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

Author

Listed:
  • 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
    Technical University of Denmark)

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.

Suggested Citation

  • David Heckmann & Colton J. Lloyd & Nathan Mih & Yuanchi Ha & Daniel C. Zielinski & Zachary B. Haiman & Abdelmoneim Amer Desouki & Martin J. Lercher & Bernhard O. Palsson, 2018. "Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:9:y:2018:i:1:d:10.1038_s41467-018-07652-6
    DOI: 10.1038/s41467-018-07652-6
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    Cited by:

    1. Alexander Kroll & Yvan Rousset & Xiao-Pan Hu & Nina A. Liebrand & Martin J. Lercher, 2023. "Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-14, December.
    2. Gi Bae Kim & Ji Yeon Kim & Jong An Lee & Charles J. Norsigian & Bernhard O. Palsson & Sang Yup Lee, 2023. "Functional annotation of enzyme-encoding genes using deep learning with transformer layers," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    3. Han Yu & Huaxiang Deng & Jiahui He & Jay D. Keasling & Xiaozhou Luo, 2023. "UniKP: a unified framework for the prediction of enzyme kinetic parameters," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. Philipp Wendering & Marius Arend & Zahra Razaghi-Moghadam & Zoran Nikoloski, 2023. "Data integration across conditions improves turnover number estimates and metabolic predictions," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    5. Guido Zampieri & Supreeta Vijayakumar & Elisabeth Yaneske & Claudio Angione, 2019. "Machine and deep learning meet genome-scale metabolic modeling," PLOS Computational Biology, Public Library of Science, vol. 15(7), pages 1-24, July.

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