Molecular evolution is frequently portrayed by structural relationships, but delineation of separate functional species is more elusive. We have generated enzyme variants by stochastic recombinations of DNA encoding two homologous detoxication enzymes, human glutathione transferases M1-1 and M2-2, and explored their catalytic versatilities. Sampled mutants were screened for activities with eight alternative substrates, and the activity fingerprints were subjected to principal component analysis. This phenotype characterization clearly identified at least three distributions of substrate selectivity, where one was orthogonal to those of the parent-like distributions. This approach to evolutionary data mining serves to identify emerging molecular quasi-species and indicates potential trajectories available for further protein evolution. directed evolution ͉ DNA shuffling ͉ glutathione transferase ͉ library ͉ multivariate analysis M olecular evolution is based on rearranged polynucleotide sequences and point mutations, which in turn give rise to novel phenotypes manifested as altered structures and functions. Biological mechanisms of genetic recombinations have counterparts in DNA shuffling and similar techniques for in vitro evolution (1). For evolution to occur, the mutants arising from progenitors undergo a Darwinian selection of the fittest. This functional filtering of the offspring in combination with structural boundary conditions governs the pathways of evolutionary change. Directed evolution similarly aims at gratifying qualities such as enhanced catalytic efficiency or increased thermal stability (2). Theoretical and experimental studies indicate that evolution operates on ensembles of mutants with a stochastic distribution of structural and functional properties rather than on single individuals that fit the prevailing conditions. These evolving units can be considered molecular ''quasi-species'' (3). Here, we show how such divergent subpopulations can be identified by screening and analysis of functional data by multivariate methods, illustrating the emergence of new distributions of functional properties in enzyme evolution.We approached molecular evolution by analyzing a library of enzyme variants obtained by recombination of DNA from two homologous glutathione transferases, GST M1-1 and GST M2-2 (4). The GSTs belong to a large family of enzymes that catalyze the conjugation of the nucleophilic tripeptide glutathione with a wide variety of genotoxic substrates. These conjugation reactions are prominent in the cellular inactivation of electrophilic compounds and promote the excretion of toxicants (5). Facile adaptation of substrate selectivities would appear to have selective advantage in particular for enzymes such as GSTs, which mount a protective response to a wide variety of toxic challenges. Results and DiscussionEnzyme Variants Represented in a Multidimensional Substrate-Activity Space. A library of mutant enzymes was produced by shuffling of cDNAs encoding human GST M1-1 (M1) and GST M2-2 (M2), ...

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A functional enzyme displays activity with at least one substrate and can be represented by a vector in substrate-activity space. Many enzymes, including GSTs (glutathione transferases), are promiscuous in the sense that they act on alternative substrates, and the corresponding vectors operate in multidimensional space. The direction of the vector is governed by the relative activities of the diverse substrates. Stochastic mutations of already existing enzymes generate populations of variants, and clusters of functionally similar mutants can serve as parents for subsequent generations of enzymes. The proper evolving unit is a functional quasi-species, which may not be identical with the 'best' variant in its generation. The manifestation of the quasi-species is dependent on the substrate matrix used to explore catalytic activities. Multivariate analysis is an approach to identifying quasi-species and to investigate evolutionary trajectories in the directed evolution of enzymes for novel functions.

A library of recombinant glutathione transferases (GSTs) generated by shuffling of DNA encoding human GST M1-1 and GST M2-2 was screened with eight alternative substrates, and the activities were subjected to multivariate analysis. Assays were made in lysates of bacteria in which the GST variants had been expressed. The primary data showed clustering of the activities in eight-dimensional substrate-activity space. For an incisive analysis, the rows of the data matrix, corresponding to the different enzyme variants, were individually scaled to unit length, thus accounting for different expression levels of the enzymes. The columns representing the activities with alternative substrates were subsequently individually normalized to unit variance and a zero mean. By this standardization, the data were adjusted to comparable orders of magnitude. Three molecular quasi-species were recognized by multivariate K-means and principal component analyses. Two of them encompassed the parental GST M1-1 and GST M2-2. A third one diverged functionally by displaying enhanced activities with some substrates and suppressed activities with signature substrates for GST M1-1 and GST M2-2. A fourth cluster contained mutants with impaired functions and was not regarded as a quasi-species. Sequence analysis of representatives of the mutant clusters demonstrated that the majority of the variants in the diverging novel quasi-species were structurally similar to the M1-like GSTs, but distinguished themselves from GST M1-1 by a Ser to Thr substitution in the active site. The data show that multivariate analysis of functional profiles can identify small structural changes influencing the evolution of enzymes with novel substrate-activity profiles.

a b s t r a c tWe propose that the proper evolving unit in enzyme evolution is not a single ''fittest molecule", but a cluster of related variants denoted a ''quasi-species". A distribution of variants provides genetic variability and thereby reduces the risk of inbreeding and evolutionary dead-ends. Different matrices of substrates or activity modulators will lead to different selection criteria and divergent evolutionary trajectories. We provide examples from our directed evolution of glutathione transferases illustrating the interplay between libraries of enzyme variants and ligand matrices in the identification of quasi-species. The ligand matrix is shown to be crucial to the outcome of the search for novel activities. In this sense the experimental system resembles the biological environment in governing the evolution of enzymes.