Multivariate-activity mining for molecular quasi-species in a glutathione transferase mutant library

Abstract

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. © The Author 2007. Published by Oxford University Press. All rights reserved.