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 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.


DOI: 10.1039/D3SC03922F

Research Groups: SIMPLAIX

Publication type: Journal

Journal: Chem. Sci.

Publisher: The Royal Society of Chemistry

Citation: Chem. Sci.,10.1039.D3SC03922F

Date Published: 16th Jan 2024


Registered Mode: imported from a bibtex file

Authors: Kai Riedmiller, Patrick Reiser, Elizaveta Bobkova, Kiril Maltsev, Ganna Gryn’ova, Pascal Friederich, Frauke Gräter

Riedmiller, K., Reiser, P., Bobkova, E., Maltsev, K., Gryn'ova, G., Friederich, P., & Gräter, F. (2024). Substituting density functional theory in reaction barrier calculations for hydrogen atom transfer in proteins. In Chemical Science (Vol. 15, Issue 7, pp. 2518–2527). Royal Society of Chemistry (RSC).

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Created: 8th Feb 2024 at 08:06

Last updated: 5th Mar 2024 at 21:25

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