Identification Of Intact Glycopeptides Research Articles

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

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. 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/.

Computational Framework for Identification of Intact Glycopeptides in Complex Samples

Glycosylation is an important protein modification that involves enzymatic attachment of sugars to amino acid residues. Understanding the structure of these sugars and the effects of glycosylation are vital for developing indicators of disease development and progression. Although computational methods based on mass spectrometric data have proven to be effective in monitoring changes in the glycome, developing such methods for the glycoproteome are challenging, largely due to the inherent complexity in simultaneously studying glycan structures with their corresponding glycosylation sites. This paper introduces a computational framework for identifying intact N-linked glycopeptides, i.e. glycopeptides with N-linked glycans attached to their glycosylation sites, in complex proteome samples. Scoring algorithms are presented for tandem mass spectra of glycopeptides resulting from collision-induced dissociation (CID), higher-energy C-trap dissociation (HCD), and electron transfer dissociation (ETD) fragmentation modes. An empirical false-discovery rate estimation method, based on a target-decoy search approach, is derived for assigning confidence. The power of our method is further enhanced when multiple data sets are pooled together to increase identification confidence. Using this framework, 103 highly confident N-linked glycopeptides from 53 sites across 33 glycoproteins were identified in complex human serum proteome samples using conventional proteomic platforms with standard depletion of the 7-most abundant proteins. These results indicate that our method is ready to be used for characterizing site-specific protein glycosylation in complex samples.

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. 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.