MAGIC: An Automated N-Linked Glycoprotein Identification Tool Using a Y1-Ion Pattern Matching Algorithm and in Silico MS² Approach

Abstract

Glycosylation is a highly complex modification influencing the functions and activities of proteins. Interpretation of intact glycopeptide spectra is crucial but challenging. In this paper, we present a mass spectrometry-based automated glycopeptide identification platform (MAGIC) to identify peptide sequences and glycan compositions directly from intact N-linked glycopeptide collision-induced-dissociation spectra. The identification of the Y1 (peptideY0 + GlcNAc) ion is critical for the correct analysis of unknown glycoproteins, especially without prior knowledge of the proteins and glycans present in the sample. To ensure accurate Y1-ion assignment, we propose a novel algorithm called Trident that detects a triplet pattern corresponding to [Y0, Y1, Y2] or [Y0-NH3, Y0, Y1] from the fragmentation of the common trimannosyl core of N-linked glycopeptides. To facilitate the subsequent peptide sequence identification by common database search engines, MAGIC generates in silico spectra by overwriting the original precursor with the naked peptide m/z and removing all of the glycan-related ions. Finally, MAGIC computes the glycan compositions and ranks them. For the model glycoprotein horseradish peroxidase (HRP) and a 5-glycoprotein mixture, a 2- to 31-fold increase in the relative intensities of the peptide fragments was achieved, which led to the identification of 7 tryptic glycopeptides from HRP and 16 glycopeptides from the mixture via Mascot. In the HeLa cell proteome data set, MAGIC processed over a thousand MS(2) spectra in 3 min on a PC and reported 36 glycopeptides from 26 glycoproteins. Finally, a remarkable false discovery rate of 0 was achieved on the N-glycosylation-free Escherichia coli data set. MAGIC is available at http://ms.iis.sinica.edu.tw/COmics/Software_MAGIC.html .

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

“…In either case, users can with care develop data acquisition methods in which a series of monosaccharide losses is observed from the precursor ion. This will result in product ions corresponding to a series of peptide + monosaccharide units, for which the peptide mass can be calculated reliably [58,37]. The peptide mass can then be used to limit the range of peptide sequences and facilitate database search.…”

“…The MAGIC algorithm [37] automates the analysis of glycopeptide tandem mass spectra generated on beam-type (QTOF) instruments using collisional dissociation. The algorithm requires neither a glycosylated protein sequences nor a glycan database.…”

“…For many years, the general consensus in the field was that collisional dissociation of glycopeptides did not dissociate the peptide backbone to an appreciable degree. We now know that peptide bond dissociation occurs provided that sufficiently high collision energy is used [34–37]. Nonetheless, the relative abundances of such peptide backbone cleavage product ions resulting from collisional dissociation is relatively low.…”

A Brief Review of Bioinformatics Tools for Glycosylation Analysis by Mass Spectrometry

“…MAGIC also provides fast computing power for large-scale complex proteome data sets. 44) "GlycoSeq" uses a heuristic iterated glycan sequencing algorithm that incorporates prior knowledge of the N-linked glycan synthetic pathway to achieve rapid glycan sequencing. e limiting factor for GlycoSeq is glycan and glycopeptides is that peptides containing more than one potential glycosylation site cannot be distinguished due to the lack of signature fragment ions to identify peptide backbone information.…”

Maturing Glycoproteomics Technologies Provide Unique Structural Insights into the N-glycoproteome and Its Regulation in Health and Disease

“…The identification process was, until recently, largely driven by manual expert annotation of the resulting MS/MS spectra. As described in topical reviews (122,(147)(148)(149) (161). The proprietary Byonic (Protein Metrics), which recently was integrated as a node into Proteome Discoverer v2.0 (Thermo Scientific), appears to be the most widely used search engine in glycoproteomics because it allows semi-automated identification of N-and O-glycopeptides (and other post-translational modifications) using fine control of the search space in a probability based manner from high resolution MS/MS data (see Table I).…”