文章基本信息

标题：Kinetic profiling of metabolic specialists demonstrates stability and consistency of in vivo enzyme turnover numbers
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作者：David Heckmann ; Anaamika Campeau ; Colton J. Lloyd 等
期刊名称：Proceedings of the National Academy of Sciences
印刷版ISSN：0027-8424
电子版ISSN：1091-6490
出版年度：2020
卷号：117
期号：37
页码：23182-23190
DOI：10.1073/pnas.2001562117
出版社：The National Academy of Sciences of the United States of America
摘要：Enzyme turnover numbers ( k cat s) are essential for a quantitative understanding of cells. Because k cat s are traditionally measured in low-throughput assays, they can be inconsistent, labor-intensive to obtain, and can miss in vivo effects. We use a data-driven approach to estimate in vivo k cat s using metabolic specialist Escherichia coli strains that resulted from gene knockouts in central metabolism followed by metabolic optimization via laboratory evolution. By combining absolute proteomics with fluxomics data, we find that in vivo k cat s are robust against genetic perturbations, suggesting that metabolic adaptation to gene loss is mostly achieved through other mechanisms, like gene-regulatory changes. Combining machine learning and genome-scale metabolic models, we show that the obtained in vivo k cat s predict unseen proteomics data with much higher precision than in vitro k cat s. The results demonstrate that in vivo k cat s can solve the problem of inconsistent and low-coverage parameterizations of genome-scale cellular models.
关键词：in vivo ; turnover number ; proteomics ; k cat ; gene knockout