Convergent evolution of similar enzymatic function on different protein folds: The hexokinase, ribokinase, and galactokinase families of sugar kinases

Kinases that catalyze phosphorylation of sugars, called here sugar kinases, can be divided into at least three distinct nonhomologous families. The first is the hexokinase family, which contains many prokaryotic and eukaryotic sugar kinases with diverse specificities, including a new member, rhamnokinase from Salmonella typhimurium. The three-dimensional structure of hexokinase is known and can be used to build models of functionally important regions of other kinases in this family. The second is the ribokinase family, of unknown three-dimensional structure, and comprises pro- and eukaryotic ribokinases, bacterial fructokinases, the minor 6-phosphofructokinase 2 from Escherichia coli, 6-phosphotagatokinase, 1-phosphofructokinase, and, possibly, inosine-guanosine kinase. The third family, also of unknown three-dimensional structure, contains several bacterial and yeast galactokinases and eukaryotic mevalonate and phosphomevalonate kinases and may have a substrate binding region in common with homoserine kinases. Each of the three families of sugar kinases appears to have a distinct three-dimensional fold, since conserved sequence patterns are strikingly different for the three families. Yet each catalyzes chemically equivalent reactions on similar or identical substrates. The enzymatic function of sugar phosphorylation appears to have evolved independently on the three distinct structural frameworks, by convergent evolution. In addition, evolutionary trees reveal that (1) fructokinase specificity has evolved independently in both the hexokinase and ribokinase families and (2) glucose specificity has evolved independently in different branches of the hexokinase family. These are examples of independent Darwinian adaptation of a structure to the same substrate at different evolutionary times. The flexible combination of active sites and three-dimensional folds observed in nature can be exploited by protein engineers in designing and optimizing enzymatic function.