Modeling the Hydrolysis of Iron−Sulfur Clusters - Archive ouverte HAL
Article Dans Une Revue Journal of Chemical Information and Modeling Année : 2020

Modeling the Hydrolysis of Iron−Sulfur Clusters

Résumé

Iron-sulfur (FeS) clusters are essential metal cofactors involved in a wide variety of biological functions. Their catalytic efficiency, biosynthesis, and regulation depend on FeS stability in aqueous solution. Here, molecular modeling is used to investigate the hydrolysis of an oxidized (ferric) mononuclear FeS cluster by bare dissociation and water substitution mechanisms in neutral and acidic solution. First, approximate electronic structure descriptions of FeS reactions by density functional theory are validated against high-level wave function CCSD(T) calculations. Solvation contributions are included by an all-atom model with hybrid quantum chemical/molecular mechanical (QM/MM) potentials and enhanced sampling molecular dynamics simulations. The free energy profile obtained for FeS cluster hydrolysis indicates that the hybrid functional M06 together with an implicit solvent correction capture the most important aspects of FeS cluster reactivity in aqueous solution. Then, 20 reaction channels leading to two consecutive Fe-S bond ruptures were explored with this calibrated model. For all protonation states, nucleophilic substitution with concerted bond breaking and forming to iron is the preferred mechanism, both kinetic and thermodynamically. In neutral solution, proton transfer from water to the sulfur leaving group is also concerted. Dissociative reactions show higher barriers and will not be relevant for FeS reactivity when exposed to solvent. These hydrolysis mechanisms may help to explain the stability and catalytic mechanisms of FeS clusters of multiple sizes and proteins.
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Dates et versions

hal-02494443 , version 1 (24-10-2022)

Identifiants

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Murilo H. Teixeira, Felipe Curtolo, Sofia R. G. Camilo, Martin J. Field, Peng Zheng, et al.. Modeling the Hydrolysis of Iron−Sulfur Clusters. Journal of Chemical Information and Modeling, 2020, 60 (2), pp.653-660. ⟨10.1021/acs.jcim.9b00881⟩. ⟨hal-02494443⟩
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