Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models

David Heckmann (), Colton J. Lloyd, Nathan Mih, Yuanchi Ha, Daniel C. Zielinski, Zachary B. Haiman, Abdelmoneim Amer Desouki, Martin J. Lercher and Bernhard O. Palsson ()
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David Heckmann: University of California, San Diego
Colton J. Lloyd: University of California, San Diego
Nathan Mih: University of California, San Diego
Yuanchi Ha: University of California, San Diego
Daniel C. Zielinski: University of California, San Diego
Zachary B. Haiman: University of California, San Diego
Abdelmoneim Amer Desouki: Heinrich Heine University
Martin J. Lercher: Heinrich Heine University
Bernhard O. Palsson: University of California, San Diego

Nature Communications, 2018, vol. 9, issue 1, 1-10

Abstract: Abstract 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 both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.

Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07652-6

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DOI: 10.1038/s41467-018-07652-6

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