Identification Of Glycopeptides Research Articles

GlycoHybridSeq: Automated Identification of N-Linked Glycopeptides Using Electron Transfer/High-Energy Collision Dissociation (EThcD).

Glycosylation is one of the most common post-translational modifications (PTM) occurring in a large variety of proteins with important biological functions in human and other higher organisms. Liquid chromatography tandem mass spectrometry (LC-MS/MS) has been routinely used to characterize site-specific protein glycosylation at high throughput in complex glycoproteomic samples. Recently, electron transfer/high-energy collision dissociation (EThcD) was introduced for glycopeptide identification, which offers rich structural information on glycopepides with the fragment ions from the cleavages of both the glycan and the peptide backbone. Herein, we present the software GlycoHybridSeq for automated interpretation of EThcD-MS/MS spectra from glycoproteomic data using a customized scoring function, which enables the functionalities of identifying glycopeptides, characterizing glycosylation sites, and distinguishing some isomeric glycans. We evaluate GlycoHybridSeq on glycoproteomic data collected for cancer biomarker discovery. The results showed that it achieved comparable or better performance than that of Byonic and MSFragger. GlycoHybridSeq is released as an open source software and is ready to be used in large-scale glycoproteomic data analyses.

Semi-Automated Glycoproteomic Data Analysis of LC-MS Data Using GlycopeptideGraphMS in Process Development of Monoclonal Antibody Biologics.

The glycosylation of antibody-based proteins is vital in translating the right therapeutic outcomes of the patient. Despite this, significant infrastructure is required to analyse biologic glycosylation in various unit operations from biologic development, process development to QA/QC in bio-manufacturing. Simplified mass spectrometers offer ease of operation as well as the portability of method development across various operations. Furthermore, data analysis would need to have a degree of automation to relay information back to the manufacturing line. We set out to investigate the applicability of using a semiautomated data analysis workflow to investigate glycosylation in different biologic development test cases. The workflow involves data acquisition using a BioAccord LC-MS system with a data-analytical tool called GlycopeptideGraphMS along with Progenesis QI to semi-automate glycoproteomic characterisation and quantitation with a LC-MS1 dataset of a glycopeptides and peptides. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 min with minimal user intervention. To demonstrate the effectiveness of the antibody and biologic glycopeptide assignment in various scenarios akin to biologic development activities, we demonstrate the effectiveness in the filtering of IgG1 and IgG2 subclasses from human serum IgG as well as innovator drugs trastuzumab and adalimumab and glycoforms by virtue of their glycosylation pattern. We demonstrate a high correlation between conventional released glycan analysis with fluorescent tagging and glycopeptide assignment derived from GraphMS. GraphMS workflow was then used to monitor the glycoform of our in-house trastuzumab biosimilar produced in fed-batch cultures. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.

Automated Intact Glycopeptide Enrichment Method Facilitating Highly Reproducible Analysis of Serum Site-Specific N-Glycoproteome.

Bottom-up proteomics has been increasingly applied in clinical research to study the disease pathophysiology and to discover disease biomarkers. However, glycoproteomic analysis always requires tedious experimental steps for intact glycopeptide enrichment, which has been the technique bottleneck for large-scale analysis of clinical samples. Herein, we developed an automated glycopeptide enrichment method for the analysis of serum site-specific N-glycoproteome. This automated method allowed for processing one sample within 20 min. It showed higher enrichment specificity, more intact glycopeptide identifications, and better quantitative reproducibility than the traditional manual method using microtip enrichment devices. We further applied this method to investigate the serum site-specific N-glycosylation changes between four patients with pancreatic cancer and seven healthy controls. The principal component analysis of intact N-glycopeptides showed good clustering across cancer and normal groups. Furthermore, we found that the site-specific glycoforms, monofucosylated and nonsialylated oligosaccharides, on IgG1 site 180 expressed a significant decrease in pancreatic cancer patients compared to healthy controls. Together, the automated method is a powerful tool for site-specific N-glycoproteomic analysis of complex biological samples, and it has great potential for clinical utilities.

Methods to improve quantitative glycoprotein coverage from bottom-up LC-MS data.

Advances in mass spectrometry instrumentation, methods development, and bioinformatics have greatly improved the ease and accuracy of site-specific, quantitative glycoproteomics analysis. Data-dependent acquisition is the most popular method for identification and quantification of glycopeptides; however, complete coverage of glycosylation site glycoforms remains elusive with this method. Targeted acquisition methods improve the precision and accuracy of quantification, but at the cost of throughput and discoverability. Data-independent acquisition (DIA) holds great promise for more complete and highly quantitative site-specific glycoproteomics analysis, while maintaining the ability to discover novel glycopeptides without prior knowledge. We review additional features that can be used to increase selectivity and coverage to the DIA workflow: retention time modeling, which would simplify the interpretation of complex tandem mass spectra, and ion mobility separation, which would maximize the sampling of all precursors at a giving chromatographic retention time. The instrumentation and bioinformatics to incorporate these features into glycoproteomics analysis exist. These improvements in quantitative, site-specific analysis will enable researchers to assess glycosylation similarity in related biological systems, answering new questions about the interplay between glycosylation state and biological function.

Gold nanoparticle-glutathione functionalized MOFs as hydrophilic materials for the selective enrichment of glycopeptides

Herein, a novel zwitterionic hydrophilic metal-organic framework (MOF)-functionalized material was synthesized through grafting l-glutathione (GSH) onto the Au which acts as the intermediate layer to modify the base material (PEI-ZIF-8) by the sulfhydryl group provided by GSH and the affinity provided by Au (denoted as PEI-ZIF-8@Au@GSH). The obtained product was employed to capture glycopeptides. Benefit from its excellent hydrophilic properties, abundant amphoteric ions, and unique large specific surface area, this material demonstrated amazing ability in the enrichment and identification of glycopeptides. As a result, the PEI-ZIF-8@Au@GSH displayed high sensitivity (as low as 2 fmol), excellent binding capacity (500 mg/g), outstanding enrichment selectivity (maximum mass ratio HRP to BSA is 1:1000) toward glycopeptides, and the ability to recycle at least five times. Furthermore, 35 and 51 glycopeptides were successfully detected from 5 μL human saliva and human serum respectively in the examination of the actual sample by MALDI-TOF MS. The above results indicated that the PEI-ZIF-8@Au@GSH had a satisfactory potential in the field of glycoproteomics.

Glycoproteomic Sample Processing, LC-MS, and Data Analysis Using GlycReSoft.

Identification of N- and O-glycosylation on specific sites of proteins, along with glycan structural information, is necessary to determine the roles glycoproteins play in normal and pathologic cellular functions. Because such glycosylation is macro- and micro-heterogeneous and alters the dissociation behavior of glycopeptides, specific sample preparation, mass spectrometry, and data analysis techniques are required. Advanced tandem mass spectrometry-based glycoproteomics coupled with powerful data mining algorithms are key elements for characterization of protein glycosylation. This article includes the detailed, streamlined sample preparation method for liquid chromatography-mass spectrometry data acquisition and subsequent bioinformatics-based data annotation using the publicly available GlycReSoft program for highly efficient identification and quantification of glycoprotein glycosylation. © 2021 Wiley Periodicals LLC. Basic Protocol 1: Characterization of glycans and site occupancy on purified glycoprotein Support Protocol 1: In-gel digestion of glycoproteins Support Protocol 2: Detection of glycoproteins from cells/tissue through glycopeptide enrichment Basic Protocol 2: Acquisition of glycopeptides through high-resolution nano-LC-MS/MS Basic Protocol 3: Identification and quantification of glycopeptides using GlycReSoft.

Enrichment of Intact Glycopeptides Using Strong Anion Exchange and Electrostatic Repulsion Hydrophilic Interaction Chromatography.

Glycosylation is a biologically important and complex protein posttranslational modification. The emergence of glycoproteomic technologies to identify and characterize glycans on proteins has the potential to enable a better understanding the role of glycosylation in biology, disease states, and other areas of interest. In particular, the analysis of intact glycopeptides by mass spectrometry allows information about glycan location and composition to be ascertained. However, such analysis is often complicated by extensive glycan diversity and the low abundance of glycopeptides in a complex mixture relative to nonglycosylated peptides. Enrichment of glycopeptides from a protein enzymatic digest is an effective approach to overcome such challenges. In this chapter, we described a glycopeptide enrichment method combining strong anion exchange, electrostatic repulsion, and hydrophilic interaction chromatography (SAX-ERLIC). Following enzymatic digestion of proteins into peptides, SAX-ERLIC is performed by solid phase extraction to enrich glycopeptides from biological samples with subsequent LC-MS/MS analysis. Glycopeptide data generated using the SAX-ERLIC enrichment yields a high number of total and unique glycopeptide identifications which can be mapped back to proteins. The enrichment strategy is robust, easy to perform, and does not require cleavage of glycans prior to LC-MS/MS analysis.

Glycan Compositions with GlyConnect Compozitorto Enhance Glycopeptide Identification.

Glycoproteomics is unquestionably on the rise and its current development benefits from past experience in proteomics, in particular when attending to bioinformatics needs. An extensive range of software solutions is available, but the reproducibility of mass spectrometry data processing remains challenging. One of the key issues in running automated glycopeptide identification software is the selection of a reference glycan composition file. The default choices are often too broad, and a fastidious literature search to properly target this selection can be avoided. This chapter suggests the use of GlyConnect Compozitor to collect relevant information on glycosylation in a given tissue or cell line and shape an appropriate glycan composition set that can be input in the majority of search engines accommodating user-defined compositions.

Semiautomated glycoproteomics data analysis workflow for maximized glycopeptide identification and reliable quantification.

Glycoproteomic data are often very complex, reflecting the high structural diversity of peptide and glycan portions. The use of glycopeptide-centered glycoproteomics by mass spectrometry is rapidly evolving in many research areas, leading to a demand in reliable data analysis tools. In recent years, several bioinformatic tools were developed to facilitate and improve both the identification and quantification of glycopeptides. Here, a selection of these tools was combined and evaluated with the aim of establishing a robust glycopeptide detection and quantification workflow targeting enriched glycoproteins. For this purpose, a tryptic digest from affinity-purified immunoglobulins G and A was analyzed on a nano-reversed-phase liquid chromatography–tandem mass spectrometry platform with a high-resolution mass analyzer and higher-energy collisional dissociation fragmentation. Initial glycopeptide identification based on MS/MS data was aided by the Byonic software. Additional MS1-based glycopeptide identification relying on accurate mass and retention time differences using GlycopeptideGraphMS considerably expanded the set of confidently annotated glycopeptides. For glycopeptide quantification, the performance of LaCyTools was compared to Skyline, and GlycopeptideGraphMS. All quantification packages resulted in comparable glycosylation profiles but featured differences in terms of robustness and data quality control. Partial cysteine oxidation was identified as an unexpectedly abundant peptide modification and impaired the automated processing of several IgA glycopeptides. Finally, this study presents a semiautomated workflow for reliable glycoproteomic data analysis by the combination of software packages for MS/MS- and MS1-based glycopeptide identification as well as the integration of analyte quality control and quantification.

Glycine additive facilitates site-specific glycosylation profiling of biopharmaceuticals by ion-pairing hydrophilic interaction chromatography mass spectrometry.

Many biotherapeutics such as monoclonal antibodies (mAb) and Fc-domain fusion proteins contain heterogeneous glycan contents at one or multiple glycosylation site(s). Site-specific glycan profile characterization is critical for monitoring the quality of these molecules during different stages of drug development. Hydrophilic interaction chromatography (HILIC) as an orthogonal separation method to reversed-phase liquid chromatography (RPLC) can achieve better glycopeptide identification due to the effective separation between individual glycoforms as well as the separation of glycopeptides from high-abundance non-glycosylated peptides, which can be further improved by modifying the mobile phases with ion-pairing agents (IP-HILIC). However, an online IP-HILIC coupled to mass spectrometry (MS) detection may suffer from the suppression of mass spectrometry signal during electrospray ionization due to the trifluoroacetic acid (TFA), commonly used as an ion-pairing agent. Here, we reported an optimized experimental condition for IP-HILIC-MS where glycine is added in the TFA-containing mobile phases to enhance the MS detection sensitivity for glycopeptides up to ~ 50-fold by eliminating the ion-suppression effect of an ion-pairing agent while still retaining excellent separation capacity. We demonstrated that with enhanced detection sensitivity, IP-HILIC-MS can confidently identify an increased number of site-specific N-linked glycans for IgG1, and IgG4 mAbs as well as an Fc-domain fusion protein (containing five N-glycosylation sites) through MS/MS-based search in the data-dependent acquisition mode, meanwhile, achieve comparable quantitative results compared with the traditional methods. We also demonstrated that IP-HILIC-MS can be used to identify low-level O-glycosylation and non-consensus N-glycosylation on mAbs without any enrichment prior to LC-MS analysis. Graphical abstract.

Comparison of Three Glycoproteomic Methods for the Analysis of the Secretome of CHO Cells Treated with 1,3,4-O-Bu3ManNAc.

Comprehensive analysis of the glycoproteome is critical due to the importance of glycosylation to many aspects of protein function. The tremendous complexity of this post-translational modification, however, makes it difficult to adequately characterize the glycoproteome using any single method. To overcome this pitfall, in this report we compared three glycoproteomic analysis methods; first the recently developed N-linked glycans and glycosite-containing peptides (NGAG) chemoenzymatic method, second, solid-phase extraction of N-linked glycoproteins (SPEG), and third, hydrophilic interaction liquid chromatography (HILIC) by characterizing N-linked glycosites in the secretome of Chinese hamster ovary (CHO) cells. Interestingly, the glycosites identified by SPEG and HILIC overlapped considerably whereas NGAG identified many glycosites not observed in the other two methods. Further, utilizing enhanced intact glycopeptide identification afforded by the NGAG workflow, we found that the sugar analog 1,3,4-O-Bu3ManNAc, a “high flux” metabolic precursor for sialic acid biosynthesis, increased sialylation of secreted proteins including recombinant human erythropoietin (rhEPO).

On-Tissue Spatially-Resolved Glycoproteomics Guided by N-Glycan Imaging Reveal Global Dysregulation of Canine Glioma Glycoproteomic Landscape

We identify N-linked glycoproteins and deduce their spatial localization using spatially-resolved glycoproteomic guided by MALDI mass spectrometry N-glycan imaging on formalin-fixed paraffin embedded (FFPE) canine high-grade glioma tissues. The glycan HexNAc4-Hex5-NeuAc2 was found to be predominantly expressed in necrotic regions, whereas high mannose HexNAc2-Hex5 was predominantly found in benign regions. To determine the underlying sialo-glycoprotein, various regions in adjacent tissue sections were analysed. Haptoglobin is the protein associated with HexNAc4-Hex5-NeuAc2. Additionally, we identified several high-mannose (Hex2-HexNAc5) glycopeptides enriched in benign regions. This is the first report that directly links glycan imaging with intact glycopeptide identification. Over 400 N-glycosylated, O-GalNAcylated, O mannosylated, and S- and O- GlcNAcylated glycopeptides from over 30 proteins were identified. While N-glycosylation and O- mannosylation were similar between benign and tumor/necrotic sections, S- and O-GlcNAc glycopeptides were significantly decreased in tumor/necrotic sections, whereas sialylated O-GalNAc glycopeptides were significantly upregulated.

A streamlined pipeline for multiplexed quantitative site-specific N-glycoproteomics

Regulation of protein N-glycosylation is essential in human cells. However, large-scale, accurate, and site-specific quantification of glycosylation is still technically challenging. We here introduce SugarQuant, an integrated mass spectrometry-based pipeline comprising protein aggregation capture (PAC)-based sample preparation, multi-notch MS3 acquisition (Glyco-SPS-MS3) and a data-processing tool (GlycoBinder) that enables confident identification and quantification of intact glycopeptides in complex biological samples. PAC significantly reduces sample-handling time without compromising sensitivity. Glyco-SPS-MS3 combines high-resolution MS2 and MS3 scans, resulting in enhanced reporter signals of isobaric mass tags, improved detection of N-glycopeptide fragments, and lowered interference in multiplexed quantification. GlycoBinder enables streamlined processing of Glyco-SPS-MS3 data, followed by a two-step database search, which increases the identification rates of glycopeptides by 22% compared with conventional strategies. We apply SugarQuant to identify and quantify more than 5,000 unique glycoforms in Burkitt’s lymphoma cells, and determine site-specific glycosylation changes that occurred upon inhibition of fucosylation at high confidence.

Examining and Fine-tuning the Selection of Glycan Compositions with GlyConnect Compozitor

A key point in achieving accurate intact glycopeptide identification is the definition of the glycan composition file that is used to match experimental with theoretical masses by a glycoproteomics search engine. At present, these files are mainly built from searching the literature and/or querying data sources focused on posttranslational modifications. Most glycoproteomics search engines include a default composition file that is readily used when processing MS data. We introduce here a glycan composition visualizing and comparative tool associated with the GlyConnect database and called GlyConnect Compozitor. It offers a web interface through which the database can be queried to bring out contextual information relative to a set of glycan compositions. The tool takes advantage of compositions being related to one another through shared monosaccharide counts and outputs interactive graphs summarizing information searched in the database. These results provide a guide for selecting or deselecting compositions in a file in order to reflect the context of a study as closely as possible. They also confirm the consistency of a set of compositions based on the content of the GlyConnect database. As part of the tool collection of the Glycomics@ExPASy initiative, Compozitor is hosted at https://glyconnect.expasy.org/compozitor/ where it can be run as a web application. It is also directly accessible from the GlyConnect database.

Large-scale site-specific mapping of the O-GalNAc glycoproteome.

Protein glycosylation is one of the most common protein modifications. A major type of protein glycosylation is O-GalNAcylation, in which GalNAc-type glycans are attached to protein Ser or Thr residues via an O-linked glycosidic bond. O-GalNAcylation is thought to play roles in protein folding, stability, trafficking and protein interactions, and identification of the site-specific O-GalNAc glycoproteome is a crucial step toward understanding the biological significance of the modification. However, lack of suitable methodology, absence of consensus sequon of O-GalNAcylation sites and complex O-GalNAc glycan structures pose analytical challenges. We recently developed a mass spectrometry-based method called extraction of O-linked glycopeptides (EXoO) that enables large-scale mapping of site-specific mucin-type O-GalNAcylation sites. Here we provide a detailed protocol for EXoO, which includes seven stages of: (1) extraction and proteolytic digestion of proteins to peptides, (2) sequential guanidination and de-salting of peptides, (3) enrichment of glycopeptides, (4) solid-phase peptide conjugation and release of O-GalNAc glycopeptides using the OpeRATOR protease, (5) liquid chromatography with tandem mass spectrometry analysis of O-GalNAc glycopeptides, (6) identification of O-GalNAc glycopeptides by database search and (7) quantification of O-GalNAc glycopeptides. Using this protocol, thousands of O-GalNAcylation sites from hundreds of glycoproteins with information regarding site-specific O-GalNAc glycan can be identified and quantified from complex samples. The protocol can be performed by a researcher with basic proteomics skills and takes about 4 d to complete.

Low Collision Energy Fragmentation in Structure-Specific Glycoproteomics Analysis.

Glycosylation is a major post-translational modification of proteins that regulates many biological processes including protein folding, structure stability, receptor activation, and immune responses. The glycans attached to proteins represent an important determinant of the protein interaction-specificity and maintain the 3D structure of proteins. Mass spectrometry (MS) is one of the most efficient tools used in the current studies of glycoproteins and structure of their glycoforms. Collision energy (CE) is a crucial instrument parameter that can be exploited to improve structural resolution because different linkages of glycan units show different stabilities under CID/HCD fragmentation. Here we report the utility of CE modulation for qualitative and quantitative analysis of site- and structure-specific glycoforms of proteins. Using CE modulation, we were able to break selectively specific glycan linkages on intact glycopeptides and get, to some degree, structure-specific mass spectrometric signals. Structure- and CE-specific oxonium ions provide sufficient information for the resolution of outer arm structure motifs with recognized biological functions. The complementary Y-ions, generated under optimized low CE (soft) conditions, provide additional structural information including features specific to the chitobiose core. This methodology of multiple CE fragmentation without merging spectral information can significantly improve confidence of glycopeptide identification and structural resolution by providing additional information to the established glycopeptide-search algorithms and tools.

Glycopeptide epitope facilitates HIV-1 envelope specific humoral immune responses by eliciting T cell help

The inherent molecular complexity of human pathogens requires that mammals evolved an adaptive immune system equipped to handle presentation of non-conventional MHC ligands derived from disease-causing agents, such as HIV-1 envelope (Env) glycoprotein. Here, we report that a CD4+ T cell repertoire recognizes a glycopeptide epitope on gp120 presented by MHCII pathway. This glycopeptide is strongly immunogenic in eliciting glycan-dependent cellular and humoral immune responses. The glycopeptide specific CD4+ T cells display a prominent feature of Th2 and Th17 differentiation and exert high efficacy and potency to help Env trimer humoral immune responses. Glycopeptide-induced CD4+ T cell response prior to Env trimer immunization elicits neutralizing antibody development and production of antibodies facilitating uptake of immunogens by antigen-presenting cells. Our identification of gp120 glycopeptide–induced, T cell–specific immune responses offers a foundation for developing future knowledge-based vaccines that elicit strong and long-lasting protective immune responses against HIV-1 infection.

Quantitative Analysis of α-1-Antitrypsin Glycosylation Isoforms in HCC Patients Using LC-HCD-PRM-MS.

The change in glycosylation of serum proteins is often associated with the development of various diseases and thus can be used for diagnosis. In this study, a liquid chromatography-tandem mass spectrometry-based method is used for accurate structural analysis and quantification of site-specific glycoforms of serum α-1-antitrypsin (A1AT) in early-stage HCC and cirrhosis patients. Serum protein A1AT was purified from patient sera by immunoprecipitation with anti-A1AT antibody conjugated agarose beads, and the isolated A1AT protein was digested and analyzed by LC-MS/MS. Two tandem mass spectrometry strategies are integrated in this study: a nontargeted stepped HCD strategy for structural analysis of A1AT glycopeptides and a targeted parallel reaction monitoring (PRM) strategy for quantification of site-specific glycoforms of A1AT in HCC and cirrhosis patient sera. Accordingly, pGlyco2.0 software was used for glycopeptide identification, and Skyline software was used for glycoform quantification using the Y1 ion (peptide+GlcNAc) in MS/MS spectra. Ten site-specific glycopeptides of A1AT were identified with stepped HCD-MS/MS in patient samples, 7 of which were further quantified using HCD-PRM-MS among patient samples. We found that our strategy was able to distinguish isomers of glycopeptides where several isomers showed distinctly different patterns between cirrhosis and HCC patients. We also found that the ratio of different charge states (2+/3+) of one glycopeptide of A1AT can significantly discriminate early-stage HCC from cirrhosis with the area under the receiver operating characteristic curve AUC of 0.9. Further analysis showed that the difference may be related to the sialic acid/galactose linkage of the glycan motif.

Strategic Applications of Negative-Mode LC-MS/MS Analyses to Expedite Confident Mass Spectrometry-Based Identification of Multiple Glycosylated Peptides.

Although recent advances in mass spectrometry (MS) have enabled meaningful glycoproteomic undertakings, many technical limitations remain unsolved. Among these, the ability to efficiently sequence the peptide backbone for de novo identification, delineating multiple N- and O-glycosylation sites on single glycopeptides, and deriving more glycan structure information to discriminate isomeric glycoforms are well acknowledged practical problems to be tackled. To address these issues, we explored the use of negative-mode MS2/MS3 fragmentation to supplement current nanoLC-MS2-based sequencing and identification of intact glycopeptides largely performed in positive mode. Consistent with previous reports by others, we found that sulfation and sialylation drastically alter the MS2 fragmentation pattern of glycopeptides in negative mode and the characteristic features identified can be utilized to program the most informative MS3 on the glycan moiety itself. Importantly, direct elimination of one or more O-glycans under negative-mode MS2 affords an easy way to discover additional O-glycosylations on a multiply glycosylated peptide by virtue of enumerating the dehydration scars imprinted on the O-glycosylated sites. Moreover, the characteristic peptide core ion carrying a ring cleavage remnant of the innermost amino sugar residue of an N-glycan can be relied upon to filter out all related N-glycopeptides carrying additional O-glycans defined by specific mass increments. Such enhanced ability to advance from definitive identification of single to multiple site-specific glycosylation on the same peptide backbones is anticipated to have a significant impact on the level of structural and biological insights one can gain in glycoproteomic applications.

Large-scale Identification of N-linked Intact Glycopeptides in Human Serum using HILIC Enrichment and Spectral Library Search

Large-scale identification of N-linked intact glycopeptides by liquid chromatography coupled with tandem mass spectrometry (LC-MS/MS) in human serum is challenging because of the wide dynamic range of serum protein abundances, the lack of a complete serum N-glycan database and the existence of proteoforms. In this regard, a spectral library search method was presented for the identification of N-linked intact glycopeptides from N-linked glycoproteins in human serum with target-decoy and motif-specific false discovery rate (FDR) control. Serum proteins were firstly separated into low-abundance and high-abundance proteins by acetonitrile (ACN) precipitation. After digestion, the N-linked intact glycopeptides were enriched by hydrophilic interaction liquid chromatography (HILIC) and a portion of the enriched N-linked intact glycopeptides were processed by Peptide-N-Glycosidase F (PNGase F) to generate N-linked deglycopeptides. Both N-linked intact glycopeptides and deglycopeptides were analyzed by LC-MS/MS. From N-linked deglycopeptides data sets, 764 N-linked glycoproteins, 1699 N-linked glycosites and 3328 unique N-linked deglycopeptides were identified. Four types of N-linked glycosylation motifs (NXS/T/C/V, X≠P) were used to recognize the N-linked deglycopeptides. The spectra of these N-linked deglycopeptides were utilized for N-linked deglycopeptides library construction and identification of N-linked intact glycopeptides. A database containing 739 N-glycan masses was constructed and utilized during spectral library search for the identification of N-linked intact glycopeptides. In total, 526 N-linked glycoproteins, 1036 N-linked glycosites, 22,677 N-linked intact glycopeptides and 738 N-glycan masses were identified under 1% FDR, representing the most in-depth serum N-glycoproteome identified by LC-MS/MS at N-linked intact glycopeptide level.