Characterizing and predicting carboxylic acid reductase activity for diversifying bioaldehyde production

Area of Science:

• Biocatalysis and enzyme engineering
• Synthetic organic chemistry
• Metabolic engineering

Background:

• Aldehydes are crucial building blocks in synthesizing polymers, pharmaceuticals, pesticides, flavors, and fragrances.
• Traditional chemical synthesis of aldehydes from carboxylic acids faces thermodynamic and specificity challenges.
• Enzymatic reductive bioaldehyde synthesis offers an environmentally friendly alternative, utilizing ATP hydrolysis under mild conditions.

Purpose of the Study:

• To expand the substrate scope for four Carboxylic Acid Reductases (Cars).
• To investigate the mechanistic similarity of the Lys2 enzyme family to Cars.
• To develop a predictive model for Car reactivity.

Main Methods:

• Enzyme assays were performed to determine substrate permissivity for four Cars and two Lys2 isozymes.
• A Support Vector Classifier (SVC) was trained using existing and newly generated substrate data.
• The SVC model was applied to predict Car reactivity for all carboxylic acid metabolites in iAF1260 and Model SEED.

Main Results:

• Carboxylic Acid Reductases (Cars) exhibit a preference for substrates where the carboxylic acid is the sole polar or charged functional group.
• The Lys2 enzyme family demonstrated mechanistic similarity to Cars.
• A predictive classifier was successfully developed to forecast enzyme activity on diverse carboxylic acid substrates.

Conclusions:

• The study enhances understanding of Car substrate specificity, aiding in enzyme engineering and biocatalytic process design.
• The developed Support Vector Classifier (SVC) provides a valuable tool for predicting enzyme-substrate interactions in metabolic contexts.
• These findings facilitate the broader application of biocatalysis for sustainable aldehyde production.