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

Kroll, Alexander and Engqvist, Martin K. M. and Heckmann, David and Lercher, Martin J. and Locasale, Jason W. (2021) Deep learning allows genome-scale prediction of Michaelis constants from structural features. PLOS Biology, 19 (10). e3001402. ISSN 1545-7885

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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.

Item Type: Article
Subjects: European Repository > Biological Science
Depositing User: Managing Editor
Date Deposited: 22 Dec 2022 12:22
Last Modified: 11 Mar 2024 04:44
URI: http://go7publish.com/id/eprint/1199

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