Study of protein folds offers insight into metabolic evolution
May 20, 2007 - 3:59:37 AM
Researchers at the University of Illinois have constructed the first global family tree of metabolic protein architecture. Their approach offers a new window on the evolutionary history of metabolism.
The study appears this week in the online edition of the Proceedings of the National Academy of Sciences.
Their work relies on established techniques of phylogenetic analysis developed in the past decade to plot the evolution of genes and organisms but which have never before been used to work out the evolutionary history of protein architecture across biological networks.
We are interested in how structure evolves, not how organisms evolve, said professor of crop sciences Gustavo Caetano-Anoll's, principal researcher on the study, which was co-written by graduate student Hee Shin Kim and emeritus professor of cell and developmental biology Jay E. Mittenthal. We are using the techniques of phylogenetic analysis that systematicists used to build the tree of life, and we are applying it to a biochemical problem, a systems biology problem.
To get at the roots of protein evolution, the researchers examined metabolic proteins at the level of their component structures: easily recognizable folds in the proteins that have known enzymatic activities. These protein domains catalyze a range of functions, breaking down or combining metabolites, small molecules that include the building blocks of all life.
Their findings relied on a fundamental assumption: that the most widely utilized protein folds (they looked at proteins in more than 200 species) were also the most ancient.
Protein architecture has preserved ancient structural designs as fossils of ancient biochemistries, the authors wrote.
The team used data from two international compilations of genetic and proteomic information: the metabolic pathways database of the Kyoto Encyclopedia of Genes and Genomes, and the Structural Classification of Proteins database. They combined these two data sets with phylogenetic reconstructions, or family trees, of protein fold architectures in metabolism.
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