Computational and AI-Driven Discovery, Characterization, and Design of Microbial Enzymes

The field of microbial enzyme research is rapidly evolving as biotechnology and industrial processes increasingly depend on the exploitation of microbial enzymes for diverse applications—from sustainable biomanufacturing to novel drug development. While traditional enzyme discovery approaches have yielded valuable biocatalysts, their pace and scale are often limited by labor-intensive experimental methods and the vast, untapped diversity accessible only through advanced computational means. In recent years, the integration of bioinformatics, machine learning, and artificial intelligence has begun to revolutionize the landscape, empowering researchers to mine vast genomic and metagenomic datasets, predict enzyme function with unprecedented accuracy, and rationally engineer enzymes for improved performance. Despite these advances, many challenges remain, including bridging the gap between in silico predictions and laboratory validation, refining algorithms to better capture the complexity of enzyme structure-function relationships, and fully leveraging interdisciplinary techniques to accelerate design and engineering workflows.This Research Topic aims to catalyze progress at the intersection of computational biology, artificial intelligence, and microbial enzyme biotechnology. It seeks to showcase, synthesize, and promote innovative research that demonstrates how data-driven and AI-assisted methodologies can systematically advance the discovery, functional elucidation, and rational engineering of microbial enzymes. Key objectives include elucidating mechanisms by which computational tools can improve resource mining, function annotation, and enzyme design, as well as fostering interdisciplinary dialogue that pushes the boundaries from classical discovery toward intelligent design and application.The scope of this Research Topic is broad yet focused on computational and AI-powered strategies related to microbial enzymes. Research should emphasize methodological advances, interdisciplinary integration, and the coupling of computational predictions with experimental approaches. To gather further insights in microbial enzyme discovery, engineering, and application, we welcome articles addressing, but not limited to, the following themes:• Intelligent mining and classification of microbial enzyme resources using genomic and metagenomic data• Machine learning for enzyme function prediction, catalytic mechanism elucidation, and substrate specificity assessment• Computational simulation and AI-assisted strategies for enzyme engineering and directed evolution• Interdisciplinary design and integration of enzyme modules in synthetic biology and metabolic engineering• Novel paradigms for integrating experimental validation with computational prediction for comprehensive enzyme characterization• AI modeling of gene transcription to enable expression-guided enzyme regulationPlease note that Microbiotechnology does not consider descriptive studies that are solely based on amplicon (e.g., 16S rRNA) profiles or comparisons of nucleic acid extracts (e.g., metagenomics), unless they are accompanied by a clear hypothesis and experimentation and provide insight into the microbiological system or process being studied. Topic editors: Zhi-Kai Yang, Xi Zhang Source: https://www.frontiersin.org/research-topics/78901/computational-and-ai-driven-discovery-characterization-and-design-of-microbial-enzymes