A systems-based framework to computationally describe putative transcription factors and signaling pathways regulating glycan biosynthesis

• Theodore Groth,
• Rudiyanto Gunawan and
• Sriram Neelamegham

Beilstein J. Org. Chem. 2021, 17, 1712–1724, doi:10.3762/bjoc.17.119

• /glycoproteomics-based mass spectrometry, would further support the proposed TF–glycogene relationships [43]. Some caveats in our analysis are important to note. First, we only used selected values of RP and ρ to filter TF–glycogene relationships from the Cistrome Cancer DB. Further studies are needed in order to

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

• Steffen Lippold,
• Arnoud H. de Ru,
• Jan Nouta,
• Peter A. van Veelen,
• Magnus Palmblad,
• Manfred Wuhrer and
• Noortje de Haan

Beilstein J. Org. Chem. 2020, 16, 3038–3051, doi:10.3762/bjoc.16.253

• 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

• quantification. Keywords: bioinformatics; cysteine oxidation; glycoproteomics; immunoglobulins; mass spectrometry; Introduction Protein glycosylation mainly occurs in the form of N- and O-glycosylation. N-Glycans are attached to Asn within an amino acid consensus sequence (Asn-Xxx-Ser/Thr, Xxx ≠ Pro) and O

• features, such as the linkage position and anomeric configuration, make protein glycosylation a highly complex posttranslational modification (PTM). Glycoproteomics has become important for many life science disciplines, in particular for biomedical and biopharmaceutical research [3][4][5]. Glycopeptide

Leveraging glycomics data in glycoprotein 3D structure validation with Privateer

• Haroldas Bagdonas,
• Daniel Ungar and
• Jon Agirre

Beilstein J. Org. Chem. 2020, 16, 2523–2533, doi:10.3762/bjoc.16.204

• alternative glycan composition exists in the form of, e.g., mass spectrometry data [14]. Harnessing glycomics and glycoproteomics results to inform glycan model building Current methods used to obtain accurate atomistic descriptions of molecules fall short in dealing with the heterogeneity of glycoproteins

• glycoproteins [42]. The mass spectrometric analysis of glycosylated proteins can be with (glycomics) or without (glycoproteomics) the release of oligosaccharides from the glycoprotein. Usually, glycomics and glycoproteomics experiments are carried out together to obtain a complete description of the

• glycoprotein profile. Glycomics experiments are required to distinguish stereoisomers and the linkage information in order to obtain a full structural description about a glycan, whereas glycoproteomics are required to establish the glycan variability and occupancy at the glycosylation sites of the protein [43

Computational tools for drawing, building and displaying carbohydrates: a visual guide

• Kanhaya Lal,
• Rafael Bermeo and
• Serge Perez

Beilstein J. Org. Chem. 2020, 16, 2448–2468, doi:10.3762/bjoc.16.199

• this tool ideal for integrated use with various glycoinformatics software and also for applications in glycoproteomics, glycomics and mass spectrometry (MS). One of the illustrations involves combined use with the gpAnnotate application, dedicated to score and annotate MS/MS glycopeptide spectrums in

GlypNirO: An automated workflow for quantitative N- and O-linked glycoproteomic data analysis

• Toan K. Phung,
• Cassandra L. Pegg and
• Benjamin L. Schulz

Beilstein J. Org. Chem. 2020, 16, 2127–2135, doi:10.3762/bjoc.16.180

• Queensland, St. Lucia, QLD 4072, Australia 10.3762/bjoc.16.180 Abstract Mass spectrometry glycoproteomics is rapidly maturing, allowing unprecedented insights into the diversity and functions of protein glycosylation. However, quantitative glycoproteomics remains challenging. We developed GlypNirO, an

• statistical workflows. We used GlypNirO to analyse a published plasma glycoproteome dataset and identified changes in site-specific N- and O-glycosylation occupancy and structure associated with hepatocellular carcinoma as putative biomarkers of disease. Keywords: glycoproteomics; mass spectrometry; N

• are few efficient, robust, and automated workflows for glycopeptide quantification. There are several freely available software programs for quantitative label-free glycoproteomics using MS1 or data-dependent acquisition. These include LaCyTools [17], MassyTools [18], and GlycoSpectrumScan [19], which