Calculation of Mass Spectra with the QCxMS Method for Negatively and Multiply Charged Molecules

Comprehensive and Explainable Fragmentation: A Machine Learning Approach for Fast and Accurate Mass Spectrum Prediction

Mass spectrometry (MS) is a fundamental tool for chemical identification. The current in-silico prediction tools can handle broad instrument conditions, large molecular libraries or fragment structures only on a very limited level. In this work, we propose a dual-model machine learning strategy that can solve this problem by jointly a classification model for fragment identification and noise filtering, and a regression model for spectral prediction. With the help of attention mechanism, our method outperforms other algorithms in accuracy and efficiency, providing a deeper understanding of the molecular fragmentation behavior in mass spectra. Our method can facilitate the large-scale in-silico spectra calculations and the analysis of unknown molecular structures, which may promote wider applications for MS.

QCxMS2 - a program for the calculation of electron ionization mass spectra via automated reaction network discovery

We present a new fully-automated computational workflow for the calculation of electron ionization mass spectra by automated reaction network discovery, transition state theory and Monte-Carlo simulations. Compared to its predecessor QCxMS [S. Grimme, Angew. Chem., Int. Ed., 52, 6306-6312] based on extensive molecular dynamics (MD) simulations, QCxMS2's more efficient approach of using stationary points on the potential energy surface (PES) enables the usage of accurate quantum chemical methods. Fragment geometries and reaction paths are optimized with fast semi-empirical quantum mechanical (SQM) methods and reaction barriers are refined at the density functional theory (DFT) level. This composite approach using GFN2-xTB geometries in combination with energies at the ωB97X-3c level proved to be an efficient combination. On a small but diverse test set of 16 organic and inorganic molecules, QCxMS2 spectra are more accurate than ones from QCxMS yielding on average a higher mass spectral matching of 0.700 compared to QCxMS with 0.622, and is more robust with a minimal matching of 0.498 versus 0.100. Further improvements were observed when both geometries and energies were computed at the ωB97X-3c level, yielding an average matching score of 0.730 and a minimal score of 0.527. Due to its higher accuracy and robustness while maintaining computational efficiency, we propose QCxMS2 as a complementary, more reliable and systematically improvable successor to QCxMS for elucidating fragmentation pathways and predicting electron ionization mass spectra of unknown chemical substances, e.g., in analytical chemistry applications. If coupled to currently developed improved SQM methods, QCxMS2 opens an efficient route to accurate, and routine mass spectra predictions. The QCxMS2 program suite is freely available on GitHub.

Overview on Building Blocks and Applications of Efficient and Robust Extended Tight Binding

The extended tight binding (xTB) family of methods opened many new possibilities in the field of computational chemistry. Within just 5 years, the GFN2-xTB parametrization for all elements up to Z = 86 enabled more than a thousand applications, which were previously not feasible with other electronic structure methods. The xTB methods provide a robust and efficient way to apply quantum mechanics-based approaches for obtaining molecular geometries, computing free energy corrections or describing noncovalent interactions and found applicability for many more targets. A crucial contribution to the success of the xTB methods is the availability within many simulation packages and frameworks, supported by the open source development of its program library and packages. We present a comprehensive summary of the applications and capabilities of xTB methods in different fields of chemistry. Moreover, we consider the main software packages for xTB calculations, covering their current ecosystem, novel features, and usage by the scientific community.

Predicting Collision-Induced-Dissociation Tandem Mass Spectra (CID-MS/MS) Using Ab Initio Molecular Dynamics

Compound identification is at the center of metabolomics, usually by comparing experimental mass spectra against library spectra. However, most compounds are not commercially available to generate library spectra. Hence, for such compounds, MS/MS spectra need to be predicted. Machine learning and heuristic models have largely failed except for lipids. Here, quantum chemistry software can be used to predict mass spectra. However, quantum chemistry predictions for collision induced dissociation (CID) mass spectra in LC-MS/MS are rare. We present the CIDMD (Collision-Induced Dissociation via Molecular Dynamics) framework to model CID-based MS/MS spectra. It uses first-principles molecular dynamics (MD) to simulate the physical process of molecular collisions in CID tandem mass spectrometry. First, molecular ions are constructed at specific protonation sites. Using density functional theory, these protonated ions are targeted by argon collider gas atoms at user-specified velocities. Subsequent bond breakages are simulated over time for at least 1,000 fs. Each simulation is repeated multiple times from various collisional directions. Fragmentations are accumulated over those repeated collisions to generate CIDMD in silico mass spectra. Twelve small metabolites (<205 Da) were selected to test the accuracy of this framework in comparison to experimental MS/MS spectra. When testing different protomers, collider velocities, number of simulations, simulation time and impact factor b cutoffs, we yielded 261 predicted mass spectra. These in silico spectra resulted in entropy similarity scores of an average 624 ± 189 for all 261 spectra compared to their corresponding experimental spectra, which improved to 828 ± 77 when using optimal parameters of the most probable protomers for 12 molecules. With increasing molecular mass, higher velocities achieved better results. Similarly, different protomers showed large differences in fragmentation; hence, with increasing numbers of protomers and tautomers, the average CIDMD prediction accuracy decreased. Mechanistic details showed that specific fragment ions can be produced from different protomers via multiple fragmentation pathways. We propose that CIDMD is a suitable tool to predict mass spectra of small metabolites like produced by the gut microbiome.

Identifying isomers in Chinese traditional medicine via density functional theory and ion fragmentation simulation software QCxMS

In the realm of electrospray ionization mass spectrometry (ESI-MS), distinguishing among isomers poses a significant challenge due to the minimal spectral differences that often arise from their subtle structural differences. This makes the accurate identification of these compounds through solely experimental spectra a daunting task. Computational chemistry has emerged as a pivotal tool in bridging the gap between experimental observations and theoretical understanding. This study used the MS fragmentation simulation software, QCxMS, to model the spectra of five groups of isomers, encompassing 11 compounds, found in the traditional Chinese medicine, Zhishi Xiebai Guizhi Decoction. By comparing the spectra predicted through computational methods with those derived from Ultra-high performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS) experiments, it was observed that, following the optimization of simulation parameters, QCxMS was capable of generating reliable spectra for all examined compounds. Notably, the data calculated under both GFN1-xTB and GFN2-xTB levels exhibited no significant discrepancies. Further analysis enabled the identification of the principal fragments of the 11 compounds from the theoretical data, facilitating the deduction of their fragmentation pathways. The Density Functional Theory (DFT) method was subsequently applied to compute the primary fragmentation energies of these compounds. The findings revealed a congruence between the energy data calculated using both thermodynamic and kinetic approaches and the observed fragment abundance of the isomers. This alignment providing a more precise theoretical framework for understanding the mechanisms underlying the generation of fragment ion differences among isomers.

Studying the Intrinsic Reactivity of Chromanes by Gas-Phase Infrared Spectroscopy

Tandem mass spectrometry is routinely used for the structural analysis of organic molecules, but many fragmentation reactions are not well understood. Because several potential structures can correspond to a measured mass, the assignment of product ions is ambiguous using mass spectrometry alone. Here, we combine mass spectrometry with high-resolution gas-phase infrared spectroscopy and computational chemistry tools to identify product ion structures and derive collision-induced fragmentation mechanisms of the chromane derivatives Trolox and Methyltrolox. We find that protonated Trolox and Methyltrolox fragment identically via dehydration and decarbonylation, while deprotonated ions display substantially diverging reactivities. For deprotonated Methyltrolox, we observe unusual radical fragmentation reactions and suggest a [1,2]-Wittig rearrangement involving aryl migration in the gas phase. Overall, the combined experimental and theoretical approach presented here revealed complex proton dynamics and intramolecular rearrangement reactions, which expand our understanding on structure-reactivity relationships of isolated molecules in different protonation states.

Ab initio molecular dynamics calculations on electron ionization induced fragmentations of C4F7N and C5F10O for understanding their decompositions under discharge conditions

C₄F₇N and C₅F₁₀O are the most promising SF₆ alternatives as eco-friendly insulating gaseous mediums in electrical engineering. It is necessary to clarify their electrical stability and decomposition mechanisms. In this work, we first introduced our experimental results for decomposition products of C₄F₇N/CO₂ and C₅F₁₀O/synthetic air mixtures under partial discharge and spark discharge conditions. Then, we performed ab initio molecular dynamics (AIMD) simulations on the typical decomposition products. The simulations were performed under standard electron impact mass spectrometry (EI-MS); thus, the statistical results of the mass spectra were compared with those of the experimentally obtained standard mass spectra from the NIST database. The AIMD simulation method in simulating the electron-induced ionization process was verified and found to be reliable. Finally, the calculations were also performed for C₄F₇N and C₅F₁₀O with incident electron energies of 20 eV and 70 eV, respectively. The dominant pathway for both gases is the formation of CF₃⁺ with the fracture of the C-C bond. The AIMD simulation is able to predict the decomposition channels after electron-impact ionization without any preconceived knowledge of fragmentation pathways, which provides a novel insight into understanding the decomposition mechanisms of C₄F₇N and C₅F₁₀O under different discharge conditions with different energies.

Understanding of protomers/deprotomers by combining mass spectrometry and computation

Multifunctional compounds may form different prototropic isomers under different conditions, which are known as protomers/deprotomers. In biological systems, these protomer/deprotomer isomers affect the interaction modes and conformational landscape between compounds and enzymes and thus present different biological activities. Study on protomers/deprotomers is essentially the study on the acidity/basicity of each intramolecular functional group and its effect on molecular structure. In recent years, the combination of mass spectrometry (MS) and computational chemistry has been proven to be a powerful and effective means to study prototropic isomers. MS-based technologies are developed to discriminate and characterize protomers/deprotomers to provide structural information and monitor transformations, showing great superiority than other experimental methods. Computational chemistry is used to predict the thermodynamic stability of protomers/deprotomers, provide the simulated MS/MS spectra, infrared spectra, and calculate collision cross-section values. By comparing the theoretical data with the corresponding experimental results, the researchers can not only determine the protomer/deprotomer structure, but also investigate the structure-activity relationship in a given system. This review covers various MS methods and theoretical calculations and their devotion to isomer discrimination, structure identification, conformational transformation, and phase transition investigation of protomers/deprotomers.