Efficient Synthesis of γ-Aminobutyric Acid from Monosodium Glutamate Using an Engineered Glutamate Decarboxylase Active at a Neutral pH

Abstract

The efficient production of γ-aminobutyric acid (GABA) at a neutral pH remains a challenge due to the pH sensitivity of glutamate decarboxylase (GAD) enzymes. Our study addressed this limitation by identifying and engineering GAD enzymes with high activity under neutral conditions. Through gene mining, we discovered a wild-type GAD from Enterococcus faecalis (EfGAD) with high activity at pH 7.0 and, using zero-shot (ZS) predictor-guided mutagenesis and C-terminal truncation, we developed an EfGAD variant with a significantly enhanced catalytic efficiency. This variant demonstrated a 1.3-fold increase in GABA production (~300 g/L) from monosodium glutamate (MSG) compared to the wild-type EfGAD in 5 L bioreactor experiments. The ability to operate at a neutral pH without the need for acidic conditions reduces production costs and facilitates scalability. Our findings underscore the potential of integrating machine learning tools for enzyme optimization and provide a sustainable approach to GABA biosynthesis using MSG as a substrate.