Artificial Intelligence, Synthetic Biology, and Robotics Combine to Improve Enzymes

Researchers at the Center for Advanced Bioenergy and Bioproducts Innovation (CABBI) have developed an automated platform that integrates artificial intelligence, synthetic biology, and robotics to optimize industrial enzymes. By utilizing the iBioFAB biofoundry, the team created a closed-loop system capable of predicting, building, and testing protein variants with minimal human intervention. This advancement addresses critical bottlenecks in enzyme engineering, such as high costs and slow development cycles, while significantly boosting the efficiency of biocatalysts essential for the global bioeconomy.

The research team at CABBI, based at the Carl R. Woese Institute for Genomic Biology (IGB) at the University of Illinois, utilized the iBioFAB biofoundry to streamline the engineering of industrial enzymes. This facility integrates robotics, computer-aided design, and informatics to overcome traditional limitations like low enzyme efficiency and poor target specificity. By combining these technologies, the scientists have moved closer to a fully self-driving laboratory where AI-powered systems manage the entire design-build-test-learn cycle, drastically reducing the time and specialized expertise typically required for protein engineering.

The workflow begins with an AI tool that analyzes datasets of known enzyme structures to suggest specific sequence changes aimed at improving performance. These designs are then physically realized by automated protein-building machines within the biofoundry. Once produced, the machinery rapidly characterizes the functions of the new enzymes, feeding the resulting data back into a secondary AI model. This iterative process allows the system to refine its suggestions for the next round of protein designs, creating a continuous loop of improvement that accelerates the discovery of high-performance biocatalysts.

In a practical application of this platform, the researchers conducted case studies on two industrially relevant enzymes. The automated process successfully increased the activity of these enzymes by 16 and 26 times, respectively, achieving these results with significantly less human labor and at a faster pace than conventional methods. This breakthrough has broad implications for the synthetic biology sector, as it provides a versatile framework for enhancing a wide range of enzymes used in agriculture, domestic energy production, and various bio-based industries.

Supported by the U.S. Department of Energy (DOE) Bioenergy Research Center, this project highlights the growing role of informatics and automation in the transition to a bio-based economy. By lowering the barriers to entry for complex protein engineering, the CABBI team’s platform is expected to drive innovation across diverse sectors, ultimately adding value to rural economies and increasing the sustainability of industrial processes. The success of this AI-driven approach marks a significant milestone in the evolution of automated synthetic biology and the development of self-sufficient laboratory environments.