Directed Evolution of Glutathione Transferases Guided by Multivariate Data Analysis

Abstract: Evolution of enzymes with novel functional properties has gained much attention in recent years. Naturally evolved enzymes are adapted to work in living cells under physiological conditions, circumstances that are not always available for industrial processes calling for novel and better catalysts. Furthermore, altering enzyme function also affords insight into how enzymes work and how natural evolution operates. Previous investigations have explored catalytic properties in the directed evolution of mutant libraries with high sequence variation. Before this study was initiated, functional analysis of mutant libraries was, to a large extent, restricted to uni- or bivariate methods. Consequently, there was a need to apply multivariate data analysis (MVA) techniques in this context. Directed evolution was approached by DNA shuffling of glutathione transferases (GSTs) in this thesis. GSTs are multifarious enzymes that have detoxication of both exo- and endogenous compounds as their primary function. They catalyze the nucleophilic attack by the tripeptide glutathione on many different electrophilic substrates. Several multivariate analysis tools, e.g. principal component (PC), hierarchical cluster, and K-means cluster analyses, were applied to large mutant libraries assayed with a battery of GST substrates. By this approach, evolvable units (quasi-species) fit for further evolution were identified. It was clear that different substrates undergoing different kinds of chemical transformation can group together in a multi-dimensional substrate-activity space, thus being responsible for a certain quasi-species cluster. Furthermore, the importance of the chemical environment, or substrate matrix, in enzyme evolution was recognized. Diverging substrate selectivity profiles among homologous enzymes acting on substrates performing the same kind of chemistry were identified by MVA. Important structure-function activity relationships with the prodrug azathioprine were elucidated by segment analysis of a shuffled GST mutant library. Together, these results illustrate important methods applied to molecular enzyme evolution.