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Protein effects in non-heme iron enzyme catalysis: insights from multiscale models
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - Ångström, Theoretical Chemistry.
Uppsala University, Disciplinary Domain of Science and Technology, Chemistry, Department of Chemistry - Ångström, Theoretical Chemistry.ORCID iD: 0000-0002-1312-1202
2016 (English)In: Journal of Biological Inorganic Chemistry, ISSN 0949-8257, E-ISSN 1432-1327, Vol. 21, no 5-6, 645-657 p.Article, review/survey (Refereed) Published
Abstract [en]

Many non-heme iron enzymes have similar sets of ligands but still catalyze widely different reactions. A key question is, therefore, the role of the protein in controlling reactivity and selectivity. Examples from multiscale simulations, primarily QM/MM, of both mono- and binuclear non-heme iron enzymes are used to analyze the stability of these models and what they reveal about the protein effects. Consistent results from QM/MM modeling are the importance of the hydrogen bond network to control reactivity and electrostatic stabilization of electron transfer from second-sphere residues. The long-range electrostatic effects on reaction barriers are small for many systems. In the systems where large electrostatic effects have been reported, these lead to higher barriers. There is thus no evidence of any significant long-range electrostatic effects contributing to the catalytic efficiency of non-heme iron enzymes. However, the correct evaluation of electrostatic contributions is challenging, and the correlation between calculated residue contributions and the effects of mutation experiments is not very strong. The largest benefits of QM/MM models are thus the improved active-site geometries, rather than the calculation of accurate energies. Reported differences in mechanistic predictions between QM and QM/MM models can be explained by differences in hydrogen bonding patterns in and around the active site. Correctly constructed cluster models can give results with similar accuracy as those from multiscale models, but the latter reduces the risk of drawing the wrong mechanistic conclusions based on incorrect geometries and are preferable for all types of modeling, even when using very large QM parts.

Place, publisher, year, edition, pages
2016. Vol. 21, no 5-6, 645-657 p.
Keyword [en]
Computational chemistry, Density functional theory, Enzyme catalysis, Ligand binding, QM/MM, Transition state, Transition metal
National Category
Inorganic Chemistry Biochemistry and Molecular Biology
Identifiers
URN: urn:nbn:se:uu:diva-304673DOI: 10.1007/s00775-016-1374-7ISI: 000382127000006OAI: oai:DiVA.org:uu-304673DiVA: diva2:1033598
Available from: 2016-10-07 Created: 2016-10-07 Last updated: 2017-11-30Bibliographically approved

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