currents RAAMS for interpreting 16O/18O experiments In the quest for biomarkers, many proteomics scientists use 16 O/ 18 O labeling to find differentially regulated proteins in two samples. Although the method is fast and easy, it can result in complex spectra that are difficult to interpret. So H. Robert Bergen and colleagues at the Mayo Clinic College of Medicine and North Carolina State University have developed an algorithm called regression analysis applied to MS (RAAMS) that uses regression methods to automatically and confidently interpret these spectra. RAAMS is fast enough to be applied in real time to MS spectra to determine which ions to fragment in MS/ MS mode. Instead of using raw spectral data as input, the algorithm works with centroided peak data, and this speeds up the analysis. Although RAAMS was developed with FTICR MS data, it can be applied to data generated by other instruments with high resolving power. The researchers also demonstrate that strongly up-regulated peptides can be distinguished from strongly down-regulated peptides by lowering the amount of H 218 O in the reaction. (Mol. Cell. Proteomics 2006, doi 10.1074/mcp. M600148-MCP200)
Christopher Dupont and co-workers at Scripps Institution of Oceanography and the University of California, San Diego, analyzed the Fe-, Zn-, Mn-, and Co-binding proteins of 23 archaea, 233 bacteria, and 57 eukarya. Because 3D protein structures often are maintained over evolutionary time scales, the researchers compared the distribution of metal-binding protein structures of sequenced organisms. An extensive bioinformatics analysis revealed that the metal-binding structures within the three domains of life (ar-
Meta-All Although several popular databases to store metabolic information already exist, they often are not well structured and only store certain types of data. In addition, researchers typically cannot manipulate the data included in these repositories. So Stephan Weise and coworkers at the Leibniz Institute of Plant Genetics and Crop Plant Research (Germany) and Brookhaven National Laboratory designed Meta-All, a database that allows researchers to store and manage metabolic data. For example, information about reactions, translocations, pathways, and kinetics can be stored in the database. A web interface for data entry and queries is included. Users can download Meta-All and install it on their own computers with a sample test data set. Scientists can enter their own results or those mined from the literature. The database is freely available at http://bic‑gh.de/meta-all. (BMC Bioinformatics 2006, 7, 465)
18 Journal of Proteome Research • Vol. 6, No. 1, 2007
chaea, bacteria, and eukarya) conform to a power law. The power-law slopes calculated for each domain indicate whether the size of a given category of proteins, such as those that bind metals, scales with the size of the whole proteome or is enriched in a certain group of organisms. According to the data, Fe-, Mn-, and Co-binding structures were preferentially retained in archaea and bacteria, whereas Zn-binding structures were preferentially retained in eukarya. The researchers say that these data are compatible with a hypothesis in
The fruit fly’s sperm proteome
Sperm are more than sacs of genetic material. They also activate the egg and deliver paternal factors to the zygote. But despite years of developmental-biology research, only a handful of sperm proteins have been identified and studied. So Timothy Karr and colleagues at the University of Bath (U.K.) and the University of Virginia took a proteomics approach to discover new sperm proteins in the fruit fly Drosophila melanogaster. They identified 342 unique proteins, which is an ~60-fold increase compared with the number of previously known sperm proteins. Sperm cells were harvested from seminal vesicles and solubilized. Proteins were digested with trypsin and analyzed by LC/MS/MS. In addition to the 342 unique proteins, the researchers found 39 that were impossible to definitively assign, possibly because the proteins were members of highly conserved families. Only five of the identified proteins had been shown previously to be present in mature sperm. Gene Ontology annotations revealed seven functional categories of proteins, including those involved in energy production and use. The largest category of proteins (31%), however, was composed of proteins with unknown functions. To test the completeness of the proteome, the samples were run on 2DE gels with various pI ranges. Each time, ~600 spots were observed. This is almost double the number of proteins identified by LC/MS/MS, but Karr and colleagues say some spots represent isoforms of the same proteins. Therefore, the LC/MS/MS data probably in-
TIMOTHY KARR
Toolbox
What are you made of? Researchers define the D. melanogaster sperm proteome. The results should help researchers treat infertility.
clude most of the proteins observed on 2DE gels. Also, a set of 65 spots was analyzed by MALDI TOFMS, and most of the proteins identified this way (~90%) were found in the large-scale sperm proteome study. Many genes that encode sperm proteins were located close to each other on D. melanogaster chromosomes; this finding indicates that these genes may be regulated in a similar manner. In addition, the evolutionary rates of sperm genes were determined with an analysis of the divergence between D. melanogaster and D. simulans genes. Sperm genes are highly conserved when compared with other functional groups of proteins. Therefore, the researchers conclude that the sperm proteome is evolving slowly. All of these results shed much-needed light on the sperm proteome, which could help scientists better understand and treat male infertility. (Nat. Genet. 2006, 38, 1440–1445)
currents which prokaryotes evolved in environments devoid of oxygen and eukaryotes evolved in oxygen-rich environments. (Proc. Natl. Acad. Sci. U.S.A. 2006, 103, 17,822–17,827)
Organellar proteomes have different properties Many researchers delving into the study of subcellular proteomes fail to consider the differing properties of the proteins in various compartments and structures. So Marc Wilkins and co-workers at the University of New South Wales (Australia) systematically compared the properties of the proteins in 22 yeast subproteomes. They suggest that subcellular analyses could be improved if researchers take these insights into account. The researchers studied the properties of 3962 yeast proteins with known subcellular localizations. In each compartment, the distribution of pI values was bimodal. Some subproteomes, however, had a bias toward basic pIs, whereas others were biased toward more acidic values. Grand average hydropathy (GRAVY) values were calculated to determine
protein hydropathy trends. Not surprisingly, membranous compartments with many hydrophobic proteins had high GRAVY scores. The researchers did not observe differences in the distribution of protein masses in various compartments, but they did find a significant difference for protein abundances. Of the subproteomes studied, the nucleolus had the highest mean abundance (number of molecules per cell), but the microtubules had the lowest. On the basis of these results, the researchers say that taking pI and hydropathy into consideration should help proteomics scientists to more effectively separate various subcellular organelles. For example, subproteomes that are hydrophilic and acidic or basic will be amenable to standard 2DE analyses, but those that are hydrophobic and basic would be better separated with 16-benzyldimethyl-n-hexadecyl ammonium chloride/SDS-PAGE or LC/MS/MS. In addition, they warn that compartments with similar physicochemical properties may copurify with those that are being targeted. (Proteomics 2006, 6, 5746–5757)
A subject’s diet is known to affect the outcome of metabolomics studies. So Daniel Raftery, R. Graham Cooks, and co-workers at Purdue University and Bioanalytical Systems, Inc., used 1H NMR and extractive ESI (EESI) to find a way to reduce the effects of diet on these investigations. Urine samples were collected from rats that were rotated through three diets: an overnight fast, a typical laboratory-rat diet, or cat food (sliced turkey in gravy). The samples were analyzed by 1H NMR and EESI MS. Principal component analysis (PCA) was performed; this procedure separated the spectra into three groups on the basis of diet. Compared with the 1H NMR data, the EESI MS data points for each replicate were more tightly clustered and produced better classifications. Although the methods have different sensitivities and selectivities, they could be used for cross-validation in a PCA-based analysis, say the researchers. After additional statistical tests, the team found that metabolites in the pathways for the urea cycle
PHOTODISC
Reducing the effect of diet on metabolomics studies
Bon appétit. Researchers suggest that metabolomics studies focused on certain metabolic pathways could reduce the effect of diet.
and the metabolism of amino groups were affected by diet, but purine metabolism products were not. Therefore, the researchers say that the influence of diet can be reduced by using a focused approach and monitoring specific metabolic pathways. (Anal. Chem. 2007, 79, 89–97)
Toolbox PP-VLAM for rapid FTMS analyses To reduce the time necessary for the analysis of FTICR MS data, Ron Heeren and co-workers at FOM Institute for Atomic and Molecular Physics and Free University (both in The Netherlands) developed a parallel workflow and a new algorithm called parallel processing virtual laboratory Amsterdam (PP-VLAM). The methods allow almost-real-time analysis, improved flexibility and reproducibility, and the ability to reprocess data with various parameters. In their workflow, the researchers process data on several connected computers simultaneously. PP-VLAM locates the raw data generated by the FTICR mass spectrometer and performs a Fourier transform to obtain a mass spectrum. Isotopic peaks are identified, and the monoisotopic mass is included in a mass list. This step is repeated until all the data have been analyzed. Next, the mass lists are compiled into a group list. The output of the analysis is stored in an XML file and can be easily converted into the mzData format. (J. Am. Soc. Mass Spectrom. 2006, doi 10.1016/j.jasms.2006.09.005)
A new version of SSRCalc The sequence-specific retention calculator (SSRCalc) takes into account the effect that the hydrophobicity of N-terminal amino acids has on peptide retention in reversed-phase (RP) HPLC (Mol. Cell. Proteomics 2004, 3, 908–919). To improve the algorithm, Oleg Krokhin at the University of Manitoba (Canada) made adjustments and included several additional parameters, such as the amino acid composition of a peptide, the positions of the amino acids, the peptide length, the overall hydrophobicity, and pI. The algorithm was optimized with a test set of 2000 peptides. Because the programming code is flexible, the updated version of SSRCalc can be adapted to different chromatographic conditions. For example, Krokhin applied the algorithm to data generated with RP LC columns with 100- and 300Å pore sizes. Although the improved algorithm was tested with an off-line LC/ MALDI approach, it also can be used with ESI data. (Anal. Chem. 2006, 78, 7785–7795)
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