Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.,Nature Communications

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Machine learning applied to enzyme turnover numbers reveals protein structural correlates and improves metabolic models.
Nature Communications ( IF 14.7 ) Pub Date : 2018-12-07 , DOI: 10.1038/s41467-018-07652-6
David Heckmann ₁ , Colton J Lloyd ₁ , Nathan Mih ₁ , Yuanchi Ha ₁ , Daniel C Zielinski ₁ , Zachary B Haiman ₁ , Abdelmoneim Amer Desouki ₂ , Martin J Lercher ₂ , Bernhard O Palsson _{1,

3}

Affiliation

Knowing the catalytic turnover numbers of enzymes is essential for understanding the growth rate, proteome composition, and physiology of organisms, but experimental data on enzyme turnover numbers is sparse and noisy. Here, we demonstrate that machine learning can successfully predict catalytic turnover numbers in Escherichia coli based on integrated data on enzyme biochemistry, protein structure, and network context. We identify a diverse set of features that are consistently predictive for both in vivo and in vitro enzyme turnover rates, revealing novel protein structural correlates of catalytic turnover. We use our predictions to parameterize two mechanistic genome-scale modelling frameworks for proteome-limited metabolism, leading to significantly higher accuracy in the prediction of quantitative proteome data than previous approaches. The presented machine learning models thus provide a valuable tool for understanding metabolism and the proteome at the genome scale, and elucidate structural, biochemical, and network properties that underlie enzyme kinetics.

中文翻译：

应用于酶周转数的机器学习揭示了蛋白质结构相关性并改进了代谢模型。

了解酶的催化周转数对于了解生物体的生长速率、蛋白质组组成和生理学至关重要，但酶周转数的实验数据稀疏且嘈杂。在这里，我们证明机器学习可以根据酶生物化学、蛋白质结构和网络环境的综合数据成功预测大肠杆菌中的催化周转数。我们确定了一系列能够一致预测体内和体外酶周转率的不同特征，揭示了催化周转率的新蛋白质结构相关性。我们使用我们的预测来参数化两个用于蛋白质组限制代谢的机械基因组规模建模框架，从而使定量蛋白质组数据的预测精度比以前的方法显着提高。因此，所提出的机器学习模型为在基因组规模上理解新陈代谢和蛋白质组提供了一个有价值的工具，并阐明了酶动力学基础的结构、生化和网络特性。

更新日期：2018-12-07

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