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Molecular & Cellular Proteomics 6:S18-S19, 2007.
© 2007 by The American Society for Biochemistry and Molecular Biology, Inc.

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Abstracts

Session 1

Expression Proteomics at Last; the Determination of Proteome Wide Protein Abundance Changes by SILAC and High Resolution Mass Spectrometry

L. de Godoy, J. Cox, J. Olsen, T. Bonaldi, C. Kumar, N. Hubner, B. Macek, and M. Mann

Department of Proteomics and Signal Transduction, Max-Planck Institute for Biochemistry, Martinsried, Germany

Mass spectrometry based proteomics has become increasingly successful at the identification of a large proportion of the proteins in complex mixtures. However, the goal of quantifying complete proteomes has remained elusive for the last three decades. Here we use Stable Isotope Labeling by Amino acids in Cell culture (SILAC), together with LTQ-Orbitrap based mass spectrometry and sophisticated bioinformatics to quantitate a large proportion of the proteome. We will cover the work flow used in these experiments including automated statistical analysis of quantitation results. Results from yeast, drosophila and human proteomes will be introduced. In drosophila we demonstrate quantitation of more than 4400 proteins after dsRNA knock down of a chromatin remodeling factor. Interestingly, the second most down-regulated protein—after the RNAi target itself—is a protein in a direct complex with the target. The message level was not affected, demonstrating a key advantage of measuring proteins in addition to mRNA levels in "systems biology." In the human system we have compared the comprehensiveness of modern proteomic methods with standard microarray methods. We conclude that proteomics is still more time consuming but that it is now competitive with microarrays in terms of coverage of gene expression.

1.2

Statistical Analysis of Quantitative Proteomics Datasets via Normalized Spectral Abundance Factors

M. P. Washburn

Stowers Institute for Medical Research, Kansas City, MO

Quantitative proteomic experimentation can now yield expression data for hundreds to thousands of proteins. Often times these datasets will be generated with the intention to discover new biology that will be followed up with focused biochemical, cell biological, or molecular biological experimentation. Our laboratory has been focused on the use of a modified form of spectral counting, named the normalized spectral abundance factor (NSAF) as the basis for quantitative proteomic analysis in combination with multidimensional protein identification technology. Since proteases like trypsin largely generate more peptides per protein as any given protein increases in length. The key features of the NSAF approach is that it takes into account the length of the protein being analyzed and the total intensity of the run. NSAF values alone do not follow a normal distribution and must be natural log transformed in order to use statistical tests like the t-test. When we investigated the statistical parameters of NSAF values compared to Affymetrix GeneChip datasets, we found that data from both approaches have similar dynamic range and distribution properties of numeric values. In addition, we observed that the standard deviation (SD) of a protein's NSAF values is dependent on the average NSAF value of the protein itself, following a power law. We have begun applying these approaches to elucidate the molecular mechanisms of rapamycin, an immunosupressive and anticancer drug. Rapamycin inhibits the protein kinase TOR (Target of Rapamycin), a central controller of cell growth in eukaryotic organisms. The targets of TOR have not yet been identified and gaps in the signalling pathway remain to be filled. In order to gain insights into TOR function and a global understanding of rapamycin effects, we performed a time course analysis of protein and mRNA changes in Saccharomyces cerevisiae in response to rapamycin. Proteomic and transcriptomic profiles have been compared between Saccharomyces cerevisiae grown in N15 media (none treated) versus N14 media (treated) during 6 hours of rapamycin treatment. Interestingly, the correlation between mRNA and protein abundance changes was low at any given time point but increased in a delayed fashion when changes at the mRNA level at early time point were correlated with changes at the protein level at later time point. This seminar will focus on the application of the NSAF approach to protein expression analysis and the statistical analysis of these datasets. For more information please see the posters by Pavelka et al and Fournier et al.

1.3

Insight into Cell Biology from Physical and Genetic Protein Interactions

G. Cagney¹, N. Krogan², J. Weissman², A. Emili³, and J. Greenblatt³

¹Conway Institute, University College Dublin, Ireland; ² Department of Cellular and Molecular Pharmacology, University of California, San Francisco, CA; ³Department of Medical Research, University of Toronto, Ontario, Canada

Pathways and complexes can be considered fundamental units of cell biology, but their relationship to each other is difficult to define. Comprehensive tagging and purification experiments have generated a network of interactions that probably represents most of the stable protein complexes in mitotically-growing yeast cells. We describe this work, and show how the analysis of pairwise epistatic relationships between genes complements the physical interaction data, and furthermore can used to classify gene products into parallel and interacting pathways.

1.4

Exploiting Peptide Identification Databases for Improved Bioinformatics for Proteomics

S. Hubbard

Faculty of Life Sciences, University of Manchester, Manchester, United Kingdom

Modern proteome science and bioinformatics are closely coupled. High-throughput LC-MS/MS experiments are generating large numbers of peptide identifications which are of course dependent on bioinformatics tools to assign the most likely amino acid sequences from protein sequence databases. This growing number of peptide identifications provides an excellent resource from which to mine useful principles, learn rules and feedback into the peptide identification process. We have developed PepSeeker (www.ispider.manchester.ac.uk/pepseeker), once such peptide identifications database which captures explicit fragment ion information and supports queries over this data coupled to amino acid sequence patterns. The current version contains over 2 million spectra and 250000 peptide identifications and can be accessed via a BioMart interface. Peptide identifications contained in PepSeeker and other public repositories have been used to examine several features of proteome peptides, including distributions of fragment ion types in different instruments, sequence signals associated with missed cleavage by trypsin, and peptide "flyability" prediction for the design of quantitative QconCAT proteins. These applications will be discussed in the context of the peptide identification process, such as a simple database processing step which can improve identifications via peptide mass fingerprinting. Finally, peptide identifications themselves can be used to improve the genome annotation process and an example will be presented showing how proteomics can help rationalise different gene model predictions for the Aspergillus niger genome.

1.5

Refining Peptide LC/MS Analysis for Qualitative and Quantitative Proteomics

A. A. High and C. A. Slaughter

St. Jude Children's Research Hospital, Memphis, TN

Recent improvements in mass spectrometer sensitivity and scan rate, along with enhanced peak capacities available with a new generation of chromatographic stationary phases, offer the potential for greater sensitivity in protein identification and greater penetration of proteomes. We have coupled one- or two-dimensional liquid chromatography at high resolution using 1.7 µm stationary phases with mass analysis using a rapid scanning, linear ion-trap mass spectrometer, and have developed procedures that take advantage of the capabilities these tools provide. Specifically, we have evaluated the gains realized through attention to instrument configuration, sample loading, off-line fractionation parameters and mass spectral acquisition settings. We illustrate the effects of these gains in a qualitative study of intrinsically unstructured proteins, and in a quantitative study of the MHC-linked presentation of peptides derived from influenza proteins in virus-infected cells. We approach the task of comparing the capabilities of the new methodology with those required for global proteome analysis, and consider the factors presently limiting performance. Achieving an accurate understanding of these issues, however, is complicated by the frequency with which commonly used search engines either misassign spectra or fail to assign them. These problems highlight the need for continuing improvements in search methods and for making raw spectral data available when publishing proteomic results.

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