Opinion | Open Access

Volume 2025 |Article ID 100026 | https://doi.org/10.1016/j.bidere.2025.100026

Enzyme promiscuity in the field of synthetic biology applied to white biotechnology: Opportunities and weaknesses

Thibault Malfoy ,¹ Ceren Alkim ,^1,2 Jean Marie François ^1,2

¹Toulouse Biotechnology Institute (TBI), Universit
e de Toulouse, CNRS, INRA, INSA, 135 Avenue de Rangueil, F-31077, Toulouse, France
²Toulouse White Biotechnology Center (TWB, UMS INRAE-INSA-CNRS), 135 avenue de Rangueil, F31077, Toulouse, France

Abstract

White biotechnology stands as a major sustainable alternative to address pressing environmental issues arising from our heavy dependence on petrochemical synthesis. However, reaching this goal, both technologically and economically, will take time, resources and money. A major reason is within the biological system itself, as it has evolved into a bow-tie structure in which carbon and energy are converted, via highly regulated, complex and interconnected metabolic networks, into cellular components for growth and homeostasis. This objective is fundamentally at odds with that of biotechnology, which aims to convert carbon and energy into bioproducts. Engineering of microorganism using systems and synthetic biological systems tools has been developed to provide a compromise between these two objectives. However, these genetic and metabolic interventions have revealed often unexpected physiological behaviors, in part due to the fact that a large proportion of metabolic enzymes are catalyzing other reactions than those for which they were evolved. While this promiscuity is the source of an underground metabolism that can prove very advantageous in building high-performance production routes, it is also responsible for loss of yield and production due to metabolic disturbances, negative cross-talks between natural and heterologous pathways as well as it is at the onset of metabolic damages. Identifying these promiscuous enzymes and thus anticipating their opportunities or weaknesses in engineering microbial cell factories for bioproduction is a major challenge in order to improve their performance. It is foreseen that machine learning tools operating on databases continuously fed by genetic, metabolic, enzymatic and fermentation processes data can help to overcome these challenges and provide a better understanding of the physiological functioning of the microbial system.