Mass spectrometry is emerging as a versatile analytical tool for profiling glycan and glycopeptide structures. While the interpretation of MS data remains a challenging and difficult task, substantial efforts have been made to develop informatics tools to alleviate MS data interpretation. Here, we present a web-based tool, GlycoPep DB, designed to facilitate compositional assignment for glycopeptides by comparing experimentally measured masses to all calculated glycopeptide masses from a carbohydrate database with N-linked glycans. GlycoPep DB is an advance over current tools to assign N-linked glycans because it uses a concept of "smart searching", where only biologically relevant carbohydrate compositions are searched, when matching carbohydrate compositions with the MS data making glycopeptide compositional assignment more efficient. This is in contrast to currently used tools, where many implausible glycan structures are present in the search output, but fewer biologically relevant glycan motifs are predicted. The utility of GlycoPep DB is illustrated in the analysis of glycopeptides derived from a proteolytic digest of follicle stimulating hormone.

Metabolite identification is a necessary step in developing safe and effective drugs. Metabolite analysis typically involves rapid identification of the chemical composition of the metabolite by automated HPLC-MS methods, followed by the laborious process of identifying the structure of the metabolite. Since MS is typically utilized to identify the metabolite, it is logical to utilize MS/MS to structurally characterize the sample. However, interpretation of MS/MS data may not provide sufficient information, as fragmentation pathways are not well understood or predictable. Therefore, other more time-consuming methods of analysis are often undertaken. If the dissociation rules for low-energy MS/MS experiments were clearly defined for all classes of compounds, more information would be obtained from MS/MS data, and metabolite identification would proceed more rapidly. We are currently developing methods to define these fragmentation rules. By screening approximately 100 carboxylic acids at a time and applying knowledge of physical-organic chemistry, predictive rules are under development that describe how compounds dissociate under low-energy collision-induced dissociation conditions. Studies of carboxylic acid dissociation demonstrate that this approach is practical and reliable. Dissociation rules were predicted with a 90% success rate, when tested on acid-containing pharmaceuticals. This predictive power cannot be matched by any commercially available software. This study, and others like it, will be used to develop algorithms that more rapidly identify drug metabolites and degradation products, based on MS/MS data. Such algorithms will benefit drug development for all types of pharmaceuticals.

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We have recently developed a new mass spectrometry method, the STEP (statistical test of equivalent pathways) analysis that uses ion abundances in two tandem mass spectrometry experiments to obtain genealogy information about product ions present in mass spectra. The method requires minimal sample, and it can be performed using a conventional quadrupole ion trap mass spectrometer. To obtain genealogy information, STEP ratios are calculated by comparing the relative abundances of product ions in two MS/MS experiments. These ratios are directly related to the origin of the product ions. Product ions that result directly from the precursor ion always have STEP ratios that are