Tandem Mass Spectral Data Research Articles

Knowledge-based In Silico Fragmentation and Annotation of Mass Spectra for Natural Products with MassKG

Liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) is a potent analytical technique utilized for identifying natural products from complex sources. However, due to the structural diversity, annotating LC-MS/MS data of natural products efficiently remains challenging, hindering the discovery process of novel active structures. Here, we introduce MassKG, an algorithm that combines a knowledge-based fragmentation strategy and a deep learning-based molecule generation model to aid in rapid dereplication and the discovery of novel NP structures. Specifically, MassKG has compiled 407,720 known NP structures and, based on this, generated 266,353 new structures using chemical language models for the discovery of potential novel compounds. Furthermore, MassKG demonstrates exceptional performance in spectra annotation compared to state-of-the-art algorithms. To enhance usability, MassKG has been implemented as a web server for annotating tandem mass spectral data (MS/MS, MS2) with a user-friendly interface, automatic reporting, and fragment tree visualization. Lastly, the interpretive capability of MassKG is comprehensively validated through composition analysis and MS annotation of Panax notoginseng, Ginkgo Biloba, Codonopsis pilosula, and Astragalus membranaceus. MassKG is now accessible at https://xomics.com.cn/masskg.

MS2Lipid: A Lipid Subclass Prediction Program Using Machine Learning and Curated Tandem Mass Spectral Data

Background: Untargeted lipidomics using collision-induced dissociation-based tandem mass spectrometry (CID-MS/MS) is essential for biological and clinical applications. However, annotation confidence still relies on manual curation by analytical chemists, despite the development of various software tools for automatic spectral processing based on rule-based fragment annotations. Methods: In this study, we present a novel machine learning model, MS2Lipid, for the prediction of known lipid subclasses from MS/MS queries, providing an orthogonal approach to existing lipidomics software programs in determining the lipid subclass of ion features. We designed a new descriptor, MCH (mode of carbon and hydrogen), to increase the specificity of lipid subclass prediction in nominal mass resolution MS data. Results: The model, trained with 6760 and 6862 manually curated MS/MS spectra for the positive and negative ion modes, respectively, classified queries into one or several of 97 lipid subclasses, achieving an accuracy of 97.4% in the test set. The program was further validated using various datasets from different instruments and curators, with the average accuracy exceeding 87.2%. Using an integrated approach with molecular spectral networking, we demonstrated the utility of MS2Lipid by annotating microbiota-derived esterified bile acids, whose abundance was significantly increased in fecal samples of obese patients in a human cohort study. This suggests that the machine learning model provides an independent criterion for lipid subclass classification, enhancing the annotation of lipid metabolites within known lipid classes. Conclusions: MS2Lipid is a highly accurate machine learning model that enhances lipid subclass annotation from MS/MS data and provides an independent criterion.

Analytical profile of the lysergamide 1cP-AL-LAD and detection of impurities.

The development of novel lysergamides continues to occur, based on both the needs of psychedelic medicine and commercial interest in new recreational substances. The present study continues the authors' research on novel lysergamides and describes the analytical profile of 1-cyclopropanoyl-AL-LAD (IUPAC name: 1-(cyclopropanecarbonyl)-N,N-diethyl-6-(prop-2-en-1-yl)-9,10-didehydroergoline-8β-carboxamide; 1cP-AL-LAD), using various chromatographic, mass spectrometric, and spectroscopic methods. Analysis of a powdered sample of 1cP-AL-LAD, obtained from an online vendor, by high performance liquid chromatography-electrospray ionization-quadrupole time-of-flight mass spectrometry in full scan/AutoMS/MS mode revealed the detection of 17 impurities based on high-resolution tandem mass spectral data; tentative determination of their identity was based on mass spectral grounds alone, though detection of AL-LAD and 1P-AL-LAD was confirmed using available reference standards. Other tentative compound identifications included 1-acetyl-AL-LAD and several other substances potentially reflecting oxidation of the N6 -allyl group as well as other positions on the ergoline ring system. These data may assist those interested in the chemistry of lysergamides. Finally, 1cP-AL-LAD was also detected in samples of "blotters" sold online for recreational use.

Comprehensive profiling of phytochemicals in the fruits of Gardenia jasminoides Ellis and its variety using liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry.

The fruits of Gardenia jasminoides Ellis are an important herb medicine in Traditional Chinese Medicine (TCM) and have been used for thousands of years for clearing away heat and toxic materials. It mainly contains iridoids, pigments, organic acids, and flavonoids. Although belonging to one species, it has two kinds of cultivars and one variety widely distributed and sold. This study aims to develop anintegratedandefficientanalytical strategy for comprehensive profiling of phytochemicals and clarify the differences in all three populations. Based on reversed-phase ultra-high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC/ESI-QTOFMS), an optimized analytical approach for comprehensive profiling of phytochemicals in the fruits of G. jasminoides was established in negative ionization mode. The holistic metabolites profiling was carried out on UHPLC/ESI-QTOFMS and data analysis program Progenesis QI, and a total of 80 metabolites were obtained and interpreted by chromatographic and tandem mass spectral data. The interpretation of metabolites comprises iridoids, pigments, organic acids, and flavonoids. Principal component analysis and partial least square-discriminant analysis were performed, and 19 main different components could be obtained to distinguish the three populations. Combined with non-targeted and targeted data analysis, the integrated strategy developed in this study was feasibly applied to discern differences in the profiles of the phytochemicals accumulating in the fruits of three populations of G. jasminoides.

Repository scale classification and decomposition of tandem mass spectral data

Various studies have shown associations between molecular features and phenotypes of biological samples. These studies, however, focus on a single phenotype per study and are not applicable to repository scale metabolomics data. Here we report MetSummarizer, a method for predicting (i) the biological phenotypes of environmental and host-oriented samples, and (ii) the raw ingredient composition of complex mixtures. We show that the aggregation of various metabolomic datasets can improve the accuracy of predictions. Since these datasets have been collected using different standards at various laboratories, in order to get unbiased results it is crucial to detect and discard standard-specific features during the classification step. We further report high accuracy in prediction of the raw ingredient composition of complex foods from the Global Foodomics Project.

Recognizing Contamination Fragment Ions in Liquid Chromatography-Tandem Mass Spectrometry Data.

Tandem mass spectral (MS/MS) data in liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis are often contaminated as the selection of precursor ions is based on a low-resolution quadrupole mass filter. In this work, we developed a strategy to differentiate contamination fragment ions (CFIs) from true fragment ions (TFIs) in an MS/MS spectrum. The rationale is that TFIs should coelute with their parent ions, but CFIs should not. To assess coelution, we performed a parallel LC-MS/MS analysis in data-independent acquisition (DIA) with all-ion-fragmentation (AIF) mode. Using the DIA (AIF) data, peak-peak correlation (PPC) score is calculated between the extracted ion chromatogram (EIC) of the fragment ion using the MS/MS scans and the EIC of the precursor ion using the MS1 scans. A high PPC score is an indication of TFIs, and a low PPC score is an indication of CFIs. Tested using metabolomics data generated by high resolution QTOF and Orbitrap MS from various vendors in different LC-MS configurations, we found that more than 70% of the fragment ions have PPC scores < 0.8 and identified three common sources of CFIs, including (1) solvent contamination, (2) adjacent chemical contamination, and (3) undetermined signals from artifacts and noise. Combining PPC scores with other precursor and fragment ion information, we further developed a machine learning model that can robustly and conservatively predict CFIs. Incorporating the machine learning model, we created an R program, MS2Purifier, to automatically recognize CFIs and clean MS/MS spectra of metabolic features in LC-MS/MS data with high sensitivity and specificity.

Comprehensive profiling of semi\u2010polar phytochemicals in whole wheat grains (Triticum aestivum) using liquid chromatography coupled with electrospray ionization quadrupole time\u2010of\u2010flight mass spectrometry

IntroductionWheat (Triticum aestivum) it is one of the most important staple food crops worldwide and represents an important resource for human nutrition. Besides starch, proteins and micronutrients wheat grains accumulate a highly diverse set of phytochemicals.ObjectivesThis work aimed at the development and validation of an analytical workflow for comprehensive profiling of semi-polar phytochemicals in whole wheat grains.MethodReversed-phase ultra-high performance liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (UHPLC/ESI-QTOFMS) was used as analytical platform. For annotation of metabolites accurate mass collision-induced dissociation mass spectra were acquired and interpreted in conjunction with literature data, database queries and analyses of reference compounds.ResultsBased on reversed-phase UHPLC/ESI-QTOFMS an analytical workflow for comprehensive profiling of semi-polar phytochemicals in whole wheat grains was developed. For method development the extraction procedure and the chromatographic separation were optimized. Using whole grains of eight wheat cultivars a total of 248 metabolites were annotated and characterized by chromatographic and tandem mass spectral data. Annotated metabolites comprise hydroquinones, hydroxycinnamic acid amides, flavonoids, benzoxazinoids, lignans and other phenolics as well as numerous primary metabolites such as nucleosides, amino acids and derivatives, organic acids, saccharides and B vitamin derivatives. For method validation, recovery rates and matrix effects were determined for ten exogenous model compounds. Repeatability and linearity were assessed for 39 representative endogenous metabolites. In addition, the accuracy of relative quantification was evaluated for six exogenous model compounds.ConclusionsIn conjunction with non-targeted and targeted data analysis strategies the developed analytical workflow was successfully applied to discern differences in the profiles of semi-polar phytochemicals accumulating in whole grains of eight wheat cultivars.

Lipid Annotator: Towards Accurate Annotation in Non-Targeted Liquid Chromatography High-Resolution Tandem Mass Spectrometry (LC-HRMS/MS) Lipidomics Using A Rapid and User-Friendly Software.

Lipidomics has great promise in various applications; however, a major bottleneck in lipidomics is the accurate and comprehensive annotation of high-resolution tandem mass spectral data. While the number of available lipidomics software has drastically increased over the past five years, the reduction of false positives and the realization of obtaining structurally accurate annotations remains a significant challenge. We introduce Lipid Annotator, which is a user-friendly software for lipidomic analysis of data collected by liquid chromatography high-resolution tandem mass spectrometry (LC-HRMS/MS). We validate annotation accuracy against lipid standards and other lipidomics software. Lipid Annotator was integrated into a workflow applying an iterative exclusion MS/MS acquisition strategy to National Institute of Standards and Technology (NIST) SRM 1950 Metabolites in Frozen Human Plasma using reverse phase LC-HRMS/MS. Lipid Annotator, LipidMatch, and MS-DIAL produced consensus annotations at the level of lipid class for 98% and 96% of features detected in positive and negative mode, respectively. Lipid Annotator provides percentages of fatty acyl constituent species and employs scoring algorithms based on probability theory, which is less subjective than the tolerance and weighted match scores commonly used by available software. Lipid Annotator enables analysis of large sample cohorts and improves data-processing throughput as compared to previous lipidomics software.

Multiple Compounds Recognition from The Tandem Mass Spectral Data Using Convolutional Neural Network.

Mixtures analysis can provide more information than individual components. It is important to detect the different compounds in the real complex samples. However, mixtures are often disturbed by impurities and noise to influence the accuracy. Purification and denoising will cost a lot of algorithm time. In this paper, we propose a model based on convolutional neural network (CNN) which can analyze the chemical peak information in the tandem mass spectrometry (MS/MS) data. Compared with traditional analyzing methods, CNN can reduce steps in data preprocessing. This model can extract features of different compounds and classify multi-label mass spectral data. When dealing with MS data of mixtures based on the Human Metabolome Database (HMDB), the accuracy can reach at 98%. In 600 MS test data, 451 MS data were fully detected (true positive), 142 MS data were partially found (false positive), and 7 MS data were falsely predicted (true negative). In comparison, the number of true positive test data for support vector machine (SVM) with principal component analysis (PCA), deep neural network (DNN), long short-term memory (LSTM), and XGBoost respectively are 282, 293, 270, and 402; the number of false positive test data for four models are 318, 284, 198, and 168; the number of true negative test data for four models are 0, 23, 7, 132, and 30. Compared with the model proposed in other literature, the accuracy and model performance of CNN improved considerably by separating the different compounds independent MS/MS data through three-channel architecture input. By inputting MS data from different instruments, adding more offset MS data will make CNN models have stronger universality in the future.

Characterization of Disulfide-Linked Peptides Using Tandem Mass Spectrometry Coupled with Automated Data Analysis Software.

Disulfide bond formation is critical for maintaining structure stability and function of many peptides and proteins. Mass spectrometry has become an important tool for the elucidation of molecular connectivity. However, the interpretation of the tandem mass spectral data of disulfide-linked peptides has been a major challenge due to the lack of appropriate tools. Developing proper data analysis software is essential to quickly characterize disulfide-linked peptides. A thorough and in-depth understanding of how disulfide-linked peptides fragment in mass spectrometer is a key in developing software to interpret the tandem mass spectra of these peptides. Two model peptides with inter- and intra-chain disulfide linkages were used to study fragmentation behavior in both collisional-activated dissociation (CAD) and electron-based dissociation (ExD) experiments. Fragments generated from CAD and ExD can be categorized into three major types, which result from different S-S and C-S bond cleavage patterns. DiSulFinder is a computer algorithm that was newly developed based on the fragmentation observed in these peptides. The software is vendor neutral and capable of quickly and accurately identifying a variety of fragments generated from disulfide-linked peptides. DiSulFinder identifies peptide backbone fragments with S-S and C-S bond cleavages and, more importantly, can also identify fragments with the S-S bond still intact to aid disulfide linkage determination. With the assistance of this software, more comprehensive disulfide connectivity characterization can be achieved. Graphical Abstract ᅟ.

Matchmaking in Proteomics.

Featured Article: Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom 1994;5:976–89. 2 Our 1994 report established a fast and accurate method to interpret the tandem mass spectra of peptides. In the early 1960s, as mass spectrometers became important tools for the elucidation of molecular structures, particularly in the natural products area, there was an interest in applying the technology to the challenge of protein sequence analysis. This task presented many technical barriers, including how to get polar, zwitterionic molecules into the gas phase inside a mass spectrometer. Many of the early solutions to this problem, such as extensive chemical modification of peptides, were elegant but not particularly practical. It was not until new ionization techniques were discovered did it become easier to introduce peptides and proteins into the gas phase, which allowed substantial progress to be made. The invention of the tandem mass …

Comprehensive metabolite profiling of onion bulbs (Allium cepa) using liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry

Onion (Allium cepa) represents one of the most important horticultural crops and is used as food, spice and medicinal plant almost worldwide. Onion bulbs accumulate a broad range of primary and secondary metabolites which impact nutritional, sensory and technological properties. To complement existing analytical methods targeting individual compound classes this work aimed at the development and validation of an analytical workflow for comprehensive metabolite profiling of onion bulbs. Metabolite profiling was performed by liquid chromatography coupled with electrospray ionization quadrupole time-of-flight mass spectrometry (LC/ESI-QTOFMS). For annotation of metabolites accurate mass tandem mass spectrometry experiments were carried out. On the basis of LC/ESI-QTOFMS and two chromatographic methods an analytical workflow was developed which facilitates profiling of polar and semi-polar onion metabolites including fructooligosaccharides, proteinogenic amino acids, peptides, S-substituted cysteine conjugates, flavonoids and saponins. To minimize enzymatic conversion of S-alk(en)ylcysteine sulfoxides, a sample preparation and extraction protocol for fresh onions was developed comprising cryohomogenization and a low-temperature quenching step. A total of 123 metabolites were annotated and characterized by chromatographic and tandem mass spectral data. For validation, recovery rates and matrix effects were determined for 15 model compounds. Repeatability and linearity were assessed for more than 80 endogenous metabolites. As exemplarily demonstrated by comparative metabolic analysis of six onion cultivars the established analytical workflow in combination with targeted and non-targeted data analysis strategies can be successfully applied for comprehensive metabolite profiling of onion bulbs.

Benefits of multidimensional fractionation for the study and characterization of natural organic matter

The belief that chromatographic separation of complex environmental mixtures or natural organic matter (NOM) produces featureless humps from which little, if anything, can be learned is still pervasive. Meanwhile improvements in chromatography and the use of information-rich detection methods have led to meaningful fractionation and revealed consistent data. Here, we build on this work and developed a robust, facile two-dimensional separation with high orthogonality between dimensions. We illustrate that re-injections of fractions (both in the first and in the second dimension) leads to individual peaks at the expected retention times and use information-rich detection to investigate the basis on which NOM is fractionated. We demonstrate unprecedentedly feature-rich chromatograms are observed even with standard UV detection for polar NOM fractions. The second stage of fractionation is demonstrated to separate isomers, providing a direct look at isomeric complexity in NOM as well as a tool to reduce it. Consistent with expectation, but confirmed for the first time through mass spectral data, radicals were detected for NOM components that were generally nonpolar and grouped in the condensed aromatic structure – like region of van Krevelen plots. High-resolution tandem mass spectral data, furthermore, suggests that many higher-MW components of fulvic acids (especially the highly oxidized ones) have formulas that do not match any known compounds in the literature, supporting the hypothesis that fulvic acids are a unique compound-class. Combined, the data illustrate that meaningful reduction in complexity reveals new compositional and structural detail and avails new avenues of investigation.

Traditional Aboriginal Preparation Alters the Chemical Profile of Carica papaya Leaves and Impacts on Cytotoxicity towards Human Squamous Cell Carcinoma

Carica papaya leaf decoction, an Australian Aboriginal remedy, has been used widely for its healing capabilities against cancer, with numerous anecdotal reports. In this study we investigated its in vitro cytotoxicity on human squamous cell carcinoma cells followed by metabolomic profiling of Carica papaya leaf decoction and leaf juice/brewed leaf juice to determine the effects imparted by the long heating process typical of the Aboriginal remedy preparation. MTT assay results showed that in comparison with the decoction, the leaf juice not only exhibited a stronger cytotoxic effect on SCC25 cancer cells, but also produced a significant cancer-selective effect as shown by tests on non-cancerous human keratinocyte HaCaT cells. Furthermore, evidence from testing brewed leaf juice on these two cell lines suggested that the brewing process markedly reduced the selective effect of Carica papaya leaf on SCC25 cancer cells. To tentatively identify the compounds that contribute to the distinct selective anticancer activity of leaf juice, an untargeted metabolomic approach employing Ultra High Performance Liquid Chromatography-Quadrupole Time of Flight-Mass Spectrometry followed by multivariate data analysis was applied. Some 90 and 104 peaks in positive and negative mode respectively were selected as discriminatory features from the chemical profile of leaf juice and >1500 putative compound IDs were obtained via database searching. Direct comparison of chromatographic and tandem mass spectral data to available reference compounds confirmed one feature as a match with its proposed authentic standard, namely pheophorbide A. However, despite pheophorbide A exhibiting cytotoxic activity on SCC25 cancer cells, it did not prove to be the compound contributing principally to the selective activity of leaf juice. With promising results suggesting stronger and more selective anticancer effects when compared to the Aboriginal remedy, Carica papaya leaf juice warrants further study to explore its activity on other cancer cell lines, as well as investigation to confirm the identity of compounds contributing to its selective effect, particularly those compounds altered by the long heating process applied during the traditional Aboriginal remedy preparation.

A review of methods for interpretation of glycopeptide tandem mass spectral data.

Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature, causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.

Compound Identification in Forensic Toxicological Analysis with Untargeted LC–MS-Based Techniques

Untargeted LC-MS/MS techniques have become indispensable tools for systematic toxicological analysis. Compound identification is based on the mass spectrometric information obtained, and this may include m/z, isotopic pattern, retention time and fragmentation information. All these different kinds of analytical features can be stored in libraries and databases. Currently, the most competent approach for compound identification involves tandem mass spectral library search. State-of-the-art databases were shown to be sensitive, specific, robust and instrument-independent. Low- and high-resolution instruments can both be used to develop efficient screening workflows. For automated and unattended acquisition of tandem mass spectral data, data-dependent acquisition control is the method of choice. Due to their impressive detection sensitivity, data-independent acquisition techniques are finding increased applicability.

Mining Novel Allergens from Coconut Pollen Employing Manual De Novo Sequencing and Homology-Driven Proteomics.

Coconut pollen, one of the major palm pollen grains is an important constituent among vectors of inhalant allergens in India and a major sensitizer for respiratory allergy in susceptible patients. To gain insight into its allergenic components, pollen proteins were analyzed by two-dimensional electrophoresis, immunoblotted with coconut pollen sensitive patient sera, followed by mass spectrometry of IgE reactive proteins. Coconut being largely unsequenced, a proteomic workflow has been devised that combines the conventional database-dependent analysis of tandem mass spectral data and manual de novo sequencing followed by a homology-based search for identifying the allergenic proteins. N-terminal acetylation helped to distinguish "b" ions from others, facilitating reliable sequencing. This led to the identification of 12 allergenic proteins. Cluster analysis with individual patient sera recognized vicilin-like protein as a major allergen, which was purified to assess its in vitro allergenicity and then partially sequenced. Other IgE-sensitive spots showed significant homology with well-known allergenic proteins such as 11S globulin, enolase, and isoflavone reductase along with a few which are reported as novel allergens. The allergens identified can be used as potential candidates to develop hypoallergenic vaccines, to design specific immunotherapy trials, and to enrich the repertoire of existing IgE reactive proteins.

Accurate identification of mass peaks for tandem mass spectra using MCMC model

In proteomics, many methods for the identification of proteins have been developed. However, because of limited known genome sequences, noisy data, incomplete ion sequences, and the accuracy of protein identification,it is challenging to identify peptides using tandem mass spectral data. Noise filtering and removing thus play a key role in accurate peptide identification from tandem mass spectra. In this paper, we employ a Bayesian model to identify proteins based on the prior information of bond cleavages. A Markov Chain Monte Carlo(MCMC)algorithm is used to simulate candidate peptides from the posterior distribution and to estimate the parameters for the Bayesian model. Our simulation and computational experimental results show that the model can identify peptide with a higher accuracy.

The H-Index of ‘An Approach to Correlate Tandem Mass Spectral Data of Peptides with Amino Acid Sequences in a Protein Database’

Over 20years ago a remarkable paper was published in the Journal of American Society for Mass Spectrometry. This paper from Jimmy Eng, Ashley McCormack, and John Yates described the use of protein databases to drive the interpretation of tandem mass spectra of peptides. This paper now has over 3660 citations and continues to average more than 260 per year over the last decade. This is an amazing scientific achievement. The reason for this is the paper was a cutting edge development at the moment in time when genomes of organisms were being sequenced, protein and peptide mass spectrometry was growing into the field of proteomics, and the power of computing was growing quickly in accordance with Moore's law. This work by the Yates lab grew in importance as genomics, proteomics, and computation all advanced and eventually resulted in the widely used SEQUEST algorithm and platform for the analysis of tandem mass spectrometry data. This commentary provides an analysis of the impact of this paper by analyzing the citations it has generated and the impact of these citing papers. Graphical Abstract ᅟ.

Unblocking the sink: improved CID-based analysis of phosphorylated peptides by enzymatic removal of the basic C-terminal residue.

A one-step enzymatic reaction for improving the collision-induced dissociation (CID)-based tandem mass spectrometry (MS/MS) analysis of phosphorylated peptides in an ion trap is presented. Carboxypeptidase-B (CBP-B) was used to selectively remove C-terminal arginine or lysine residues from phosphorylated tryptic/Lys-C peptides prior to their MS/MS analysis by CID with a Paul-type ion trap. Removal of this basic C-terminal residue served to limit the extent of gas-phase neutral loss of phosphoric acid (H3PO4), favoring the formation of diagnostic b and y ions as determined by an increase in both the number and relative intensities of the sequence-specific product ions. Such differential fragmentation is particularly valuable when the H3PO4 elimination is so predominant that localizing the phosphorylation site on the peptide sequence is hindered. Improvement in the quality of tandem mass spectral data generated by CID upon CBP-B treatment resulted in greater confidence both in assignment of the phosphopeptide primary sequence and for pinpointing the site of phosphorylation. Higher Mascot ion scores were also generated, combined with lower expectation values and higher delta scores for improved confidence in site assignment; Ascore values also improved. These results are rationalized in accordance with the accepted mechanisms for the elimination of H3PO4 upon low energy CID and insights into the factors dictating the observed dissociation pathways are presented. We anticipate this approach will be of utility in the MS analysis of phosphorylated peptides, especially when alternative electron-driven fragmentation techniques are not available. Figureᅟ Electronic supplementary materialThe online version of this article (doi:10.1007/s13361-013-0770-2) contains supplementary material, which is available to authorized users.