Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.

ABSTRACT: The increasing availability of enzyme turnover number measurements from experiments and of turnover number predictions from deep learning models prompts the use of these enzyme parameters in precise metabolic engineering. Yet, there is no computational approach that allows the prediction of metabolic engineering strategies that rely on the modification of turnover numbers. It is also unclear if modifications of turnover numbers without alterations in the host's transcriptional regulatory machinery suffice to increase the production of chemicals of interest. Here, we present a constraint-based modeling approach, termed Overcoming Kinetic rate Obstacles (OKO), that uses enzyme-constrained metabolic models to predict in silico strategies to increase the production of a given chemical, while e

SUBMITTER: Razaghi-Moghadam Z 

PROVIDER: S-EPMC11563432 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature