Machine learning models for the prediction of enzyme properties should be tested on proteins not used for model training

Kroll, A.; Lercher, M. J.

doi:10.1101/2023.02.06.526991

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bioRxiv · 10.1101/2023.02.06.526991

Machine learning models for the prediction of enzyme properties should be tested on proteins not used for model training

Kroll, A.·Lercher, M. J.

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Abstract

The recently published DLKcat model, a deep learning approach for predicting enzyme turnover numbers (kcat), claims to enable high-throughput kcat predictions for metabolic enzymes from any organism and to capture kcat changes for mutated enzymes. Here, we critically evaluate these claims. We show that DLKcat predictions become positively misleading for enzymes with less than 60% sequence identity to the training data, performing worse than simply assuming a mean kcat value for all reactions. Furthermore, DLKcats ability to predict mutation effects is much weaker than implied, capturing only 3% of the experimentally observed variation across mutants not included in the training data. These findings highlight significant limitations in DLKcats generalizability and its practical utility for predicting kcat values for novel enzyme families or mutants, which are crucial applications in fields such as metabolic modeling.

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Kroll, A., Lercher, M. J.. 2023-02-07. Machine learning models for the prediction of enzyme properties should be tested on proteins not used for model training. https://doi.org/10.1101/2023.02.06.526991

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