Cyclization specificity in terpene synthases by residue interaction networks

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Enzymes are essential to life and critical for industrial biotechnology efforts aimed at producing everyday pharmaceuticals, biofuels, and fine chemicals. A complete understanding of enzyme function will enable us to fully exploit enzymes to successfully adapt to an ever changing and uncertain world. We are continually making major strides in our understanding of enzyme function through focusing on the active site where the chemical reactions occur. However, it is well documented in directed evolution experiments that outer tier mutations (regions of the enzyme structure distant from the active site) greatly enhance catalytic activity. Although outer tiers of protein structure clearly exert profound influence on enzyme function, this remains poorly understood; our current knowledge rests largely on retrospective structure-based analysis while limited investigations have been conducted. From my own investigations of naturally evolved terpene synthases, enzymes that make diverse bioactive natural products, I discovered that outer tier mutations were essential for the specificity of a chemical reaction. Results from theses studies indicate that the outer tier 'communicates' to the active site through network connections in the protein structure, akin to computers linked through the Internet. Considering a folded protein as a network, this proposal will exploit well-developed graph theory tools, a branch of math to describe 'connectivity', to predict important outer tier residue positions from network analysis of protein structure. Further, we will apply structure-based combinatorial protein engineering collections of new enzymes that 'rewire' outer tier connections to begin to systematically interrogate our predictions. In addition, this work will produce rich data sets to advance our fundamental knowledge of enzymes and generate new 'parts' for synthetic biology and industrial biotechnology applications.