Characterizing and predicting carboxylic acid reductase activity for diversifying bioaldehyde production. Issue 5 (10th November 2015)

• Record Type: Journal Article
• Authors: Moura, Matthew; Pertusi, Dante; Lenzini, Stephen; Bhan, Namita; Broadbelt, Linda J.; Tyo, Keith E.J.
• Is Part Of: Biotechnology and bioengineering. Volume 113:Issue 5(2016)
• Page Start: 944
• Page End: 952
• Publication Date: 2015-11-10
• Subjects: carboxylic acid reductase -- reductive aldehyde synthesis -- enzyme promiscuity -- support vector machines; Biotechnology -- Periodicals; Bioengineering -- Periodicals; 660.6
• DOI: 10.1002/bit.25860
• Languages: English
• ISSNs: 0006-3592

Abstract

ABSTRACT: Chemicals with aldehyde moieties are useful in the synthesis of polymerization reagents, pharmaceuticals, pesticides, flavors, and fragrances because of their high reactivity. However, chemical synthesis of aldehydes from carboxylic acids has unfavorable thermodynamics and limited specificity. Enzymatically catalyzed reductive bioaldehyde synthesis is an attractive route that overcomes unfavorable thermodynamics by ATP hydrolysis in ambient, aqueous conditions. Carboxylic acid reductases (Cars) are particularly attractive, as only one enzyme is required. We sought to increase the knowledge base of permitted substrates for four Cars. Additionally, the Lys2 enzyme family was found to be mechanistically the same as Cars and two isozymes were also tested. Our results show that Cars prefer molecules where the carboxylic acid is the only polar/charged group. Using this data and other published data, we develop a support vector classifier (SVC) for predicting Car reactivity and make predictions on all carboxylic acid metabolites in iAF1260 and Model SEED. Biotechnol. Bioeng. 2016;113: 944–952. © 2015 Wiley Periodicals, Inc. Abstract : Chemicals with aldehydes have many useful applications, but chemical options for the reduction of aldehydes from carboxylic acids are limited. The enzymatically catalyzed option by carboxylic acid reductases (Cars) is an attractive route, albeit with limited experimental information available on enzyme–substrate compatibility. This study expands the known substrate promiscuity of Cars, confirming both high‐ and trace‐level catalyses. Further, the authors apply machine‐learning techniques to our broadened substrate profile to give an expansive predictive set of substrates within native metabolism.