Fungal -omics resources and the challenges of function prediction toward unlocking the potential of enzyme biocatalysts

Abstract

Fungal enzymes represent a versatile and underexploited resource for sustainable industrial biocatalysis due to their extensive functional diversity and metabolic adaptability. Recent advances in multi-omics technologies, genome mining platforms, and enzyme classification systems have significantly enhanced the identification and annotation of fungal enzymes and biosynthetic gene clusters. However, current discovery pipelines remain constrained by limited integration across predictive genome mining tools, functional genomics resources, and experimentally validated enzyme and metabolite datasets, resulting in a function prediction–validation disconnect. This review provides a critical synthesis of computational and experimental frameworks for fungal enzyme discovery, with emphasis on multi-omics integration, enzyme classification systems, and biosynthetic gene cluster analysis. Key knowledge gaps, including functional annotation ambiguity, fragmented analytical workflows, and insufficient experimental validation, are examined. For industrial re-purposing of these fungal enzymes to serve as biocatalysts, phenotypic data integration remains a major barrier to establishing direct genotype–phenotype relationships in fungal systems. Addressing these challenges will be essential for improving the predictive accuracy, functional resolution, and industrial applicability of fungal biocatalysts.