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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. [ABSTRACT FROM AUTHOR]

The advent of machine learning (ML) has significantly advanced enzyme engineering, particularly through zero‐shot (ZS) predictors that forecast the effects of amino acid mutations on enzyme properties without requiring additional labeled data for the target enzyme. This review comprehensively summarizes ZS predictors developed over the past decade, categorizing them into predictors for enzyme kinetic parameters, stability, solubility/aggregation, and fitness. It details the algorithms used, encompassing traditional ML approaches and deep learning models, emphasizing their predictive performance. Practical applications of ZS predictors in engineering specific enzymes are discussed. Despite notable advancements, challenges persist, including limited training data for ZS predictors and the necessity to incorporate environmental factors (e.g., pH, temperature) and enzyme dynamics into these models. Future directions are proposed to advance ZS prediction‐guided enzyme engineering, thereby enhancing the practical utility of these predictors. [ABSTRACT FROM AUTHOR]