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Deep learning allows genome-scale prediction of Michaelis constants from structural features

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  • Alexander Kroll
  • Martin K M Engqvist
  • David Heckmann
  • Martin J Lercher

Abstract

The Michaelis constant KM describes the affinity of an enzyme for a specific substrate and is a central parameter in studies of enzyme kinetics and cellular physiology. As measurements of KM are often difficult and time-consuming, experimental estimates exist for only a minority of enzyme–substrate combinations even in model organisms. Here, we build and train an organism-independent model that successfully predicts KM values for natural enzyme–substrate combinations using machine and deep learning methods. Predictions are based on a task-specific molecular fingerprint of the substrate, generated using a graph neural network, and on a deep numerical representation of the enzyme’s amino acid sequence. We provide genome-scale KM predictions for 47 model organisms, which can be used to approximately relate metabolite concentrations to cellular physiology and to aid in the parameterization of kinetic models of cellular metabolism.To understand the action of an enzyme, we need to know its affinity for its substrates, quantified by Michaelis constants, but these are difficult to measure experimentally. This study shows that a deep learning model that can predict them from structural features of the enzyme and substrate, providing KM predictions for all enzymes across 47 model organisms.

Suggested Citation

  • Alexander Kroll & Martin K M Engqvist & David Heckmann & Martin J Lercher, 2021. "Deep learning allows genome-scale prediction of Michaelis constants from structural features," PLOS Biology, Public Library of Science, vol. 19(10), pages 1-21, October.
  • Handle: RePEc:plo:pbio00:3001402
    DOI: 10.1371/journal.pbio.3001402
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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. Alexander Kroll & Sahasra Ranjan & Martin K. M. Engqvist & Martin J. Lercher, 2023. "A general model to predict small molecule substrates of enzymes based on machine and deep learning," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
    4. 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.

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