Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
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ABSTRACT:
SUBMITTER: Heckmann D
PROVIDER: S-EPMC6286351 | biostudies-literature | 2018 Dec
REPOSITORIES: biostudies-literature
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Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
Heckmann David D Lloyd Colton J CJ Mih Nathan N Ha Yuanchi Y Zielinski Daniel C DC Haiman Zachary B ZB Haiman Zachary B ZB Desouki Abdelmoneim Amer AA Lercher Martin J MJ Palsson Bernhard O BO
Nature communications 20181207 1
Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for bo ...[more]
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