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

Alexander Kroll, Martin K M Engqvist, David Heckmann and Martin J Lercher

PLOS Biology, 2021, vol. 19, issue 10, 1-21

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.

Date: 2021
References: Add references at CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.3001402 (text/html)
https://journals.plos.org/plosbiology/article/file ... 01402&type=printable (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:plo:pbio00:3001402

DOI: 10.1371/journal.pbio.3001402

Access Statistics for this article

More articles in PLOS Biology from Public Library of Science
Bibliographic data for series maintained by plosbiology ().