Precise, fast and comprehensive analysis of intact glycopeptides and modified glycans with pGlyco3.

Great advances have been made in mass spectrometric data interpretation for intact glycopeptide analysis. However, accurate identification of intact glycopeptides and modified saccharide units at the site-specific level and with fast speed remains challenging. Here, we present a glycan-first glycopeptide search engine, pGlyco3, to comprehensively analyze intact N- and O-glycopeptides, including glycopeptides with modified saccharide units. A glycan ion-indexing algorithm developed for glycan-first search makes pGlyco3 5-40 times faster than other glycoproteomic search engines without decreasing accuracy or sensitivity. By combining electron-based dissociation spectra, pGlyco3 integrates a dynamic programming-based algorithm termed pGlycoSite for site-specific glycan localization. Our evaluation shows that the site-specific glycan localization probabilities estimated by pGlycoSite are suitable to localize site-specific glycans. With pGlyco3, we confidently identified N-glycopeptides and O-mannose glycopeptides that were extensively modified by ammonia adducts in yeast samples. The freely available pGlyco3 is an accurate and flexible tool that can be used to identify glycopeptides and modified saccharide units.

Novel methods in glycomics: a 2019 update.

Introduction: Glycomics, which aims to define the glycome of a biological system to better assess the biological attributes of the glycans, has attracted increasing interest. However, the complexity and diversity of glycans present challenging barriers to glycome definition. Technological advances are major drivers in glycomics.Areas covered: This review summarizes the main methods and emphasizes the most recent advances in mass spectrometry-based methods regarding glycomics following the general workflow in glycomic analysis.Expert opinion: Recent mass spectrometry-based technological advances have significantly lowered the barriers in glycomics. The field of glycomics is moving toward both generic and precise analysis.

Straightforward and Highly Efficient Strategy for Hepatocellular Carcinoma Glycoprotein Biomarker Discovery Using a Nonglycopeptide-Based Mass Spectrometry Pipeline.

Efficient detection of aberrant glycoproteins in serum is particularly important for biomarker discovery. However, direct quantitation of glycoproteins in serum remains technically challenging because of the extraordinary complexity of the serum proteome. In the current work, we proposed a straightforward and highly efficient strategy by using the nonglycopeptides releasing from the specifically enriched glycoproteins for targeted glycoprotein quantification. With this so-called nonglycopeptide-based mass spectrometry (NGP-MS) strategy, a powerful and nondiscriminatory pipeline for hepatocellular carcinoma (HCC) glycoprotein biomarker discovery, verification, and validation has been developed. First, a data set of 234 NGPs was strictly established for multiple-reaction monitoring (MRM) quantification in serum. Second, the NGPs enriched from 20 HCC serum mixtures and 20 normal serum mixtures were labeled with mTRAQ reagents (Δ0 and Δ8, respectively) to find the differentially expressed glycoproteins in HCC. A total of 97 glycoprotein candidates were preliminarily screened and submitted for absolute quantitation with NGP-based stable-isotope-labeled (SID)-MRM in the individual samples of 38 HCC serum and 24 normal controls. Finally, 21 glycoproteins were absolutely quantified with high quality. The diagnostic sensitivity results showed that three glycoproteins, ß-2-glycoprotein 1 (APOH), α-1-acid glycoprotein 2 (ORM2), and complement C3 (C3), could be used for the discrimination between HCC patients and healthy people. A novel glycoprotein biomarker panel [APOH, ORM2, C3, and α-fetoprotein (AFP)] has proven to outperform AFP, the known HCC serum biomarker, alone, in this study. We believe that this strategy and the panel of glycoproteins might hold great clinical value for HCC detection in the future.

pGlyco 2.0 enables precision N-glycoproteomics with comprehensive quality control and one-step mass spectrometry for intact glycopeptide identification.

The precise and large-scale identification of intact glycopeptides is a critical step in glycoproteomics. Owing to the complexity of glycosylation, the current overall throughput, data quality and accessibility of intact glycopeptide identification lack behind those in routine proteomic analyses. Here, we propose a workflow for the precise high-throughput identification of intact N-glycopeptides at the proteome scale using stepped-energy fragmentation and a dedicated search engine. pGlyco 2.0 conducts comprehensive quality control including false discovery rate evaluation at all three levels of matches to glycans, peptides and glycopeptides, improving the current level of accuracy of intact glycopeptide identification. The N-glycoproteome of samples metabolically labeled with 15N/13C were analyzed quantitatively and utilized to validate the glycopeptide identification, which could be used as a novel benchmark pipeline to compare different search engines. Finally, we report a large-scale glycoproteome dataset consisting of 10,009 distinct site-specific N-glycans on 1988 glycosylation sites from 955 glycoproteins in five mouse tissues.Protein glycosylation is a heterogeneous post-translational modification that generates greater proteomic diversity that is difficult to analyze. Here the authors describe pGlyco 2.0, a workflow for the precise one step identification of intact N-glycopeptides at the proteome scale.