Ailurus vec™ (A. vec™) rethinks the vector not as a single, passive vehicle, but as an intelligent carrier built for library-scale screening — an engine to scale and accelerate protein expression in a tube.
protein expression can be more reliable?
Recombinant expression is a powerful technology in life sciences. However, in practice, it is not as straightforward as simply copying and pasting your gene of interest into the cloning site.
No single expression vector suits every gene, and the available options are limited. Researchers often need to screen various expression systems and cultivation conditions to achieve satisfactory expression levels. The search space is vast.
For decades, vectors have been passive vehicles derived from natural plasmids, lacking built-in logic and offering limited flexibility. This forces researchers to screen different constructs one by one in monoclonal isolations.
Ailurus vec seeks to redefine the standards.
Libraries designed for E. coli — from fundamental science to protein bioproduction.
Gene expression is a combinatorial optimization problem. A. vec libraries draw on diverse genetic elements, providing a broad initial search space tailored to expression screening.
Biology provides a powerful solution through parallelism and selection. A. vec libraries incorporate built-in genetic logic to automatically rank or select the winners, eliminating the need for manual picking or individual characterization.
With next-generation sequencing (NGS), or additional readout modes where applicable, quantitatively measure the relative fitness of each variant in the population — structured data ready for supervised learning or other AI/ML methods.
See results for expression optimization, and the data they generate.
Client: A startup working in the biomaterial space.
Challenge: Self-assembling proteins have a low expression level with commonly used expression vectors.
Solution:
High-throughput screening of 15,000 combinations.
Results:
Identified optimal designs with 250× improvement in production, while generating structured data for future AI training.
Impacts:
Covering a range of optimal solutions, enhancing IP protection. Created a high-quality, structured dataset for ongoing AI model enhancement.
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Discovering the power of biological programming for expression.