Insights &
News

Research updates, company milestones, and perspective on the future of enzyme engineering and industrial biotech.

24
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2025
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PRESS
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LATEST POSTS
Company
June 10, 2026

Welcome Dr. Patrick Gerlinger, Senior Bioengineer

Enzidia welcomes Dr. Patrick Gerlinger as Senior Bioengineer, bringing biocatalysis and enzyme engineering expertise from Prof. Tobias Erb's lab at MPI and Bay Area startup Rubi Laboratories — selected from over 600 applicants.
1
min read
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Company
June 16, 2026

Enzidia Named to Hello Tomorrow's Top 100 Deep Tech Startups to Watch in 2026

Enzidia has been named a Finalist in Hello Tomorrow's 2026 Challenge, selected from 4,800+ startups across 108 countries in the Industrial Biotech & New Materials track — competing in Amsterdam for the €100,000 grand prize.
2
min read
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Research
June 2, 2026

Enzidia Licenses MillionFull from DTU — and What It's Unlocking with Frances Arnold's Lab and eXoZymes

"Enzidia has signed an exclusive license with DTU for MillionFull, a technology generating millions of enzyme sequence-to-performance datapoints per run — now powering a collaboration with Nobel laureate Frances Arnold's lab and eXoZymes on ML-driven enzyme design.
3
min read
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Company
May 20, 2026

Welcome to the Team: Dr. Carina Prell and Arsa Burhan Join Enzidia

"Enzidia welcomes two new team members: Dr. Carina Prell, a senior bioengineer previously at Ginkgo Bioworks, and Arsa Burhan, a bioengineer whose unconventional path — from lab training to delivery driving to rejoining biotech — reflects Enzidia's hiring philosophy.
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min read
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Press
June 16, 2026

Enzidia, Refine Biology, and Asia Aroma Partner to Develop and Commercialize Low-Cost, Sustainable Bio-Vanillin

Enzidia has signed a long-term joint development agreement with Refine Biology and Asia Aroma to bring fermentation-derived bio-vanillin to market — combining enzyme innovation, fermentation scale-up, and global flavor-industry distribution into one partnership.
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min read
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Research
June 25, 2026

The protein-LLM hype problem: what two new studies actually found

Two independent teams — UT Austin, and a Microsoft / NVIDIA / Profluent / Caltech / Duke / Oxford collaboration (FLIP2) — reached the same conclusion: protein language models rarely beat simple baselines when real experimental data exists, and their rankings collapse to near-random when pushing toward genuinely new function.
2
min read
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