Jayantha Gunaratne1*, Alexander Schmidt2*#, Andreas Quandt3*, Suat Peng Neo1, Ömer Sinan Saraç4, Tannia Gracia5, Salvatore Loguercio4, Erik Ahrné2, Rachel Li Hai Xia1, Keng Hwa Tan1, Christopher Lößner1, Jürg Bähler5, Andreas Beyer4, Walter Blackstock1 and Ruedi Aebersold#3,6
1 - Quantitative Proteomics Group, Institute of Molecular and Cell Biology, Agency for Science
Technology and Research, 61 Biopolis Drive, Singapore 138673
2 - Proteomics Core Facility, Biozentrum, University of Basel, CH-4056 Basel, Switzerland
3 - Departments of Biology, Institute of Molecular Systems Biology, ETH Zurich, CH-8093 Zurich,
4 - Biotechnology Center, TU Dresden, Dresden, Germany
5 - University College London, Department of Genetics, Evolution & Environment and UCL Cancer
Institute, London WC1E 6BT, United Kingdom
6 - Faculty of Science, University of Zurich, CH-8057 Zurich, Switzerland
* Equal contribution
# Corresponding authors
Published in Mol Cell Proteomics on 15 March 2013. [Epub ahead of print]
We report a high-quality and system-wide proteome catalogue covering 71% (3,542 proteins) of the predicted genes of fission yeast, Schizosaccharomyces pombe, presenting the largest protein dataset to date for this important model organism. We obtained this high proteome and peptide (11.4 peptides/protein) coverage by a combination of extensive sample fractionation, high-resolution Orbitrap mass spectrometry and combined database searching using the iProphet software as part of the Trans-Proteomics Pipeline. All raw and processed data are made accessible in the S. pombe PeptideAtlas. The identified proteins showed no biases in functional properties and allowed global estimation of protein abundances. The high coverage of the PeptideAtlas allowed correlation with transcriptomic data in a system-wide manner indicating that post-transcriptional processes control the levels of at least half of all identified proteins. Interestingly, the correlation was not equally tight for all functional categories ranging from rs> 0.80 for proteins involved in translation to rs<0.45 for signal transduction proteins. Moreover, many proteins involved in DNA damage repair could not be detected in the PeptideAtlas despite their high mRNA levels, strengthening the translation-on-demand hypothesis for members of this protein class. In summary, the extensive and public available S. pombe PeptideAtlas together with the generated proteotypic peptide spectral library will be a useful resource for future targeted, in-depth and quantitative proteomic studies on this microorganism.
Figure: (A) Abundance distribution of cell-cycle proteins. Proteins were mapped to the cell cycle pathway according to the KEGG database together with their abundances classes determined from the analysis of unsynchronized proliferating cells. Proteins not identified in the PeptideAtlas are indicated in white. (B) Hierarchical clustering of protein levels of S. pombe genes and their orthologous genes in S. cerevisiae and human. Protein clusters were subjected to GO-term enrichment analysis using DAVID (http://david.abcc.ncifcrf.gov). Only clusters with significant terms (p<0.05) are displayed.
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