Introduction

Glycosylation is one of the most common and important post-translational modifications and is found in more than 50% of all eukaryotic proteins, including antibodies, receptors, hormones, and structural proteins. Glycoproteins have diverse functions and are involved in numerous biological processes. Their glycan moieties are either directly involved in regulatory processes or influence physicochemical properties of the glycoprotein.This study focuses on N -glycans, which are covalently bound to asparagine residues. The asparagine is found in the consensus sequence [asparagine] [X] [serine/threonine] where X denotes any amino acid except proline. N -glycans are subdivided into complex, high mannose and hybrid types that share a common trimannose-chitobiose core (Manα1-6(Manα1-3)Manβ1-4GlcNAcβ1-4GlcNAc1-N) (Varki et al. 2008; Figure 1). High mannose type structures consist entirely of mannose units, whereas complex type structures are characterized by a variable number of N- acetyllactosamine (Gal(β1-3/4)GlcNAc(β1-)) antennae and possible additional fucosylation, sialylation, galactosylation and/or a bisecting N -acetyl-D-glucosamine. Hybrid type structures are a combination of high mannose and complex type structures.

Mass spectrometry (MS) techniques such as MALDI-MS and LC ESI-MS offer analytical solutions for glycopeptide

Application Note # LCMS-66

Straightforward N-glycopeptide analysis combining fast ion trap data acquisition with new ProteinScape functionalities

and glycan analysis (Wuhrer et al. 2007). However, due to the large variety of glycan structures present at one glycosylation site, MS analysis of N -glycopeptides is challenging. In addition, the generally lower ionization efficiency of glycopeptides compared to non-glycosylated peptides leads to signal suppression effects. Furthermore, N -glycopeptides have considerably higher masses and therefore higher m/z-values than non-glycosylated peptides. This means that a combination of high sensitivity, good resolution, fast MS data acquisition and a large accessible mass range is essential for efficient glycopeptide analysis.

For this study we elucidated the N -glycosylation pattern of the murine monoclonal antibody MOPC-21, which represents a model analyte for biopharmaceutical applications (Huhn et al. 2009). The MOPC-21 antibody has one N -glycosylation site at asparagine 294 and a highly heterogeneous N -glycan pattern with the typical trimannose-chitobiose core. Additional galactosylation and sialylation of the antennae and core fucosylation has been described (Kontermann & Dübel 2010).

Here we demonstrate the beneficial and effective combination of fast ion trap data acquisition (amaZon speed ETD) equipped with CaptiveSpray ionization and the new glycoanalysis features of ProteinScape software.

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Experimental

Sample preparation and LC ESI-MS

The monoclonal antibody MOPC-21 (Sigma-Aldrich) was reduced, carbamidomethylated and digested with trypsin according to the supplied protocol (Sigma-Aldrich, Technical Bulletin). The tryptic peptides were separated on a Dionex Ultimate 3000 nanoRSLC system with a Dionex Acclaim® PepMap C18 column (see Table 1 for more details). MS experiments were carried out using an amaZon speed ETD ion trap system equipped with a CaptiveSpray™ source (Bruker-Michrom). CID fragmentation experiments were performed in autoMSn mode using the Enhanced resolution mode for MS and UltraScan mode for MS/MS acquisition. In comparison to peptide analysis, mass ranges in MS and MS/MS mode were enlarged and the fragmentation amplitude was enhanced. Experimental details are given in Table 2.

Data processing and glycan identification

MS data were processed with DataAnalysis 4.0 using default settings for glycopeptide analysis. The resulting peak lists were classified in ProteinScape 3.0. Glycopeptide spectra were then searched against the Glycome DB

LC settings

System Dionex RSLCnano

Trap column Dionex Acclaim® PepMap RSLC, 75 µm x 2 cm, C18, 3 µm, 100 Å

Analytical column Dionex Acclaim® PepMap RSLC, 75 µm x 25 cm, C18, 2 µm, 100 Å

Flow rate 300 nL/min

Gradient 5-30% acetonitrile in 0.1% formic acid in 60 min

MS settings

Source CaptiveSpray

MS conditions Enhanced resolution mode (8100 m/z s-1)

400-1700 m/z scan range

200000 ICC target

5 Spectra Averages (2 Rolling Averaging)

Target Mass 1200 m/z (CID)

MS/MS conditions UltraScan mode (32500 m/z s-1)

Scan begin 100 m/z – scan end 3 x precursor

Smart isolation

5 precursor (Active Exclusion after 2 spectra)

Fragmentation amplitude: 70% (SmartFrag active)

Table 1: LC-settings used for separation of N-glycopeptides and non-glycosylated peptides.

Table 2: MS and MS/MS settings used for aquisition with the amaZon speed.

database with the integrated search engine GlycoQuest using the parameters listed in Table 3.

Results

In order to discriminate glycopeptide spectra from those of non-glycosylated peptides, a spectra classification was performed in ProteinScape (cf. Figure 2). Glycan fragment distances and specific low mass signals originating from fragmentation within the glycan moiety – so-called oxonium ions– were used to classify MS/MS spectra as potential glycopeptide spectra. Furthermore, consecutive glycan fragment distances were used to determine the mass of the peptide backbone for each glycopeptide. The theoretical fragmentation scheme for an N -glycopeptide of the complex type is shown in Figure 3. The corresponding MS/MS spectrum with the annotated oxonium ions and glycan distances is shown in Figure 4. Peptide masses were automatically calculated during the classification workflow from the signal of the peptide plus GlcNAc. The corresponding glycan masses were used for glycan database searches of the classified N -glycopeptide spectra. This resulted in several glycan structure proposals for each MS/MS spectrum.

Parameter Value

Submitted to search Only classified spectra

Glycan type N-glycan

Taxonomy No restriction

Database GlycomeDB

Composition restriction Hex < 8; HexNAc < 6; NeuAc < 3; Fuc < 1; NeuGc < 3

Derivatization Underivatized

Ions H+ up to 4, charge permutation 1 to 4

MS tolerance 0.3 Da

MS/MS tolerance 0.35 Da

#13C 1

Fragmentation A, B, Y; max. 2 cleavages; max. 2 cross ring

Table 3: GlycoQuest search parameters.

An overview of the glycan database search results obtained with ProteinScape is shown in Figure 5. Several different glycan structures with and without core fucosylation, with additional galactosylation and with attached N -glycolylneuraminic acid were identified for the N -glycosylation site at asparagine 294 of MOPC-21. The ten most abundant glycan structures are listed in Table 4. The score for each glycan was calculated from the intensity and the fragmentation coverage.

The glycopeptides are chromatographically separated in two groups. The main group around 29.2 min contains N -glycopeptides without N -glycolylneuraminic acid and the second group around 33.6 min contains N -glycopeptides with N -glycolylneuraminic acid. An annotated base peak chromatogram representing the ten most abundant N -glycopeptides is shown in Figure 6. Despite the fact that most of the glycopeptides actually co-elute, the fast MS/MS duty cycle of the amaZon speed ETD ion trap enabled high quality MS/MS spectra to be obtained.

In addition to the identification of various glycan structures, a protein sequence coverage of around 85% for the MOPC- 21 heavy chain and 90% for the light chain was achieved, even though the LC-MS method was not optimized for non-glycosylated tryptic peptides.

Figure 1: Basic forms of N-linked glycans. The boxes denote the common chitobiose core that is covalently bound to the amide nitrogen of the asparagine residue.

N-glycans

D-mannose N-acetyl-D-glucosamine

D-galactose

high-mannose type

complex type

hybrid type

N-glycans

GlycoQuest Search

LC-MS/MS data acquisition of glycoprotein digest

LC-MS/MS data set

Potential glycopeptide spectraResult: Identified glycan moieties and calculated peptide masses

EEQFNSTFR

EEQFNSTFR

EEQFNSTFR

EEQFNSTFR

Classification

Figure 2: General workflow used for N-glycopeptide characterization by mass spectrometry.

Theoretical fragmentation scheme

Figure 3 : CID fragmentation scheme of an MOPC-21 N-glycopeptide. Monosaccharide symbols are used according to CFG recommendations (CFG URL).

N

S

TF

F

QE

E

Oxonium ions

Peptide +

GlcNAcGlycopeptide fragments

GlcNAcAmino acidsX

ManGal

Rt [min] Composition m/z measured z Δ MH+ [Da] Score

29.34 Hex3HexNAc4dHex1 1301.58 2 0.00 76.8

29.34 Hex3HexNAc3 1127.01 2 0.00 42.8

29.35 Hex3HexNAc3dHex1 1200.03 2 -0.02 75.1

28.93 Hex4HexNAc4dHex1 1382.61 2 0.00 77.9

29.09 Hex3HexNAc4 1228.54 2 -0.02 85.3

29.14 Hex4HexNAc4 1309.57 2 -0.02 79.1

29.35 Hex4HexNAc3 1208.02 2 -0.04 72.2

34.21 Hex4HexNAc3NeuGc1 1361.57 2 -0.02 23.4

29.54 Hex4HexNAc3dHex1 1281.03 2 0.02 67.9

28.77 Hex5HexNAc4dHex1 1463.61 2 -0.04 64.2 Table 4: Overview of the 10 most abundant N-glycopeptides detected for MOPC-21.

EEQFNSTFR

EEQFNSTFR

EEQFNSTFR

EEQFNSTFR

Conclusion

The current approach demonstrates the performance of the amaZon speed ETD system for the analysis of N -glycosylated peptides. The instrument combines new hardware and software features such as faster precursor isolation and reduced fragmentation time to deliver a significantly faster MS/MS duty cycle compared to the previous platform, providing high quality MS/MS spectra, even from heterogeneous co-eluting glycopeptides.

MS/MS spectrum with the annotated oxonium ions and glycan distances

Figure 4: Ion trap MS/MS spectrum of the doubly charged N-glycopeptide with m/z 1309.57. Oxonium ions and glycan mass distances used for glycopeptide classification are highlighted. The peptide mass is calculated from the signal corresponding to peptide+GlcNAc.

366.171+

528.221+ 690.24

1+ 893.341+

1055.411+

1258.541+

1563.821+ 1887.82

1+

2049.861+

2253.001+

2415.011+

0.0

0.5

1.0

1.5

2.0

2.5

5x10

Intens.

250 500 750 1000 1250 1500 1750 2000 2250 2500 m/z

HexNAcHex

Glycopeptide fragments

Hex Hex HexNAc

1725.831+

Hex+HexNAc

Oxonium ions

HexHexNAc Hex Hex HexHexNAc

Peptide + GlcNAc

1 + 1360.71

Data processing was performed using ProteinScape 3.0, a sophisticated bioinformatics platform. The workflow comprised classification of N -glycopeptide MS/MS spectra from the LC-MS/MS data set, automatic determination of peptide masses, and glycan database search using the search engine GlycoQuest. This led to the identification of a total of 22 different glycan structures for the glycopeptide EEQFNSTFR from MOPC-21 in one LC-MS/MS run.

Overview of the glycan database search results

Figure 5: GlycoQuest search result of MOPC-21 tryptic digest in ProteinScape. Project Navigator (upper left), list of identified glycans and their fragments (upper right), spectrum view with annotated fragments (lower right) and Glycan Structure view (lower left) are shown. Fragmentation spectra are annotated according to the nomenclature of Domon & Costello.

Annotated Spectrum

Fragment Ion List

Project Navigator

Glycan Structure View

Glycan List

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Keywords

N-glycosylation

Glycan structure analysis

bioinformatics

Instrumentation & Software

amaZon speed

ProteinScape 3.0

GlycoQuest

For research use only. Not for use in diagnostic procedures.

AuthorsKristina Neue, Andrea Kiehne, Markus Meyer, Marcus Macht,

Ulrike Schweiger-Hufnagel, Anja Resemann

Bruker Daltonik GmbH

Bruker Daltonik GmbH

Bremen · GermanyPhone +49 (0)421-2205-0 Fax +49 (0)421-2205-103 sales@bdal.de

Bruker Daltonics Inc.

Billerica, MA · USAPhone +1 (978) 663-3660 Fax +1 (978) 667-5993 ms-sales@bdal.com

Bruker Daltonics Inc.

Fremont, CA · USAPhone +1 (510) 683-4300 Fax +1 (510) 490-6586ms-sales@bdal.com

www.bruker.com/ms

References

B. Domon and C. Costello; A systematic nomenclature for

carbohydrate fragmentations in FAB-MS/MS spectra of

glycoconjugates; Glycoconjugate, 5, 397 409 (1988).

C. Huhn, M.H. J. Selman, L. Renee Ruhaak, A.M. Deelder, and

Manfred Wuhrer; IgG glycosylation analysis; Proteomics, 9, 882–913

(2009).

R. Kontermann and S. Dübel (eds.), Antibody Engineering Vol. 1,

Springer-Verlag Berlin Heidelberg 2010.

M. Wuhrer, M.I. Catalina, A.M. Deelder, C.H. Hokke;

Glycoproteomics based on tandem mass spectrometry of

glycopeptides; J. Chrom. B, 849, 115 – 128 (2007).

Varki , R.D. Cummings, J.D. Esko, H.H. Freeze, P. Stanley, C.R.

Bertozzi, G.W. Hart, M.E. Etler; 2008, Essentials of Glycobiology,

2nd edition, CSH press.

CFG: http://www.functionalglycomics.org/static/consortium/

Nomenclature.shtml

The ten most abundant N-glycopeptides

Figure 6: Annotated base peak chromatogram representing the ten most intense glycopeptide precursors linked to the N-glycosylation site N294.

EEQFNSTFR

EEQFNSTFR

1

2

0

1

2

3

4

8x10

Intens.

20 25 30 35 40 45 Time [min]