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Reduction pathway of glutaredoxin 1 investigated with QM/MM molecular dynamics using a neural network correction

Böser, Julian 1; Kubař, Tomáš 1; Elstner, Marcus 1,2; Maag, Denis ORCID iD icon 1
1 Institut für Physikalische Chemie (IPC), Karlsruher Institut für Technologie (KIT)
2 Institut für Biologische Grenzflächen (IBG), Karlsruher Institut für Technologie (KIT)

Abstract:

Glutaredoxins are small enzymes that catalyze the oxidation and reduction of protein disulfide bonds by the thiol–disulfide exchange mechanism. They have either one or two cysteines in their active site, resulting in different catalytic reaction cycles that have been investigated in many experimental studies. However, the exact mechanisms are not yet fully known, and to our knowledge, no theoretical studies have been performed to elucidate the underlying mechanism. In this study, we investigated a proposed mechanism for the reduction of the disulfide bond in the protein HMA4n by a mutated monothiol Homo sapiens glutaredoxin and the co-substrate glutathione. The catalytic cycle involves three successive thiol–disulfide exchanges that occur between the molecules. To estimate the regioselectivity of the different attacks, classical molecular dynamics simulations were performed and the trajectories analyzed regarding the sulfur–sulfur distances and the attack angles between the sulfurs. The free energy profile of each reaction was obtained with hybrid quantum mechanical/molecular mechanical metadynamics simulations. Since this required extensive phase space sampling, the semi-empirical density functional tight-binding method was used to describe the reactive cysteines. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000152325
Veröffentlicht am 07.11.2022
Originalveröffentlichung
DOI: 10.1063/5.0123089
Scopus
Zitationen: 4
Web of Science
Zitationen: 4
Dimensions
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Biologische Grenzflächen (IBG)
Institut für Physikalische Chemie (IPC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2022
Sprache Englisch
Identifikator ISSN: 1089-7690, 0021-9606, 1520-9032
KITopen-ID: 1000152325
Erschienen in The Journal of Chemical Physics
Verlag American Institute of Physics (AIP)
Band 157
Heft 15
Seiten Art.-Nr.: 154104
Vorab online veröffentlicht am 21.10.2022
Schlagwörter Modern Semiempirical Electronic Structure Methods, Molecular dynamics, Free energy calculations, Catalysts and Catalysis, Metadynamics, Artificial neural networks, Quantum chemistry, Enzymes, Machine learning, Quantum mechanical/molecular mechanical calculations, Density-functional tight-binding
Nachgewiesen in Web of Science
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Scopus
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