A computational learning paradigm to targeted discovery of biocatalysts from metagenomic data: A case study of lipase identification.

The growing adoption of enzymes as biocatalysts in various industries has accentuated the demand for acquiring access to the great natural diversity and, in the meantime, the advent and advancements of metagenomics and high-throughput sequencing technologies have offered an unprecedented opportunity to explore this extensive resource. Lipases, enzymes responsible for the biological turnover of lipids, are among the most commercialized biocatalysts with numerous applications in different domains and therefore are of high industrial value. The relatively costly and time-consuming wet-lab experimental pipelines commonly used for novel enzyme discovery, highlight the necessity of agile in silico approaches to keep pace with the exponential growth of available sequencing data.

A novel thermostable cellulase cocktail enhances lignocellulosic bioconversion and biorefining in a broad range of pH.

Lignocellulose is the most abundant biomass in nature, and the effective biorefining of them is dependent upon enzymes with high catalytic activity and stability in extreme pH and high temperatures. Due to the molecular constraints for a single enzyme, obtaining a more excellent active pH range can be more easily achievable through the simultaneous activity of two or more enzymes in a cocktail. To address this, we attempted to develop a cocktail of novel thermostable cellulases with high hydrolytic ability and stability.