A general model to predict small molecule substrates of enzymes based on machine and deep learning

Alexander Kroll, Sahasra Ranjan, Martin K. M. Engqvist and Martin J. Lercher ()
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Alexander Kroll: Heinrich Heine University
Sahasra Ranjan: Indian Institute of Technology Bombay
Martin K. M. Engqvist: Chalmers University of Technology
Martin J. Lercher: Heinrich Heine University

Nature Communications, 2023, vol. 14, issue 1, 1-13

Abstract: Abstract For most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze. Experimental characterizations of potential substrates are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine-learning model for the prediction of enzyme-substrate pairs with an accuracy of over 91% on independent and diverse test data. ESP can be applied successfully across widely different enzymes and a broad range of metabolites included in the training data, outperforming models designed for individual, well-studied enzyme families. ESP represents enzymes through a modified transformer model, and is trained on data augmented with randomly sampled small molecules assigned as non-substrates. By facilitating easy in silico testing of potential substrates, the ESP web server may support both basic and applied science.

Date: 2023
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DOI: 10.1038/s41467-023-38347-2

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