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T. Borowski and M. Szaleniec
statistics also hold for enzymes, and as much as ca. 40% of the structurally characterized enzymes turn out to be metal-dependent. Furthermore, metalloenzymes can be
found among 76% of the EC-based subclasses that were structurally characterized
[4]. These facts underline high catalytic versatility and widespread occurrence of
metalloenzymes in various forms of life.
Metalloenzymes are often the most efficient catalysts in terms of their activity,
selectivity and ability to operate at mild conditions. Additionally, due to the active
participation of metal ions in the catalytic process, the scope of the available molecular transformations is greatly enhanced. Furthermore, it is also possible that presence
of the metal ions inside the enzymes’ active sites significantly contributes to their
overall stability and thus increases their operational robustness [5].
The majority of enzymatic reactions tend to be multistep processes proceeding
through enzyme-bound intermediates, some of which are very short-lived, and hence
rather difficult to characterize directly. Even though in some cases it is possible to trap
and structurally characterize such intermediates in crystallo, e.g. peracid intermediate in 2-oxoglutarate-dependent dioxygenases [6] or peroxo-bridged intermediate
in extradiol dioxygenases [7], in most cases only some indirect experimental evidence, e.g. from spectroscopy or kinetic isotope effect measurements, is available
from which one can only infer geometric and electronic structure of the key species.
Hence, computational methods that can aid in interpretation of the available indirect
data concerning some intermediates and fill in the gaps in the reaction mechanism
by describing most likely structures of the remaining intermediates and transition
structures are very helpful. For examples on how spectroscopy and computational
methods can be efficiently used in tandem for metal containing systems see Chapters
“Anisotropic Magnetic Spin Interactions of Transition Metal Complexes and Metalloenzymes from Spectroscopy and Quantum Chemistry”, “Computational Versus
Experimental Spectroscopy for Transition-Metals” and “Assessing Electronically
Excited States of Cobalamins via Absorption Spectroscopy and Time-Dependent
Density Functional Theory” in this volume. Chapters “The Quest for Accurate Theoretical Models of Metalloenzymes: An Aid to Experiment”, “Applications of Computational Chemistry to Selected Problems of Transition-Metal Catalysis in Biological and Non-biological Systems” and “How Metal Coordination in the Ca-, Ce- and
Eu-Containing Methanol Dehydrogenase Enzymes Can Influence the Catalysis: A
Theoretical Point of View” provide a good number of examples of computational
studies on reaction mechanism of various metalloenzymes. Hence, instead of providing further examples of this kind, in this chapter we decided to limit their number to
necessary minimum and instead put more emphasis on: (a) various auxiliary methods
that are useful when preparing computational model of metalloenzymes and (b) illustrate some challenges one may face when trying to investigate reaction mechanism
of redox-active metalloenzymes.
T. Borowski and M. Szaleniec
statistics also hold for enzymes, and as much as ca. 40% of the structurally characterized enzymes turn out to be metal-dependent. Furthermore, metalloenzymes can be
found among 76% of the EC-based subclasses that were structurally characterized
[4]. These facts underline high catalytic versatility and widespread occurrence of
metalloenzymes in various forms of life.
Metalloenzymes are often the most efficient catalysts in terms of their activity,
selectivity and ability to operate at mild conditions. Additionally, due to the active
participation of metal ions in the catalytic process, the scope of the available molecular transformations is greatly enhanced. Furthermore, it is also possible that presence
of the metal ions inside the enzymes’ active sites significantly contributes to their
overall stability and thus increases their operational robustness [5].
The majority of enzymatic reactions tend to be multistep processes proceeding
through enzyme-bound intermediates, some of which are very short-lived, and hence
rather difficult to characterize directly. Even though in some cases it is possible to trap
and structurally characterize such intermediates in crystallo, e.g. peracid intermediate in 2-oxoglutarate-dependent dioxygenases [6] or peroxo-bridged intermediate
in extradiol dioxygenases [7], in most cases only some indirect experimental evidence, e.g. from spectroscopy or kinetic isotope effect measurements, is available
from which one can only infer geometric and electronic structure of the key species.
Hence, computational methods that can aid in interpretation of the available indirect
data concerning some intermediates and fill in the gaps in the reaction mechanism
by describing most likely structures of the remaining intermediates and transition
structures are very helpful. For examples on how spectroscopy and computational
methods can be efficiently used in tandem for metal containing systems see Chapters
“Anisotropic Magnetic Spin Interactions of Transition Metal Complexes and Metalloenzymes from Spectroscopy and Quantum Chemistry”, “Computational Versus
Experimental Spectroscopy for Transition-Metals” and “Assessing Electronically
Excited States of Cobalamins via Absorption Spectroscopy and Time-Dependent
Density Functional Theory” in this volume. Chapters “The Quest for Accurate Theoretical Models of Metalloenzymes: An Aid to Experiment”, “Applications of Computational Chemistry to Selected Problems of Transition-Metal Catalysis in Biological and Non-biological Systems” and “How Metal Coordination in the Ca-, Ce- and
Eu-Containing Methanol Dehydrogenase Enzymes Can Influence the Catalysis: A
Theoretical Point of View” provide a good number of examples of computational
studies on reaction mechanism of various metalloenzymes. Hence, instead of providing further examples of this kind, in this chapter we decided to limit their number to
necessary minimum and instead put more emphasis on: (a) various auxiliary methods
that are useful when preparing computational model of metalloenzymes and (b) illustrate some challenges one may face when trying to investigate reaction mechanism
of redox-active metalloenzymes.