Assessment of enzyme diversity in the fermented food microbiome

Microbial bioactivity is essential for the flavor, appearance, quality, and safety of fermented foods. However, the diversity and distribution of enzymatic resources in fermentation remain poorly understood. This study explored 10,202 metagenome-assembled genomes from global fermented foods using machine learning, identifying over 5 million enzyme sequences grouped into 98,693 homologous clusters, representing over 3,000 enzyme types. Functional analysis revealed that 84.4% of these clusters were unannotated in current databases, with high novelty in terpenoid and polyketide metabolism enzymes. Peptide hydrolases exhibited broad environmental adaptability based on predicted optimal temperatures and pH, and niche breadth calculations indicated 31.3% of enzyme clusters displayed food-type specificity. Additionally, we developed a machine learning model to classify fermented food sources by enzyme clusters, highlighting key enzymes differentiating habitats. Our findings emphasize the untapped potential of fermented food environments for enzyme resource exploration, offering valuable insights into microbial functions for future food research. A record of this paper's transparent peer review process is included in the supplemental information.