2021
DOI: 10.1038/s41467-021-24075-y
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Physical constraints and functional plasticity of cellulases

Abstract: Enzyme reactions, both in Nature and technical applications, commonly occur at the interface of immiscible phases. Nevertheless, stringent descriptions of interfacial enzyme catalysis remain sparse, and this is partly due to a shortage of coherent experimental data to guide and assess such work. In this work, we produced and kinetically characterized 83 cellulases, which revealed a conspicuous linear free energy relationship (LFER) between the substrate binding strength and the activation barrier. The scaling … Show more

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Cited by 27 publications
(52 citation statements)
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“…21 This type of tradeoff between binding strength and rate is well-known within inorganic, heterogeneous catalysis, and was originally coined as the Sabatier principle, which states that efficient catalysis occurs when the catalyst binds its reactant with intermediate strength. 22,23 The Sabatier principle has recently proven useful for predicting and rationalizing catalytic properties of cellulases 24,25 and as a guide for computer-aided enzyme design and discovery. 26…”
Section: Introductionmentioning
confidence: 99%
“…21 This type of tradeoff between binding strength and rate is well-known within inorganic, heterogeneous catalysis, and was originally coined as the Sabatier principle, which states that efficient catalysis occurs when the catalyst binds its reactant with intermediate strength. 22,23 The Sabatier principle has recently proven useful for predicting and rationalizing catalytic properties of cellulases 24,25 and as a guide for computer-aided enzyme design and discovery. 26…”
Section: Introductionmentioning
confidence: 99%
“…100 cellulases with experimental binding values. 3 For a more efficient training, all binding energies were linearly scaled with a minimum value of −13 kJ/mol and a maximum value of 17 kJ/mol, resulting in values well between 0-1 for all datasets.…”
Section: Experimental Procedures Data Set and Data Curationmentioning
confidence: 99%
“…It is known that the residues W38 and W40 are critical for binding and that their mutation to alanine leads to a drastic decrease in binding energy. 3,19 Software The deep learning model was built with Tensorflow (2.4) using python (3.8.11), CUDA (11.3), scikit-learn (0.24.2). 20,21 The module 3D-GuidedGradCAM was used to perform guided back-propagation.…”
Section: Transfer-learning For Classification Modelmentioning
confidence: 99%
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