Development and application of GlycanDIA workflow for glycomic analysis.

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.

Analogies between photochemical reactions and ground-state post-transition-state bifurcations shed light on dynamical origins of selectivity.

Revealing the origins of kinetic selectivity is one of the premier tasks of applied theoretical organic chemistry, and for many reactions, doing so involves comparing competing transition states. For some reactions, however, a single transition state leads directly to multiple products, in which case non-statistical dynamic effects influence selectivity control. The selectivity of photochemical reactions-where crossing between excited-state and ground-state surfaces occurs near ground-state transition structures that interconvert competing products-also should be controlled by the momentum of the reacting molecules as they return to the ground state in addition to the shape of the potential energy surfaces involved. Now, using machine-learning-assisted non-adiabatic molecular dynamics and multiconfiguration pair-density functional theory, these factors are examined for a classic photochemical reaction-the deazetization of 2,3-diazabicyclo[2.2.2]oct-2-ene-for which we demonstrate that momentum dominates the selectivity for hexadiene versus [2.2.2] bicyclohexane products.

Automatic Assignment of Molecular Ion Species to Elemental Formulas in Gas Chromatography/Methane Chemical Ionization Accurate Mass Spectrometry.

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.

Beyond the Ground State: Predicting Electron Ionization Mass Spectra Using Excited-State Molecular Dynamics.

Here, we provide an algorithm that introduces excited states into the molecular dynamics prediction of the 70 eV electron ionization mass spectra. To decide the contributions of different electronic states, the ionization cross section associated with relevant molecular orbitals was calculated by the binary-encounter-Bethe (BEB) model. We used a fast orthogonalization model/single and double state configuration interaction (OM2/CISD) method to implement excited states calculations and combined this with the GFN1-xTB semiempirical model. Demonstrated by predicting the mass spectrum of urocanic acid, we showed better accuracies to experimental spectra using excited-state molecular dynamics than calculations that only used the ground-state occupation. For several histidine pathway intermediates, we found that excited-state corrections yielded an average of 73% more true positive ions compared to the OM2 method when matching to experimental spectra and 16% more true positive ions compared to the GFN method. Importantly, the exited state models also correctly predict several fragmentation reactions that were missing from both ground-state methods. Overall, for 48 calculated molecules, we found the best average mass spectral similarity scores for the mixed excited-state method compared to the ground-state methods using either cosine, weighted dot score, or entropy similarity calculations. Therefore, we recommend adding excited-state calculations for predicting the electron ionization mass spectra of small molecules in metabolomics.

How Well Can We Predict Mass Spectra from Structures? Benchmarking Competitive Fragmentation Modeling for Metabolite Identification on Untrained Tandem Mass Spectra.

Competitive Fragmentation Modeling for Metabolite Identification (CFM-ID) is a machine learning tool to predict in silico tandem mass spectra (MS/MS) for known or suspected metabolites for which chemical reference standards are not available. As a machine learning tool, it relies on both an underlying statistical model and an explicit training set that encompasses experimental mass spectra for specific compounds. Such mass spectra depend on specific parameters such as collision energies, instrument types, and adducts which are accumulated in libraries. Yet, ultimately prediction tools that are meant to cover wide expanses of entities must be validated on cases that were not included in the initial training and testing sets. Hence, we here benchmarked the performance of CFM-ID 4.0 to correctly predict MS/MS spectra for spectra that were not included in the CFM-ID training set and for different mass spectrometry conditions. We used 609,456 experimental tandem spectra from the NIST20 mass spectral library that were newly added to the previous NIST17 library version. We found that CFM-ID's highest energy prediction output would maximize the capacity for library generation. Matching the experimental collision energy with CFM-ID's prediction energy produced the best results, even for HCD-Orbitrap instruments. For benzenoids, better MS/MS predictions were achieved than for heterocyclic compounds. However, when exploring CFM-ID's performance on 8,305 compounds at 40 eV HCD-Orbitrap collision energy, >90% of the 20/80 split test compounds showed <700 MS/MS similarity score. Instead of a stand-alone tool, CFM-ID 4.0 might be useful to boost candidate structures in the greater context of identification workflows.

Quantum Chemical Prediction of Electron Ionization Mass Spectra of Trimethylsilylated Metabolites.

Chemical derivatization, especially silylation, is widely used in gas chromatography coupled to mass spectrometry (GC-MS). By introducing the trimethylsilyl (TMS) group to substitute active hydrogens in the molecule, thermostable volatile compounds are created that can be easily analyzed. While large GC-MS libraries are available, the number of spectra for TMS-derivatized compounds is comparatively small. In addition, many metabolites cannot be purchased to produce authentic library spectra. Therefore, computationally generated in silico mass spectral databases need to take TMS derivatizations into account for metabolomics. The quantum chemistry method QCEIMS is an automatic method to generate electron ionization (EI) mass spectra directly from compound structures. To evaluate the performance of the QCEIMS method for TMS-derivatized compounds, we chose 816 trimethylsilyl derivatives of organic acids, alcohols, amides, amines, and thiols to compare in silico-generated spectra against the experimental EI mass spectra from the NIST17 library. Overall, in silico spectra showed a weighted dot score similarity (1000 is maximum) of 635 compared to the NIST17 experimental spectra. Aromatic compounds yielded a better prediction accuracy with an average similarity score of 808, while oxygen-containing molecules showed lower accuracy with only an average score of 609. Such similarity scores are useful for annotation of small molecules in untargeted GC-MS-based metabolomics, suggesting that QCEIMS methods can be extended to compounds that are not present in experimental databases. Despite this overall success, 37% of all experimentally observed ions were not found in QCEIMS predictions. We investigated QCEIMS trajectories in detail and found missed fragmentations in specific rearrangement reactions. Such findings open the way forward for future improvements to the QCEIMS software.

Quantum Chemistry Calculations for Metabolomics.

A primary goal of metabolomics studies is to fully characterize the small-molecule composition of complex biological and environmental samples. However, despite advances in analytical technologies over the past two decades, the majority of small molecules in complex samples are not readily identifiable due to the immense structural and chemical diversity present within the metabolome. Current gold-standard identification methods rely on reference libraries built using authentic chemical materials ("standards"), which are not available for most molecules. Computational quantum chemistry methods, which can be used to calculate chemical properties that are then measured by analytical platforms, offer an alternative route for building reference libraries, i.e., in silico libraries for "standards-free" identification. In this review, we cover the major roadblocks currently facing metabolomics and discuss applications where quantum chemistry calculations offer a solution. Several successful examples for nuclear magnetic resonance spectroscopy, ion mobility spectrometry, infrared spectroscopy, and mass spectrometry methods are reviewed. Finally, we consider current best practices, sources of error, and provide an outlook for quantum chemistry calculations in metabolomics studies. We expect this review will inspire researchers in the field of small-molecule identification to accelerate adoption of in silico methods for generation of reference libraries and to add quantum chemistry calculations as another tool at their disposal to characterize complex samples.

Predicting in silico electron ionization mass spectra using quantum chemistry.

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.