(502c) Automating the Engineering of Improved Enzymes for Biomanufacturing

A key challenge in cellular biomanufacturing of fuels, chemicals, and pharmaceuticials is that many pathway enzymes have very low activity, limiting overall titers and productivities. This is a known problem in at least twelve different systems. 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.

In this talk 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 mutagenesis¹, deep sequencing to evaluate the functional effect of nearly all possible single point mutants on solubility², Bayesian methods to discriminate stable, catalytically neutral from deleterious mutations², and computational design to combine up to 50 mutations at once³.

I will show unpublished work on application of this method to improve the pathway productivity of a medicinal alkaloid pathway in Saccharomyces cerevisiae, and end with the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.

References Cited

1. Wrenbeck EE, KlesmithJR, AdeniranA, StapletonJA, Tyo KJ, Whitehead TA, (2016) “Plasmid-based single-pot saturation mutagenesis”, Nature Methods 13(11): 928-930 doi:10.1038/nmeth.4029

2. Klesmith JR, Bacik JP, Wrenbeck EE, Michalczyk R, Whitehead TA (2017) “Trade-offs between enzyme fitness and solubility illuminated by deep mutational scanning”, PNAS 114:2265-2270 doi: 10.1073/pnas.1614437114

3. Klesmith JR, Bacik JP, Michalczyk R, Whitehead TA (2015) “High-resolution sequence function mapping of a levoglucosan utilization pathway in E. coli”, ACS Synthetic Biology 4 (11), 1235-1243 DOI: 10.1021/acssynbio.5b001