Kinetic Characterization of L-threonine Transaldolases with Enhanced Affinity for L-threonine

Researcher(s)

  • Anoushka Buddhikot, Chemical Engineering, University of Delaware

Faculty Mentor(s)

  • Aditya Kunjapur, Chemical and Biomolecular Engineering, University of Delaware

Abstract

ꞵ-hydroxy non-standard amino acids (ꞵ-OH nsAAs) have applications as small-molecule pharmaceuticals, precursors for antibiotics, and building blocks for polypeptides in protein engineering. L-threonine transaldolases (L-TTAs) are an enzyme useful for the synthesis of  ꞵ-OH nsAAs from an aromatic aldehyde and L-Threonine due to their broad substrate specificity and stereoselectivity. However, L-TTAs are limited by their low co-substrate affinity (high L-threonine Km), causing the enzyme to require high concentrations of supplemental L-threonine to catalyze the reaction. This constraint limits our ability to perform live-cell synthesis of ꞵ-OH nsAAs due to the sub-millimolar intracellular concentration of L-threonine of 200 µM in E. coli.

Through bioprospecting, we have identified a wild-type L-TTA known as TTA P with an L-threonine Km of 0.43 mM, which is significantly lower than any previously characterized L-TTAs reported in literature. However, to make this enzyme more suitable for live cell biosynthesis, we aim to engineer a TTA P variant with enhanced affinity for L-threonine, with a Km below the intracellular L-threonine concentration of E. coli. Firstly, we screened a library of wildtype L-TTAs by assessing their productivity in live E. coli cells without supplemental L-threonine. High activity of an L-TTA under L-threonine limiting conditions suggests that the enzyme has a high affinity for L-threonine. Once we identified the top performing TTAs, we purified them for further biochemical characterization in vitro. TTA activity was monitored under varied L-threonine conditions using an alcohol dehydrogenase-coupled assay and the L-threonine Km was determined using the Michaelis-Menten kinetic model. This work will inform an iterative machine learning model that seeks to understand the L-TTA sequence context and activity relationships and predict improved L-TTA mutants. Ultimately, the development of an L-TTA with enhanced L-threonine affinity is a key step toward improving our capacity for synthesis of ꞵ-OH nsAAs and their derivatives in live E. coli cells.