Computational approach for identification, characterization, three-dimensional structure modelling and machine learning-based thermostability prediction of xylanases from the genome of Aspergillus fumigatus

Powerful cell wall biomass degradation enzymatic system from saprotrophic Aspergillus fumigatus

Cell wall biomass, Earth's most abundant natural resource, holds significant potential for sustainable biofuel production. Composed of cellulose, hemicellulose, lignin, pectin, and other polymers, the plant cell wall provides essential structural support to diverse organisms in nature. In contrast, non-plant species like insects, crustaceans, and fungi rely on chitin as their primary structural polysaccharide. The saprophytic fungus Aspergillus fumigatus has been widely recognized for its adaptability to various environmental conditions. It achieves this by secreting different cell wall biomass degradation enzymes to obtain essential nutrients. This review compiles a comprehensive collection of cell wall degradation enzymes derived from A. fumigatus, including cellulases, hemicellulases, various chitin degradation enzymes, and other polymer degradation enzymes. Notably, these enzymes exhibit biochemical characteristics such as temperature tolerance or acid adaptability, indicating their potential applications across a spectrum of industries.

Precision enzyme discovery through targeted mining of metagenomic data

Metagenomics has opened new avenues for exploring the genetic potential of uncultured microorganisms, which may serve as promising sources of enzymes and natural products for industrial applications. Identifying enzymes with improved catalytic properties from the vast amount of available metagenomic data poses a significant challenge that demands the development of novel computational and functional screening tools. The catalytic properties of all enzymes are primarily dictated by their structures, which are predominantly determined by their amino acid sequences. However, this aspect has not been fully considered in the enzyme bioprospecting processes. With the accumulating number of available enzyme sequences and the increasing demand for discovering novel biocatalysts, structural and functional modeling can be employed to identify potential enzymes with novel catalytic properties. Recent efforts to discover new polysaccharide-degrading enzymes from rumen metagenome data using homology-based searches and machine learning-based models have shown significant promise. Here, we will explore various computational approaches that can be employed to screen and shortlist metagenome-derived enzymes as potential biocatalyst candidates, in conjunction with the wet lab analytical methods traditionally used for enzyme characterization.