Imagine the power of knowing the three-dimensional structures of all proteins. The 3D-structure can provide information about critical protein-protein interactions both from a global perspective as well as all the way down to the level of minuscule molecular and biochemical detail. In much the same way, structural information can reveal a lot about the protein’s evolutionary relationships and functions. Even to provide this information about all the proteins in one organism—its proteome—would offer a more global view of these relationships, but solving each structure individually would be a formidable task.
However, in a new study published online this week in the open access journal PLoS Biology, Lars Malmström, David Baker, and colleagues have done precisely this for the model organism yeast. These researchers divided all Saccharomyces cerevisiae proteins into nearly 15,000 distinct “domains” (regions of a protein that fold into a distinct quaternary globular structure). They then applied their own de novo structure prediction methods together with worldwide distributed computing to predict three-dimensional structures for all domains lacking sequence similarity to proteins of known structure.
To overcome the uncertainties in de novo structure prediction, Lars Malmström and colleagues combined these predictions with data on the biological process, function, and localization of the proteins from previous experimental studies to assign the domains to families of evolutionarily related proteins. These genome-wide domain predictions and superfamily assignments provide the basis for the generation of experimentally testable hypotheses about the mechanism of action for a large number of yeast proteins.
Citation: Malmstro¨m L, Riffle M, Strauss CEM, Chivian D, Davis TN, et al. (2007) Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology. PLoS Biol 5(4): e76. doi:10.1371/journal.pbio.0050076.
David Baker
University of Washington
Box 357350
Seattle, WA 98195-7350
+1-206-543-1295
+1-206-685-1792 (fax)
dabaker@u.washington.edu
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