Sino Biological's Cell-Free Protein Synthesis Supports Tencent AI for Life Sciences Lab's Protein Design Study Published in Nature Communications
Bridging AI Protein Design and Experimental Validation
Artificial intelligence has accelerated protein amino acid sequence design; however, translating these computational designs into functional proteins remains a key challenge in protein engineering. Protein activity, stability, folding, and expression are influenced by complex structural and biochemical factors, often causing discrepancies between in silico predictions and experiments.
To address this gap, the study introduced an Ontology Reinforcement Iteration (ORI) framework, integrating protein ontology with reinforcement learning from wet-lab feedback. Experimental data, including protein expression levels and functional activity, were continuously fed back into the model enabling iterative optimization of protein sequences and improved design accuracy.
Cell-Free Protein Synthesis Accelerates the
The researchers subsequently utilized
Using this workflow, the team engineered a lysozyme with over 100-fold higher activity than the natural enzyme, developed a thermostable chitinase retaining activity at 85°C, and expressed bifunctional enzymes with improved performance compared with naturally occurring multifunctional enzymes.
XPressMAX™ Cell-Free Protein Synthesis Kit
Key features include:
- Ultra-fast synthesis: proteins expressed in as little as 3 hours
- High screening efficiency: optimized for VHH, scFv, and miniproteins
- Flexible template support: compatible with plasmid and linear DNA templates
- Disulfide bond friendly: enables expression of complex disulfide-bonded proteins without additional enhancers
- Cost-effective performance: reduced operational time and cost
- Scalable supply: suitable for high-throughput and industrial applications
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