Synthetic metabolic pathways often suffer from low specific productivity, and new methods that quickly assess pathway functionality for many thousands of variants are urgently needed. In my talk I will present a deep sequencing-based tool called Fluxscan, a combination of metabolic flux and deep mutational scanning, which enables the rapid and parallel determination of sequence effects on flux for complete gene-encoding sequences. I will first show application of this method to determine the function of 93% of all possible single point mutants (over 8,000) in a levoglucosan utilization pathway implanted in Escherichia coli. Efficient levoglucosan utilization is a necessary component for hybrid thermochemical/biochemical processing schemes. Experimental sequence-function datasets predicted whether fitness-enhancing mutations to the enzyme levoglucosan kinase resulted from enhanced catalytic efficiency or enzyme stability. Next, I will detail how the integration of these experimental datasets with computational protein design resulted in a 15-fold improvement in growth rate and greater than 24-fold improvement in enzyme activity for the modified pathway. We solved a structure of one enzyme design incorporating 38 mutations, providing insight into the structural basis of high fitness mutations. Our current implementation of Fluxscan resolves the sequence-function relationship through a functional growth selection which may not exist for enzymes within fermentative pathways. I will conclude my talk with early benchmarking of a new integrated computational and experimental tool to improve metabolic pathway performance in the absence of a selection coupling pathway flux to growth.

Deep sequencing-guided assessment and computational design of synthetic metabolic pathways

251st National Meeting (2016)

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