New Glycoproteomics Software, GlycoPep Evaluator, Generates Decoy Glycopeptides de Novo and Enables Accurate False Discovery Rate Analysis for Small Data Sets

Zhu, Zhongkui; Su, Xiaomeng; Go, Eden P.; Desaire, Heather

doi:10.1021/ac502176n

Cited by 42 publications

(55 citation statements)

References 41 publications

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“…For example, glycopeptide SVQEIQEIQTFFYFTPN(HexNAc)KTEDTIFLR had a decoy LFITDEN(HexNAc)KTPTFYFFTQIEQIEQVSR. Confidence scores (q-values) were calculated as described by Käll et al (46, 104). …”

Section: Resultsmentioning

confidence: 99%

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Khatri

Klein

White

et al. 2016

Molecular & Cellular Proteomics

114

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Despite sustained biomedical research effort, influenza A virus remains an imminent threat to the world population and a major healthcare burden. The challenge in developing vaccines against influenza is the ability of the virus to mutate rapidly in response to selective immune pressure. Hemagglutinin is the predominant surface glycoprotein and the primary determinant of antigenicity, virulence and zoonotic potential. Mutations leading to changes in the number of HA glycosylation sites are often reported. Such genetic sequencing studies predict at best the disruption or creation of sequons for N-linked glycosylation; they do not reflect actual phenotypic changes in HA structure. Therefore, combined analysis of glycan micro and macro-heterogeneity and bioassays will better define the relationships among glycosylation, viral bioactivity and evolution. We present a study that integrates proteomics, glycomics and glycoproteomics of HA before and after adaptation to innate immune system pressure. We combined this information with glycan array and immune lectin binding data to correlate the phenotypic changes with biological activity. Underprocessed glycoforms predominated at the glycosylation sites found to be involved in viral evolution in response to selection pressures and interactions with innate immune-lectins. To understand the structural basis for site-specific glycan microheterogeneity at these sites, we performed structural modeling and molecular dynamics simulations. We observed that the presence of immature, high-mannose type glycans at a particular site correlated with reduced accessibility to glycan remodeling enzymes. Further, the high mannose glycans at sites implicated in immune lectin recognition were predicted to be capable of forming trimeric interactions with the immune-lectin surfactant protein-D.

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Section: Resultsmentioning

confidence: 99%

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Khatri

Klein

White

et al. 2016

Molecular & Cellular Proteomics

114

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“…GlycoPep Evaluator [89] was developed to address the problem that target-decoy analysis is not appropriate for relatively small datasets, such as those for many glycopeptide samples. This algorithms generates decoy glycopeptides to facilitate determination of FDR.…”

Section: Database Search Methods For Glycoproteomicsmentioning

confidence: 99%

A review of methods for interpretation of glycopeptide tandem mass spectral data

Khatri

Klein

et al. 2015

Glycoconj J

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Despite the publication of several software tools for analysis of glycopeptide tandem mass spectra, there remains a lack of consensus regarding the most effective and appropriate methods. In part, this reflects problems with applying standard methods for proteomics database searching and false discovery rate calculation. While the analysis of small post-translational modifications (PTMs) may be regarded as an extension of proteomics database searching, glycosylation requires specialized approaches. This is because glycans are large and heterogeneous by nature causing glycopeptides to exist as multiple glycosylated variants. Thus, the mass of the peptide cannot be calculated directly from that of the intact glycopeptide. In addition, the chemical nature of the glycan strongly influences product ion patterns observed for glycopeptides. As a result, glycopeptidomics requires specialized bioinformatics methods. We summarize the recent progress towards a consensus for effective glycopeptide tandem mass spectrometric analysis.

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“…the peptide, site and monosaccharide composition. However, assigning appropriate and meaningful FDRs to glycopeptide identifications involves more variables than generating FDRs of nonmodified peptide identifications and has consequently proven challenging and is still in its infancy (158).…”

Section: Ms Acquisition Strategies In Glycoproteomics-lc-ms/mentioning

confidence: 99%

Maturing Glycoproteomics Technologies Provide Unique Structural Insights into the N-glycoproteome and Its Regulation in Health and Disease

Thaysen‐Andersen

Packer

Schulz

2016

Molecular & Cellular Proteomics

180

196

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New Glycoproteomics Software, GlycoPep Evaluator, Generates Decoy Glycopeptides de Novo and Enables Accurate False Discovery Rate Analysis for Small Data Sets

Cited by 42 publications

References 41 publications

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

Integrated Omics and Computational Glycobiology Reveal Structural Basis for Influenza A Virus Glycan Microheterogeneity and Host Interactions

A review of methods for interpretation of glycopeptide tandem mass spectral data

Maturing Glycoproteomics Technologies Provide Unique Structural Insights into the N-glycoproteome and Its Regulation in Health and Disease

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