Molecular dynamics simulation of the electron ionization mass spectrum of tabun

Quantum Chemistry-based Molecular Dynamics Simulations as a Tool for the Assignment of ESI-MS/MS Spectra of Drug Molecules

In organic mass spectrometry, fragment ions provide important information on the analyte as a central part of its structure elucidation. With increasing molecular size and possible protonation sites, the potential energy surface (PES) of the analyte can become very complex, which results in a large number of possible fragmentation patterns. Quantum chemical (QC) calculations can help here, enabling the fast calculation of the PES and thus enhancing the mass spectrometry-based structure elucidation processes. In this work, the previously unknown fragmentation pathways of the two drug molecules Nateglinide (45 atoms) and Zopiclone (51 atoms) were investigated using a combination of generic formalisms and calculations conducted with the Quantum Chemical Mass Spectrometry (QCxMS) program. The computations of the de novo fragment spectra were conducted with the semi-empirical GFNn-xTB (n=1, 2) methods and compared against Orbitrap measured electrospray ionization (ESI) spectra in positive ion mode. It was found that the unbiased QC calculations are particularly suitable to predict non-evident fragment ion structures, sometimes contrasting the accepted generic formulation of fragment ion structures from electron migration rules, where the "true" ion fragment structures are approximated. For the first time, all fragment and intermediate structures of these large-sized molecules could be elucidated completely and routinely using this merger of methods, finding new undocumented mechanisms, that are not considered in common rules published so far. Given the importance of ESI for medicinal chemistry, pharmacokinetics, and metabolomics, this approach can significantly enhance the mass spectrometry-based structure elucidation processes and contribute to the understanding of previously unknown fragmentation pathways.

Simulation of the electron ionization mass spectra of the Novichok nerve agent

Novichok is one of the most feared and controversial nerve agents, which existence was confirmed only after the Salisbury attack in 2018. A new attack on August 2020, in Russia, was confirmed. After the 2018 attack, the agent was included in the list of the most dangerous chemicals of the Chemical Weapons Convention (CWC). However, information related to its electron ionization mass spectrometry (EI/MS), essential for unambiguous identification, is scarce. Therefore, investigations about Novichok EI/MS are urgent. In this work, we employed Born-Oppenheimer molecular dynamics through the Quantum Chemistry Electron Ionization Mass Spectrometry (QCEIMS) method to simulate and rationalize the EI/MS spectra and fragmentation pathways of 32 Novichok molecules recently incorporated into the CWC. The comparison of additional simulations with the measured EI spectrum of another Novichok analog is very favorable. A general scheme of the fragmentation pathways derived from simulation results was presented. The present results will be useful for elucidation and prediction of the EI spectra and fragmentation pathways of the dangerous Novichok nerve agent.

From QCEIMS to QCxMS: A Tool to Routinely Calculate CID Mass Spectra Using Molecular Dynamics

Mass spectrometry (MS) is a powerful tool in chemical research and substance identification. For the computational modeling of electron ionization MS, we have developed the quantum-chemical electron ionization mass spectra (QCEIMS) program. Here, we present an extension of QCEIMS to calculate collision-induced dissociation (CID) spectra. The more general applicability is accounted for by the new name QCxMS, where "x" refers to EI or CID. To this end, fragmentation and rearrangement reactions are computed "on-the-fly" in Born-Oppenheimer molecular dynamics (MD) simulations with the semiempirical GFN2-xTB Hamiltonian, which provides an efficient quantum mechanical description of all elements up to Z = 86 (Rn). Through the explicit modeling of multicollision processes between precursor ions and neutral gas atoms as well as temperature-induced decomposition reactions, QCxMS provides detailed insight into the collision kinetics and fragmentation pathways. In combination with the CREST program to determine the preferential protonation sites, QCxMS becomes the first standalone MD-based program that can predict mass spectra based solely on molecular structures as input. We demonstrate this for six organic molecules with masses ranging from 159 to 296 Da, for which QCxMS yields CID spectra in reasonable agreement with experiments.

A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics

Metabolomics, the comprehensive study of the metabolome, and lipidomics-the large-scale study of pathways and networks of cellular lipids-are major driving forces in enabling personalized medicine. Complicated and error-prone data analysis still remains a bottleneck, however, especially for identifying novel metabolites. Comparing experimental mass spectra to curated databases containing reference spectra has been the gold standard for identification of compounds, but constructing such databases is a costly and time-demanding task. Many software applications try to circumvent this process by utilizing cutting-edge advances in computational methods-including quantum chemistry and machine learning-and simulate mass spectra by performing theoretical, so called in silico fragmentations of compounds. Other solutions concentrate directly on experimental spectra and try to identify structural properties by investigating reoccurring patterns and the relationships between them. The considerable progress made in the field allows recent approaches to provide valuable clues to expedite annotation of experimental mass spectra. This review sheds light on individual strengths and weaknesses of these tools, and attempts to evaluate them-especially in view of lipidomics, when considering complex mixtures found in biological samples as well as mass spectrometer inter-instrument variability.