Parameter Reliability and Understanding Enzyme Function

McDonald, Andrew G.; Tipton, Keith F.

doi:10.3390/molecules27010263

Cited by 23 publications

(13 citation statements)

References 111 publications

(123 reference statements)

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“…14 is responsible for the high correlation observed between reaction fluxes and k cat by Heckmann et al. Computational estimates of k cat values are highly relevant for the functional and kinetic study of individual enzymes 40 , and TurNuP can provide a first estimate of k cat before performing labor-intensive experiments. Another major use case of TurNuP is the prediction of k cat values for genome-scale metabolic models.…”

Section: Discussionmentioning

confidence: 99%

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

Kroll

Liebrand

et al. 2022

Preprint

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The turnover number kcat, a measure of enzyme efficiency, is central to understanding cellular physiology and resource allocation. As experimental kcat estimates are unavailable for the vast majority of enzymatic reactions, the development of accurate computational prediction methods is highly desirable. However, existing machine learning models are limited to a single, well-studied organism, or they provide inaccurate predictions except for enzymes that are highly similar to proteins in the training set. Here, we present TurNuP, a general and organism-independent model that successfully predicts turnover numbers for natural reactions of wild-type enzymes. We constructed model inputs by representing complete chemical reactions through difference fingerprints and by representing enzymes through a modified and re-trained Transformer Network model for protein sequences. TurNuP outperforms previous models and generalizes well even to enzymes that are not similar to proteins in the training set. Parameterizing metabolic models with TurNuP-predicted kcat values leads to improved proteome allocation predictions. To provide a powerful and convenient tool for the study of molecular biochemistry and physiology, we implemented a TurNuP web server at https://turnup.cs.hhu.de.

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Section: Discussionmentioning

confidence: 99%

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

Kroll

Liebrand

et al. 2022

Preprint

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“…The Haldane equation has been commonly used to describe substrate inhibition kinetics depending on the amount of substrate ( Sonnad and Goudar, 2004 ; McDonald and Tipton, 2022 ). The Haldane equation has provided a better description of enzyme-based kinetics in microbes for a long-term period with higher substrate concentrations than the basic Michaelis-Menten equation ( Koper et al., 2010 ).…”

Section: Methodsmentioning

confidence: 99%

Physicochemical and fertility characteristics of microalgal soil ameliorants using harvested cyanobacterial microalgal sludge from a freshwater ecosystem, Republic of Korea

Ahn

Lee

Park

et al. 2022

Heliyon

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“…However, in order for new biocatalysts to be worthy of industrial large-scale production, reliable and comprehensive methods for the initial kinetic characterization of possible enzyme variants are necessary. In search of an optimal enzyme variant, the en-zyme with the highest activity (highest V m value) or highest affinity for the substrate (lowest K m value) is often sought [41]. This is only valid when Michaelis-Menten kinetics are applied, however, in practice, the situation is rarely that simple.…”

Section: Choosing the Best Enzyme Variant For The Reactionmentioning

confidence: 99%

Enzyme Reaction Engineering as a Tool to Investigate the Potential Application of Enzyme Reaction Systems

Miličić¹,

Ćevid²,

Çakar³

et al. 2022

Hung. J. Ind. Chem.

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It is widely recognized and accepted that although biocatalysis is an exquisite tool to synthesize natural and unnatural compounds under mild process conditions, much can be done to better understand these processes as well as detect resulting bottlenecks and help to resolve them. This is the precise purpose of enzyme reaction engineering, a scientific discipline that focuses on investigating enzyme reactions with the goal of facilitating their implementation on an industrial scale. Even though reaction schemes of enzyme reactions often seem simple, in practice, the interdependence of differentvariables is unknown, very complex and may prevent further applications. Therefore, in this work, important aspects of the implementation of enzyme reactions are discussed using simple and complex examples, along with principles of mathematical modelling that provide explanations for why some reactions do not proceed as planned.

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Parameter Reliability and Understanding Enzyme Function

Cited by 23 publications

References 111 publications

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

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

Physicochemical and fertility characteristics of microalgal soil ameliorants using harvested cyanobacterial microalgal sludge from a freshwater ecosystem, Republic of Korea

Enzyme Reaction Engineering as a Tool to Investigate the Potential Application of Enzyme Reaction Systems

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