Asymchem, a global contract development and manufacturing organization (CDMO), has created an artificial intelligence (AI) platform to address key challenges in protein design. This platform, known as STAR (Sequence Recommendation via Artificial Intelligence), was developed by Asymchem’s Center of Synthetic Biology Technology (CSBT) and AI team.
 
A significant hurdle in protein engineering is the volume of mutations that must be generated and tested, a process that can last from 3 to 6 months. However, STAR significantly speeds up this procedure. Utilizing machine learning, the platform rapidly pinpoints essential mutation regions. This subsequently limits the experimental samples to a few hundred each round, allowing researchers to uncover mutants with enhanced activity over 50 times faster in as little as a month.
 
Technical advantages of the STAR platform include:
 

• Intelligent algorithms: By integrating active learning and virtual directed evolution, the system allows researchers to identify mutation sites beyond experimental data and recommends advantageous mutations
• Data-driven insights: With years of proprietary protein engineering data accumulated by Asymchem’s CSBT, STAR has trained models with highly accurate predictive capabilities
• High-level automation: From data processing to model training and sequence recommendation, STAR offers a fully integrated, end-to-end protein design workflow

 
Asymchem’s CSBT team successfully applied the STAR platform to the evolution of glucose dehydrogenase, increasing the enzyme’s activity twofold and improving its stability tenfold. They further used STAR to predict combination mutations, resulting in a fivefold increase in activity and another tenfold boost in stability. Compared to traditional methods, STAR significantly reduced the time needed for enzyme evolution while improving efficiency in analyzing mutation relationships.
 
As the STAR platform continues to evolve, it will play a pivotal role in advancing Asymchem’s biopharmaceutical research, unlocking opportunities for breakthroughs in the field.