MRMPath and MRMutation, Facilitating Discovery of Mass Transitions for Proteotypic Peptides in Biological Pathways Using a Bioinformatics Approach

Quantitative proteomics applications in mass spectrometry depend on the knowledge of the mass-to-charge ratio (m/z) values of proteotypic peptides for the proteins under study and their product ions. MRMPath and MRMutation, web-based bioinformatics software that are platform independent, facilitate the recovery of this information by biologists. MRMPath utilizes publicly available information related to biological pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. All the proteins involved in pathways of interest are recovered and processed in silico to extract information relevant to quantitative mass spectrometry analysis. Peptides may also be subjected to automated BLAST analysis to determine whether they are proteotypic. MRMutation catalogs and makes available, following processing, known (mutant) variants of proteins from the current UniProtKB database. All these results, available via the web from well-maintained, public databases, are written to an Excel spreadsheet, which the user can download and save. MRMPath and MRMutation can be freely accessed. As a system that seeks to allow two or more resources to interoperate, MRMPath represents an advance in bioinformatics tool development. As a practical matter, the MRMPath automated approach represents significant time savings to researchers. 1. Introduction A feature of the last two decades of biomedical research has been the generation of “–omics” data, a result of the pursuit of discovery. The introduction of soft ionization techniques for analysis of peptides and proteins by mass spectrometry in the 1980s [1, 2] led to a plethora of applications related to the identification of proteins from a wide variety of proteomes, from microorganisms to plants to mammals. These studies largely defined the measurable peptidome and by implication the proteome. They were also designed to “discover” significant protein changes, such as abundance and modifications. Because of the complications resulting from multiple hypotheses testing, however, detecting differences between treated and control samples has often met with limited success [3]. Concern has been expressed, for example, over the failure of different participating laboratories to systematically determine the same proteins that distinguish cancer patients from controls [4]. The next phase of proteomics is moving towards targeted, hypothesis-driven experiments. It integrates knowledge from previous proteomics discovery endeavors (2D-gel/peptide mass fingerprinting, MuDPIT, and GeLC-tandem mass spectrometry), microarray analysis