Year: 2025
Type: article
Abstract: The use of enzymes for synthetic purposes typically relies on well-known or commercially available proteins, valued for their established properties. However, these enzymes may not always be ideal for specific reactions, prompting researchers to explore the vast diversity of enzymes within biodiversity. Genome and metagenome mining offers a rich reservoir of sequences, revealing novel enzymes with enhanced properties such as thermostability and substrate specificity, which are crucial for industrial applications. Advances in large-scale sequencing have exponentially increased available protein sequences, with over 2.4 billion reported in 2023 compared to 123 million in 2018. Despite its potential, enzyme discovery from metagenomic data remains challenging due to the immense volume of sequences. This necessitates innovative computational tools and bioinformatics workflows to streamline the identification of biocatalysts. Bioinformatics plays a pivotal role in predicting enzyme functions, analyzing protein superfamilies, and selecting key enzymes via biosynthetic gene clusters. Integration of artificial intelligence (AI) further enhances enzyme discovery and retrosynthetic pathway design, enabling the customization of enzymes for specific applications. Case studies from our laboratory illustrate the efficiency of genome mining and bioinformatics in discovering enzymes complementary to known ones, modifying metabolic reactions, and identifying novel scaffolds. These methods have expanded the diversity of enzymes available for synthesis, underscoring the importance of synergizing bioinformatics with biocatalysis to harness biodiversity and develop a versatile enzymatic toolbox.
Source: Comptes Rendus Chimie
DOI: 10.5802/crchim.409
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4411731832