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Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

Biology

Turnover number predictions for kinetically uncharacterized enzymes using machine and deep learning

A. Kroll, Y. Rousset, et al.

Discover TurNuP, a groundbreaking model developed by Alexander Kroll, Yvan Rousset, Xiao-Pan Hu, Nina A. Liebrand, and Martin J. Lercher that predicts enzyme turnover numbers (kcat) with remarkable generalizability. This innovative tool integrates reaction fingerprints with advanced protein sequence analysis, enhancing metabolic model predictions and providing easy web access to vital biochemical insights.

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Playback language: English
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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