GPSE

GPSE predicts substrate-specific enzymes from whole-genome and next-generation sequencing datasets to identify and analyze enzymes (e.g., Baeyer–Villiger monooxygenases and carboxylester hydrolases) involved in mycotoxin degradation such as zearalenone (ZEA) in bacteria, yeast, and fungi.

Key Features:

• Whole-Genome and NGS integration: Leverages whole-genome sequencing and next-generation sequencing datasets from bacteria, yeast, and fungi to identify genomic candidates for mycotoxin degradation.
• Bioinformatics pipeline: Predicts substrate-specific enzymes using sequence analysis to identify enzymes implicated in mycotoxin degradation, including Baeyer–Villiger monooxygenases (BVMOs) and carboxylester hydrolases.
• Homologous structural modeling: Builds homology models to infer enzyme function and substrate specificity at the structural level.
• Molecular docking: Performs molecular docking simulations to model and evaluate enzyme–substrate interactions quantitatively and structurally.
• Configuration: Requires editing GPSEcfg.py to set system-specific paths prior to execution.

Scientific Applications:

• Biological detoxification research: Enables molecular analysis of enzymes involved in microbial mycotoxin degradation mechanisms.
• Enzyme discovery and characterization: Facilitates identification and structural characterization of candidate detoxifying enzymes from microbial genomes.
• Agricultural and food-safety biotechnology: Provides molecular targets and interaction models to support development of biotechnological approaches for mitigating mycotoxin contamination.

Methodology:

Workflow steps explicitly include genomic data preparation from mycotoxin-degrading microorganisms, editing GPSEcfg.py for system-specific paths, sequence-based enzyme prediction and structural modeling, homologous model building, and molecular docking simulations.