Novel LC-MS² product dependent parallel data acquisition function and data analysis workflow for sequencing and identification of intact glycopeptides.

Data dependent acquisition (DDA) of higher collision energy dissociation (HCD)-MS(2) followed by electron transfer dissociation (ETD)-MS(2) upon detection of glycan-specific oxonium is one of the better approaches in current LC-MS(2) analysis of intact glycopeptides. Although impressive numbers of glycopeptide identification by a direct database search have been reported, false positives remained high and difficult to determine. Even in cases when the peptide backbones were correctly identified, the exact glycan moieties were often erroneously assigned. Any attempt to fit the best glycosyl composition match by mass only is problematic particularly when the correct monoisotopic precursor cannot be determined unambiguously. Taking advantage of a new trihybrid Orbitrap configuration, we experimented with adding in a parallel ion trap collision induced dissociation (CID)-MS(2) data acquisition to the original HCD-product dependent (pd)-ETD function. We demonstrated the feasibility and advantage of identifying the peptide core ion directly from edited HCD-MS(2) data as an easy way to reduce false positives without compromising much sensitivity in intact glycopeptide positive spectrum matches. Importantly, the additional CID-MS(2) data allows one to validate the glycan assignment and provides insight into possible glycan modifications. Moreover, it is a viable alternative to deduce the glycopeptide backbone particularly in cases when the peptide backbone cannot be identified by ETD/HCD. The novel HCD-pd-CID/ETD workflow combines the best possible decision tree dependent MS(2) data acquisition modes currently available for glycoproteomics within a rapid Top Speed DDA duty cycle. Additional informatics can conceivably be developed to mine and integrate the rich information contained within for simultaneous N- and O-glycopeptide analysis.

An adaptive workflow coupled with Random Forest algorithm to identify intact N-glycopeptides detected from mass spectrometry.

MOTIVATION: Despite many attempts for algorithm development in recent years, automated identification of intact glycopeptides from LC-MS(2) spectral data is still a challenge in both sensitivity and precision. RESULTS: We implemented a supervised machine learning algorithm, Random Forest, in an automated workflow to identify N-glycopeptides using spectral features derived from ion trap-based LC-MS(2) data. The workflow streamlined high-confident N-glycopeptide spectral data and enabled adaptive model optimization with respect to different sampling strategies, training sample size and feature set. A critical evaluation of the features important for glycopeptide identification further facilitated effective feature selection for model improvement. Using split sample testing method from 577 high-confident N-glycopeptide spectral data, we demonstrated that an optimal true-positive rate, precision and false-positive rate of 73, 88 and 10%, respectively, can be attained for overall N-glycopeptide identification Availability and implementation: The workflow developed in this work and the application suite, Sweet-Heart, that the workflow supports for N-glycopeptide identification are available for download at http://sweet-heart.glycoproteomics.proteome.bc.sinica.edu.tw/.

Mass spectrometry-based quantitative proteomics for dissecting multiplexed redox cysteine modifications in nitric oxide-protected cardiomyocyte under hypoxia.

AIMS: Distinctive states of redox-dependent cysteine (Cys) modifications are known to regulate signaling homeostasis under various pathophysiological conditions, including myocardial injury or protection in response to ischemic stress. Recent evidence further implicates a dynamic interplay among these modified forms following changes in cellular redox environment. However, a precise delineation of multiplexed Cys modifications in a cellular context remains technically challenging. To this end, we have now developed a mass spectrometry (MS)-based quantitative approach using a set of novel iodoacetyl-based Cys-reactive isobaric tags (irreversible isobaric iodoacetyl Cys-reactive tandem mass tag [iodoTMT]) endowed with unique irreversible Cys-reactivities. RESULTS: We have established a sequential iodoTMT-switch procedure coupled with efficient immunoenrichment and advanced shotgun liquid chromatography-MS/MS analysis. This workflow allows us to differentially quantify the multiple redox-modified forms of a Cys site in the original cellular context. In one single analysis, we have identified over 260 Cys sites showing quantitative differences in multiplexed redox modifications from the total lysates of H9c2 cardiomyocytes experiencing hypoxia in the absence and presence of S-nitrosoglutathione (GSNO), indicative of a distinct pattern of individual susceptibility to S-nitrosylation or S-glutathionylation. Among those most significantly affected are proteins functionally implicated in hypoxic damage from which we showed that GSNO would protect. INNOVATION: We demonstrate for the first time how quantitative analysis of various Cys-redox modifications occurring in biological samples can be performed precisely and simultaneously at proteomic levels. CONCLUSION: We have not only developed a new approach to map global Cys-redoxomic regulation in vivo, but also provided new evidences implicating Cys-redox modifications of key molecules in NO-mediated ischemic cardioprotection.

Sweet-Heart - an integrated suite of enabling computational tools for automated MS2/MS3 sequencing and identification of glycopeptides.

High efficiency identification of intact glycopeptides from a shotgun glycoproteomic LC-MS(2) dataset remains problematic. The prevalent mode of identifying the de-N-glycosylated peptides is littered with false positives and addresses only the issue of site occupancy. Here, we present Sweet-Heart, a computational tool set developed to tackle the heart of the problems in MS(2) sequencing of glycopeptide. It accepts low resolution and low accuracy ion trap MS(2) data, filters for glycopeptides, couples knowledge-based de novo interpretation of glycosylation-dependent fragmentation pattern with protein database search, and uses machine-learning algorithm to score the computed glyco and peptide combinations. Higher ranking candidates are then compiled into a list of MS(2)/MS(3) entries to drive subsequent rounds of targeted MS(3) sequencing of putative peptide backbone, allowing its validation by database search in a fully automated fashion. With additional fishing out of all related glycoforms and final data integration, the platform proves to be sufficiently sensitive and selective, conducive to novel glycosylation discovery, and robust enough to discriminate, among others, N-glycolyl neuraminic acid/fucose from N-acetyl neuraminic acid/hexose. A critical appraisal of its computing performance shows that Sweet-Heart allows high sensitivity comprehensive mapping of site-specific glycosylation for isolated glycoproteins and facilitates analysis of glycoproteomic data. BIOLOGICAL SIGNIFICANCE: The biological relevance of protein site-specific glycosylation cannot be meaningfully addressed without first defining its pattern by direct analysis of glycopeptides. Sweet-Heart is a novel suite of computational tools allowing for automated analysis of mass spectrometry-based glycopeptide sequencing data. It is developed to accept ion trap MS2/MS3 data and uses a machine learning algorithm to score and rank the candidate peptide core and glycosyl substituent combinations. By eliminating the need for manual, labor-intensive, and subjective data interpretation, it facilitates high throughput shotgun glycoproteomic data analysis and is conducive to identification of unanticipated glycosylation, as demonstrated here with a recombinant EGFR.

Evaluation of Drosophila metabolic labeling strategies for in vivo quantitative proteomic analyses with applications to early pupa formation and amino acid starvation.

Although stable isotope labeling by amino acids in cell culture (SILAC)-based quantitative proteomics was first developed as a cell culture-based technique, stable isotope-labeled amino acids have since been successfully introduced in vivo into select multicellular model organisms by manipulating the feeding diets. An earlier study by others has demonstrated that heavy lysine labeled Drosophila melanogaster can be derived by feeding with an exclusive heavy lysine labeled yeast diet. In this work, we have further evaluated the use of heavy lysine and/or arginine for metabolic labeling of fruit flies, with an aim to determine its respective quantification accuracy and versatility. In vivo conversion of heavy lysine and/or heavy arginine to several nonessential amino acids was observed in labeled flies, leading to distorted isotope pattern and underestimated heavy to light ratio. These quantification defects can nonetheless be rectified at protein level using the normalization function. The only caveat is that such a normalization strategy may not be suitable for every biological application, particularly when modified peptides need to be individually quantified at peptide level. In such cases, we showed that peptide ratios calculated from the summed intensities of all isotope peaks are less affected by the heavy amino acid conversion and therefore less sequence-dependent and more reliable. Applying either the single Lys8 or double Lys6/Arg10 metabolic labeling strategy to flies, we quantitatively mapped the proteomic changes during the onset of metamorphosis and upon amino acid deprivation. The expression of a number of steroid hormone 20-hydroxyecdysone regulated proteins was found to be changed significantly during larval-pupa transition, while several subunits of the V-ATPase complex and components regulating actomyosin were up-regulated under starvation-induced autophagy conditions.