Simple modification of a protein database for mass spectral identification of N‐linked glycopeptides

Atwood, James; Sahoo, Satya S.; Álvarez-Manilla, Gerardo; Weatherly, D. Brent; Kolli, Kumar; Orlando, Ron; York, William S.

doi:10.1002/rcm.2162

Cited by 19 publications

(22 citation statements)

References 16 publications

(31 reference statements)

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“…Thus, an approach which simply limits the 'identified' N-linked glycosylation sites to Asn residues found within a consensus sequence may yield erroneous results. 29 These ambiguities led us to examine the PGIP data in greater detail. One characteristic shared by the majority of falsely identified glycosylation sites was their close proximity to the C-terminus of large tryptic peptides.…”

Section: Resultsmentioning

confidence: 99%

“…Thus, this approach has eliminated all of the apparent false positive N-linked glycosylation site identifications without affecting the discovery of actual glycosylation sites. One potential detrimental effect from using this computational solution is that increasing the number of possible peptides searched has been shown to increase the probability of incorrect peptide identifications; 29,31 however, this topic is outside the scope of this communication.…”

Section: (A) and 3(b) Respectively) Although Unexpected This Incormentioning

confidence: 96%

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A potential pitfall in ¹⁸O‐based N‐linked glycosylation site mapping

Angel

Lim

Wells

et al. 2007

Rapid Comm Mass Spectrometry

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A common procedure for identifying N-linked glycosylation sites involves tryptic digestion of the glycoprotein, followed by the conversion of glycosylated asparagine residues into (18)O-labeled aspartic acids by PNGase F digestion in (18)O water. The 3 Da mass tag created by this process is readily observable by liquid chromatography/tandem mass spectrometry (LC/MS/MS) analysis, and is often used to identify the sites of N-linked glycosylation. While using this procedure, we noticed that 60% of the asparagines identified as being glycosylated were not part of the consensus sequence required for N-linked glycosylation, and thus were not biologically possible. Investigation into the source of this unacceptably high false positive rate demonstrated that even after reversed-phase cleanup and heat denaturation, the trypsin used for proteolysis was still active and led to the incorporation of (18)O into the C-termini of the peptides during the deglycosylation step. The resulting mass shift accounted for most of the false positive sites, as the database search algorithm confused it with an (18)O-labeled Asp residue near the C-terminus of a peptide. This problem can be overcome by eliminating trypsin from the solution prior to performing the deglycosylation process, by resuspending the peptides in natural abundance water following deglycosylation, or by allowing (18)O incorporation into the C-terminus as a variable modification during the database search. These methods have been demonstrated on a model protein, and are applicable to the analyses of glycoproteins that are digested with trypsin or another serine protease prior to enzymatic release of the carbohydrate side chains. This study should alert investigators in the field to this potential and unexpected pitfall and provide strategies to overcome this phenomenon.

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Section: Resultsmentioning

confidence: 99%

Section: (A) and 3(b) Respectively) Although Unexpected This Incormentioning

confidence: 96%

A potential pitfall in ¹⁸O‐based N‐linked glycosylation site mapping

Angel

Lim

Wells

et al. 2007

Rapid Comm Mass Spectrometry

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“…We then incorporated PI scan-driven IDA to selectively identify and structurally characterize the glycopeptides, glycosylation sites, and glycoforms with the assistance of the in silico tool for all HILIC fractions. The identified glycopeptides and occupancy of each glycosylation site were determined after treatment of five pooled HILIC fractions with PNGase A and subsequent MS/MS analysis on a high resolution instrument capable of distinguishing between peptides incorporating an Asp (glycoform) or an Asn (native amide) (31). In addition, comparative bioinformatic analysis was performed for the data acquired from the shotgun proteomic and direct glycosylation analysis approaches on three different lectins samples.…”

Section: Workflow and Experimental Design-mentioning

confidence: 99%

A Comparative Study of Lectin Affinity Based Plant N-Glycoproteome Profiling Using Tomato Fruit as a Model

Ruiz‐May

Hucko

Howe

et al. 2014

Molecular & Cellular Proteomics

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Lectin affinity chromatography (LAC) can provide a valuable front-end enrichment strategy for the study of N-glycoproteins and has been used to characterize a broad range eukaryotic N-glycoproteomes. Moreover, studies with mammalian systems have suggested that the use of multiple lectins with different affinities can be particularly effective. A multi-lectin approach has also been reported to provide a significant benefit for the analysis of plant N-glycoproteins; however, it has yet to be determined whether certain lectins, or combinations of lectins are optimal for plant N-glycoproteome profiling; or whether specific lectins show preferential association with particular N-glycosylation sites or N-glycan structures. We describe here a comparative study of three mannose-binding lectins, concanavalin A, snowdrop lectin, and lentil lectin, to profile the N-glycoproteome of mature green stage tomato (Solanum lycopersicum) fruit pericarp. Through coupling lectin affinity chromatography with a shotgun proteomics strategy, we identified 448 putative N-glycoproteins, whereas a parallel lectin affinity chromatography plus hydrophilic interaction chromatography analysis revealed 318 putative N-glycosylation sites on 230 N-glycoproteins, of which 100 overlapped with the shotgun analysis, as well as 17 N-glycan structures. The use of multiple lectins substantially increased N-glycoproteome coverage and although there were no discernible differences in the structures of N-glycans, or the charge, isoelectric point (pI) or hydrophobicity of the glycopeptides that differentially bound to each lectin, differences were observed in the amino acid frequency at the ؊1 and ؉1 subsites of the N-glycosylation sites. We also demonstrated an alternative and complementary in planta recombinant expression strategy, followed by affinity MS analysis, to identify the putative N-glycan structures of glycoproteins whose abundance is too low to be readily determined by a shotgun approach, and/or combined with deglycosylation for predicted deamidated sites, using a xyloglucan-specific endoglucanase inhibitor protein as an example. Molecular & Cellular

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“…We searched for all tandem mass spectra in pFind studio 2.8 against the Swiss-Prot database (2015_03, mouse, 16711 entries), replacing the N in the sequon N-!P-[S|T|C] (where !P is ‘not proline’) with J. J was defined as having the same monoisotopic mass as asparagine, and variable modifications of 0.9840 were allowed only for J during database searching [41]. Static modification of carbamidomethyl (Cys) was set, along with dynamic modifications of deamidation (J), oxidation (Met), and acetylation (N-Terminal).…”

Section: Methodsmentioning

confidence: 99%

In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins

Pan

Wang

et al. 2016

Oncotarget

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N-glycosylation is one of the most prominent and abundant posttranslational modifications of proteins. It is estimated that over 50% of mammalian proteins undergo glycosylation. However, the analysis of N-glycoproteins has been limited by the available analytical technology. In this study, we comprehensively mapped the N-glycosylation sites in the mouse brain proteome by combining complementary methods, which included seven protease treatments, four enrichment techniques and two fractionation strategies. Altogether, 13492 N-glycopeptides containing 8386 N-glycosylation sites on 3982 proteins were identified. After evaluating the performance of the above methods, we proposed a simple and efficient workflow for large-scale N-glycosylation site mapping. The optimized workflow yielded 80% of the initially identified N-glycosylation sites with considerably less effort. Analysis of the identified N-glycoproteins revealed that many of the mouse brain proteins are N-glycosylated, including those proteins in critical pathways for nervous system development and neurological disease. Additionally, several important biomarkers of various diseases were found to be N-glycosylated. These data confirm that N-glycosylation is important in both physiological and pathological processes in the brain, and provide useful details about numerous N-glycosylation sites in brain proteins.

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Simple modification of a protein database for mass spectral identification of N‐linked glycopeptides

Cited by 19 publications

References 16 publications

A potential pitfall in ¹⁸O‐based N‐linked glycosylation site mapping

A potential pitfall in ¹⁸O‐based N‐linked glycosylation site mapping

A Comparative Study of Lectin Affinity Based Plant N-Glycoproteome Profiling Using Tomato Fruit as a Model

In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins

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Simple modification of a protein database for mass spectral identification of N‐linked glycopeptides

Cited by 19 publications

References 16 publications

A potential pitfall in 18O‐based N‐linked glycosylation site mapping

A potential pitfall in 18O‐based N‐linked glycosylation site mapping

A Comparative Study of Lectin Affinity Based Plant N-Glycoproteome Profiling Using Tomato Fruit as a Model

In-depth mapping of the mouse brain N-glycoproteome reveals widespread N-glycosylation of diverse brain proteins

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A potential pitfall in ¹⁸O‐based N‐linked glycosylation site mapping

A potential pitfall in ¹⁸O‐based N‐linked glycosylation site mapping