Generative AI-driven enzyme engineering unlocks sustainable valorization of broccoli waste into sulforaphane
生成式人工智能驱动的酶工程实现西兰花废弃物向萝卜硫素的可持续高值化转化
- 关键词：
- 来源：
- Bioresource Technology
- 类型：
- 学术文献
- 语种：
- 英语
- 原文发布日期：
- 2026-05-29
- 摘要：
- Current production of sulforaphane (SFN), a health-promoting isothiocyanate, relies mainly on energy-intensive plant extraction or chemical synthesis, limiting sustainable large-scale manufacture. Here, a waste-to-value framework integrating generative artificial intelligence (AI), multi-scale modeling, and life-cycle assessment (LCA) was established to engineer a robust microbial biocatalyst for low-carbon valorization of broccoli waste. Genome mining identified a novel Lactobacillus-derived β-thioglucosidase as a promising scaffold. A ProteinMPNN–AlphaFold design pipeline targeting flexible loops near the active site yielded an improved variant with markedly enhanced catalytic performance and thermal robustness, with kcat increasing from 98.5 ± 3.2 to 421.3 ± 9.1 min−1 and Km decreasing from 4.6 ± 0.2 to 0.9 ± 0.1 mM. Molecular dynamics and hybrid quantum mechanics/molecular mechanics (QM/MM) analyses indicated that loop rigidification, improved hydrophobic packing, and a reshaped electrostatic environment promoted transition-state stabilization and reduced the calculated activation barrier for SC bond cleavage from 19.2 to 17.5 kcal mol−1. When applied to homogenized broccoli residues in a heterogeneous solid–liquid system, the engineered enzyme showed high SFN yields and broad tolerance across pH and temperature conditions. Comparative LCA further revealed ∼70% and ∼60% reductions in global warming potential and cumulative energy demand, respectively, relative to conventional routes. As the first study to integrate AI-driven protein engineering with rigorous sustainability metrics for SFN production, this work offers a scalable paradigm for the green biomanufacturing of high-value natural products from agricultural residues.
- 所属专题：
- 173
