Glycoproteomics based on tandem mass spectrometry of glycopeptides

Next to the identification of proteins and the determination of their expression levels, the analysis of post-translational modifications (PTM) is becoming an increasingly important aspect in proteomics. Here, we review mass spectrometric (MS) techniques for the study of protein glycosylation at the glycopeptide level. Enrichment and separation techniques for glycoproteins and glycopeptides from complex (glyco-)protein mixtures and digests are summarized. Various tandem MS (MS/MS) techniques for the analysis of glycopeptides are described and compared with respect to the information they provide on peptide sequence, glycan attachment site and glycan structure. Approaches using electrospray ionization and matrix-assisted laser desorption/ionization (MALDI) of glycopeptides are presented and the following fragmentation techniques in glycopeptide analysis are compared: collision-induced fragmentation on different types of instruments, metastable fragmentation after MALDI ionization, infrared multi-photon dissociation, electron-capture dissociation and electron-transfer dissociation. This review discusses the potential and limitations of tandem mass spectrometry of glycopeptides as a tool in structural glycoproteomics.     

Postexperiment monoisotopic mass filtering and refinement (PE-MMR) of tandem mass spectrometric data increases accuracy of peptide identification in LC/MS/MS

Methods for treating MS/MS data to achieve accurate peptide identification are currently the subject of much research activity. In this study we describe a new method for filtering MS/MS data and refining precursor masses that provides highly accurate analyses of massive sets of proteomics data. This method, coined "postexperiment monoisotopic mass filtering and refinement" (PE-MMR), consists of several data processing steps: 1) generation of lists of all monoisotopic masses observed in a whole LC/MS experiment, 2) clusterization of monoisotopic masses of a peptide into unique mass classes (UMCs) based on their masses and LC elution times, 3) matching the precursor masses of the MS/MS data to a representative mass of a UMC, and 4) filtration of the MS/MS data based on the presence of corresponding monoisotopic masses and refinement of the precursor ion masses by the UMC mass. PE-MMR increases the throughput of proteomics data analysis, by efficiently removing "garbage" MS/MS data prior to database searching, and improves the mass measurement accuracies (i.e. 0.05 +/- 1.49 ppm for yeast data (from 4.46 +/- 2.81 ppm) and 0.03 +/- 3.41 ppm for glycopeptide data (from 4.8 +/- 7.4 ppm)) for an increased number of identified peptides. In proteomics analyses of glycopeptide-enriched samples, PE-MMR processing greatly reduces the degree of false glycopeptide identification by correctly assigning the monoisotopic masses for the precursor ions prior to database searching. By applying this technique to analyses of proteome samples of varying complexities, we demonstrate herein that PE-MMR is an effective and accurate method for treating massive sets of proteomics data.     

A face in the crowd: recognizing peptides through database search

Peptide identification via tandem mass spectrometry sequence database searching is a key method in the array of tools available to the proteomics researcher. The ability to rapidly and sensitively acquire tandem mass spectrometry data and perform peptide and protein identifications has become a commonly used proteomics analysis technique because of advances in both instrumentation and software. Although many different tandem mass spectrometry database search tools are currently available from both academic and commercial sources, these algorithms share similar core elements while maintaining distinctive features. This review revisits the mechanism of sequence database searching and discusses how various parameter settings impact the underlying search.     

综述: N-连接完整糖肽的质谱分析策略和研究方法

蛋白质糖基化作为最普遍、最重要的蛋白质修饰,一直是组学研究的焦点之一．近十几年来,N-连接糖蛋白质组学研究普遍采用的方法是将糖链与所修饰的多肽分开进行分析．该策略虽降低了分析难度,却也丢失了糖链与蛋白质糖基化位点间重要的对应关系信息．近年来,完整糖肽的质谱分析策略和方法逐步建立起来．总体而言,要实现对完整糖肽的直接质谱分析,首先需要从复杂样品中富集完整糖肽以消除非糖基化多肽对完整糖肽分析的影响,然后在质谱分析中还需要根据糖肽特性调整相应质谱分析参数,最后在后续数据分析中还需要开发相应的分析软件以完成完整糖肽中多肽序列和糖链组成或结构的鉴定．本文即从以上三个主要方面系统阐述目前N-完整糖肽分析中常用的质谱和数据分析策略和方法,并进一步在糖肽谱图识别、母离子单同位素分子质量校正、数据库选择以及假阳性率评估和控制等方面都进行了逐一探讨．完整糖肽的直接质谱分析有助于获取糖链和糖基化位点间的对应关系信息,可为生物标志物发现和疾病致病机理等研究提供更有力的糖蛋白质组学研究工具．     

False discovery rates and related statistical concepts in mass spectrometry-based proteomics

Development of statistical methods for assessing the significance of peptide assignments to tandem mass spectra obtained using database searching remains an important problem. In the past several years, several different approaches have emerged, including the concept of expectation values, target-decoy strategy, and the probability mixture modeling approach of PeptideProphet. In this work, we provide a background on statistical significance analysis in the field of mass spectrometry-based proteomics, and present our perspective on the current and future developments in this area.     

Tools for glycoproteomic analysis: size exclusion chromatography facilitates identification of tryptic glycopeptides with N-linked glycosylation sites

Proteomic techniques, such as HPLC coupled to tandem mass spectrometry (LC-MS/MS), have proved useful for the identification of specific glycosylation sites on glycoproteins (glycoproteomics). Glycosylation sites on glycopeptides produced by trypsinization of complex glycoprotein mixtures, however, are particularly difficult to identify both because a repertoire of glycans may be expressed at a particular glycosylation site, and because glycopeptides are usually present in relatively low abundance (2% to 5%) in peptide mixtures compared to nonglycosylated peptides. Previously reported methods to facilitate glycopeptide identification require either several pre-enrichment steps, involve complex derivatization procedures, or are restricted to a subset of all the glycan structures that are present in a glycoprotein mixture. Because the N-linked glycans expressed on tryptic glycopeptides contribute substantially to their mass, we demonstrate that size exclusion chromatography (SEC) provided a significant enrichment of N-linked glycopeptides relative to nonglycosylated peptides. The glycosylated peptides were then identified by LC-MS/MS after treatment with PNGase-F by the monoisotopic mass increase of 0.984 Da caused by the deglycosylation of the peptide. Analyses performed on human serum showed that this SEC glycopeptide isolation procedure results in at least a 3-fold increase in the total number of glycopeptides identified by LC-MS/MS, demonstrating that this simple, nonselective, rapid method is an effective tool to facilitate the identification of peptides with N-linked glycosylation sites.     

Flying blind,or just flying under the radar? The underappreciated power of de novo methods of mass spectrometric peptide identification

Mass spectrometry‐based proteomics is a popular and powerful method for precise and highly multiplexed protein identification. The most common method of analyzing untargeted proteomics data is called database searching, where the database is simply a collection of protein sequences from the target organism, derived from genome sequencing. Experimental peptide tandem mass spectra are compared to simplified models of theoretical spectra calculated from the translated genomic sequences. However, in several interesting application areas, such as forensics, archaeology, venomics, and others, a genome sequence may not be available, or the correct genome sequence to use is not known. In these cases, de novo peptide identification can play an important role. De novo methods infer peptide sequence directly from the tandem mass spectrum without reference to a sequence database, usually using graph‐based or machine learning algorithms. In this review, we provide a basic overview of de novo peptide identification methods and applications, briefly covering de novo algorithms and tools, and focusing in more depth on recent applications from venomics, metaproteomics, forensics, and characterization of antibody drugs.     

Semi-automated identification of N-Glycopeptides by hydrophilic interaction chromatography, nano-reverse-phase LC-MS/MS, and glycan database search

Glycoproteins fulfill many indispensable biological functions, and changes in protein glycosylation have been observed in various diseases. Improved analytical methods are needed to allow a complete characterization of this complex and common post-translational modification. In this study, we present a workflow for the analysis of the microheterogeneity of N-glycoproteins that couples hydrophilic interaction and nanoreverse-phase C18 chromatography to tandem QTOF mass spectrometric analysis. A glycan database search program, GlycoPeptideSearch, was developed to match N-glycopeptide MS/MS spectra with the glycopeptides comprised of a glycan drawn from the GlycomeDB glycan structure database and a peptide from a user-specified set of potentially glycosylated peptides. Application of the workflow to human haptoglobin and hemopexin, two microheterogeneous N-glycoproteins, identified a total of 57 distinct site-specific glycoforms in the case of haptoglobin and 14 site-specific glycoforms of hemopexin. Using glycan oxonium ions and peptide-characteristic glycopeptide fragment ions and by collapsing topologically redundant glycans, the search software was able to make unique N-glycopeptide assignments for 51% of assigned spectra, with the remaining assignments primarily representing isobaric topological rearrangements. The optimized workflow, coupled with GlycoPeptideSearch, is expected to make high-throughput semiautomated glycopeptide identification feasible for a wide range of users.     

Simultaneous glycosylation analysis of human serum glycoproteins by high-performance liquid chromatography/tandem mass spectrometry

Changes in the glycosylation of some serum proteins are associated with certain diseases. In this study, we performed simultaneous site-specific glycosylation analysis of abundant serum glycoproteins by LC/Qq-TOF MS of human serum tryptic digest, the albumin of which was depleted. The glycopeptide peaks on the chromatogram were basically assigned by database searching with modified peak-list text files of MS/MS spectra and then based on mass differences of glycan units from characterized glycopeptides. Glycopeptide of IgG, haptoglobin and ceruloplasmin were confirmed by means of a comparison of their retention times and m/z values with those obtained by LC/MS of commercially available glycoproteins. Mass spectrometric carbohydrate heterogeneity in the assigned glycopeptides was analyzed by an additional LC/MS. We successfully demonstrated site-specific glycosylation of 23 sites in abundant serum glycoproteins.     

Liquid chromatography-tandem mass spectrometry-based fragmentation analysis of glycopeptides

The use of liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MSⁿ) for the glycoproteomic characterization of glycopeptides is a growing field of research. The N- and O-glycosylated peptides (N- and O-glycopeptides) analyzed typically originate from protease-digested glycoproteins where many of them are expected to be biomedically important. Examples of LC-MS² and MS³ fragmentation strategies used to pursue glycan structure, peptide identity and attachment-site identification analyses of glycopeptides are described in this review. MS² spectra, using the CID and HCD fragmentation techniques of a complex biantennary N-glycopeptide and a core 1 O-glycopeptide, representing two examples of commonly studied glycopeptide types, are presented. A few practical tips for accomplishing glycopeptide analysis using reversed-phase LC-MSⁿ shotgun proteomics settings, together with references to the latest glycoproteomic studies, are presented.     

N-Glycan structure annotation of glycopeptides using a linearized glycan structure database (GlyDB)

While glycoproteins are abundant in nature, and changes in glycosylation occur in cancer and other diseases, glycoprotein characterization remains a challenge due to the structural complexity of the biopolymers. This paper presents a general strategy, termed GlyDB, for glycan structure annotation of N-linked glycopeptides from tandem mass spectra in the LC-MS analysis of proteolytic digests of glycoproteins. The GlyDB approach takes advantage of low-energy collision-induced dissociation of N-linked glycopeptides that preferentially cleaves the glycosidic bonds while the peptide backbone remains intact. A theoretical glycan structure database derived from biosynthetic rules for N-linked glycans was constructed employing a novel representation of branched glycan structures consisting of multiple linear sequences. The commonly used peptide identification program, Sequest, could then be utilized to assign experimental tandem mass spectra to individual glycoforms. Analysis of synthetic glycopeptides and well-characterized glycoproteins demonstrate that the GlyDB approach can be a useful tool for annotation of glycan structures and for selection of a limited number of potential glycan structure candidates for targeted validation.     

Recent advances in computational algorithms and software for large-scale glycoproteomics

Glycoproteomics, or characterizing glycosylation events at a proteome scale, has seen rapid advances in methods for analyzing glycopeptides by tandem mass spectrometry in recent years. These advances have enabled acquisition of far more comprehensive and large-scale datasets, precipitating an urgent need for improved informatics methods to analyze the resulting data. A new generation of glycoproteomics search methods has recently emerged, using glycan fragmentation to split the identification of a glycopeptide into peptide and glycan components and solve each component separately. In this review, we discuss these new methods and their implications for large-scale glycoproteomics, as well as several outstanding challenges in glycoproteomics data analysis, including validation of glycan assignments and quantitation. Finally, we provide an outlook on the future of glycoproteomics from an informatics perspective, noting the key challenges to achieving widespread and reproducible glycopeptide annotation and quantitation.     

Efficiency improvement of peptide identification for an organism without complete genome sequence, using expressed sequence tag database and tandem mass spectral data

We compared peptide identification by database (DB) search methods with de novo sequencing results for proteomics study in an organism without genome sequence information. When the former was done by searching the Expressed Sequence Tag (EST) DB of the sample organism or the NCBI nonredundant (nr) protein DB of green plants using either the MASCOT or SEQUEST software program, it was confirmed that the former is as accurate as the latter. Peptides identified from EST DB were twice as many as those from the nr protein DB, in spite of the fact that the EST DB has less data (26 222 EST) than the NCBI nr protein DB (224 238). This study demonstrates that EST DB with tandem mass spectra can be used reliably for high-throughput proteomics studies in an organism without genome information.     

Large improvements in MS/MS-based peptide identification rates using a hybrid analysis

We report a hybrid search method combining database and spectral library searches that allows for a straightforward approach to characterizing the error rates from the combined data. Using these methods, we demonstrate significantly increased sensitivity and specificity in matching peptides to tandem mass spectra. The hybrid search method increased the number of spectra that can be assigned to a peptide in a global proteomics study by 57-147% at an estimated false discovery rate of 5%, with clear room for even greater improvements. The approach combines the general utility of using consensus model spectra typical of database search methods with the accuracy of the intensity information contained in spectral libraries. A common scoring metric based on recent developments linking data analysis and statistical thermodynamics is used, which allows the use of a conservative estimate of error rates for the combined data. We applied this approach to proteomics analysis of Synechococcus sp. PCC 7002, a cyanobacterium that is a model organism for studies of photosynthetic carbon fixation and biofuels development. The increased specificity and sensitivity of this approach allowed us to identify many more peptides involved in the processes important for photoautotrophic growth.     

Characterization of a high field Orbitrap mass spectrometer for proteome analysis

The field of proteomics continues to be driven by improvements in analytical technology, notably in peptide separation, quantitative MS, and informatics. In this study, we have characterized a hybrid linear ion trap high field Orbitrap mass spectrometer (Orbitrap Elite) for proteomic applications. The very high resolution available on this instrument allows 95% of all peptide masses to be measured with sub‐ppm accuracy that in turn improves protein identification by database searching. We further confirm again that mass accuracy in tandem mass spectra is a valuable parameter for improving the success of protein identification. The new CID rapid scan type of the Orbitrap Elite achieves similar performance as higher energy collision induced dissociation fragmentation and both allow the identification of hundreds of proteins from as little as 0.1 ng of protein digest on column. The new instrument outperforms its predecessor the Orbitrap Velos by a considerable margin on each metric assessed that makes it a valuable and versatile tool for MS‐based proteomics.     

Peptide identification by database search of mixture tandem mass spectra

In high-throughput proteomics the development of computational methods and novel experimental strategies often rely on each other. In certain areas, mass spectrometry methods for data acquisition are ahead of computational methods to interpret the resulting tandem mass spectra. Particularly, although there are numerous situations in which a mixture tandem mass spectrum can contain fragment ions from two or more peptides, nearly all database search tools still make the assumption that each tandem mass spectrum comes from one peptide. Common examples include mixture spectra from co-eluting peptides in complex samples, spectra generated from data-independent acquisition methods, and spectra from peptides with complex post-translational modifications. We propose a new database search tool (MixDB) that is able to identify mixture tandem mass spectra from more than one peptide. We show that peptides can be reliably identified with up to 95% accuracy from mixture spectra while considering only a 0.01% of all possible peptide pairs (four orders of magnitude speedup). Comparison with current database search methods indicates that our approach has better or comparable sensitivity and precision at identifying single-peptide spectra while simultaneously being able to identify 38% more peptides from mixture spectra at significantly higher precision.     

OLAV: towards high-throughput tandem mass spectrometry data identification

Mass spectrometry combined with database searching has become the preferred method for identifying proteins in proteomics projects. Proteins are digested by one or several enzymes to obtain peptides, which are analyzed by mass spectrometry. We introduce a new family of scoring schemes, named OLAV, aimed at identifying peptides in a database from their tandem mass spectra. OLAV scoring schemes are based on signal detection theory, and exploit mass spectrometry information more extensively than previously existing schemes. We also introduce a new concept of structural matching that uses pattern detection methods to better separate true from false positives. We show the superiority of OLAV scoring schemes compared to MASCOT, a widely used identification program. We believe that this work introduces a new way of designing scoring schemes that are especially adapted to high-throughput projects such as GeneProt large-scale human plasma project, where it is impractical to check all identifications manually.     

Potential for false positive identifications from large databases through tandem mass spectrometry

The biomedical research community at large is increasingly employing shotgun proteomics for large-scale identification of proteins from enzymatic digests. Typically, the approach used to identify proteins and peptides from tandem mass spectral data is based on the matching of experimentally generated tandem mass spectra to the theoretical best match from a protein database. Here, we present the potential difficulties of using such an approach without statistical consideration of the false positive rate, especially when large databases, as are encountered in eukaryotes are considered. This is illustrated by searching a dataset generated from a multidimensional separation of a eukaryotic tryptic digest against an in silico generated random protein database, which generated a significant number of positive matches, even when previously suggested score filtering criteria are used.