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Predicting reaction barriers of 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 activ...

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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) Chapter/Article
Language:English
Published: Feb 13, 2023
Edition:Version 1
In: ChemRxiv
Year: 2023, Pages: 1-8
DOI:10.26434/chemrxiv-2023-7hntk
Online Access:Resolving-System, kostenfrei, Volltext: https://doi.org/10.26434/chemrxiv-2023-7hntk
Verlag, kostenfrei, Volltext: https://chemrxiv.org/engage/chemrxiv/article-details/63e60790fcfb27a31f87e9c0
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Author Notes:Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn'ova, Pascal Friederich, 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 activation energies of HAT reactions in proteins. It is 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 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). As the inference speed is high, this model enables evaluations of many chemical situations in rapid succession. When combined with molecular dynamics in a kinetic Monte-Carlo scheme, the model paves the way toward reactive simulations.
Item Description:Gesehen am 14.02.2023
Physical Description:Online Resource
DOI:10.26434/chemrxiv-2023-7hntk

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