A phosphohistidine proteomics strategy based on elucidation of a unique gas-phase phosphopeptide fragmentation mechanism.

Protein histidine phosphorylation is increasingly recognized as a critical posttranslational modification (PTM) in central metabolism and cell signaling. Still, the detection of phosphohistidine (pHis) in the proteome has remained difficult due to the scarcity of tools to enrich and identify this labile PTM. To address this, we report the first global proteomic analysis of pHis proteins, combining selective immunoenrichment of pHis peptides and a bioinformatic strategy based on mechanistic insight into pHis peptide gas-phase fragmentation during LC-MS/MS. We show that collision-induced dissociation (CID) of pHis peptides produces prominent characteristic neutral losses of 98, 80, and 116 Da. Using isotopic labeling studies, we also demonstrate that the 98 Da neutral loss occurs via gas-phase phosphoryl transfer from pHis to the peptide C-terminal α-carboxylate or to Glu/Asp side chain residues if present. To exploit this property, we developed a software tool that screens LC-MS/MS spectra for potential matches to pHis-containing peptides based on their neutral loss pattern. This tool was integrated into a proteomics workflow for the identification of endogenous pHis-containing proteins in cellular lysates. As an illustration of this strategy, we analyzed pHis peptides from glycerol-fed and mannitol-fed Escherichia coli cells. We identified known and a number of previously speculative pHis sites inferred by homology, predominantly in the phosphoenolpyruvate:sugar transferase system (PTS). Furthermore, we identified two new sites of histidine phosphorylation on aldehyde-alcohol dehydrogenase (AdhE) and pyruvate kinase (PykF) enzymes, previously not known to bear this modification. This study lays the groundwork for future pHis proteomics studies in bacteria and other organisms.

Software tool for researching annotations of proteins: open-source protein annotation software with data visualization.

In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP), a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP results include meta-information on the protein in addition to complementary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger data set. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie charts (biological process, cellular component, and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large data sets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net.

STRAP PTM: Software Tool for Rapid Annotation and Differential Comparison of Protein Post-Translational Modifications.

The identification of protein post-translational modifications (PTMs) is an increasingly important component of proteomics and biomarker discovery, but very few tools exist for performing fast and easy characterization of global PTM changes and differential comparison of PTMs across groups of data obtained from liquid chromatography-tandem mass spectrometry experiments. STRAP PTM (Software Tool for Rapid Annotation of Proteins: Post-Translational Modification edition) is a program that was developed to facilitate the characterization of PTMs using spectral counting and a novel scoring algorithm to accelerate the identification of differential PTMs from complex data sets. The software facilitates multi-sample comparison by collating, scoring, and ranking PTMs and by summarizing data visually. The freely available software (beta release) installs on a PC and processes data in protXML format obtained from files parsed through the Trans-Proteomic Pipeline. The easy-to-use interface allows examination of results at protein, peptide, and PTM levels, and the overall design offers tremendous flexibility that provides proteomics insight beyond simple assignment and counting.