Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.

Glycans modify protein, lipid, and even RNA molecules to form the regulatory outer coat on cells called the glycocalyx. The changes in glycosylation have been linked to the initiation and progression of many diseases. Thus, while the significance of glycosylation is well established, a lack of accessible methods to characterize glycans has hindered the ability to understand their biological functions. Mass spectrometry (MS)-based methods have generally been at the core of most glycan profiling efforts; however, modern data-independent acquisition (DIA), which could increase sensitivity and simplify workflows, has not been benchmarked for analyzing glycans. Herein, we developed a DIA-based glycomic workflow, termed GlycanDIA, to identify and quantify glycans with high sensitivity and accuracy. The GlycanDIA workflow combined higher energy collisional dissociation (HCD)-MS/MS and staggered windows for glycomic analysis, which facilitates the sensitivity in identification and the accuracy in quantification compared to conventional data-dependent acquisition (DDA)-based glycomics. To facilitate its use, we also developed a generic search engine, GlycanDIA Finder, incorporating an iterative decoy searching for confident glycan identification and quantification from DIA data. The results showed that GlycanDIA can distinguish glycan composition and isomers from N -glycans, O -glycans, and human milk oligosaccharides (HMOs), while it also reveals information on low-abundant modified glycans. With the improved sensitivity, we performed experiments to profile N -glycans from RNA samples, which have been underrepresented due to their low abundance. Using this integrative workflow to unravel the N -glycan profile in cellular and tissue glycoRNA samples, we found that RNA-glycans have specific forms as compared to protein-glycans and are also tissue-specific differences, suggesting distinct functions in biological processes. Overall, GlycanDIA can provide comprehensive information for glycan identification and quantification, enabling researchers to obtain in-depth and refined details on the biological roles of glycosylation. Graphical

Protein–protein interactions (PPIs) are crucial for comprehending the molecular mechanisms and signaling pathways underlying diverse biological processes and disease progression. However, investigating PPIs involving membrane proteins is challenging due to the complexity and heterogeneity of glycosylation. To tackle this challenge, we developed an approach termed glycan-dependent affinity purification coupled with mass spectrometry (GAP–MS), specifically designed to characterize changes in glycoprotein PPIs under varying glycosylation conditions. GAP–MS integrates metabolic control of glycan profiles in cultured cells using small molecules referred to as glycan modifiers with affinity purification followed by mass spectrometry analysis (AP–MS). Here, GAP–MS was applied to characterize and compare the interaction networks under five different glycosylation states for four bait glycoproteins: BSG, CD44, EGFR, and SLC3A2. This analysis identified a network comprising 156 interactions, of which 131 were determined to be glycan dependent. Notably, the GAP–MS analysis of BSG provided distinct information regarding glycosylation-influenced interactions compared to the commonly used glycosylation site mutagenesis approach combined with AP–MS, emphasizing the unique advantages of GAP–MS. Collectively, GAP–MS presents distinct insights over existing methods in elucidating how specific glycosylation forms impact glycoprotein interactions. Additionally, the glycan-dependent interaction networks generated for these four glycoproteins serve as a valuable resource for guiding future functional investigations and therapeutic developments targeting the glycoproteins discussed in this study.

SUMMARY: Protein-protein interactions (PPIs) provide essential insights into the complex molecular mechanisms and signaling pathways within cells that regulate development and disease-related phenotypes. However, for membrane proteins, the impact of various forms of glycosylation has often been overlooked in PPI studies. In this study, we introduce a novel approach, glycan-dependent affinity purification followed by mass spectrometry (GAP-MS), to assess variations in PPIs for any glycoprotein of interest under different glycosylation conditions. As a proof of principle, we selected four glycoproteins—BSG, CD44, EGFR, and SLC3A2—as baits to compare their co-purified partners across five metabolically controlled glycan conditions. The findings demonstrate the capability of GAP-MS to identify PPIs influenced by altered glycosylation states, establishing a foundation for systematically exploring the Glycan-Dependent Protein Interactome (GDPI) for other glycoproteins of interest.

Gas chromatography–mass spectrometry (GC-MS) usually employs hard electron ionization, leading to extensive fragmentations that are suitable to identify compounds based on library matches. However, such spectra are less useful to structurally characterize unknown compounds that are absent from libraries, due to the lack of readily recognizable molecular ion species. We tested methane chemical ionization on 369 trimethylsilylated (TMS) derivatized metabolites using a quadrupole time-of-flight detector (QTOF). We developed an algorithm to automatically detect molecular ion species and tested SIRIUS software on how accurate the determination of molecular formulas was. The automatic workflow correctly recognized 289 (84%) of all 345 detected derivatized standards. Specifically, strong [M − CH₃]⁺ fragments were observed in 290 of 345 derivatized chemicals, which enabled the automatic recognition of molecular adduct patterns. Using Sirius software, correct elemental formulas were retrieved in 87% of cases within the top three hits. When investigating the cases for which the automatic pattern analysis failed, we found that several metabolites showed a previously unknown [M + TMS]⁺ adduct formed by rearrangement. Methane chemical ionization with GC-QTOF mass spectrometry is a suitable avenue to identify molecular formulas for abundant unknown peaks.

Compound identification by mass spectrometry needs reference mass spectra. While there are over 102 million compounds in PubChem, less than 300,000 curated electron ionization (EI) mass spectra are available from NIST or MoNA mass spectral databases. Here, we test quantum chemistry methods (QCEIMS) to generate in-silico EI mass spectra (MS) by combining molecular dynamics (MD) with statistical methods. To test the accuracy of predictions, in-silico mass spectra of 451 small molecules were generated and compared to experimental spectra from the NIST 17 mass spectral library. The compounds covered 43 chemical classes, ranging up to 358 Da. Organic oxygen compounds had a lower matching accuracy, while computation time exponentially increased with molecular size. The parameter space was probed to increase prediction accuracy including initial temperatures, the number of MD trajectories and impact excess energy (IEE). Conformational flexibility was not correlated to the accuracy of predictions. Overall, QCEIMS can predict 70 eV electron ionization spectra of chemicals from first principles. Improved methods to calculate potential energy surfaces (PES) are still needed before QCEIMS mass spectra of novel molecules can be generated at large scale.