A key challenge in cellular biomanufacturing of fuels, chemicals, and Pharmaceuticals is that many pathway enzymes have very low activity, limiting overall titers and productivities. One reason is that enzymes are marginally stable under their native conditions, and expression in a different environment can thermodynamically favor the unfolded state. Additionally, overexpression can result in aggregation because natively expressed proteins are close to their solubility limit. This challenge suggests an engineering solution: engineer pathways enzymes to be stable in their biomanufacturing chassis. However, this is difficult because: (a.) many enzymes do not have high-throughput activity screens needed for directed evolution; (b.) there are few or no structures available; (c.) there are often multiple limiting enzymes; (d.) most mutations confer small benefits to stability; and (e.) the plurality of stability-enhancing mutations decrease catalytic efficiency. I will present a culmination of my group's approach to solve the above challenges, in effect automating the design of stable, active enzymes from limited combinatorial datasets. This engineering strategy involves user-defined precise mutagenesis, deep sequencing to evaluate the functional effect of nearly all possible single point mutants on solubility, Bayesian methods to discriminate stable, catalytically neutral from deleterious mutations, and computational design to combine up to 50 mutations at once.I will show recently published work on application of this method to improve the pathway productivity of a medicinal alkaloid pathway in Saccharomyces cerevisiae, and end with the description of a computational pipeline to automate our process for any enzyme of interest.
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