Properties of isotope patterns and their utility for peptide identification in large-scale proteomic experiments

Unsupervised Identification of Isotope-Labeled Peptides

In vivo isotopic labeling coupled with high-resolution proteomics is used to investigate primary metabolism in techniques such as stable isotope probing (protein-SIP) and peptide-based metabolic flux analysis (PMFA). Isotopic enrichment of carbon substrates and intracellular metabolism determine the distribution of isotopes within amino acids. The resulting amino acid mass distributions (AMDs) are convoluted into peptide mass distributions (PMDs) during protein synthesis. With no a priori knowledge on metabolic fluxes, the PMDs are therefore unknown. This complicates labeled peptide identification because prior knowledge on PMDs is used in all available peptide identification software. An automated framework for the identification and quantification of PMDs for nonuniformly labeled samples is therefore lacking. To unlock the potential of peptide labeling experiments for high-throughput flux analysis and other complex labeling experiments, an unsupervised peptide identification and quantification method was developed that uses discrete deconvolution of mass distributions of identified peptides to inform on the mass distributions of otherwise unidentifiable peptides. Uniformly (13)C-labeled Escherichia coli protein was used to test the developed feature reconstruction and deconvolution algorithms. The peptide identification was validated by comparing MS(2)-identified peptides to peptides identified from PMDs using unlabeled E. coli protein. Nonuniformly labeled Glycine max protein was used to demonstrate the technology on a representative sample suitable for flux analysis. Overall, automatic peptide identification and quantification were comparable or superior to manual extraction, enabling proteomics-based technology for high-throughput flux analysis studies.

Product ion isotopologue pattern: A tool to improve the reliability of elemental composition elucidations of unknown compounds in complex matrices

RATIONALE

Elucidation of the elemental compositions of unknown compounds (e.g., in metabolomics) generally relies on the availability of accurate masses and isotopic ratios. This study focuses on the information provided by the abundance ratio within a product ion pair (monoisotopic versus the first isotopic peak) when isolating and fragmenting the first isotopic ion (first isotopic mass spectrum) of the precursor.

METHODS

This process relies on the capability of the quadrupole within the Q Orbitrap instrument to isolate a very narrow mass window. Selecting only the first isotopic peak (first isotopic mass spectrum) leads to the observation of a unique product ion pair. The lighter ion within such an isotopologue pair is monoisotopic, while the heavier ion contains a single carbon isotope. The observed abundance ratio is governed by the percentage of carbon atoms lost during the fragmentation and can be described by a hypergeometric distribution.

RESULTS

The observed carbon isotopologue abundance ratio (product ion isotopologue pattern) gives reliable information regarding the percentage of carbon atoms lost in the fragmentation process. It therefore facilitates the elucidation of the involved precursor and product ions. Unlike conventional isotopic abundances, the product ion isotopologue pattern is hardly affected by isobaric interferences. Furthermore, the appearance of these pairs greatly aids in cleaning up a 'matrix-contaminated' product ion spectrum.

CONCLUSIONS

The product ion isotopologue pattern is a valuable tool for structural elucidation. It increases confidence in results and permits structural elucidations for heavier ions. This tool is also very useful in elucidating the elemental composition of product ions. Such information is highly valued in the field of multi-residue analysis, where the accurate mass of product ions is required for the confirmation process. Copyright © 2016 John Wiley & Sons, Ltd.

QUDeX-MS: hydrogen/deuterium exchange calculation for mass spectra with resolved isotopic fine structure

Background

Hydrogen/deuterium exchange (HDX) coupled to mass spectrometry permits analysis of structure, dynamics, and molecular interactions of proteins. HDX mass spectrometry is confounded by deuterium exchange-associated peaks overlapping with peaks of heavy, natural abundance isotopes, such as carbon-13. Recent studies demonstrated that high-performance mass spectrometers could resolve isotopic fine structure and eliminate this peak overlap, allowing direct detection and quantification of deuterium incorporation.

Results

Here, we present a graphical tool that allows for a rapid and automated estimation of deuterium incorporation from a spectrum with isotopic fine structure. Given a peptide sequence (or elemental formula) and charge state, the mass-to-charge ratios of deuterium-associated peaks of the specified ion is determined. Intensities of peaks in an experimental mass spectrum within bins corresponding to these values are used to determine the distribution of deuterium incorporated. A theoretical spectrum can then be calculated based on the estimated distribution of deuterium exchange to confirm interpretation of the spectrum. Deuterium incorporation can also be detected for ion signals without a priori specification of an elemental formula, permitting detection of exchange in complex samples of unidentified material such as natural organic matter. A tool is also incorporated into QUDeX-MS to help in assigning ion signals from peptides arising from enzymatic digestion of proteins. MATLAB-deployable and standalone versions are available for academic use at qudex-ms.sourceforge.net and agarlabs.com.

Conclusion

Isotopic fine structure HDX-MS offers the potential to increase sequence coverage of proteins being analyzed through mass accuracy and deconvolution of overlapping ion signals. As previously demonstrated, however, the data analysis workflow for HDX-MS data with resolved isotopic fine structure is distinct. QUDeX-MS we hope will aid in the adoption of isotopic fine structure HDX-MS by providing an intuitive workflow and interface for data analysis.