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

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.

SUBMITTER: Kroll A

PROVIDER: S-EPMC10185530 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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

Kroll Alexander A Ranjan Sahasra S Engqvist Martin K M MKM Lercher Martin J MJ

Nature communications 20230515 1

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 pai ...[more]

PMID: 37188731

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Dataset Information

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

Publications

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

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