Enzymes are nature’s master artisans, directing nearly every chemical reaction that keeps organisms alive. Their catalytic power drives cellular energy production and industrial bioprocesses, yet each enzyme works best under narrow environmental conditions. When these proteins are moved from their native settings to manufacturing lines or diagnostic kits, performance often plummets. Cracking the biochemical code that preserves stability, selectivity, and high activity is essential for modern biotechnology.
Molecular biologist David Schönauer has dedicated his career to this challenge. Realizing that traditional trial-and-error methods were too slow, he founded Aminoverse to merge wet-lab experimentation with machine-learning models that scan enormous sequence databases for functional candidates. By combining high-quality laboratory data with predictive algorithms, his team reduces billions of theoretical variants to a handful of promising enzymes in weeks rather than years. This workflow turns the vastness of protein space into an actionable shortlist.
The data-driven strategy is already reshaping industries that rely on custom catalysts. Cold-water laundry detergents, mRNA vaccine production, and eco-friendly biofuels now depend on enzymes engineered to withstand extreme pH, temperature, or solvent conditions. Aminoverse’s NZNAV platform guides researchers to optimal sequences and validates them through high-throughput assays, offering a faster and more sustainable path to solve pressing biochemical problems.
From University Bench to Chief Executive
Early fascination with molecular machinery
Schönauer’s passion for enzymes began at university. He recognized enzymes as “the small molecular workers of life,” responsible for every metabolic reaction in humans, animals, plants, and microbes. This appreciation for nature’s molecular engineering spurred him to explore how enzymes could be adapted for real-world applications.
Choosing entrepreneurship over academia
While many classmates pursued academic careers, Schönauer realized he preferred shaping practical solutions over chasing fundamental discoveries. A professor introduced the idea of starting a company, allowing him to remain close to science without spending his life on the bench. That suggestion set him on an entrepreneurial path long before graduation.
Lessons from an early spin-off
He took over a university spin-off during his studies and learned the essentials of running a business. The company grew from one person to a team of ten over seven years. Managing finances, negotiating with customers, and coordinating scientific work laid the foundation for his current role. This experience convinced him that building a strong team and controlling expenses were prerequisites for success in biotech.
Founding Aminoverse
Schönauer launched Aminoverse to unite enzyme science with modern machine-learning tools. The goal is to navigate the vast combinatorial space of possible enzyme sequences and rapidly deliver tailored biocatalysts for industrial clients. Unlike heavily funded competitors, Aminoverse grew organically and reached profitability in just three and a half years, allowing the team to prioritize long-term relationships over aggressive short-term milestones.
Enzymes: Nature’s Programmable Catalysts
The necklace analogy
Schönauer explained enzyme diversity through a simple image. Picture a necklace of 100 pearls. For each position, you may pick one out of 20 distinct beads. The number of possible necklaces quickly exceeds the number of stars in the universe. Likewise, proteins consist of amino acids, and re-arranging even a few positions creates astronomical diversity.
Industrial relevance
Enzymes already underpin many everyday technologies
- Insulin synthesis and blood-glucose tests rely on specific catalytic proteins
- mRNA vaccine production uses RNA-generating enzymes in place of slower chemical methods
- Laundry detergents contain specialized proteases and lipases that enable cold-water washing
- Lactase tablets help lactose-intolerant consumers digest dairy products
Enzymes are basically the small molecular workers of life. Every living being relies on enzymes. They basically govern every process that happens inside our bodies, but also in the bodies of every animal, every insect, every microbe. And the key fundamental that you would have to know about them, which is also nature's brilliance, you can think of a pearl necklace, and this necklace maybe consists of, let's say, 100 pearls. And for each pearl, you can choose one out of 20 different ones. So different color, different shape.
The engineering challenge
Natural enzymes are suited explicitly for conditions inside living cells, not reactors, washing machines, or pharmaceutical pipelines. Many lose activity after a few degrees of temperature shift or exposure to solvents. Engineering them for industrial conditions requires screening thousands of natural variants or redesigning amino-acid sequences. Both tasks are ripe for computational assistance.
Navigating Centillions of Possibilities with AI
The sequence universe
Scientists estimate roughly 10²⁰⁰ possible enzyme sequences. Schönauer compared that figure to the 10²⁴ stars in the universe. In this space lies an ideal enzyme for any reaction, yet brute-force screening is impossible.
Aminoverse’s NZNAV platform
Aminoverse created an in-house platform called NZNAV (Enzyme Navigator).
The process follows a funnel:
- Start with public and proprietary databases containing more than one billion natural enzyme sequences
- Apply clustering algorithms to eliminate redundant families
- Filter candidates by reaction type, catalytic residues, and other functional motifs
- Use machine-learning models to predict stability, solubility, and initial activity
- Deliver roughly 100 promising variants to the wet-lab team for synthesis and testing
Balancing in-silico and wet-lab work
Schönauer emphasised that computation has limits. Accurate prediction of catalytic activity remains one of the most challenging tasks in protein science. Structure predictions and stability estimates are reliable, but final rankings still need experimental confirmation.
Therefore, Aminoverse invests heavily in generating high-quality assay data. These measurements train the next generation of models, creating a virtuous cycle of improved prediction followed by tighter lab validation.
Assay robustness
Reliable screening is as important as clever algorithms. Assays must be sensitive, reproducible, and able to cut through biological noise. Aminoverse tailors each protocol to the customer’s reaction. Minor inaccuracies in data collection can derail an entire modelling effort, so the team focuses on eliminating variability before running large libraries.
Choosing and Testing the Right Enzyme
Typical client workflow
- A customer approaches with a desired chemical transformation.
- Aminoverse analyses the reaction mechanism to identify relevant enzyme classes.
- NZNAV narrows the billion-sequence search to a tractable list of candidates.
- Wet-lab teams express, purify, and assay the shortlist.
- Data feed back into machine-learning models for iterative refinement.
- The client receives a set of validated enzymes suited to scale-up.
Pitfalls to avoid
Schönauer advised companies to scrutinize partners:
- Seek teams that take ownership of scientific risk rather than over-promising timelines
- Demand transparent discussion of wet-lab capacity, assay quality, and data handling
- Be realistic about complex or exploratory projects that may exceed budgets or deadlines
- Maintain close communication to ensure the enzyme fits downstream manufacturing needs
Real-World Impact of Engineered Enzymes
Sustainable chemistry
Cold-wash detergents already cut household energy usage. Next-generation enzymes could reduce industrial water and heat requirements, lowering carbon footprints.
Biopharmaceutical production
mRNA vaccines demonstrated how enzyme-driven synthesis surpasses chemical routes. Optimized RNA polymerases and capping enzymes will streamline future vaccine campaigns and gene therapy manufacturing.
Nutrition and health
Engineered lactases, transglutaminases, or lipases can create hypoallergenic foods, novel plant-based textures, and personalized nutrition solutions.
Environmental applications
Once stability and efficiency hurdles are solved, enzymes capable of degrading plastics or fixing carbon dioxide could transform waste management and climate-mitigation strategies.
The Entrepreneur’s Perspective
Motivations beyond titles
For Schönauer, the CEO label is secondary. He seeks meaningful work, a collaborative team, and projects that benefit society. Entrepreneurship offers the freedom to align scientific curiosity with practical value.
Building a profitable biotech without massive funding
Key principles included
- Tight budget discipline from day one
- Selecting projects that generate revenue early rather than long research tails
- Hiring people who combine scientific skills with team spirit
- Saying no when a project’s risk-to-reward ratio is unrealistic
Advice for aspiring founders
- Validate whether you genuinely enjoy the problem space before launching a company.
- Gather operational experience inside a small organization to learn sales, finance, and HR.
- Cultivate mentors who warn against common start-up traps.
- Focus on delivering measurable benefits, not just scientific novelty.
- Treat wet-lab data as precious fuel for computational progress.
What we definitely have solved is structure prediction and especially stability. So that's something where we, I think also others are extremely good at, but really accurately predicting the behavior and the performance of enzymes is still something we're working on, which is also why we still have this reliance on wet lab data. This is also why Aminoverse really specialized in generating high quality wet lab data sets, not just for testing purposes, but also to provide training data for machine learning to make it even better.
What AI Still Cannot Do
Schönauer is optimistic yet cautious. Machine learning has solved structural prediction and stability forecasting, but precise catalytic performance remains elusive. Accurate modelling demands far larger, well-annotated datasets than currently exist. Aminoverse contributes by generating proprietary high-throughput measurements, yet the field requires collective effort and data sharing to reach design-from-scratch capabilities.
Looking Ahead
A future in which engineers design bespoke enzymes at the press of a button grows nearer each year. When that vision arrives, risks will fall, development cycles will shorten, and greener processes will replace many petrochemical routes. Schönauer expects AI to be the driver, with continuous feedback from robust wet-lab assays ensuring that predictions translate into reality.
Final Remarks
David Schönauer’s journey illustrates how passion for fundamental biology can evolve into entrepreneurial leadership. By combining machine-learning algorithms with meticulous laboratory work, Aminoverse helps clients discover high-performance enzymes faster.
His story underscores three enduring truths: enzymes hold enormous untapped potential, artificial intelligence accelerates but never replaces experimentation, and successful biotech ventures rest on disciplined teamwork. For researchers and start-ups alike, embracing these lessons could unlock the next wave of sustainable, enzyme-powered innovation.
About David Schönauer
David discovered his passion for enzymes while studying Molecular and Applied Biotechnology at RWTH Aachen University. Already during his Master's studies, he took over the role as CEO in a university spinoff focusing on enzyme development. 4 years ago, he sought to combine enzyme development with the latest advancements in machine learning/artificial intelligence and founded the biotech startup Aminoverse.
Connect with David Schönauer on LinkedIn.
David Brühlmann is a strategic advisor who helps C-level biotech leaders reduce development and manufacturing costs to make life-saving therapies accessible to more patients worldwide.
He is also a biotech technology innovation coach, technology transfer leader, and host of the Smart Biotech Scientist podcast—the go-to podcast for biotech scientists who want to master biopharma CMC development and biomanufacturing.
Hear It From The Horse’s Mouth
Want to listen to the full interview? Go to Smart Biotech Scientist Podcast.
Want to hear more? Do visit the podcast page and check out other episodes.
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