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Novel Approach Used to Assign Functions to Previously Unknown Proteins in a Microbe
Published: January 10, 2005
Posted: January 13, 2005

An interdisciplinary team of scientists at ORNL, PNNL and BIATECH in Seattle used a combination approach mixing experimental and computational analyses in the microbe Shewanella omeidensis. Integrative approaches such as this one offer valuable means to undertake the enormous challenge of characterizing the rapidly growing number of hypothetical proteins continuing to be found in each newly sequenced genome. The bacterium Shewanella oneidensis strain MR-1 is a metabolically versatile microbe that can reduce a wide range of organic compounds, metal ions, and radionuclides and thus offers great promise to help clean up contaminated DOE sites. Similar to almost all other sequenced microbes, about 40% of the predicted 4324 genes in the S. oneidensis genome are unknown and therefore cannot be assigned potential roles and functions in the microbe. The work will be published within the next two weeks in the Proceedings of the National Academy of Sciences. The resulting analyses identified about one-third of the 1600 previously 'hypothetical' genes. The scientists (part of the DOE "Shewanella Federation") were able to identify similar proteins in other sequenced organisms for nearly all of these 538 'hypothetical' proteins, but could confidently assign exact biochemical functions for only 16 of them. Computational and experimental evidence provided less exact but plausible functional assignments or insights for an additional 240 genes. These functional annotations significantly reduce the "search space" within which the exact functions of these genes, about which nothing was previously known, can be experimentally determined and advance our understanding of genes involved in vital cellular processes.

Contact: Dan Drell, SC-72, 301-903-4742
Topic Areas:

  • Research Area: Subsurface Biogeochemical Research
  • Research Area: Genomic Analysis and Systems Biology
  • Research Area: Microbes and Communities
  • Research Area: Computational Biology, Bioinformatics, Modeling

Division: SC-33.2 Biological Systems Science Division, BER
      (formerly SC-72 Life Sciences Division, OBER)

 

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