(485ad) Computational Design and Selection of Glucose Oxidase

Computational protein design has shown its potential to efficiently create proteins with improved properties, including protein stability and antibody affinity. However, the computational redesign of enzymes has proven to be far more challenging because of poor understanding of the catalytic process and inability to accurately predict the interactions at the transition states. Thus, experimental approaches like directed evolution have been the preferred method to create new enzymes. Advances in DNA synthesis technology, however, have made possible the rapid creation of genetic material from scratch, giving synthetic biologists opportunities to combine the benefits of the computational and experimental approaches. Here, we report a computational design and selection of glucose oxidase. To this end, we computationally searched the sequence space of candidate mutations, combinatorially assembled the selected sequences, and experimentally screened the created library. We will present computational and experimental results, including the methods used to generate the novel mutants and the structure-activity relationship of the newly created glucose oxidases.