BACKGROUND: Selection on proteins is typically measured with the assumption that each protein acts independently. However, selection more likely acts at higher levels of biological organization, requiring an integrative view of protein function. Here, we built a kinetic model for de novo pyrimidine biosynthesis in the yeast Saccharomyces cerevisiae to relate pathway function to selective pressures on individual protein-encoding genes. RESULTS: Gene families across yeast were constructed for each member of the pathway and the ratio of nonsynonymous to synonymous nucleotide substitution rates (dN/dS) was estimated for each enzyme from S. cerevisiae and closely related species. We found a positive relationship between the influence that each enzyme has on pathway function and its selective constraint. CONCLUSIONS: We expect this trend to be locally present for enzymes that have pathway control, but over longer evolutionary timescales we expect that mutation-selection balance may change the enzymes that have pathway control.

The most widely used classification system describing enzyme-catalysed reactions is the Enzyme Commission (EC) number. Understanding enzyme function is important for both fundamental scientific and pharmaceutical reasons. The EC classification is essentially unrelated to the reaction mechanism. In this work we address two important questions related to enzyme function diversity. First, to investigate the relationship between the reaction mechanisms as described in the MACiE (Mechanism, Annotation, and Classification in Enzymes) database and the main top-level class of the EC classification. Second, how well these enzymes biocatalysis are adapted in nature. In this thesis, we have retrieved 335 enzyme reactions from the MACiE database. We consider two ways of encoding the reaction mechanism in descriptors, and three approaches that encode only the overall chemical reaction. To proceed through my work, we first develop a basic model to cluster the enzymatic reactions. Global study of enzyme reaction mechanism may provide important insights for better understanding of the diversity of chemical reactions of enzymes. Clustering analysis in such research is very common practice. Clustering algorithms suffer from various issues, such as requiring determination of the input parameters and stopping criteria, and very often a need to specify the number of clusters in advance. Using several well known metrics, we tried to optimize the clustering outputs for each of the algorithms, with equivocal results that suggested the existence of between two and over a hundred clusters. This motivated us to design and implement our algorithm, PFClust (Parameter-Free Clustering), where no prior information is required to determine the number of cluster. The analysis highlights the structure of the enzyme overall and mechanistic reaction. This suggests that mechanistic similarity can influence approaches for function prediction and automatic annotation of newly discovered protein and gene sequences. We then develop and evaluate the method for enzyme function prediction using machine learning methods. Our results suggest that pairs of similar enzyme reactions tend to proceed by different mechanisms. The machine learning method needs only chemoinformatics descriptors as an input and is applicable for regression analysis. The last phase of this work is to test the evolution of chemical mechanisms mapped onto ancestral enzymes. This domain occurrence and abundance in modern proteins has showed that the / architecture is probably the oldest fold design. These observations have important implications for the origins of biochemistry and for exploring structure-function relationships. Over half of the known mechanisms are introduced before architectural diversification over the evolutionary time. The other halves of the mechanisms are invented gradually over the evolutionary timeline just after organismal diversification. Moreover, many common mechanisms includes fundamental building blocks of enzyme chemistry were found to be associated with the ancestral fold.

Synthesis of the CCA end of essential tRNAs is performed either by CCA-adding enzymes or as a collaboration between enzymes restricted to CC- and A-incorporation. While the occurrence of such tRNA nucleotidyltransferases with partial activities seemed to be restricted to Bacteria, the first example of such split CCA-adding activities was reported in Schizosaccharomyces pombe. Here, we demonstrate that the choanoflagellate Salpingoeca rosetta also carries CC- and A-adding enzymes. However, these enzymes have distinct evolutionary origins. Furthermore, the restricted activity of the eukaryotic CC-adding enzymes has evolved in a different way compared to their bacterial counterparts. Yet, the molecular basis is very similar, as highly conserved positions within a catalytically important flexible loop region are missing in the CC-adding enzymes. For both the CC-adding enzymes from S. rosetta as well as S. pombe, introduction of the loop elements from closely related enzymes with full activity was able to restore CCA-addition, corroborating the significance of this loop in the evolution of bacterial as well as eukaryotic tRNA nucleotidyltransferases. Our data demonstrate that partial CC- and A-adding activities in Bacteria and Eukaryotes are based on the same mechanistic principles but, surprisingly, originate from different evolutionary events.

Today, enzymes are extensively used for many industrial applications, this includes bulk and fine-chemical synthesis, pharmaceuticals and consumer products. Though Nature has perfected enzymes for many millions of years, they seldom reach industrial performance targets. Natural enzymes could benefit from protein redesign experiments to gain novel functions or optimize existing functions. Glutathione transferases (GSTs) are detoxification enzymes, they also display disparate functions. Two Mu-class GSTs, M1-1 and M2-2, are closely related but display dissimilar substrate selectivity profiles. Saturation mutagenesis of a previously recognized hypervariable amino acid in GST M2-2, generated twenty enzyme variants with altered substrate selectivity profiles, as well as modified thermostabilities and expressivities. This indicates an evolutionary significance; GST Mu-class enzymes could easily alter functions in a duplicate gene by a single-point mutation. To further identify residues responsible for substrate selectivity in the GST M2-2 active site, three residues were chosen for iterative saturation mutagenesis. Mutations in position10, identified as highly conserved, rendered enzyme variants with substrate selectivity profiles resembling that of specialist enzymes. Ile10 could be conserved to sustain the broad substrate acceptance displayed by GST Mu-class enzymes. Enzymes are constructed from primarily twenty amino acids, it is a reasonable assumption that expansion of the amino acid repertoire could result in functional properties that cannot be accomplished with the natural set of building blocks. A combination approach of site-directed mutagenesis and chemical modifications in GST M2-2 and GST M1-1 resulted in novel enzyme variants that displayed altered substrate selectivity patterns as well as improved enantioselectivities. The results presented in this thesis demonstrate the use of different protein redesign techniques to modulate various functions in Mu-class GSTs. These techniques could be useful in search of optimized enzyme variants for industrial targets. / biokemi och organisk kemi