ChemFrag: Chemically meaningful annotation of fragment ion mass spectra

Schüler, Jördis-Ann; Neumann, Steffen; Müller‐Hannemann, Matthias; Brandt, Wolfgang

doi:10.1002/jms.4278

Cited by 17 publications

(14 citation statements)

References 33 publications

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“…However, QCEIMS required hours to predict a single spectrum, 27 whereas CFM is competitive with standard combinatorial methods. Suspect screening may involve hundreds or thousands of candidates, 48 and since combinatorial fragmentation can match hundreds of spectra within minutes, 49 it does not appear to be worthwhile to use methods such as QCEIMS for suspect screening at present. However, for de novo structure elucidation, QCEIMS may well become an indispensable tool for environmental chemists in the future, as discussed in the next section.…”

Section: ■ Results and Discussionmentioning

confidence: 99%

Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Schreckenbach

Anderson

Koopman

et al. 2021

J. Am. Soc. Mass Spectrom.

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Organic pollutants can be identified by comparing their electron ionization (EI) mass spectra with those in libraries or obtained from authentic standards. Nevertheless, libraries are incomplete; standards may be unavailable or too costly, or their synthesis may be too time-consuming. This study evaluates the performance of quantum chemical electron ionization mass spectrometry (QCEIMS) vis-à-vis competitive fragmentation modeling (CFM) for suspect screening and unknown identification. EI mass spectra of 35 compounds, including halogenated organics, organophosphorus flame retardants (OPFRs), and disinfection byproducts were computed. Computational results were compared with EI mass spectra compiled in the NIST Library as well as collision-induced dissociation (CID) mass spectra obtained from radical cations M•+ generated by charge-exchange atmospheric pressure chemical ionization (APCI). The results indicate that QCEIMS performs equivalently or better than CFM. Average match factors between computed and experimental (NIST) EI mass spectra were 656 vs 503 for the halogenated organics, and on average, QCEIMS predicted 55% of the products generated by CID vs 17% predicted by CFM. QCEIMS predicted 37% of the OPFR CID products whereas CFM predicted 29%. QCEIMS performed comparably to a commercial combinatorial fragmentation method for suspect screening of a dust sample, identifying 19/20 targets. Examples of unknown pollutants, whose reference spectra were unavailable at the time of discovery, are also presented. The computational results suggest that QCEIMS can help guide the analyst in obtaining authentic standards and raise the possibility that, with advances in computing, an unknown may eventually be confirmed in hours as opposed to the days or months required to obtain authentic standards.

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Section: ■ Results and Discussionmentioning

confidence: 99%

Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Schreckenbach

Anderson

Koopman

et al. 2021

J. Am. Soc. Mass Spectrom.

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“…Calculated masses are searched against fragment peaks in the experimental mass spectrum. Software tools based upon combinatorial approaches are among the fastest fragmentation prediction tools available, although they suffer from accuracy problems, including matching chemically unlikely or impossible fragment ions 32 and not accounting for rearrangements. There is a trade-off between speed and accuracy when it comes to fragmentation prediction.…”

Section: Suspect Screening Methods Developmentmentioning

confidence: 99%

Data-Independent Identification of Suspected Organic Pollutants Using Gas Chromatography–Atmospheric Pressure Chemical Ionization–Mass Spectrometry

et al. 2020

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The identity of an unknown environmental pollutant is reflected by the mass and dissociation chemistry of its (quasi)molecular ion. Gas chromatography–atmospheric pressure chemical ionization–mass spectrometry (GC-APCI-MS) increases the yield of molecular ions (compared to conventional electron ionization) by collisional cooling. Scanning quadrupole data-independent acquisition (SQDIA) permits unbiased, unattended selection of (quasi)molecular ions and acquisition of structure-diagnostic collision-induced dissociation mass spectra, while minimizing interferences, by sequentially cycling a quadrupole isolation window through the m/z range. This study reports on the development of a suspect screening method based on industrial compounds with bioaccumulation potential. A comparison of false and correct identifications in a mixed standard containing 30 analytes suggests that SQDIA results in a markedly lower false-positive rate than standard DIA: 5 for SQDIA and 82 for DIA. Electronic waste dust was analyzed using GC and quadrupole time-of-flight MS with APCI and SQDIA acquisition. A total of 52 brominated, chlorinated, and organophosphorus compounds were identified by suspect screening; 15 unique elemental compositions were identified using nontargeted screening; 17 compounds were confirmed using standards and others identified to confidence levels 2, 3, or 4. SQDIA reduced false-positive identifications, compared to experiments without quadrupole isolation. False positives also varied by class: 20% for Br, 37% for Cl, 75% for P, and >99% for all other classes. The structure proposal of a previously reported halogenated compound was revisited. The results underline the utility of GC-SQDIA experiments that provide information on both the (quasi)molecular ions and its dissociation products for a more confident structural assignment.

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“… 284 , 285 So far, only cheminformatics, machine learning approaches, 286 − 288 or hybrid models have been able to perform successful spectrum-to-structure analysis. 289 …”

Section: Current Applications and Real Examplesmentioning

confidence: 99%

Quantum Chemistry Calculations for Metabolomics

et al. 2021

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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.

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ChemFrag: Chemically meaningful annotation of fragment ion mass spectra

Cited by 17 publications

References 33 publications

Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Predicting the Mass Spectra of Environmental Pollutants Using Computational Chemistry: A Case Study and Critical Evaluation

Data-Independent Identification of Suspected Organic Pollutants Using Gas Chromatography–Atmospheric Pressure Chemical Ionization–Mass Spectrometry

Quantum Chemistry Calculations for Metabolomics

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