Automated N-glycopeptide identification using a combination of single- and tandem-MS

Recent advances in N-glycan biomarker discovery among human diseases

N-glycans play important roles in a variety of biological processes. In recent years, analytical technologies with high resolution and sensitivity have advanced exponentially, enabling analysts to investigate N-glycomic changes in different states. Specific glycan and glycosylation signatures have been identified in multiple diseases, including cancer, autoimmune diseases, nervous system disorders, and metabolic and cardiovascular diseases. These glycans demonstrate comparable or superior indicating capability in disease diagnosis and prognosis over routine biomarkers. Moreover, synchronous glycan alterations concurrent with disease initiation and progression provide novel insights into pathogenetic mechanisms and potential treatment targets. This review elucidates the biological significance of N-glycans, compares the existing glycomic technologies, and delineates the clinical performance of N-glycans across a range of diseases.

Glycopeptide characterization of Sf9-derived SARS-CoV-2 spike protein recombinant vaccine candidates expedited by the use of glycopeptide libraries

RATIONALE

We report the N-glycosylation pattern of Sf9 insect cell-derived recombinant spike proteins being developed as candidate vaccine antigens for SARS-CoV-2 (COVID-19) (Sanofi). The method has been optimised to produce peptides with single, isolated glycosylation sites using multiple protease digests. The development and use of glycopeptide libraries from previous developmental phases allowed for faster analysis than processing datasets from individual batches from first principles.

METHODS

Purified spike proteins were reduced, alkylated, and digested with proteolytic enzymes. Three different protease digests were utilised to generate peptides with isolated glycosylation sites. The glycopeptides were then analysed using a Waters Q-TOF while using a data-dependent acquisition mass spectrometry experiment. Glycopeptide mapping data processing and glycan classification were performed using Genedata Expressionist via a specialised workflow that used libraries of previously detected glycopeptides to greatly reduce processing time.

RESULTS

Two different spike proteins from six manufacturers were analysed. There was a strong similarity at each site across batches and manufacturers. The majority of the glycans present were of the truncated class, although at sites N61, N234, and N717/714 high mannose structures were dominant and at N1173/1170 aglycosylation was dominant for both variant proteins. A comparison was performed on a commercially available spike protein and our results were found to be similar to those of earlier reports.

CONCLUSIONS

Our data clearly show that the overall glycosylation pattern of both spike protein variants was highly similar from batch to batch, and between materials produced at different manufacturing facilities. The use of our glycopeptide libraries greatly expedited the generation of site-specific glycan occupancy data for a large glycoprotein. We compared our method with previously obtained data from a commercially available insect cell-derived spike protein and the results were comparable to published findings.

High-Throughput Glycomic Methods

Glycomics aims to identify the structure and function of the glycome, the complete set of oligosaccharides (glycans), produced in a given cell or organism, as well as to identify genes and other factors that govern glycosylation. This challenging endeavor requires highly robust, sensitive, and potentially automatable analytical technologies for the analysis of hundreds or thousands of glycomes in a timely manner (termed high-throughput glycomics). This review provides a historic overview as well as highlights recent developments and challenges of glycomic profiling by the most prominent high-throughput glycomic approaches, with N-glycosylation analysis as the focal point. It describes the current state-of-the-art regarding levels of characterization and most widely used technologies, selected applications of high-throughput glycomics in deciphering glycosylation process in healthy and disease states, as well as future perspectives.

Protonated α-N-Acetyl Galactose Glycopeptide Dissociation Chemistry

We recently provided mass spectrometric, H/D labeling, and computational evidence of pyranose to furanose N-acetylated ion isomerization reactions that occurred prior to glycosidic bond cleavage in both O- and N-linked glycosylated amino acid model systems (Guan et al. Phys. Chem. Chem. Phys., 2021, 23, 23256-23266). These reactions occurred irrespective of the glycosidic linkage stereochemistry (α or β) and the N-acetylated hexose structure (GlcNAc or GalNAc). In the present article, we test the generality of the preceding findings by examining threonyl α-GalNAc-glycosylated peptides. We utilize computational chemistry to compare the various dissociation and isomerization pathways accessible with collisional activation. We then interrogate the structure(s) of the resulting charged glycan and peptide fragments with infrared "action" spectroscopy. Isomerization of the original pyranose, the protonated glycopeptide [AT(GalNAc)A+H]⁺, is predicted to be facile compared to direct dissociation, as is the glycosidic bond cleavage of the newly formed furanose form, i.e., furanose oxazolinium ion structures are predicted to predominate. IR action spectra for the m/z 204, C₈H₁₄N₁O₅⁺, glycan fragment population support this prediction. The IR action spectra of the complementary m/z 262 peptide fragment were assigned as a mixture of the lowest-energy structures of [ATA+H]⁺ consistent with the literature. If general, the change to a furanose m/z 204 product ion structure fundamentally alters the ion population available for MS³ dissociation and glycopeptide sequence identification.

Evidence of gas-phase pyranose-to-furanose isomerization in protonated peptidoglycans

Peptidoglycans are diverse co- and post-translational modifications of key importance in myriad biological processes. Mass spectrometry is employed to infer their biomolecular sequences and stereochemisties, but little is known about the critical gas-phase dissociation processes involved. Here, using tandem mass spectrometry (MS/MS and MSⁿ), isotopic labelling and high-level simulations, we identify and characterize a facile isomerization reaction that produces furanose N-acetylated ions. This reaction occurs for both O- and N-linked peptidoglycans irrespective of glycosidic linkage stereochemistry (α/β). Dissociation of the glycosidic and other bonds thus occur from the furanose isomer critically altering the reaction feasibility and product ion structures.

Recent advances in glycoinformatic platforms for glycomics and glycoproteomics

Protein glycosylation is the most complex and prevalent post-translation modification in terms of the number of proteins modified and the diversity generated. To understand the functional roles of glycoproteins it is important to gain an insight into the repertoire of oligosaccharides present. The comparison and relative quantitation of glycoforms combined with site-specific identification and occupancy are necessary steps in this direction. Computational platforms have continued to mature assisting researchers with the interpretation of such glycomics and glycoproteomics data sets, but frequently support dedicated workflows and users rely on the manual interpretation of data to gain insights into the glycoproteome. The growth of site-specific knowledge has also led to the implementation of machine-learning algorithms to predict glycosylation which is now being integrated into glycoproteomics pipelines. This short review describes commercial and open-access databases and software with an emphasis on those that are actively maintained and designed to support current analytical workflows.

Global and site-specific analysis of protein glycosylation in complex biological systems with Mass Spectrometry

Protein glycosylation is ubiquitous in biological systems and plays essential roles in many cellular events. Global and site-specific analysis of glycoproteins in complex biological samples can advance our understanding of glycoprotein functions and cellular activities. However, it is extraordinarily challenging because of the low abundance of many glycoproteins and the heterogeneity of glycan structures. The emergence of mass spectrometry (MS)-based proteomics has provided us an excellent opportunity to comprehensively study proteins and their modifications, including glycosylation. In this review, we first summarize major methods for glycopeptide/glycoprotein enrichment, followed by the chemical and enzymatic methods to generate a mass tag for glycosylation site identification. We next discuss the systematic and quantitative analysis of glycoprotein dynamics. Reversible protein glycosylation is dynamic, and systematic study of glycoprotein dynamics helps us gain insight into glycoprotein functions. The last part of this review focuses on the applications of MS-based proteomics to study glycoproteins in different biological systems, including yeasts, plants, mice, human cells, and clinical samples. Intact glycopeptide analysis is also included in this section. Because of the importance of glycoproteins in complex biological systems, the field of glycoproteomics will continue to grow in the next decade. Innovative and effective MS-based methods will exponentially advance glycoscience, and enable us to identify glycoproteins as effective biomarkers for disease detection and drug targets for disease treatment. © 2019 Wiley Periodicals, Inc. Mass Spec Rev 9999: XX-XX, 2019.

DecoyDeveloper: An On-Demand, De Novo Decoy Glycopeptide Generator

Glycopeptide analysis is a growing field that is struggling to adopt effective, automated tools. Many creative workflows and software apps have emerged recently that offer promising capabilities for assigning glycopeptides to MS data in an automated fashion. The effectiveness of these tools is best measured and improved by determining how often they would select a glycopeptide decoy as a spectral match, instead of its correct assignment; yet generating the appropriate number and type of glycopeptide decoys can be challenging. To address this need, we have designed DecoyDeveloper, an on-demand decoy glycopeptide generator that can produce a high volume of decoys with low mass differences. DecoyDeveloper has a simple user interface and is capable of producing large sets of decoys containing complete, biologically relevant glycan and peptide sequences. We demonstrate the tool's efficiency by applying it to a set of 80 glycopeptide targets. This tool is freely available and can be found at http://glycopro.chem.ku.edu/J1.php .

GlycopeptideGraphMS: Improved Glycopeptide Detection and Identification by Exploiting Graph Theoretical Patterns in Mass and Retention Time

The leading proteomic method for identifying N-glycosylated peptides is liquid chromatography coupled with tandem fragmentation mass spectrometry (LCMS/MS) followed by spectral matching of MS/MS fragment masses to a database of possible glycan and peptide combinations. Such database-dependent approaches come with challenges such as needing high-quality informative MS/MS spectra, ignoring unexpected glycan or peptide sequences, and making incorrect assignments because some glycan combinations are equivalent in mass to amino acids. To address these challenges, we present GlycopeptideGraphMS, a graph theoretical bioinformatic approach complementary to the database-dependent method. Using the AXL receptor tyrosine kinase (AXL) as a model glycoprotein with multiple N-glycosylation sites, we show that those LCMS features that could be grouped into graph networks on the basis of glycan mass and retention time differences were actually N-glycopeptides with the same peptide backbone but different N-glycan compositions. Conversely, unglycosylated peptides did not exhibit this grouping behavior. Furthermore, MS/MS sequencing of the glycan and peptide composition of just one N-glycopeptide in the graph was sufficient to identify the rest of the N-glycopeptides in the graph. By validating the identifications with exoglycosidase cocktails and MS/MS fragmentation, we determined the experimental false discovery rate of identifications to be 2.21%. GlycopeptideGraphMS detected more than 500 unique N-glycopeptides from AXL, triple the number found by a database search with Byonic software, and detected incorrect assignments due to a nonspecific protease cleavage. This method overcomes some limitations of the database approach and is a step closer to comprehensive automated glycoproteomics.

An Improved Approach for N-Linked Glycan Structure Identification from HCD MS/MS Spectra

Glycosylation is a frequently observed post-translational modification on proteins. Currently, tandem mass spectrometry (MS/MS) serves as an efficient analytical technique for characterizing structures of oligosaccharides. However, developing effective computational approaches for identifying glycan structures from mass spectra is still a great challenge in glycoproteomics research. In this study, we proposed an approach for matching the input spectra with glycan structures acquired from a glycan structure database by incorporating a de novo sequencing assisted ranking scheme. The proposed approach is implemented as a software tool, GlycoNovoDB, for automated glycan structure identification from HCD MS/MS of glycopeptides. Experimental results showed that GlycoNovoDB can identify glycans effectively and has better performance than our previously proposed de novo sequencing algorithm as well as another software GlycoMaster DB.

Algorithms and design strategies towards automated glycoproteomics analysis

Glycoproteomics involves the study of glycosylation events on protein sequences ranging from purified proteins to whole proteome scales. Understanding these complex post-translational modification (PTM) events requires elucidation of the glycan moieties (monosaccharide sequences and glycosidic linkages between residues), protein sequences, as well as site-specific attachment of glycan moieties onto protein sequences, in a spatial and temporal manner in a variety of biological contexts. Compared with proteomics, bioinformatics for glycoproteomics is immature and many researchers still rely on tedious manual interpretation of glycoproteomics data. As sample preparation protocols and analysis techniques have matured, the number of publications on glycoproteomics and bioinformatics has increased substantially; however, the lack of consensus on tool development and code reuse limits the dissemination of bioinformatics tools because it requires significant effort to migrate a computational tool tailored for one method design to alternative methods. This review discusses algorithms and methods in glycoproteomics, and refers to the general proteomics field for potential solutions. It also introduces general strategies for tool integration and pipeline construction in order to better serve the glycoproteomics community. © 2016 Wiley Periodicals, Inc. Mass Spec Rev 36:475-498, 2017.

Glycoprotein Enrichment Analytical Techniques: Advantages and Disadvantages

Protein glycosylation is one of the most important posttranslational modifications. Numerous biological functions are related to protein glycosylation. However, analytical challenges remain in the glycoprotein analysis. To overcome the challenges associated with glycoprotein analysis, many analytical techniques were developed in recent years. Enrichment methods were used to improve the sensitivity of detection, while HPLC and mass spectrometry methods were developed to facilitate the separation of glycopeptides/proteins and enhance detection, respectively. Fragmentation techniques applied in modern mass spectrometers allow the structural interpretation of glycopeptides/proteins, while automated software tools started replacing manual processing to improve the reliability and throughput of the analysis. In this chapter, the current methodologies of glycoprotein analysis were discussed. Multiple analytical techniques are compared, and advantages and disadvantages of each technique are highlighted.

GlycoPep MassList: software to generate massive inclusion lists for glycopeptide analyses

Protein glycosylation drives many biological processes and serves as markers for disease; therefore, the development of tools to study glycosylation is an essential and growing area of research. Mass spectrometry can be used to identify both the glycans of interest and the glycosylation sites to which those glycans are attached, when proteins are proteolytically digested and their glycopeptides are analyzed by a combination of high-resolution mass spectrometry (MS) and tandem mass spectrometry (MS/MS) methods. One major challenge in these experiments is collecting the requisite MS/MS data. The digested glycopeptides are often present in complex mixtures and in low abundance, and the most commonly used approach to collect MS/MS data on these species is data-dependent acquisition (DDA), where only the most intense precursor ions trigger MS/MS. DDA results in limited glycopeptide coverage. Semi-targeted data acquisition is an alternative experimental approach that can alleviate this difficulty. However, due to the massive heterogeneity of glycopeptides, it is not obvious how to expediently generate inclusion lists for these types of analyses. To solve this problem, we developed the software tool GlycoPep MassList, which can be used to generate inclusion lists for liquid chromatography tandem-mass spectrometry (LC-MS/MS) experiments. The utility of the software was tested by conducting comparisons between semi-targeted and untargeted data-dependent analysis experiments on a variety of proteins, including IgG, a protein whose glycosylation must be characterized during its production as a biotherapeutic. When the GlycoPep MassList software was used to generate inclusion lists for LC-MS/MS experiments, more unique glycopeptides were selected for fragmentation. Generally, ∼30 % more unique glycopeptides can be analyzed per protein, in the simplest cases, with low background. In cases where background ions from proteins or other interferents are high, usage of an inclusion list is even more advantageous. The software is freely publically accessible. Graphical abstract Software increases the number of glycopeptides that get selected for MS/MS analysis.

Quantitative profiling of glycans and glycopeptides: an informatics' perspective

Experimental techniques to identify and quantify glycan structures in a given sample are continuously improving. However, as they advance data analysis and annotation seems to become more complex. To address this issue, much progress has been made in developing software for interpretation of quantitative glycan profiles. Here, we focus on these informatics tools for high/ultra performance liquid chromatography (H/UPLC), mass spectrometry (MS), tandem mass spectrometry (MSⁿ) and combinations thereof. Software for biomarker discovery, pathway, genomic and disease analysis and a final note on some future prospects for glycoinformatics are also mentioned.

Automated Glycan Sequencing from Tandem Mass Spectra of N-Linked Glycopeptides

Mass spectrometry has become a routine experimental tool for proteomic biomarker analysis of human blood samples, partly due to the large availability of informatics tools. As one of the most common protein post-translational modifications (PTMs) in mammals, protein glycosylation has been observed to alter in multiple human diseases and thus may potentially be candidate markers of disease progression. While mass spectrometry instrumentation has seen advancements in capabilities, discovering glycosylation-related markers using existing software is currently not straightforward. Complete characterization of protein glycosylation requires the identification of intact glycopeptides in samples, including identification of the modification site as well as the structure of the attached glycans. In this paper, we present GlycoSeq, an open-source software tool that implements a heuristic iterated glycan sequencing algorithm coupled with prior knowledge for automated elucidation of the glycan structure within a glycopeptide from its collision-induced dissociation tandem mass spectrum. GlycoSeq employs rules of glycosidic linkage as defined by glycan synthetic pathways to eliminate improbable glycan structures and build reasonable glycan trees. We tested the tool on two sets of tandem mass spectra of N-linked glycopeptides cell lines acquired from breast cancer patients. After employing enzymatic specificity within the N-linked glycan synthetic pathway, the sequencing results of GlycoSeq were highly consistent with the manually curated glycan structures. Hence, GlycoSeq is ready to be used for the characterization of glycan structures in glycopeptides from MS/MS analysis. GlycoSeq is released as open source software at https://github.com/chpaul/GlycoSeq/ .

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.

Advancements in mass spectrometry-based glycoproteomics and glycomics

Protein N-glycosylation plays a crucial role in a considerable number of important biological processes. Research studies on glycoproteomes and glycomes have already characterized many glycoproteins and glycans associated with cell development, life cycle, and disease progression. Mass spectrometry (MS) is the most powerful tool for identifying biomolecules including glycoproteins and glycans, however, utilizing MS-based approaches to identify glycoproteomes and glycomes is challenging due to the technical difficulties associated with glycosylation analysis. In this review, we summarize the most recent developments in MS-based glycoproteomics and glycomics, including a discussion on the development of analytical methodologies and strategies used to explore the glycoproteome and glycome, as well as noteworthy biological discoveries made in glycoproteome and glycome research. This review places special emphasis on China, where scientists have made sizeable contributions to the literature, as advancements in glycoproteomics and glycomincs are occurring quite rapidly.

Photo-lysine captures proteins that bind lysine post-translational modifications

Post-translational modifications (PTMs) have key roles in regulating protein-protein interactions in living cells. However, it remains a challenge to identify these PTM-mediated interactions. Here we develop a new lysine-based photo-reactive amino acid, termed photo-lysine. We demonstrate that photo-lysine, which is readily incorporated into proteins by native mammalian translation machinery, can be used to capture and identify proteins that recognize lysine PTMs, including 'readers' and 'erasers' of histone modifications.

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.

Engaging challenges in glycoproteomics: recent advances in MS-based glycopeptide analysis

The proteomic analysis of glycosylation is uniquely challenging. The numerous and varied biological roles of protein-linked glycans have fueled a tremendous demand for technologies that enable rapid, in-depth structural examination of glycosylated proteins in complex biological systems. In turn, this demand has driven many innovations in wide ranging fields of bioanalytical science. This review will summarize key developments in glycoprotein separation and enrichment, glycoprotein proteolysis strategies, glycopeptide separation and enrichment, the role of mass measurement accuracy in glycopeptide detection, glycopeptide ion dissociation methods for MS/MS, and informatic tools for glycoproteomic analysis. In aggregate, this selection of topics serves to encapsulate the present status of MS-based analytical technologies for engaging the challenges of glycoproteomic analysis.

Carbohydrates on Proteins: Site-Specific Glycosylation Analysis by Mass Spectrometry

Glycosylation on proteins adds complexity and versatility to these biologically vital macromolecules. To unveil the structure-function relationship of glycoproteins, glycopeptide-centric analysis using mass spectrometry (MS) has become a method of choice because the glycan is preserved on the glycosylation site and site-specific glycosylation profiles of proteins can be readily determined. However, glycopeptide analysis is still challenging given that glycopeptides are usually low in abundance and relatively difficult to detect and the resulting data require expertise to analyze. Viewing the urgent need to address these challenges, emerging methods and techniques are being developed with the goal of analyzing glycopeptides in a sensitive, comprehensive, and high-throughput manner. In this review, we discuss recent advances in glycoprotein and glycopeptide analysis, with topics covering sample preparation, analytical separation, MS and tandem MS techniques, as well as data interpretation and automation.

Automating mass spectrometry-based quantitative glycomics using aminoxy tandem mass tag reagents with SimGlycan

Protein glycosylation is a common post-translational modification, which serves critical roles in the biological processes of organisms. Monitoring of changes in the abundance and structure of glycans may be necessary to explain the correlations between protein glycosylation and various diseases. Hence, the growing importance of glycoproteomics necessitates in-depth qualitative and quantitative studies of glycans. One of the emerging trends in glycomics research is the innovation related to accurate mass spectrometry based quantitative analysis of glycans. Recently, we have introduced aminoxyTMT reagents, which enable efficient relative quantitation of carbohydrates, improved glycan ionization efficiency and increased analytical throughput. These reagents can be used for quantitative analysis of N-glycans by direct infusion or liquid chromatography (LC)-coupled to electrospray ionization mass spectrometry (ESI-MS). However, unlike in proteomics, one of the major challenges left unaddressed is the lack of informatics tools to automate the qualitative and quantitative analysis of generated data. This analysis typically includes identification/quantitation of glycans using MS/MS data and differential analysis across biological samples. We have developed software modules to streamline such protocols for quantitative analysis of aminoxyTMT labeled-glycans derived from complex mixtures. This article is part of a Special Issue entitled: Proteomics in India.

Integration of systems glycobiology with bioinformatics toolboxes, glycoinformatics resources, and glycoproteomics data

The glycome constitutes the entire complement of free carbohydrates and glycoconjugates expressed on whole cells or tissues. 'Systems Glycobiology' is an emerging discipline that aims to quantitatively describe and analyse the glycome. Here, instead of developing a detailed understanding of single biochemical processes, a combination of computational and experimental tools are used to seek an integrated or 'systems-level' view. This can explain how multiple biochemical reactions and transport processes interact with each other to control glycome biosynthesis and function. Computational methods in this field commonly build in silico reaction network models to describe experimental data derived from structural studies that measure cell-surface glycan distribution. While considerable progress has been made, several challenges remain due to the complex and heterogeneous nature of this post-translational modification. First, for the in silico models to be standardized and shared among laboratories, it is necessary to integrate glycan structure information and glycosylation-related enzyme definitions into the mathematical models. Second, as glycoinformatics resources grow, it would be attractive to utilize 'Big Data' stored in these repositories for model construction and validation. Third, while the technology for profiling the glycome at the whole-cell level has been standardized, there is a need to integrate mass spectrometry derived site-specific glycosylation data into the models. The current review discusses progress that is being made to resolve the above bottlenecks. The focus is on how computational models can bridge the gap between 'data' generated in wet-laboratory studies with 'knowledge' that can enhance our understanding of the glycome.

Software eyes for protein post-translational modifications

Post-translational modifications (PTMs) are critical to almost all aspects of complex processes of the cell. Identification of PTMs is one of the biggest challenges for proteomics, and there have been many computational studies for the analysis of PTMs from tandem mass spectrometry (MS/MS). Most early PTM identification studies have been performed by matching MS/MS data to protein databases, using database search tools, but they are prohibitively slow when a large number of PTMs is given as a search parameter. In this article, we present recent developments to search for more types of PTMs and to speed up the search, and discuss many computational issues and solutions in terms of identifying multiply modified peptides or searching for all possible modifications at once in unrestrictive mode. Apart from the most common type of PTMs involving covalent addition of functional groups to proteins, PTMs such as disulfide linkage require dedicated software for the analysis because they may involve cross-linking between two different parts of proteins. Finally, methods for identification of protein disulfide bonds are presented.

New glycoproteomics software, GlycoPep Evaluator, generates decoy glycopeptides de novo and enables accurate false discovery rate analysis for small data sets

Glycoproteins are biologically significant large molecules that participate in numerous cellular activities. In order to obtain site-specific protein glycosylation information, intact glycopeptides, with the glycan attached to the peptide sequence, are characterized by tandem mass spectrometry (MS/MS) methods such as collision-induced dissociation (CID) and electron transfer dissociation (ETD). While several emerging automated tools are developed, no consensus is present in the field about the best way to determine the reliability of the tools and/or provide the false discovery rate (FDR). A common approach to calculate FDRs for glycopeptide analysis, adopted from the target-decoy strategy in proteomics, employs a decoy database that is created based on the target protein sequence database. Nonetheless, this approach is not optimal in measuring the confidence of N-linked glycopeptide matches, because the glycopeptide data set is considerably smaller compared to that of peptides, and the requirement of a consensus sequence for N-glycosylation further limits the number of possible decoy glycopeptides tested in a database search. To address the need to accurately determine FDRs for automated glycopeptide assignments, we developed GlycoPep Evaluator (GPE), a tool that helps to measure FDRs in identifying glycopeptides without using a decoy database. GPE generates decoy glycopeptides de novo for every target glycopeptide, in a 1:20 target-to-decoy ratio. The decoys, along with target glycopeptides, are scored against the ETD data, from which FDRs can be calculated accurately based on the number of decoy matches and the ratio of the number of targets to decoys, for small data sets. GPE is freely accessible for download and can work with any search engine that interprets ETD data of N-linked glycopeptides. The software is provided at https://desairegroup.ku.edu/research.

GlycoMaster DB: software to assist the automated identification of N-linked glycopeptides by tandem mass spectrometry

Glycosylation is one of the most commonly observed post-translational modifications (PTMs) in eukaryotes. It is believed that more than 50% eukaryotic proteins are glycosylated. To reveal the biological functions of protein-linked glycans involved in numerous biological processes, the high-throughput identification of both glycoproteins and the attached glycan structures becomes fundamentally important. Tandem mass spectrometry (MS/MS) is an effective method for glycoproteomic analysis because of its high sensitivity and selectivity. Two experimental approaches exist to obtain MS/MS spectral data of glycopeptides. One consists of isolating glycans from glycopeptides and generating MS/MS spectra of the glycans and peptides separately. The other approach produces spectra directly from intact glycopeptides. The latter approach has the advantage of retaining the glycosylation site information. However, the spectral data cannot be readily analyzed because of the lack of software specifically designed for the identification of intact glycopeptides. To address this need, we developed a novel software tool, GlycoMaster DB, to assist the automated and high-throughput identification of intact N-linked glycopeptides from MS/MS spectra. The software simultaneously searches a protein sequence database and a glycan structure database to find the best pair of peptide and glycan for each input spectrum. GlycoMaster DB can analyze mass spectral data produced with HCD/ETD mixed fragmentation, where HCD spectra are used to identify glycans and ETD spectra are used to determine peptide sequences. When only HCD spectra are available, GlycoMaster DB can still help to identify the glycans, and a list of possible peptide sequences are reported according to the accurate precursor mass and the N-linked glycopeptide sequon. GlycoMaster DB is freely accessible at http://www-novo.cs.uwaterloo.ca:8080/GlycoMasterDB .

Advances in LC-MS/MS-based glycoproteomics: getting closer to system-wide site-specific mapping of the N- and O-glycoproteome

Site-specific structural characterization of glycoproteins is important for understanding the exact functional relevance of protein glycosylation. Resulting partly from the multiple layers of structural complexity of the attached glycans, the system-wide site-specific characterization of protein glycosylation, defined as glycoproteomics, is still far from trivial leaving the N- and O-linked glycoproteomes significantly under-defined. However, recent years have seen significant advances in glycoproteomics driven, in part, by the developments of dedicated workflows and efficient sample preparation, including glycopeptide enrichment and prefractionation. In addition, glycoproteomics has benefitted from the continuous performance enhancement and more intelligent use of liquid chromatography and tandem mass spectrometry (LC-MS/MS) instrumentation and a wider selection of specialized software tackling the unique challenges of glycoproteomics data. Together these advances promise more streamlined N- and O-linked glycoproteome analysis. Tangible examples include system-wide glycoproteomics studies detecting thousands of intact glycopeptides from hundreds of glycoproteins from diverse biological samples. With a strict focus on the system-wide site-specific analysis of protein N- and O-linked glycosylation, we review the recent advances in LC-MS/MS based glycoproteomics. The review opens with a more general discussion of experimental designs in glycoproteomics and sample preparation prior to LC-MS/MS based data acquisition. Although many challenges still remain, it becomes clear that glycoproteomics, one of the last frontiers in proteomics, is gradually maturing enabling a wider spectrum of researchers to access this new emerging research discipline. The next milestone in analytical glycobiology is being reached allowing the glycoscientist to address the functional importance of protein glycosylation in a system-wide yet protein-specific manner.

Software for automated interpretation of mass spectrometry data from glycans and glycopeptides

The purpose of this review is to provide those interested in glycosylation analysis with the most updated information on the availability of automated tools for MS characterization of N-linked and O-linked glycosylation types. Specifically, this review describes software tools that facilitate elucidation of glycosylation from MS data on the basis of mass alone, as well as software designed to speed the interpretation of glycan and glycopeptide fragmentation from MS/MS data. This review focuses equally on software designed to interpret the composition of released glycans and on tools to characterize N-linked and O-linked glycopeptides. Several websites have been compiled and described that will be helpful to the reader who is interested in further exploring the described tools.

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.

Exploring site-specific N-glycosylation microheterogeneity of haptoglobin using glycopeptide CID tandem mass spectra and glycan database search

Glycosylation is a common protein modification with a significant role in many vital cellular processes and human diseases, making the characterization of protein-attached glycan structures important for understanding cell biology and disease processes. Direct analysis of protein N-glycosylation by tandem mass spectrometry of glycopeptides promises site-specific elucidation of N-glycan microheterogeneity, something that detached N-glycan and deglycosylated peptide analyses cannot provide. However, successful implementation of direct N-glycopeptide analysis by tandem mass spectrometry remains a challenge. In this work, we consider algorithmic techniques for the analysis of LC-MS/MS data acquired from glycopeptide-enriched fractions of enzymatic digests of purified proteins. We implement a computational strategy that takes advantage of the properties of CID fragmentation spectra of N-glycopeptides, matching the MS/MS spectra to peptide-glycan pairs from protein sequences and glycan structure databases. Significantly, we also propose a novel false discovery rate estimation technique to estimate and manage the number of false identifications. We use a human glycoprotein standard, haptoglobin, digested with trypsin and GluC, enriched for glycopeptides using HILIC chromatography, and analyzed by LC-MS/MS to demonstrate our algorithmic strategy and evaluate its performance. Our software, GlycoPeptideSearch (GPS), assigned glycopeptide identifications to 246 of the spectra at a false discovery rate of 5.58%, identifying 42 distinct haptoglobin peptide-glycan pairs at each of the four haptoglobin N-linked glycosylation sites. We further demonstrate the effectiveness of this approach by analyzing plasma-derived haptoglobin, identifying 136 N-linked glycopeptide spectra at a false discovery rate of 0.4%, representing 15 distinct glycopeptides on at least three of the four N-linked glycosylation sites. The software, GlycoPeptideSearch, is available for download from http://edwardslab.bmcb.georgetown.edu/GPS .

N- and O-glycosylation in the murine synaptosome

We present the first large scale study characterizing both N- and O-linked glycosylation in a site-specific manner on hundreds of proteins. We demonstrate that a lectin-affinity fractionation step using wheat germ agglutinin enriches not only peptides carrying intracellular O-GlcNAc, but also those bearing ER/Golgi-derived N- and O-linked carbohydrate structures. Liquid chromatography-MS (LC/MS) analysis with high accuracy precursor mass measurements and high sensitivity ion trap electron-transfer dissociation (ETD) were utilized for structural characterization of glycopeptides. Our results reveal both the identity of the precise sites of glycosylation and information on the oligosaccharide structures possible on these proteins. We report a novel iterative approach that allowed us to interpret the ETD data set directly without making prior assumptions about the nature and distribution of oligosaccharides present in our glycopeptide mixture. Over 2500 unique N- and O-linked glycopeptides were identified on 453 proteins. The extent of microheterogeneity varied extensively, and up to 19 different oligosaccharides were attached at a given site. We describe the presence of the well-known mucin-type structures for O-glycosylation, an EGF-domain-specific fucosylation and a rare O-mannosylation on the transmembrane phosphatase Ptprz1. Finally, we identified three examples of O-glycosylation on tyrosine residues.

Automated assignments of N- and O-site specific glycosylation with extensive glycan heterogeneity of glycoprotein mixtures

Site-specific glycosylation (SSG) of glycoproteins remains a considerable challenge and limits further progress in the areas of proteomics and glycomics. Effective methods require new approaches in sample preparation, detection, and data analysis. While the field has advanced in sample preparation and detection, automated data analysis remains an important goal. A new bioinformatics approach implemented in software called GP Finder automatically distinguishes correct assignments from random matches and complements experimental techniques that are optimal for glycopeptides, including nonspecific proteolysis and high mass resolution liquid chromatography/tandem mass spectrometry (LC/MS/MS). SSG for multiple N- and O-glycosylation sites, including extensive glycan heterogeneity, was annotated for single proteins and protein mixtures with a 5% false-discovery rate, generating hundreds of nonrandom glycopeptide matches and demonstrating the proof-of-concept for a self-consistency scoring algorithm shown to be compliant with the target-decoy approach (TDA). The approach was further applied to a mixture of N-glycoproteins from unprocessed human milk and O-glycoproteins from very-low-density-lipoprotein (vLDL) particles.

Plant cell wall proteomics: the leadership of Arabidopsis thaliana

Plant cell wall proteins (CWPs) progressively emerged as crucial components of cell walls although present in minor amounts. Cell wall polysaccharides such as pectins, hemicelluloses, and cellulose represent more than 90% of primary cell wall mass, whereas hemicelluloses, cellulose, and lignins are the main components of lignified secondary walls. All these polymers provide mechanical properties to cell walls, participate in cell shape and prevent water loss in aerial organs. However, cell walls need to be modified and customized during plant development and in response to environmental cues, thus contributing to plant adaptation. CWPs play essential roles in all these physiological processes and particularly in the dynamics of cell walls, which requires organization and rearrangements of polysaccharides as well as cell-to-cell communication. In the last 10 years, plant cell wall proteomics has greatly contributed to a wider knowledge of CWPs. This update will deal with (i) a survey of plant cell wall proteomics studies with a focus on Arabidopsis thaliana; (ii) the main protein families identified and the still missing peptides; (iii) the persistent issue of the non-canonical CWPs; (iv) the present challenges to overcome technological bottlenecks; and (v) the perspectives beyond cell wall proteomics to understand CWP functions.

GlycoPep Detector: a tool for assigning mass spectrometry data of N-linked glycopeptides on the basis of their electron transfer dissociation spectra

Electron transfer dissociation (ETD) is commonly used in fragmenting N-linked glycopeptides in their mass spectral analyses to complement collision-induced dissociation (CID) experiments. The glycan remains intact through ETD, while the peptide backbone is cleaved, providing the sequence of amino acids for a glycopeptide. Nonetheless, data analysis is a major bottleneck to high-throughput glycopeptide identification based on ETD data, due to the complexity and diversity of ETD mass spectra compared to CID counterparts. GlycoPep Detector (GPD) is a web-based tool to address this challenge. It filters out noise peaks that interfere with glycopeptide sequencing, correlates input glycopeptide compositions with the ETD spectra, and assigns a score for each candidate. By considering multiple ion series (c-, z-, and y-ions) and scoring them separately, the software gives more weighting to the ion series that matches peaks of high intensity in the spectra. This feature enables the correct glycopeptide to receive a high score while keeping scores of incorrect compositions low. GPD has been utilized to interpret data collected on six model glycoproteins (RNase B, avidin, fetuin, asialofetuin, transferrin, and AGP) as well as a clade C HIV envelope glycoprotein, C.97ZA012 gp140ΔCFI. In every assignment made by GPD, the correct glycopeptide composition earns a score that is about 2-fold higher than other incorrect glycopeptide candidates (decoys). The software can be accessed at http://glycopro.chem.ku.edu/ZZKHome.php .

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.

Using glycome databases for drug discovery

BACKGROUND

The glycomics field has made great advancements in the last decade due to technologies for their synthesis and analysis including carbohydrate microarrays. Accordingly, databases for glycomics research have also emerged and been made publicly available by many major institutions worldwide.

OBJECTIVE

This review introduces these and other useful databases on which new methods for drug discovery can be developed.

METHODS

The scope of this review covers current documented and accessible databases and resources pertaining to glycomics. These were selected with the expectation that they may be useful for drug discovery research.

RESULTS/CONCLUSION

There is a plethora of glycomics databases that have much potential for drug discovery. This may seem daunting at first but this review helps to put some of these resources into perspective. Additionally, some thoughts on how to integrate these resources to allow more efficient research are presented.

A classifier based on accurate mass measurements to aid large scale, unbiased glycoproteomics

Determining which glycan moieties occupy specific N-glycosylation sites is a highly challenging analytical task. Arguably, the most common approach involves LC-MS and LC-MS/MS analysis of glycopeptides generated by proteases with high cleavage site specificity; however, the depth achieved by this approach is modest. Nonglycosylated peptides are a major challenge to glycoproteomics, as they are preferentially selected for data-dependent MS/MS due to higher ionization efficiencies and higher stoichiometric levels in moderately complex samples. With the goal of improving glycopeptide coverage, a mass defect classifier was developed that discriminates between peptides and glycopeptides in complex mixtures based on accurate mass measurements of precursor peaks. By using the classifier, glycopeptides that were not fragmented in an initial data-dependent acquisition run may be targeted in a subsequent analysis without any prior knowledge of the glycan or protein species present in the mixture. Additionally, from probable glycopeptides that were poorly fragmented, tandem mass spectra may be reacquired using optimal glycopeptide settings. We demonstrate high sensitivity (0.892) and specificity (0.947) based on an in silico dataset spanning >100,000 tryptic entries. Comparable results were obtained using chymotryptic species. Further validation using published data and a fractionated tryptic digest of human urinary proteins was performed, yielding a sensitivity of 0.90 and a specificity of 0.93. Lists of glycopeptides may be generated from an initial proteomics experiment, and we show they may be efficiently targeted using the classifier. Considering the growing availability of high accuracy mass analyzers, this approach represents a simple and broadly applicable means of increasing the depth of MS/MS-based glycoproteomic analyses.

Elucidation of N-glycosites within human plasma glycoproteins for cancer biomarker discovery

Glycans are an important class of post-translational modifications that decorate a wide array of protein substrates. These cell-type specific molecules, which are modulated during developmental and disease processes, are attractive biomarker candidates as biology regarding altered glycosylation can be used to guide the experimental design. The mass spectrometry (MS)-based workflow described here incorporates chromatography on affinity matrices formed from lectins, proteins that bind specific glycan motifs. The goal was to design a relatively simple method for the rapid analysis of small plasma volumes (e.g., clinical specimens). As increases in sialylation and fucosylation are prominent among cancer-associated modifications, we focused on Sambucus nigra agglutinin and AAL, which bind sialic acid- and fucose-containing structures, respectively. Positive controls (fucosylated and sialylated human lactoferrin glycopeptides), and negative controls (high-mannose glycopeptides from Saccharomyces cerevisiae invertase) were used to monitor the specificity of lectin capture and optimize the workflow. Multiple Affinity Removal System 14-depleted, trypsin-digested human plasma from healthy donors served as the target analyte. Samples were loaded onto the lectin columns and separated by high performance liquid chromatography (HPLC) into flow through and bound fractions, which were treated with PNGase F, an amidase that removes N-linked glycans and marks the underlying asparagine glycosite by a +1 Da mass shift. The deglycosylated peptide fractions were interrogated by HPLC ESI-MS/MS on a quadrupole time-of-flight mass spectrometer. The method allowed identification of 122 human plasma glycoproteins containing 247 unique glycosites. Notably, glycoproteins that circulate at ng/mL levels (e.g., cadherin-5 at 0.3-4.9 ng/mL, and neutrophil gelatinase-associated lipocalin which is present at ∼2.5 ng/mL) were routinely observed, suggesting that this method enables the detection of low-abundance cancer-specific glycoproteins.