactions, we extracted sequences by taxon and presented the number of sequences for each. We successfully extracted multiple sequences that included several with high similarity to the enzymes used in the paper.

Acknowledgments

We utilized data from the following paper for this synthetic pathway exploration:

Tateyama et al. (2016). Ultrastrong, Transparent Polytruxillamides Derived from Microbial Photodimers. Macromolecules.