代谢工程与深度学习驱动的蛋白质工程技术在大肠杆菌中实现N-乙酰神经氨酸生物合成

Abstract

N-Acetylneuraminic acid (NeuAc) is a sialic acid valued in pharmaceuticals and infant nutrition, and microbial synthesis offers a scalable route once the pathway’s catalytic bottlenecks are relieved. We combine deep learning with metabolic engineering to build a high-titer NeuAc-producing E.coli strain. Modular pathway engineering reaches 1.25 g L −1 and pinpoints N-acetylglucosamine 2-epimerase (AGE) as the rate-limiting step. We develop DLCatalysis, a deep learning framework that predicts k cat K m −1 directly from protein sequence and substrate, and use it to mine AGE Bf that lifts the titer to 6.84 g L −1 . DLCatalysis-guided redesign of AGE Bf and NeuB Nm raises NeuAc to 9.27 g L⁻¹, and identifying and deleting exuT , a previously unannotated NeuAc transporter, blocks product reuptake. In 5-L fed-batch fermentation the optimized strain produces 85.5 g L −1 , showing that AI-guided enzyme discovery can resolve the bottlenecks that have limited microbial NeuAc production.