AI-Driven Metabolic Engineering of γ-Aminobutyric Acid: Biosynthetic Advances and Industrial Applications

Abstract

Gamma-aminobutyric acid (GABA) is the most significant inhibitory neurotransmitter in the mammalian central nervous system and plays crucial roles in regulating neural excitation, mood, and muscle activity. Beyond mammals, GABA is also pivotal in plant stress responses and microbial metabolism. It has wide applications in the pharmaceutical, agricultural, and food industries. In recent years, metabolic engineering strategies combined with synthetic biology, gene editing technologies, and artificial intelligence have significantly advanced the understanding and production of GABA. Notably, the integration of machine learning into microbial engineering has enabled rational design and optimization of biosynthetic pathways, enzyme functions, and fermentation conditions. This review summarizes the current knowledge of GABA biosynthetic pathways, enzyme engineering strategies, microbial strain optimization, and production conditions, with a particular focus on the emerging role of AI in GABA biosynthesis. The goal is to provide a reference framework for future research that leverages AI-driven approaches to enhance GABA production.