Sino Biological, Inc. has announced that its gene synthesis and cell-free protein expression workflow was used in a recent study by Tencent AI for Life Sciences Lab, published in Nature Communications. The research demonstrates how artificial intelligence can accelerate protein design, but faces challenges in translating computational designs into functional proteins due to complex structural and biochemical factors affecting activity, stability, folding, and expression.
To address this gap, the study introduced an Ontology Reinforcement Iteration (ORI) framework that integrates protein ontology with reinforcement learning from wet-lab feedback. By continuously feeding experimental data—including protein expression levels and functional activity—back into the model, the researchers achieved iterative optimization of protein sequences, improving design accuracy.
The researchers utilized Sino Biological’s XPressMAX™ Cell-Free Protein Synthesis Kit to enable rapid protein expression and functional screening. Protein-coding sequences were cloned into the kit’s expression vector and added to the proprietary cell-free reaction system, supporting rapid design–build–test cycles. This workflow allowed the team to engineer a lysozyme with over 100-fold higher activity than the natural enzyme, develop a thermostable chitinase retaining activity at 85°C, and successfully express bifunctional enzymes with improved performance compared with naturally occurring multifunctional enzymes.
The implications of this study are significant for the field of protein engineering. By bridging the gap between AI design and experimental validation, researchers can now rapidly test and optimize AI-generated proteins, potentially accelerating the development of novel enzymes for industrial applications, therapeutics, and synthetic biology. The ability to engineer proteins with drastically improved activity and stability could lead to more efficient biocatalysts, cost-effective production processes, and new bioproducts. For industries relying on enzymatic processes, this means faster innovation cycles and reduced development costs.
Sino Biological’s XPressMAX™ Cell-Free Protein Synthesis Kit offers ultra-fast synthesis with protein expression completed in as little as 3 hours, high screening efficiency optimized for VHH, scFv, and miniproteins, flexible template support for plasmid and linear DNA templates, disulfide bond-friendly expression without additional enhancers, cost-effective performance, and scalable supply suitable for high-throughput and industrial applications.
This collaboration underscores the importance of combining AI with high-throughput experimental platforms to overcome bottlenecks in protein engineering. As AI continues to advance, the ability to rapidly validate and iterate on computational designs will be crucial for realizing the full potential of AI-driven biotechnology. The study highlights a practical pathway for integrating wet-lab feedback into AI models, setting a precedent for future research in the field.
