Sino Biological's Cell-Free Kit Powers AI-Designed Protein Breakthrough

Sino Biological, Inc., a leading reagent supplier and CRO service provider listed on the Shenzhen Stock Exchange (301047.SZ), has announced that its gene synthesis and cell-free protein expression workflow was utilized in a recent study by Tencent AI for Life Sciences Lab, published in Nature Communications. The study introduced an Ontology Reinforcement Iteration (ORI) framework that integrates protein ontology with reinforcement learning from wet-lab feedback, enabling iterative optimization of AI-designed proteins. To bridge the gap between computational design and functional validation, the researchers employed Sino Biological’s XPressMAX™ Cell-Free Protein Synthesis Kit, which supports rapid protein expression in as little as three hours. This workflow allowed the team to engineer a lysozyme with over 100-fold higher activity than the natural enzyme, a thermostable chitinase retaining activity at 85°C, and bifunctional enzymes with improved performance. The XPressMAX™ kit features ultra-fast synthesis, high screening efficiency for VHH, scFv, and miniproteins, compatibility with plasmid and linear DNA templates, disulfide bond-friendly expression, cost-effective performance, and scalable supply for high-throughput applications.

Sino Biological, with its US-based Center for Bioprocessing (C4B) in Houston and SignalChem Biotech in Canada, delivers tailored solutions to researchers in over 90 countries. The company’s stringent quality management system ensures reliability across all products. This collaboration highlights the critical role of experimental validation in AI-driven protein design, a field where in silico predictions often diverge from actual outcomes due to complex structural and biochemical factors. By enabling rapid design–build–test cycles, the XPressMAX™ kit accelerates the iterative optimization process, bringing AI-designed proteins closer to real-world applications in biotechnology and therapeutics.

The study, accessible via Nature Communications, underscores the synergy between computational biology and experimental wet-lab techniques. As AI continues to advance protein design, tools like cell-free protein synthesis are essential for validating and refining these designs efficiently. This breakthrough could expedite the development of novel enzymes and therapeutic proteins, impacting industries ranging from pharmaceuticals to industrial biotechnology.