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Nathan C. VerBerkmoes 1,3, Jonathan L. Bundy 1, Loren Hauser 2,3,
Keiji G. Asano 1, Jane Razumovskaya 2,3, Frank Larimer 2, Robert L. Hettich 1, James L. Stephenson, Jr. 1
1Organic and Biological Mass Spectrometry Group, Chemical Sciences Division
P.O. Box 2008, Oak Ridge, TN 37831-6365
2Life Sciences Division
P.O. Box 2008, Oak Ridge, TN 37831
3Graduate School of Genome Science and Technology
The University of Tennessee-Oak Ridge National Laboratory
1060 Commerce Park
Oak Ridge, Tennessee 37830-8026
Abstract:
Shewanella oneidensis MR-1 is a metal reducing microbe of potential importance to the field of bioremediation. This microbe has been shown to be capable of reducing a wide variety of heavy metals and organic compounds. The exact mechanism of this microbe’s ability to reduce these chemicals is only now beginning to be understood. Recently, the S. oneidensis genome was sequenced by TIGR and fully annotated by both TIGR and DOE. We have been the first group to identify a major portion of the proteome of wild-type S. oneidensis MR-1 grown under aerobic conditions. Cellular lysates were examined directly by “shotgun” proteomics in attempt to identify proteins in a high-throughput, comprehensive manner. Our goal for this study was to evaluate and compare 1D and 2D LC-MS/MS techniques for comprehensive microbial proteome characterization. Our initial analysis of this microbe has consisted of 10 unique 1D or 2D LC-MS/MS experiments. Currently, we have identified 868 proteins representing virtually every functional class as well as a large number of hypotheticals and conserved hypotheticals. We then compiled the proteins by predicted function into KEGG maps, in order to evaluate what metabolic pathways were identified from this organism. On-going studies have focused on reproducibility and improving these LC-MS/MS techniques for identifying a large number of proteins from S. oneidensis in a shorter amount of time. The use of bioinformatics tools such as the SEQUEST algorithm for searching raw MS/MS spectrum, DTASelect for compiling the output files, and the insertion of these proteins into KEGG maps has been of prime importance for the success of this project. This presentation will focus on our current use of bioinformatics tools for proteome analysis and how we feel these tools can be improved.