Metabolic In Silico Network Expansions to Predict and Exploit Enzyme Promiscuity

Area of Science:

• Biochemistry
• Metabolic Engineering
• Bioinformatics

Background:

• Enzymes exhibit promiscuous activities, catalyzing reactions beyond their canonical function.
• These enzymatic capacities are often hidden in native biological systems but can emerge in engineered metabolic contexts with novel substrates or high intermediate concentrations.

Purpose of the Study:

• To describe the application of metabolic in silico network expansion (MINE) databases for predicting novel enzymatic biotransformations.
• To guide scientists in detecting, exploiting, or avoiding these predicted enzyme activities in metabolic engineering and synthetic biology.

Main Methods:

• Utilizing metabolic in silico network expansion (MINE) databases.
• Searching MINE databases by structural similarity to known compounds.
• Analyzing metabolomics data to identify potential novel biotransformations.

Main Results:

• Prediction of a wide range of novel biotransformations catalyzed by enzymes.
• Identification of potential new metabolites resulting from these promiscuous enzymatic reactions.
• Development of a framework for leveraging MINE databases to explore enzymatic potential.

Conclusions:

• MINE databases are powerful tools for uncovering hidden enzymatic capabilities.
• Predictive approaches using MINEs can significantly advance metabolic engineering and synthetic biology.
• Understanding enzyme promiscuity is crucial for designing and optimizing engineered metabolic pathways.