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Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins

Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energ...

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Main Authors: Riedmiller, Kai (Author) , Reiser, Patrick (Author) , Bobkova, Elizaveta (Author) , Maltsev, Kiril (Author) , Gryn’ova, Ganna (Author) , Friederich, Pascal (Author) , Gräter, Frauke (Author)
Format: Article (Journal)
Language:English
Published: 16 Jan 2024
In: Chemical science
Year: 2024, Volume: 15, Issue: 7, Pages: 2518-2527
ISSN:2041-6539
DOI:10.1039/D3SC03922F
Online Access:Verlag, kostenfrei, Volltext: https://doi.org/10.1039/D3SC03922F
Verlag, kostenfrei, Volltext: https://pubs.rsc.org/en/content/articlelanding/2024/sc/d3sc03922f
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Author Notes:Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn'ova, Pascal Friederich and Frauke Gräter
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Summary:Hydrogen atom transfer (HAT) reactions are important in many biological systems. As these reactions are hard to observe experimentally, it is of high interest to shed light on them using simulations. Here, we present a machine learning model based on graph neural networks for the prediction of energy barriers of HAT reactions in proteins. As input, the model uses exclusively non-optimized structures as obtained from classical simulations. It was trained on more than 17 000 energy barriers calculated using hybrid density functional theory. We built and evaluated the model in the context of HAT in collagen, but we show that the same workflow can easily be applied to HAT reactions in other biological or synthetic polymers. We obtain for relevant reactions (small reaction distances) a model with good predictive power (R2 ∼ 0.9 and mean absolute error of <3 kcal mol−1). As the inference speed is high, this model enables evaluations of dozens of chemical situations within seconds. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.
Item Description:Gesehen am 27.03.2024
Physical Description:Online Resource
ISSN:2041-6539
DOI:10.1039/D3SC03922F

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