Microbial lipases: advances in metagenomics and artificial intelligence for enzyme discovery and engineering

Microbial lipases are versatile biocatalysts with high catalytic efficiency, substrate specificity, stability, and ability to catalyze a wide range of processes under mild environmental conditions, which make them highly valuable in various industrial and biotechnological applications. However, traditional methods of enzyme discovery and engineering rely on cultured microorganisms and labor-intensive experimental processes. This study highlights recent developments in metagenomics and AI technologies for microbial lipase discovery and engineering and providing a brief overview of the sources, structural features, physicochemical properties, and industrial applications of lipases. Recent breakthroughs in metagenomics have provided new access to novel enzymes from non-cultivable microbial communities, and the rising significance of artificial intelligence in enzyme discovery, structure prediction, protein engineering, and bioprocess optimization is presented. This study also highlights the important synergy between metagenomics and artificial intelligence technologies for the identification and rational design of enzymes, integrating extensive sequence databases with predictive computational modeling tools. In addition, there are still various challenges, such as low heterologous expression levels, a lack of quality information, and limited industrial-scale validation. We anticipate that future advances in protein language models, generative artificial intelligence, synthetic biology, and multi-omics integration will accelerate enzyme discovery, engineering, and large-scale industrial implementation. Overall, the use of metagenomics, artificial intelligence, and experimental approaches has tremendous potential for developing efficient and economically viable lipases for sustainable biotechnological applications.