"High-throughput screening of catalytically active inclusion bodies using laboratory automation and Bayesian...

Area of Science:

• Biotechnology
• Enzyme Engineering
• Protein Production

Background:

• Catalytically active inclusion bodies (CatIBs) offer cost-efficient, stable, and recyclable protein production.
• Optimizing CatIBs requires screening various linker and tag combinations, a process currently lacking predictive models.
• Extensive screening is necessary to identify optimal CatIB variants for specific enzymes.

Purpose of the Study:

• To develop and validate a semi-automated cloning and screening workflow for CatIBs.
• To accelerate the identification of high-performing CatIB variants.
• To enable automated Design-Build-Test-Learn cycles for enzyme engineering.

Main Methods:

• Implemented a semi-automated cloning workflow for rapid generation of 63 Bacillus subtilis glucose dehydrogenase (BsGDH) CatIB variants.
• Developed and optimized an automated screening workflow, including temperature adjustments to mitigate plate position effects.
• Utilized a Bayesian process model and Thompson sampling for efficient screening and identification of top performers.

Main Results:

• Successfully generated 63 BsGDH CatIB variants.
• Reduced manual workload for strain construction by 88% (from 59 to 7 hours for 48 variants).
• Achieved high reproducibility (1.9% relative standard deviation) and analyzed 63 variants within three batch experiments, identifying TDoT-PT-BsGDH as a highly promising candidate.

Conclusions:

• The developed CatIB toolbox facilitates rapid construction and screening of enzyme variants.
• The methodology significantly shortens the time required to find optimal CatIB producers from large libraries.
• Enables automated Design-Build-Test-Learn cycles for efficient enzyme structure/function studies.