Synergizing Enzyme and Encapsulation Engineering to Develop Superoptimal Biocatalysts

Tufts University, Medford MA

Investigators

Abstract

Enzymes are proteins that promote chemical reactions. They can produce biofuels, chemicals, and medicines. They can be designed to perform well under unnatural conditions. Design efforts usually result in trade-offs between properties. For example, increasing enzyme stability often results in reduced activity. A primary objective of the project is to evaluate a new design strategy. This strategy might improve certain properties without degrading others. The strategy combines a computational approach with a novel starting material. This work will also support recruitment and engagement of undergraduate researchers and underrepresented groups through hands-on research, workshops, and lectures. The overall goal of this work is to develop a “super-optimal” biocatalytic system where enzymes can operate beyond their characterized optimal temperatures, turnover frequency (TOF, kcat), and conversion. Achieving this requires enzyme engineering to directly modify their catalytic properties and encapsulation to alter stability and substrate and product transport rates and partitioning. The project will use a 4-methylideneimidazole-5-one (MIO)-containing amino acid ammonia-lyases (XAL) as a model enzyme. Three different hypotheses will be tested. The first is that novel enzyme activity can be achieved by relaxing stability criteria during engineering. The second is that MIO-containing XAL enzymes can accept non-aromatic substrates. The third is that an engineered encapsulation system can enhance enzyme stability, reaction rates, and conversion. A directed evolution workflow will identify XAL variants with high activity. Stability requirements will be relaxed during directed evolution. Identifying variants with activity on non-cognate amino acid substrates will be another objective. XAL enzymes have never previously been engineered to act on non-aromatic substrates. The enzyme engineering work will use a deep mutational scanning (DMS)-guided experimental workflow as well as computational modeling. Encapsulations for enzymes to enhance their stability, reaction rate, and product yield will be designed and evaluated. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.