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Abstract
Predicting enzyme turnover numbers (kcat) is crucial for understanding cellular processes. Existing machine learning models often lack generalizability. This paper introduces TurNuP, an organism-independent model that predicts kcat using reaction fingerprints and a modified Transfer Network model for protein sequences. TurNuP outperforms previous models and generalizes well to dissimilar enzymes. Integrating TurNuP predictions into metabolic models improves proteome allocation predictions. A TurNuP web server facilitates easy access to the model.
Publisher
Nature Communications
Published On
Jul 12, 2023
Authors
Alexander Kroll, Yvan Rousset, Xiao-Pan Hu, Nina A. Liebrand, Martin J. Lercher
Tags
enzyme turnover number
kcat prediction
machine learning
TurNuP
metabolic models
biochemistry
protein sequences

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