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

Abstract:

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.

SEEK ID: https://publications.h-its.org/publications/1770

DOI: 10.1039/D3SC03922F

Research Groups: Molecular Biomechanics

Publication type: Journal

Journal: Chemical Science

Publisher: RSC

Citation: Chemical Science

Date Published: 16th Jan 2024

URL: https://doi.org/10.1039/D3SC03922F

Registered Mode: manually

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

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Citation
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). https://doi.org/10.1039/d3sc03922f
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Created: 16th Jan 2024 at 12:32

Last updated: 5th Mar 2024 at 21:25

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