Extremozymes for food fermentation: Integrating AI, metagenomics, and protein engineering.
TL;DR
This review identifies current knowledge gaps, technological bottlenecks, and future research priorities for developing robust, programmable, and energy-efficient fermentation systems capable of sustaining product quality, process reliability, and sustainable food production under increasingly variable environmental conditions.
Abstract
Climate change-induced fluctuations in temperature, pH, salinity, and water activity are increasingly compromising microbial metabolism and fermentation efficiency, exposing the limitations of conventional mesophilic enzymes in maintaining process stability and product consistency. Extremozymes, derived from extremophilic microorganisms, exhibit exceptional structural stability and catalytic activity under harsh physicochemical conditions, making them promising biocatalysts for climate-resilient food fermentation. Although considerable progress has been achieved in extremozyme discovery and engineering, challenges remain in bridging computational prediction with experimental validation, functional characterization, large-scale production, and industrial deployment. This review critically examines the diversity, biochemical properties, and functional roles of extremozymes in food fermentation while evaluating the influence of climate-induced process stresses on microbial performance, enzyme functionality, and fermentation outcomes. It further synthesizes recent advances in Artificial Intelligence (AI)-assisted metagenomics, machine learning, transformer-based protein modelling, generative protein design, multi-omics (MO) integration, and high-throughput screening platforms, including microfluidics, droplet-based systems, and cell-free expression technologies, that are accelerating enzyme discovery, engineering, and validation. Particular emphasis is placed on the integration of computational and experimental workflows to improve the accuracy, scalability, and industrial translation of next-generation extremozymes. Unlike previous reviews that primarily describe individual enzyme classes or AI methodologies, this review provides a comprehensive and critical framework linking climate-driven fermentation challenges with emerging computational and biotechnological solutions. It identifies current knowledge gaps, technological bottlenecks, and future research priorities for developing robust, programmable, and energy-efficient fermentation systems capable of sustaining product quality, process reliability, and sustainable food production under increasingly variable environmental conditions.