Expected Practical Applications of the digzyme Custom Enzyme Lab: Approaches to Glycan Structure Construction and Recalcitrant Substance Degradation
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.
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.
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.
Answers to Questions Received at the ifia JAPAN 2025 Exhibition
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!
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.
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/
List of answers to the questions we received at our booth at ifia JAPAN 2024 venue.
Our company exhibited at "ifia JAPAN 2024 - The 29th International Food Ingredients/Additives Exhibition & Conference" (organized by Food Chemical News Co., Ltd.), held at Tokyo Big Sight from May 22nd (Wednesday) to 24th (Friday), 2024.
We would like to express our sincere gratitude to everyone who visited our exhibition booth.
In this article, we will introduce and answer the questions we received from all of you during the exhibition period, focusing on the ones that were particularly frequent. Please stay tuned until the end.
Q: What does your company do?
A: In response to our clients' needs, we conduct new enzyme exploration and enzyme modification. By employing our unique bioinformatics technology, which differs from conventional methods, we facilitate rapid enzyme development. We believe that this innovation accelerator can benefit both enzyme manufacturers and food manufacturers.
Q: Do you have any specific examples?
A: In chemical applications, we have successfully explored new enzymes needed by users and achieved significant improvements in enzyme activity. In food applications, we are currently addressing specific themes requested by multiple clients and actively working on them.
Q: What properties of enzymes can be modified in the digzyme Spotlight (enzyme modification program)?
A: Potential modifications include enhancing activity, improving heat resistance, and altering optimal pH. Modifications to substrate specificity are addressed using the digzyme Moonlight (enzyme exploration program) as needed.
Q: What is the development process like?
A: Depending on the client's situation, we set the start and end goals, but the main process typically follows these steps:
- Development Consultation: We listen to the client's challenges and select the target enzymes.
- Enzyme Design: Using supercomputers, we design the target enzymes.
- Enzyme Library Provision: Enzymes designed on the computer are produced at the lab scale using microorganisms, and the suitability of the enzymes for the intended purpose is verified and confirmed.
- Enzyme Production Provision: We scale up production from the lab to the plant, ensuring stable enzyme supply as a product.
That concludes the Q&A for this article. Thank you very much for reading until the end. If you have any further questions or inquiries, please contact us using the following contact form.
[Contact Form] https://www.digzyme.com/contact/
Compare the predictive accuracy of the machine learning model Spotlight™ for enzyme variants’ activity with prior research.
Summary
Our company offers a service called Spotlight that uses a machine learning model to suggest mutants that improve properties such as enzyme activity and thermostability. We input target enzyme sequences into a pre-trained model using various enzymes to predict mutants with improved activity and thermostability for that enzyme. In this tech blog, we have verified the predictive accuracy of Spotlight™ compared to previous research.
The previous research used for comparison.
In Li et al., 2022, a machine learning model (DLKcat) was created to predict kcat using enzyme amino acid sequences and compounds as input information. To ensure equality in the comparison, we utilized the DLKcat machine learning model algorithm and reconstructed the model using the same training data as Spotlight™, namely the kcat entries from BRENDA. We compared the predicted kcat values of the mutants by the reconstructed DLKcat and Spotlight and evaluated which values were closer to the actual measured values. For this study, we extracted entries from BRENDA, ensuring that only wild type (WT) and single mutant variants were included. Our focus was to compare the sensitivity for a single mutation between the two models.
Results
1. Construction of the machine learning model using BRENDA’s kcat (Turnover Number) data.
Entries for variants with reported kcat values were extracted from BRENDA, including entries for the corresponding wild-type (WT) sequences and information about the compounds used to measure kcat. While ensuring no bias toward specific enzyme families, the entries were divided into a 3:1 ratio of training to test data. Training data consisted of 3,969 entries with an increased kcat, 2,985 entries with an unchanged kcat, and 8,296 entries with a decreased kcat (Figure 1). The test data consisted of 792 entries with an increased kcat, 748 entries with an unchanged kcat, and 1,926 entries with a decreased kcat (Figure 2).
2. Evaluation of mutant/WT ratio of predicted kcat by DLKcat and Spotlight™.
The information from the training data was converted into the format of features required by DLKcat, and a machine learning model was constructed. We converted the training data into the required format of features for Spotlight™ and built a machine learning model (Figure 3).
In the case of DLKcat, the Pearson correlation coefficient between the measured and predicted values of the ratio of the kcat of the mutant to the wild type (WT) kcat was 0.18 (Figure 3). We believe that the reason why the predicted values in DLKcat did not correlate well with the measured values is that DLKcat converts the entire length of the sequence into a vector as a feature, making it difficult for the difference of one amino acid to be reflected in the feature.
In the case of Spotlight™, the Pearson correlation coefficient between the measured and predicted values of the ratio of the kcat of the mutant to the WT kcat was 0.66 (Figure 3). We believe that our Spotlight™ is able to accurately predict the changes caused by single mutations from the WT because it has been devised to reflect the properties of the mutant as a feature.
Conclusion
Our Spotlight™ model was found to more accurately predict changes in activity compared to previous research in cases where only one amino acid mutated.
Acknowledgments
We are grateful for the use of data from the following paper to compare the accuracy of enzyme activity prediction in this study.
Li et al., (2022) Deep learning-based kcat prediction enables improved enzyme-constrained model reconstruction. Nature Catalysis.