GlycoScape: Glycan database-free Run & Done glycoproteomics on the timsTOF platform GlycoScape™ is a real-time glycoproteomics software implementing the Myriad algorithm [1], for real-time glycopeptide identification. We show how GlycoScape accurately identifies glycopeptides without using a glycan-database, and how this database-free approach can be leveraged to identify uncommon glycans. Introduction Glycosylation, the enzymatic process that covalently attaches glycans to proteins or lipids, is a critical post-translational modification that modulates the structure and function of many biomolecules. These modifications can influence physiological and pathological factors including protein folding, cellular recognition, and immune responses [2–4]. Glycosylation is considered of significant importance as a potential source of novel biomarkers and possibly new drug targets, and there is a large body of evidence documenting altered glycosylation in a range of disorders, including cancer, autoimmune and neurodegenerative disease [4–7]. Despite its biological importance, glycosylation remains an area that has largely not received appropriate attention, likely due to the analytical challenges it poses, including mass-spectrometry based research. The study of glycoproteomics, commonly carried out by identification of glycopeptides by LC-MS/MS, is challenging due to the fragmentation behavior of glycopeptides in collision-induced dissociation experiments. Glycopeptides are hybrid amino acid–sugar copolymers, composed of a peptide-moiety and a glycan-moiety. Each moiety has a different fragmentation behavior due to its intrinsic physicochemical differences. This makes acquisition of good glycopeptide fragmentation spectra more challenging. On timsTOF instruments, the stepped-energy collision-induced dissociation (SE-CID)-based glyco-PASEF® method addresses these challenges by performing two fragmentation events, one optimized for generating peptide-moiety fragments and the other for generating glycan-moiety fragments [8, 9]. Also, SE-CID glyco-PASEF allows for the optimization of precursor selection by using a polygon suitable for glycopeptides, gated on glycopeptide-specific oxonium-ion signatures (see 1909861-lcms-221-glyco-pasef-ebook). Gad Armony 1, Fokje Zijlstra 2, Dirk Lefeber 2, Alain van Gool 2, Sven Brehmer 1, José Abuin 1, Lennard Pfenning 1, Paolo Ballesteros 1, Tharan Srikumar 1, and Hans Wessels 2; 1 Bruker Daltonics GmbH & Co. KG, Bremen, Germany; 2 RadboudUMC, the Netherlands. Keywords: Glycopeptides, glycoproteomics, data analysis, glycans, glycobiology, timsTOF, dda-PASEF, GlycoScape,

Analyzing glycoproteomics data and generating glycopeptide identification is more challenging than generating peptide identifications. Therefore, the range of available software for glycoproteomics analysis is scarcer and less mature than proteomic software solutions. Many glycoproteomic software packages use glycan databases for glycan-moiety identification. However, unlike protein identification, which is template-driven, based on the human genome sequence, glycan identification cannot similarly be predicted. Therefore, a comprehensive and exhaustive glycan database akin to the available protein...

Case 1: Antibody glycosylation identified by GlycoScape. Therapeutic monoclonal antibodies (mAB) characterization is an important step in their development and quality control. All antibodies are glycosylated, and determination of conjugated glycans, in particular at Asn297, is a critical part of their characterization. The commercially available NIST mAB standard is well established and has been extensively characterized in the field, including work from us (1889617-Glycopeptide-analysis-in-peptidemapping-workflow-ebook). SE-CID-based glyco-PASEF data were analyzed using default GlycoScape parameters...

Case 3: GlycoScape helps identify novel glycans. The Myriad algorithm in GlycoScape identifies glycan-moieties in a database-free manner, searching through all theoretically possible glycan compositions. This key feature allows GlycoScape to identify novel glycan-moieties that are missing from currently existing glycan databases. An example of such novel glycan-moieties is found in patients suffering from an ALG1-congenital disorder of glycosylation, affecting the first mannosylation step in the biosynthesis of lipid-linked oligosaccharides due to mutations in the ALG1 gene [15]. In this disorder,...

Case 4: GlycoScape correctly identifies species-specific sialic acid distribution in plasma. One of the strengths of the Myriad algorithm is that users can define which sugar building blocks it uses (Figure 4A). Any user defined building blocks can be used for glycan compositions, including multiple types of sialic acids (Neu5Ac and Neu5Gc in this example); phosphorylated glycans [16]; derivatized glycans [17]; or labeled sugars [18]. Building blocks are defined with a name, their mass within the glycan, a one-letter code used in the glycan composition and fragment annotation (Figure 3D), the...

References [1] Armony G, Brehmer S, Srikumar T, et al (2023). The GlycoPaSER Prototype as a Real-Time N-Glycopeptide Identification Tool Based on the PaSER Parallel Computing Platform. Int J Mol Sci 24:7869. https://doi.org/10.3390/ijms24097869. https://github.com/Wessels-Lab/Myriad_core [2] Shental-Bechor D, Levy Y (2008). Effect of glycosylation on protein folding: a close look at thermodynamic stabilization. Proc Natl Acad Sci U S A 105:8256–8261. https://doi.org/10.1073/pnas.0801340105 [3] Varki A (2017). Biological roles of glycans. Glycobiology 27:3–49. https://doi.org/10.1093/glycob/cww086...