Biotech company building an integrated benchtop eProtein Discovery system to accelerate protein and antibody discovery by combining expression, purification, and validation into a single workflow.
Investment Rounds
Capital activity and funding progression
| Amount | Date | Round |
|---|---|---|
| €12 M | Jan, 2026 | series-c |
Industries
Frequently Asked Questions
Where is Nuclera located?
Nuclera is located in Cambridge, United Kingdom.
What industries does Nuclera operate in?
Nuclera operates in the following industries: biotechnology, drug discovery, protein engineering.
When was Nuclera founded?
Nuclera was founded on 2026-01-14.
How much total funding has Nuclera raised?
Nuclera has raised a total of $12,000,000.
Who are the investors in Nuclera?
The investors in Nuclera are: Jonathan Milner, British Business Bank, GK Goh.
Who are the founders of Nuclera?
The founders of Nuclera are: Michael Chen, Gordon Herling-McInroy.
What is the primary purpose of the eProtein Discovery system developed by Nuclera?
The eProtein Discovery system is an automated benchtop platform designed to enable researchers to screen and produce high-quality proteins in just 48 hours.
How does Nuclera accelerate the protein research and discovery process?
By combining digital microfluidics with cell-free protein synthesis, the platform allows for rapid screening of protein constructs and expression conditions, reducing timelines from months to days.
What specific technologies are integrated into the Nuclera platform?
The platform utilizes digital microfluidics to move and mix microliter-sized droplets and cell-free protein expression technology to synthesize proteins without the need for living cells.
Who are the typical users of Nuclera's automated protein solutions?
Scientists and researchers in drug discovery, structural biology, and biotechnology companies who require fast and reliable protein expression use these solutions.
What are the main advantages of using Nuclera over traditional protein expression methods?
The main advantages include significant time savings, reduced manual labor through automation, and the ability to rapidly identify optimal expression parameters for difficult proteins.