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

Koopman, Jeroen; Grimme, Stefan

doi:10.1021/jasms.1c00098

Cited by 51 publications

(60 citation statements)

References 111 publications

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“… 22 − 26 Many attempts were undertaken to use quantum chemistry for the interpretation of fragmentation spectra. 27 − 32 Other software packages were created for the prediction of fragmentation, 33 − 36 and currently, the most advanced software package is MassFrontier (uses a rule-based approach and expert curation), MS-FINDER 37 (uses a set of hydrogen rearrangement rules), MetFrag 38 (uses iterative bond cleavage), CFM-ID, 39 , 40 and SIRIUS 4, 41 , 42 which integrates CSI:FingerID 36 (uses fragmentation trees). Since experimental MS/MS spectra contain ions formed by several fragmentation events, it is convenient to use a fragmentation tree concept.…”

Section: Introductionmentioning

confidence: 99%

PyFragMS─A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

et al. 2022

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Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS—a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or 16 O/ 18 O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.

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Section: Introductionmentioning

confidence: 99%

PyFragMS─A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

et al. 2022

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“…We assume the increased internal energy of ions due to collisions is randomly redistributed into the bath of vibrational degrees of freedom, so that subsequent fragmentation is statistical (Chen et al 2014). This enables the modeling of the collision induced fragmentation merely through molecular dynamics (MD) simulations at elevated temperatures which are also used by Koopman & Grimme (2021). The key element of MD simulations to successfully describe statistical fragmentation is the choice of a proper inter-atomic potential for determining energies and forces of a given system.…”

Section: Methodsmentioning

confidence: 99%

Top-down formation of ethylene from fragmentation of superhydrogenated polycyclic aromatic hydrocarbons

Tang

Simonsen

Jaganathan

et al. 2022

A&A

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Context.Fragmentation is an important decay mechanism for polycyclic aromatic hydrocarbons (PAHs) under harsh interstellar conditions and represents a possible formation pathway for small molecules such as H 2 , C 2 H 2 and C 2 H 4 . Aims. Our aim is to investigate the dissociation mechanism of superhydrogenated PAHs that undergo energetic processing and the formation pathway of small hydrocarbons. Methods. We obtain, experimentally, the mass distribution of protonated tetrahydropyrene (C 16 H + 15 , [py + 5H] + ) and protonated hexahydropyrene (C 16 H + 17 , [py+7H] + ) upon collision induced dissociation (CID). The IR spectra of their main fragments are recorded by infrared multiple-photon dissociation (IRMPD). Extended tight-binding (GFN2-xTB) based molecular dynamics simulations are performed in order to provide the missing structure information in experiment and identify fragmentation pathways. The pathways for fragmentation are further investigated at a hybrid-density functional theory (DFT) and dispersion corrected level. Results. A strong signal for loss of 28 mass units of [py + 7H] + is observed both in the CID experiment and the MD simulation, while [py + 5H] + shows negligible signal for the product corresponding to a mass loss of 28. The 28 mass loss from [py + 7H] + is assigned to the loss of ethylene (C 2 H 4 ) and a good fit between the calculated and experimental IR spectrum of the resulting fragment species is obtained. Further DFT calculations show favorable kinetic pathways for loss of C 2 H 4 from hydrogenated PAH configurations involving three consecutive CH 2 molecular entities. Conclusions. This joint experimental and theoretical investigation proposes a chemical pathway of ethylene formation from fragmentation of superhydrogenated PAHs. This pathway is sensitive to hydrogenated edges (e.g. the degree of hydrogenation and the hydrogenated positions). The inclusion of this pathway in astrochemical models may improve the estimated abundance of ethylene.

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“…For the discovery of unknown unknowns where no libraries or standards exist, the in-silico prediction of EI spectra (Allen et al, 2016;Spackman et al, 2018;Wang et al, 2020;Wei et al, 2019) is more straight forward than collision-induced dissociation (CID) (Koopman and Grimme, 2021) and higher energy C-trap dissociation (HCD) spectra used for LC-HRMS analysis. Although, it is important to recognize that false positive rates vary across all in silico approaches (Schreckenbach et al, 2021).…”

Section: S12 Alternative Ei Spectral Libraries and Ionization Methods...mentioning

confidence: 99%

An Actionable Annotation Scoring Framework for Gas Chromatography - High Resolution Mass Spectrometry (GCHRMS)

Koelmel

Xie

Price

et al. 2022

Preprint

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Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (exposome) as well as assessment of the corresponding biological responses at the molecular level (e.g., metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework, and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography high-resolution mass spectrometry (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar isomers are not considered false positives). This framework is easily adaptable to various workflows and provides a concise means to communicate confidence in annotations. Further validation, refinements, and adoption of this framework will ideally lead to harmonization across the field, helping to improve the quality and interpretability of compound annotations obtained in GC-HRMS.

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

Cited by 51 publications

References 111 publications

PyFragMS─A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

PyFragMS─A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways

Top-down formation of ethylene from fragmentation of superhydrogenated polycyclic aromatic hydrocarbons

An Actionable Annotation Scoring Framework for Gas Chromatography - High Resolution Mass Spectrometry (GCHRMS)

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