Evaluating the Accuracy of the QCEIMS Approach for Computational Prediction of Electron Ionization Mass Spectra of Purines and Pyrimidines

Mass spectrometry is the most commonly used method for compound annotation in metabolomics. However, most mass spectra in untargeted assays cannot be annotated with specific compound structures because reference mass spectral libraries are far smaller than the complement of known molecules. Theoretically predicted mass spectra might be used as a substitute for experimental spectra especially for compounds that are not commercially available. For example, the Quantum Chemistry Electron Ionization Mass Spectra (QCEIMS) method can predict 70 eV electron ionization mass spectra from any given input molecular structure. In this work, we investigated the accuracy of QCEIMS predictions of electron ionization (EI) mass spectra for 80 purine and pyrimidine derivatives in comparison to experimental data in the NIST 17 database. Similarity scores between every pair of predicted and experimental spectra revealed that 45% of the compounds were found as the correct top hit when QCEIMS predicted spectra were matched against the NIST17 library of >267,000 EI spectra, and 74% of the compounds were found within the top 10 hits. We then investigated the impact of matching, missing, and additional fragment ions in predicted EI mass spectra versus ion abundances in MS similarity scores. We further include detailed studies of fragmentation pathways such as retro Diels–Alder reactions to predict neutral losses of (iso)cyanic acid, hydrogen cyanide, or cyanamide in the mass spectra of purines and pyrimidines. We describe how trends in prediction accuracy correlate with the chemistry of the input compounds to better understand how mechanisms of QCEIMS predictions could be improved in future developments. We conclude that QCEIMS is useful for generating large-scale predicted mass spectral libraries for identification of compounds that are absent from experimental libraries and that are not commercially available.

Identifying Fentanyl with Mass Spectral Libraries

<div> <div> <div> <p>Synthesis, distribution and abuse of fentanyl, a synthetic opioid, has led to a critical worldwide epidemic. Mass spectral library searching for opioids remains unresolved despite being central to law-enforcement involving identification, monitoring and prosecution of opioid related crimes. In this article, two model problems are presented to illustrate difficulties associated with fentanyl identification. A collection of both currently-employed similarity measures and intuitive measures of dissimilarity are employed to simulate identifying fentanyl analogs with mass spectral library searching. </p> </div> </div> </div>

Identifying Fentanyl with Mass Spectral Libraries

<div> <div> <div> <p>Synthesis, distribution and abuse of fentanyl, a synthetic opioid, has led to a critical worldwide epidemic. Mass spectral library searching for opioids remains unresolved despite being central to law-enforcement involving identification, monitoring and prosecution of opioid related crimes. In this article, two model problems are presented to illustrate difficulties associated with fentanyl identification. A collection of both currently-employed similarity measures and intuitive measures of dissimilarity are employed to simulate identifying fentanyl analogs with mass spectral library searching. </p> </div> </div> </div>

Open, High-Resolution EI+ Spectral Library of Anthropogenic Compounds

To address the lack of high-resolution electron ionisation mass spectral libraries (HR-[EI+]-MS) for environmental chemicals, a retention-indexed HR-[EI+]-MS library has been constructed following analysis of authentic compounds via GC-Orbitrap MS. The library is freely provided alongside a compound database of predicted physicochemical properties. Currently, the library contains over 350 compounds from 56 compound classes and includes a range of legacy and emerging contaminants. The RECETOX Exposome HR-[EI+]-MS library expands the number of freely available resources for use in full-scan chemical exposure studies and is available at: https://doi.org/10.5281/zenodo.4471217.

Analyses of Skin Secretions of Vipera ammodytes (Linnaeus, 1758) (Reptilia: Serpentes), with Focus on the Complex Compounds and Their Possible Role in the Chemical Communication

Snakes rely heavily on chemical cues when foraging, searching for mates, etc. Snakes’ sex attractiveness pheromones comprise mainly heavy, semi-volatile compounds such as ketones. Here we investigated the composition of skin secretions of adult Vipera ammodytes (Linnaeus, 1758) individuals. The samples were analyzed by gas chromatography/mass spectrometry and the identification of the compounds was performed using commercial mass spectral libraries and retention times. The relative concentrations of all detected compounds were tested for significant differences between (1) male vs. female live individuals, (2) shed skin vs. live individuals, and (3) pre-reproductive vs. reproductive live individuals. We detected fifty-nine compounds of which six were ketones. Two ketones (2-pentacosanone and 2-heptacosanone) were present in many of the samples and thus may have an important role in the V. ammodytes chemical communication. We did not find significant differences between the relative concentrations of the compounds between male and female individuals (only three compounds are exceptions). Significant differences were found between extracts from shed skins and live individuals and between live pre-reproductive individuals and live reproductive individuals. The results of the study suggest that chemical communication in V. ammodytes involves less compounds in comparison to the known literature data for other species.

Annotating Nontargeted LC-HRMS/MS Data with Two Complementary Tandem Mass Spectral Libraries

Tandem mass spectral databases are indispensable for fast and reliable compound identification in nontargeted analysis with liquid chromatography–high resolution tandem mass spectrometry (LC-HRMS/MS), which is applied to a wide range of scientific fields. While many articles now review and compare spectral libraries, in this manuscript we investigate two high-quality and specialized collections from our respective institutes, recorded on different instruments (quadrupole time-of-flight or QqTOF vs. Orbitrap). The optimal range of collision energies for spectral comparison was evaluated using 233 overlapping compounds between the two libraries, revealing that spectra in the range of CE 20–50 eV on the QqTOF and 30–60 nominal collision energy units on the Orbitrap provided optimal matching results for these libraries. Applications to complex samples from the respective institutes revealed that the libraries, combined with a simple data mining approach to retrieve all spectra with precursor and fragment information, could confirm many validated target identifications and yield several new Level 2a (spectral match) identifications. While the results presented are not surprising in many ways, this article adds new results to the debate on the comparability of Orbitrap and QqTOF data and the application of spectral libraries to yield rapid and high-confidence tentative identifications in complex human and environmental samples.

Mind the Gap: Mapping Mass Spectral Databases in Genome-Scale Metabolic Networks Reveals Poorly Covered Areas

The use of mass spectrometry-based metabolomics to study human, plant and microbial biochemistry and their interactions with the environment largely depends on the ability to annotate metabolite structures by matching mass spectral features of the measured metabolites to curated spectra of reference standards. While reference databases for metabolomics now provide information for hundreds of thousands of compounds, barely 5% of these known small molecules have experimental data from pure standards. Remarkably, it is still unknown how well existing mass spectral libraries cover the biochemical landscape of prokaryotic and eukaryotic organisms. To address this issue, we have investigated the coverage of 38 genome-scale metabolic networks by public and commercial mass spectral databases, and found that on average only 40% of nodes in metabolic networks could be mapped by mass spectral information from standards. Next, we deciphered computationally which parts of the human metabolic network are poorly covered by mass spectral libraries, revealing gaps in the eicosanoids, vitamins and bile acid metabolism. Finally, our network topology analysis based on the betweenness centrality of metabolites revealed the top 20 most important metabolites that, if added to MS databases, may facilitate human metabolome characterization in the future.