Dataset Information

Glycopeptide Relative Retention Time Prediction

ABSTRACT: A re-analysis of published data-dependent acquisition glycoproteomics datasets to demonstrate relative retention time prediction on glycopeptide identification and disambiguation of adduct states.

INSTRUMENT(S): Orbitrap Fusion Lumos, Q Exactive

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Blood

SUBMITTER: Joshua Klein

LAB HEAD: Joseph Zaia

PROVIDER: PXD018208 | Pride | 2020-04-01

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	20160301_Serum_SA_glycopeptide_HCC_1.raw	Raw
	20160301_Serum_SA_glycopeptide_HCC_1_identified_glycopeptides.csv	Csv
	20160301_Serum_SA_glycopeptide_HCC_2.raw	Raw
	20160301_Serum_SA_glycopeptide_HCC_2_identified_glycopeptides.csv	Csv
	20160301_Serum_SA_glycopeptide_HCC_3.raw	Raw

Items per page:

1 - 5 of 20

Publications

Relative Retention Time Estimation Improves N-Glycopeptide Identifications by LC-MS/MS.

Klein Joshua J Zaia Joseph J

Journal of proteome research 20200410 5

Glycopeptides identified by tandem mass spectrometry rely on the identification of the peptide backbone sequence and the attached glycan(s) by the incomplete fragmentation of both moieties. This may lead to ambiguous identifications where multiple structures could explain the same spectrum equally well due to missing information in the mass spectrum or incorrect precursor mass determination. To date, approaches to solving these problems have been limited, and few inroads have been made to addres ...[more]

PMID: 32223173

Similar Datasets

Project description:Influenza A virus (IAV) mutates rapidly, preventing a single seasonal vaccine from targeting more than one strain, and year-to-year vaccine effectiveness is low. One challenge in designing effective vaccines is that genetic mutations frequently cause amino acid variations in IAV envelope protein hemagglutinin (HA) that create new N-glycosylation sequons; resulting N-glycans cause antigenic shielding, allowing viral escape from adaptive immune responses. Vaccine candidate strain selection currently involves correlating antigenicity with HA protein sequence among circulating strains, but quantitative comparison of site-specific glycosylation information is likely to improve the ability to design vaccines with broader effectiveness against evolved strains. However, there is poor understanding of the influence of glycosylation on immunodominance, antigenicity, and immunogenicity of HA, and there are no well-tested methods for comparing glycosylation similarity among virus samples. Here, we present a method for statistically rigorous quantification of similarity between two related virus strains that considers the presence and abundance of glycopeptide glycoforms. We demonstrate the strength of our approach by determining that there was a quantifiable difference in glycosylation at the protein level between wild-type IAV HA from A/Switzerland/9715293/2013 (SWZ13) and a mutant strain of SWZ13, even though no N-glycosylation sequons were changed. We determined site-specifically that WT and mutant HA have varying similarity at the glycosylation sites of the head domain, reflecting competing pressures to evade host immune response while retaining viral fitness. To our knowledge, our results are the first to quantify changes in glycosylation state that occur in related proteins of considerable glycan heterogeneity. Our results provide a method for understanding how changes in glycosylation state are correlated with variations in protein sequence, which is necessary for improving IAV vaccine strain selection. Understanding glycosylation will be especially important as we find new expression vectors for vaccine production, as glycosylation state depends greatly on the host species.

Project description:The amino acid sequences of immunodominant domains of hemagglutinin (HA) on the surface of influenza A virus (IAV) evolve rapidly, producing viral variants. HA mediates receptor recognition, binding and cell entry, and serves as the target for IAV vaccines. Glycosylation, a post-translational modification that places large, branched polysaccharide molecules on proteins, can modulate the function of HA, and can shield antigenic regions allowing for viral evasion from immune responses. Our previous work showed that subtle changes in the HA protein sequence can have a measurable change in glycosylation. Thus, being able to measure quantitatively how glycosylation changes in viral variants is critical for understanding how HA function may change throughout viral evolution. Moreover, understanding quantitatively how the choice of viral expression systems affects glycosylation can help in the process of vaccine design and manufacture. Although IAV vaccines are most commonly expressed in chicken eggs, cell-based vaccines have many advantages and the adoption of more cell-based vaccines would be an important step in mitigating seasonal influenza and protecting against future pandemics. Here, we have investigated the use of data-independent acquisition (DIA) mass spectrometry for quantitative glycoproteomics. We found that DIA improved the sensitivity of glycopeptide detection for four variants of A/Switzerland/9715293/2013 IAV: WT and mutant, each expressed in chicken eggs and MDCK cells. We used the Tanimoto similarity metric to quantify changes in glycosylation between WT and mutant, and between egg-expressed and cell-expressed virus. Our DIA site-specific glycosylation similarity comparison of WT and mutant expressed in eggs confirmed our previous analysis while achieving greater depth of coverage. We found that sequence changes resulting from different viral expression systems affected distinct glycosylation sites of HA. Our methods can be applied to track glycosylation changes in circulating IAV variants to bolster the genomic surveillance already being done, for a more complete understanding of IAV evolution.

Project description:The glycosylation of antibody-based proteins is vital in conferring 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. The 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 in complex LC-MS1 data of a glycoproteomic analysis. Data analysis which involved identifying glycopeptides and their quantitative glycosylation was performed in 30 minutes 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, and it offers valuable insights into the effect of culture media changes on bioprocess and biologic characterisation. The demonstrated utility of GraphMS to semi-automate quantitation and qualitative identification of glycopeptides proves to be an easy and scalable data analysis method that can complement emerging multi-attribute monitoring (MAM) analytical toolsets in bioprocess environments.