IDEAS home Printed from https://ideas.repec.org/a/plo/pbio00/3001402.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001402
    Download Restriction: no

    File URL: https://journals.plos.org/plosbiology/article/file?id=10.1371/journal.pbio.3001402&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pbio.3001402?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    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. 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.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:plo:pbio00:3001402. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: plosbiology (email available below). General contact details of provider: https://journals.plos.org/plosbiology/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.