Engineering synthetic auxotrophs for growth-coupled directed protein evolution

Area of Science:

• Synthetic biology
• Metabolic engineering
• Protein engineering

Background:

• Directed evolution relies on selecting desired protein variants from large libraries, which is a significant challenge.
• Current selection methods can be low-throughput and not easily compatible with continuous evolution processes.
• Developing high-throughput, in vivo selection systems is crucial for advancing protein engineering.

Purpose of the Study:

• To engineer synthetic auxotrophs that couple cellular growth to specific protein properties.
• To establish a high-throughput and continuous evolution-compatible selection system for protein variants.
• To utilize in silico simulations for designing customized synthetic auxotrophs.

Main Methods:

• Engineering synthetic auxotrophic strains where cell growth is dependent on the desired protein function.
• Implementing in vivo selection strategies that link protein property to auxotrophy.
• Employing in silico simulation-guided metabolic reprogramming to tailor auxotroph characteristics.

Main Results:

• Demonstrated successful establishment of a growth-coupled selection system for protein variants.
• Achieved high-throughput screening compatible with continuous evolution.
• Validated the utility of in silico simulations in designing customized auxotrophs for specific protein engineering goals.

Conclusions:

• Synthetic auxotrophs offer a powerful platform for high-throughput, growth-coupled in vivo selection in directed evolution.
• In silico-guided metabolic reprogramming enables the rational design of tailored auxotrophic strains.
• This approach significantly enhances the efficiency and scope of protein engineering efforts.