Biology subjects

Rangel-Angarita, V.

Publications and source records attributed to Rangel-Angarita, V..

3 recordsLinked to original sources

bioRxiv2023

False positive glycopeptide identification via in-FAIMS fragmentation

High-field asymmetric waveform ion mobility spectrometry (FAIMS) separates glycopeptides in the gas phase prior to mass spectrometry (MS) analysis, thus offering the potential to analyze glycopeptides without prior enrichment. Several studies have demonstrated the ability of FAIMS to enhance glycopeptide detection but have primarily focused on N-glycosylation. Here, we evaluated FAIMS for O-glycoprotein and mucin-domain glycoprotein analysis using samples of varying complexity. We demonstrated that FAIMS was useful in increasingly complex samples, as it allowed for the identification of more glycosylated species. However, during our analyses, we observed a phenomenon called "in FAIMS fragmentation" (IFF) akin to in source fragmentation but occurring during FAIMS separation. FAIMS experiments showed a 2-5-fold increase in spectral matches from IFF compared to control experiments. These results were also replicated in previously published data, indicating that this is likely a systemic occurrence when using FAIMS. Our study highlights that although there are potential benefits to using FAIMS separation, caution must be exercised in data analysis because of prevalent IFF, which may limit its applicability in the broader field of O-glycoproteomics. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/542648v1_ufig1.gif" ALT="Figure 1"> View larger version (51K): org.highwire.dtl.DTLVardef@d94300org.highwire.dtl.DTLVardef@1bdc6dforg.highwire.dtl.DTLVardef@4b8ea8org.highwire.dtl.DTLVardef@82beed_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

bioRxiv2022

Comprehensive analysis of platelet glycoprotein Ibα glycosylation.

BackgroundPlatelet glycoprotein (GP) Ib is the major ligand-binding subunit of the GPIb-IX-V complex that binds von Willebrand Factor (VWF). GPIb is heavily glycosylated, and its glycans have been proposed to play key roles in platelet clearance, VWF binding, and as target antigens in immune thrombocytopenia syndromes. Despite its importance in platelet biology, the glycosylation profile of GPIb is not well characterized. ObjectivesThe aim of this study was to comprehensively analyze GPIb amino acid sites of glycosylation (glycosites) and glycan structures. MethodsGPIb ectodomain that was recombinantly expressed or that was purified from human platelets was analyzed by Western blot, mass spectrometry (MS) glycomics, and MS glycoproteomics to define glycosites and the structures of the attached glycans. ResultsWe identified a diverse repertoire of N- and O-glycans, including sialoglycans, Tn antigen, T antigen, and ABH blood group antigens. In the analysis of the recombinant protein, we identified 62 unique O-glycosites. In the analysis of the endogenous protein purified from platelets, we identified at least 48 unique O-glycosites and 1 N-glycosite. The GPIb mucin domain is densely O-glycosylated. Glycosites are also located within the macroglycopeptide domain and mechanosensory domain (MSD). ConclusionsThis comprehensive analysis of GPIb glycosylation lays the foundation for further studies to determine the functional and structural roles of GPIb glycans. Essentials- Glycosylation of glycoprotein Ib (GPIb) is important for platelet function. - We report a comprehensive and site-specific analysis of human GPIb glycosylation. - GPIb carries sialoglycans, Tn antigen, T antigen, and ABO blood group (ABH) antigens. - We experimentally determined 48 O-glycosites and 1 N-glycosite by mass spectrometry.

biochemistry↗

bioRxiv2022

A systematic comparison of current bioinformatic tools for glycoproteomics data

Glycosylation is one of the most common post-translational modifications and generates an enormous amount of proteomic diversity; changes in glycosylation are associated with nearly all disease states. Intact glycoproteomics seeks to determine the site-localization and composition of glycans along a protein backbone via mass spectrometry. Following data acquisition, raw files are analyzed using search algorithms to define peptide sequence, glycan composition, and site localization. Glycoproteomics is rapidly expanding, creating the pressing need to establish bioinformatic community standards. Recently, several new search algorithms were released, many of which vary in terms of search strategy, localization system, score cutoffs, and glycan databases, thus warranting a comprehensive comparison of these new programs along with existing programs. Here, we analyzed three common samples: an enriched cell lysate, a mixture of 6 glycoproteins, and a mucin-domain glycoprotein. All raw files were searched with comparable parameters among software and the results were extensively manually validated to compare accuracy and completion of the output. Our results highlight the continued need for manual validation of glycopeptide spectral matches, especially for O-glycopeptides. Despite this, O-Pair outperformed all other programs in correct identification of O-glycopeptides and its localization system proved to be useful. On the other hand, Byonic and pGlyco performed best for N-glycoproteomics; the former was best for proteome-wide searches, but the latter identified more N-glycosites in less complex samples. Overall, we summarize the strengths, weaknesses, and potential improvements for these search algorithms. TOC Figure O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=52 SRC="FIGDIR/small/484528v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@17c371forg.highwire.dtl.DTLVardef@70532org.highwire.dtl.DTLVardef@e640b7org.highwire.dtl.DTLVardef@a2bb26_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗