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 wi

SUBMITTER: Riedmiller K 

PROVIDER: S-EPMC10866341 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature