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Evaluating the Accuracy of the QCEIMS Approach for Computational Prediction of Electron Ionization Mass Spectra of Purines and Pyrimidines. Metabolites 2022; 12:metabo12010068. [PMID: 35050190 PMCID: PMC8779335 DOI: 10.3390/metabo12010068] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 01/08/2022] [Accepted: 01/09/2022] [Indexed: 12/04/2022] Open
Abstract
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.
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Borges R, Colby SM, Das S, Edison AS, Fiehn O, Kind T, Lee J, Merrill AT, Merz KM, Metz TO, Nunez JR, Tantillo DJ, Wang LP, Wang S, Renslow RS. Quantum Chemistry Calculations for Metabolomics. Chem Rev 2021; 121:5633-5670. [PMID: 33979149 PMCID: PMC8161423 DOI: 10.1021/acs.chemrev.0c00901] [Citation(s) in RCA: 35] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Indexed: 02/07/2023]
Abstract
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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Affiliation(s)
- Ricardo
M. Borges
- Walter
Mors Institute of Research on Natural Products, Federal University of Rio de Janeiro, Rio de Janeiro 21941-901, Brazil
| | - Sean M. Colby
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Susanta Das
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Arthur S. Edison
- Departments
of Genetics and Biochemistry and Molecular Biology, Complex Carbohydrate
Research Center and Institute of Bioinformatics, University of Georgia, Athens, Georgia 30602, United States
| | - Oliver Fiehn
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Tobias Kind
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
| | - Jesi Lee
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Amy T. Merrill
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Kenneth M. Merz
- Department
of Chemistry, Michigan State University, East Lansing, Michigan 48824, United States
| | - Thomas O. Metz
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Jamie R. Nunez
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
| | - Dean J. Tantillo
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Lee-Ping Wang
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Shunyang Wang
- West
Coast Metabolomics Center for Compound Identification, UC Davis Genome
Center, University of California, Davis, California 95616, United States
- Department
of Chemistry, University of California, Davis, California 95616, United States
| | - Ryan S. Renslow
- Biological
Science Division, Pacific Northwest National
Laboratory, Richland, Washington 99352, United States
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Krettler CA, Thallinger GG. A map of mass spectrometry-based in silico fragmentation prediction and compound identification in metabolomics. Brief Bioinform 2021; 22:6184408. [PMID: 33758925 DOI: 10.1093/bib/bbab073] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/29/2021] [Accepted: 02/12/2021] [Indexed: 12/27/2022] Open
Abstract
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.
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Affiliation(s)
- Christoph A Krettler
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
| | - Gerhard G Thallinger
- Institute of Biomedical Informatics, Graz University of Technology, Stremayrgasse 16/I, 8010, Graz, Austria.,Omics Center Graz, BioTechMed-Graz, Stiftingtalstrasse 24, 8010, Graz, Austria
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