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![Protein Film Infrared Electrochemistry Demonstrated for Study of H2 Oxidation by a [NiFe] Hydrogenase](/_next/image?url=https%3A%2F%2Fcloudfront.jove.com%2FCDNSource%2Fteasers%2F55858.jpg&w=3840&q=50)
Protein Film Infrared Electrochemistry Demonstrated for Study of H2 Oxidation by a [NiFe] Hydrogenase
Published on: December 4, 2017
Protein effects in non-heme iron enzyme catalysis: insights from multiscale models
Nathalie Proos Vedin1, Marcus Lundberg2
1Department of Chemistry-Ångström Laboratory, Uppsala University, Box 518, 751 20, Uppsala, Sweden.
Abstract:
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.
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