Figure S1. Growth of E. coli after gene deletions intended to lower basal growth on MM phe,init . (a.-d.) Select aminotransferases with reported promiscuous activity on phenylalanine were deleted in an attempt to reduce the level of basal growth seen by wild-type E. coli on MM phe,init . Each deletion strain showed no changes in

Transcription factor (TF)-based biosensors are very desirable reagents for high-throughput enzyme and strain engineering campaigns. Despite their potential, they are often difficult to deploy effectively as the small molecules being detected can leak out of high-producer cells, into low-producer cells, and activate the biosensor therein. This crosstalk leads to the overrepresentation of false-positive/cheater cells in the enriched population. While the host cell can be engineered to minimize crosstalk (e.g., by deleting responsible transporters), this is not easily applicable to all molecules of interest, particularly those that can diffuse passively. One such biosensor recently reported for trans-cinnamic acid (tCA) suffers from crosstalk when used for phenylalanine ammonia-lyase (PAL) enzyme engineering by directed evolution. We report that desensitizing the biosensor (i.e., increasing the limit of detection) suppresses cheater population enrichment. Furthermore, we show that, if we couple the biosensor-based screen with an orthogonal prescreen that eliminates a large fraction of true negatives, we can successfully reduce the cheater population during the fluorescence-activated cell sorting. Using the approach developed here, we were successfully able to isolate PAL variants with ∼70% higher k cat after a single sort. These mutants have tremendous potential in phenylketonuria (PKU) treatment and flavonoid production.

Deep mutational scanning (DMS) has recently emerged as a powerful method to study protein sequence− function relationships but is not well-explored as a guide to enzyme engineering and identifying of pathways by which their catalytic cycle may be improved. We report such a demonstration in this work using a phenylalanine ammonia-lyase (PAL), which deaminates L-phenylalanine to trans-cinnamic acid and has widespread application in chemoenzymatic synthesis, agriculture, and medicine. In particular, the PAL from Anabaena variabilis (AvPAL*) has garnered significant attention as the active ingredient in Pegvaliase, the only FDA-approved drug for treating classical phenylketonuria (PKU). Although an extensive body of literature exists on the structure, substrate-specificity, and catalytic cycle, protein-wide sequence determinants of function remain unknown, as do intermediate reaction steps that limit turnover frequency, which has hindered the rational engineering of these enzymes. Here, we created a detailed sequence−function landscape of AvPAL* by performing DMS and revealed 112 mutations at 79 functionally relevant sites that affect a positive change in enzyme fitness. Using fitness values and structure−function analysis, we picked a subset of positions for comprehensive single-and multi-site saturation mutagenesis and identified combinations of mutations that led to improved reaction kinetics in cell-free and cellular contexts. We then performed quantum mechanics/molecular mechanics (QM/MM) and molecular dynamics (MD) studies to understand the mechanistic role of the most beneficial mutations and observed that different mutants confer improvements via different mechanisms, including stabilizing transition and intermediate states, improving substrate diffusion into the active site, and decreasing product inhibition. This work demonstrates how DMS can be combined with computational analysis to effectively identify significant mutations that enhance enzyme activity along with the underlying mechanisms by which these mutations confer their benefit.