Leveraging Protein Language Models and Cell-Free Protein Synthesis to Identify Effective Divalent Metal Cofactor–Polyphenol Oxidase Pairings for Optimal Catalytic Activity
Dr. David Garcia, Department of Chemical, Biochemical, and Environmental Engineering.
Biological production systems are an emerging area in industrial biomanufacturing where biological pathways aid reaction catalysis to generate chemical products. The success of these systems relies on the efficiency of biocatalysis, where correct enzyme-cofactor pairings enable greater catalytic activity resulting in cost-effective, structurally stable products, and selective reactions increasing overall yield. However, many well-characterized enzymes are studied with a narrow range of metal cofactors, potentially overlooking more effective cofactors that could enhance catalytic performance. This project employs Protein Language Models (PLMs) to predict and classify effective divalent cofactors including Cu2+, Mn2+, Mg2+, Zn2+, and Fe2+ for various polyphenol oxidases. This iterative process uses the results of experimental data to train the model and predict the next set of effective cofactors. Cell-free protein synthesis (CFPS) generates the enzymes needed to test and rapidly validate these computational predictions, feeding the results back into the PLM fine-tuning its prediction. This expedited approach intends to identify effective cofactors giving insight into structural or mechanistic flexibility for each cofactor-enzyme pairing. Initial testing demonstrates that non-canonical divalent ions can act as superior cofactors; specifically, Fe2+ paired with tyrosinase catalyzed reactions with greater efficiency than literature-established Cu2+ under specific concentration conditions. These findings suggest previously unrecognized flexibility in tyrosinase-cofactor pairings, challenging current understandings of its cofactor-binding requirements. Combining machine learning with rapid testing offers new insights into these enzyme-cofactor interactions providing a new pathway maximizing yield for biological production systems.