Halobacterium salinarum NRC-1 PeptideAtlas: Toward Strategies for Targeted Proteomics and Improved Proteome Coverage

• Published in: Journal of proteome research Vol. 7; no. 9; pp. 3755 - 3764
• Main Authors: Van, Phu T, Schmid, Amy K, King, Nichole L, Kaur, Amardeep, Pan, Min, Whitehead, Kenia, Koide, Tie, Facciotti, Marc T, Goo, Young Ah, Deutsch, Eric W, Reiss, David J, Mallick, Parag, Baliga, Nitin S
• Format: Journal Article
• Language: English
• Published: United States American Chemical Society 01.09.2008
• Subjects: Amino Acid Sequence; Archaeal Proteins - chemistry; Halobacterium salinarum - chemistry; Isoelectric Point; Mass Spectrometry - methods; Molecular Sequence Data; Proteome; Solubility; iTRAQ; Halobacterium; PeptideAtlas; proteomics; bioinformatics; archaea
• ISSN: 1535-3893; 1535-3907
• DOI: 10.1021/pr800031f

The relatively small numbers of proteins and fewer possible post-translational modifications in microbes provide a unique opportunity to comprehensively characterize their dynamic proteomes. We have constructed a PeptideAtlas (PA) covering 62.7% of the predicted proteome of the extremely halophilic archaeon Halobacterium salinarum NRC-1 by compiling approximately 636 000 tandem mass spectra from 497 mass spectrometry runs in 88 experiments. Analysis of the PA with respect to biophysical properties of constituent peptides, functional properties of parent proteins of detected peptides, and performance of different mass spectrometry approaches has highlighted plausible strategies for improving proteome coverage and selecting signature peptides for targeted proteomics. Notably, discovery of a significant correlation between absolute abundances of mRNAs and proteins has helped identify low abundance of proteins as the major limitation in peptide detection. Furthermore, we have discovered that iTRAQ labeling for quantitative proteomic analysis introduces a significant bias in peptide detection by mass spectrometry. Therefore, despite identifying at least one proteotypic peptide for almost all proteins in the PA, a context-dependent selection of proteotypic peptides appears to be the most effective approach for targeted proteomics.