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Superfamily assignments for the yeast proteome through integration of structure prediction with the gene ontology
Authors:Malmström Lars  Riffle Michael  Strauss Charlie E M  Chivian Dylan  Davis Trisha N  Bonneau Richard  Baker David
Institution:1, Department of Biochemistry, University of Washington, Seattle, Washington, United States of America;2, Los Alamos National Laboratory, Los Alamos, New Mexico, United States of America;3, Department of Biology, Department of Computer Science, and Center for Comparative Functional Genomics, New York University, New York, New York, United States of America;4, Howard Hughes Medical Institute, University of Washington, Seattle, Washington, United States of America;University of California San Francisco, United States of America
Abstract:Saccharomyces cerevisiae is one of the best-studied model organisms, yet the three-dimensional structure and molecular function of many yeast proteins remain unknown. Yeast proteins were parsed into 14,934 domains, and those lacking sequence similarity to proteins of known structure were folded using the Rosetta de novo structure prediction method on the World Community Grid. This structural data was integrated with process, component, and function annotations from the Saccharomyces Genome Database to assign yeast protein domains to SCOP superfamilies using a simple Bayesian approach. We have predicted the structure of 3,338 putative domains and assigned SCOP superfamily annotations to 581 of them. We have also assigned structural annotations to 7,094 predicted domains based on fold recognition and homology modeling methods. The domain predictions and structural information are available in an online database at http://rd.plos.org/10.1371_journal.pbio.0050076_01.
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