Computational methods are very useful tools in the study of enzymatic reactions, as they can provide a detailed understanding of reaction mechanisms and the sources of various selectivities. In this thesis, density functional theory has been employed to examine four different enzymes of potential importance for biocatalytic applications. The enzymes considered are limonene epoxide hydrolase, soluble epoxide hydrolase, arylmalonate decarboxylase and phenolic acid decarboxylase. Besides the reaction mechanisms, the enantioselectivities in three of these enzymes have also been investigated in detail. In all studies, quite large quantum chemical cluster models of the active sites have been used. In particular, the models have to account for the chiral environment of the active site in order to reproduce and rationalize the experimentally observed selectivities.

For both epoxide hydrolases, the calculated enantioselectivities are in good agreement with experiments. In addition, explanations for the change in stereochemical outcome for the mutants of limonene epoxide hydrolase, and for the observed enantioconvergency in the soluble epoxide hydrolase are presented.

The reaction mechanisms of the two decarboxylases are found to involve the formation of an enediolate- or a quinone methide intermediate, supporting thus the main features of the proposed mechanisms in both cases. For arylmalonate decarboxylase, an explanation for the observed enantioselectivity is also presented.

In addition to the obtained chemical insights, the results presented in this thesis demonstrate that the quantum chemical cluster approach is indeed a valuable tool in the field of asymmetric biocatalysis.