Computational Insights into Metal-Hydride Reactivity in Engineered Metalloenzymes

16 September 2025, Version 1
This content is an early or alternative research output and has not been peer-reviewed by Cambridge University Press at the time of posting.

Abstract

Transition metal-hydrides are key intermediates in numerous catalytic transformations mediated by molecular complexes and, more recently, in repurposed metalloenzymes. However, their fleeting nature poses significant challenges for experimental characterization, obscuring mechanistic details – particularly in distinguishing between hydrogen atom transfer (HAT) and hydride transfer pathways. Here, we employ a combination of quantum mechanics/molecular mechanics (QM/MM) and molecular dynamics (MD) simulations to investigate the catalytic mechanism of an evolved myoglobin ketoreductase. We model the formation of the iron-hydride (Fe–H) intermediate and the subsequent ketone reduction process. Our results indicate that Fe–H formation occurs via a σ-bond metathesis mechanism, while the ketone reduction proceeds through a stepwise metal-hydride hydrogen atom transfer (MHAT) followed by electron transfer, rather than a concerted hydride transfer as previously proposed. Natural Bond Orbital (NBO) analysis elucidates the electronic features governing MHAT reactivity. We further show that the nature of the axial ligand and applied oriented electric fields modulate the Fe–H bond character and steer the mechanistic preference between MHAT and hydride transfer. These findings provide mechanistic insight into transition metal-hydride reactivity in protein scaffolds and underscore opportunities to tune this reactivity through protein design and electrostatic control.

Keywords

Metal-hydride
MHAT
QM/MM
Electric Fields
Axial Ligands
Repurposed Ketoreductase

Supplementary materials

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Description
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Title
Supporting Information for Computational Insights into Metal-Hydride Reactivity in Engineered Metalloenzymes
Description
Supporting figures, tables and cartesian coordinates of optimized geometries.
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