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.

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.

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.