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Perez de Souza L, Fernie AR. Computational methods for processing and interpreting mass spectrometry-based metabolomics. Essays Biochem 2024; 68:5-13. [PMID: 37999335 PMCID: PMC11065554 DOI: 10.1042/ebc20230019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/25/2023]
Abstract
Metabolomics has emerged as an indispensable tool for exploring complex biological questions, providing the ability to investigate a substantial portion of the metabolome. However, the vast complexity and structural diversity intrinsic to metabolites imposes a great challenge for data analysis and interpretation. Liquid chromatography mass spectrometry (LC-MS) stands out as a versatile technique offering extensive metabolite coverage. In this mini-review, we address some of the hurdles posed by the complex nature of LC-MS data, providing a brief overview of computational tools designed to help tackling these challenges. Our focus centers on two major steps that are essential to most metabolomics investigations: the translation of raw data into quantifiable features, and the extraction of structural insights from mass spectra to facilitate metabolite identification. By exploring current computational solutions, we aim at providing a critical overview of the capabilities and constraints of mass spectrometry-based metabolomics, while introduce some of the most recent trends in data processing and analysis within the field.
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Affiliation(s)
- Leonardo Perez de Souza
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
| | - Alisdair R Fernie
- Max Planck Institute of Molecular Plant Physiology, Am Mühlenberg 1, 14476 Potsdam-Golm, Germany
- Center for Plant Systems Biology and Biotechnology, 4000 Plovdiv, Bulgaria
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2
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Computed Mass-Fragmentation Energy Profiles of Some Acetalized Monosaccharides for Identification in Mass Spectrometry. Symmetry (Basel) 2022. [DOI: 10.3390/sym14051074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
Our study found that quantum calculations can differentiate fragmentation energies into isomeric structures with asymmetric carbon atoms, such as those of acetalized monosaccharides. It was justified by the good results that have been published in recent years on the discrimination of structural isomers and diastereomers by correlating the calculated mass energy fragmentation profiles with their mass spectra. Based on the quantitative structure–fragmentation relationship (QSFR), this technique compares the intensities of primary ions from the experimental spectrum using the mass energy profiles calculated for the candidate structures. Maximum fit is obtained for the true structure. For a preliminary assessment of the accuracy of the identification of some di-O-isopropylidene monosaccharide diastereomers, we used fragmentation enthalpies (ΔfH) and Gibbs energies (ΔfG) as the energetic descriptors of fragmentation. Four quantum chemical methods were used: RM1, PM7, DFT ΔfH and DFT ΔfG. The mass energy database shows that the differences between the profiles of the isomeric candidate structures could be large enough to be distinguished from each other. This database allows the optimization of energy descriptors and quantum computing methods that can ensure the correct identification of these isomers.
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3
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Lebedev AV. Peculiarities of 2,6-Di-tert-butylpyridine Protonation: Mobility of Protonated Molecules. JOURNAL OF ANALYTICAL CHEMISTRY 2021. [DOI: 10.1134/s1061934821130074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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4
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Chernicharo FCS, Modesto-Costa L, Borges I. Simulation of the electron ionization mass spectra of the Novichok nerve agent. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 56:e4779. [PMID: 34407561 DOI: 10.1002/jms.4779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Revised: 07/19/2021] [Accepted: 07/26/2021] [Indexed: 06/13/2023]
Abstract
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.
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Affiliation(s)
| | - Lucas Modesto-Costa
- Departamento de Química, Instituto Militar de Engenharia, Rio de Janeiro, RJ, Brazil
| | - Itamar Borges
- Departamento de Química, Instituto Militar de Engenharia, Rio de Janeiro, RJ, Brazil
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5
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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: 40] [Impact Index Per Article: 13.3] [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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6
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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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7
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Carrà A, Spezia R. In Silico
Tandem Mass Spectrometer: an Analytical and Fundamental Tool. ACTA ACUST UNITED AC 2021. [DOI: 10.1002/cmtd.202000071] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Affiliation(s)
- Andrea Carrà
- Agilent Technologies Italia Via Piero Gobetti 2/C 20063 Cernusco SN, Milano Italy
| | - Riccardo Spezia
- Laboratoire de Chimie Théorique Sorbonne Université, UMR 7616 CNRS 4, Place Jussieu 75005 Paris France
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8
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Sammut Bartolo N, Zoidis G, Gikas E, Benaki D, Ferrito V, Serracino-Inglott A. A multi-technique analytical approach for impurity profiling during synthesis: The case of difluprednate. J Pharm Biomed Anal 2020; 190:113483. [DOI: 10.1016/j.jpba.2020.113483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 07/15/2020] [Accepted: 07/16/2020] [Indexed: 11/29/2022]
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9
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Cautereels J, Van Hee N, Chatterjee S, Van Alsenoy C, Lemière F, Blockhuys F. QCMS 2 as a new method for providing insight into peptide fragmentation: The influence of the side-chain and inter-side-chain interactions. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4446. [PMID: 31652378 DOI: 10.1002/jms.4446] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/27/2019] [Revised: 09/12/2019] [Accepted: 09/21/2019] [Indexed: 06/10/2023]
Abstract
The identification of peptides and proteins from tandem mass spectra is a difficult task and multiple tools have been developed to aid this identification. We present a new method called quantum chemical mass spectrometry for materials science (QCMS2 ), which is based on quantum chemical calculations of bond orders, reaction, and transition-state energies at the DFT/B3LYP/6-311+G* level of theory. The method was used to describe the fragmentation pathways of five X-His-Ser tripeptides with X = Asn, Asp, Glu, Ser, and Trp, thereby focusing on the influence of the side chain and inter-side-chain interactions on the fragmentation. The main features in the mass spectra of the five tripeptides were correctly reproduced, and a number of fragments were assigned to fragmentations involving the side chain and the influence of inter-side-chain interactions. Product ion spectra were recorded to evaluate the capabilities and limitations of QCMS2 and a number of conventional tools.
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Affiliation(s)
- Julie Cautereels
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | - Nils Van Hee
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | - Sneha Chatterjee
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | | | - Filip Lemière
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | - Frank Blockhuys
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
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10
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Cautereels J, Giribaldi J, Enjalbal C, Blockhuys F. Quantum chemical mass spectrometry: Ab initio study of b 2 -ion formation mechanisms for the singly protonated Gln-His-Ser tripeptide. RAPID COMMUNICATIONS IN MASS SPECTROMETRY : RCM 2020; 34:e8778. [PMID: 32144813 DOI: 10.1002/rcm.8778] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2019] [Revised: 02/28/2020] [Accepted: 03/05/2020] [Indexed: 06/10/2023]
Abstract
RATIONALE Both amide bond protonation triggering peptide fragmentations and the controversial b2 -ion structures have been subjects of intense research. The involvement of histidine (H), with its imidazole side chain that induces specific dissociation patterns involving inter-side-chain (ISC) interactions, in b2 -ion formation was investigated, focusing on the QHS model tripeptide. METHODS To identify the effect of histidine on fragmentations issued from ISC interactions, QHS was selected for a comprehensive analysis of the pathways leading to the three possible b2 -ion structures, using quantum chemical calculations performed at the DFT/B3LYP/6-311+G* level of theory. Electrospray ionization ion trap mass spectrometry allowed the recording of MS2 and MS3 tandem mass spectra, whereas the Quantum Chemical Mass Spectrometry for Materials Science (QCMS2 ) method was used to predict fragmentation patterns. RESULTS Whereas it is very difficult to differentiate among protonated oxazolone, diketopiperazine, or lactam b2 -ions using MS2 and MS3 mass spectra, the calculations indicated that the QH b2 -ion (detected at m/z 266) is probably a mixture of the lactam and oxazolone structures formed after amide nitrogen protonation, making the formation of diketopiperazine less likely as it requires an additional step for its formation. CONCLUSIONS In contrast to glycine-histidine-containing b2 -ions, known to be issued from the backbone-imidazole cyclization, we found that interactions between the side chains were not obvious to perceive, neither from a thermodynamics nor from a fragmentation perspective, emphasizing the importance of the whole sequence on the dissociation behavior usually demonstrated from simple glycine-containing tripeptides.
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Affiliation(s)
- Julie Cautereels
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
| | | | | | - Frank Blockhuys
- Department of Chemistry, University of Antwerp, Antwerp, Belgium
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11
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Chernicharo FCS, Modesto-Costa L, Borges I. Molecular dynamics simulation of the electron ionization mass spectrum of tabun. JOURNAL OF MASS SPECTROMETRY : JMS 2020; 55:e4513. [PMID: 32212286 DOI: 10.1002/jms.4513] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2019] [Revised: 03/05/2020] [Accepted: 03/11/2020] [Indexed: 06/10/2023]
Abstract
Tabun (ethyl N,N-dimethylphosphoramidocyanidate), or GA, is a chemical warfare nerve agent produced during the World War II. The synthesis of its analogs is rather simple; thus, it is a significant threat. Furthermore, experiments with tabun and other nerve agents are greatly limited by the involved life risks and the severe restrictions imposed by the Chemical Weapons Convention. For these reasons, accurate theoretical assignment of fragmentation pathways can be especially important. In this work, we employ the Quantum Chemistry Electron Ionization Mass Spectra method, which combines molecular dynamics, quantum chemistry methods, and stochastic approaches, to accurately investigate the electron ionization/mass spectrometry (EI/MS) fragmentation spectrum and pathways of the tabun molecule. We found that different rearrangement reactions occur including a McLafferty involving the nitrile group. An essential and characteristic pathway for identification of tabun and analogs, a two-step fragmentation producing the m/z 70 ion, was confirmed. The present results will be also useful to predict EI/MS spectrum and fragmentation pathways of other members of the tabun family, namely, the O-alkyl/cycloalkyl N,N-dialkyl (methyl, ethyl, isopropyl, or propyl) phosphoramidocyanidates.
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Affiliation(s)
- Francisco C S Chernicharo
- Department of Chemistry, Military Institute of Engineering, Praça Gen Tiburcio, 80, Rio de Janeiro, RJ, Brazil
| | - Lucas Modesto-Costa
- Department of Chemistry, Military Institute of Engineering, Praça Gen Tiburcio, 80, Rio de Janeiro, RJ, Brazil
| | - Itamar Borges
- Department of Chemistry, Military Institute of Engineering, Praça Gen Tiburcio, 80, Rio de Janeiro, RJ, Brazil
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12
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Moumbock AFA, Ntie-Kang F, Akone SH, Li J, Gao M, Telukunta KK, Günther S. An overview of tools, software, and methods for natural product fragment and mass spectral analysis. PHYSICAL SCIENCES REVIEWS 2019. [DOI: 10.1515/psr-2018-0126] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2023]
Abstract
Abstract
One major challenge in natural product (NP) discovery is the determination of the chemical structure of unknown metabolites using automated software tools from either GC–mass spectrometry (MS) or liquid chromatography–MS/MS data only. This chapter reviews the existing spectral libraries and predictive computational tools used in MS-based untargeted metabolomics, which is currently a hot topic in NP structure elucidation. We begin by focusing on spectral databases and the general workflow of MS annotation. We then describe software and tools used in MS, particularly those used to predict fragmentation patterns, mass spectral classifiers, and tools for fragmentation trees analysis. We then round up the chapter by looking at more advanced approaches implemented in tools for competitive fragmentation modeling and quantum chemical approaches.
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13
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Przybyłek M, Studziński W, Gackowska A, Gaca J. The use of fast molecular descriptors and artificial neural networks approach in organochlorine compounds electron ionization mass spectra classification. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2019; 26:28188-28201. [PMID: 31363975 PMCID: PMC6791912 DOI: 10.1007/s11356-019-05968-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/24/2018] [Accepted: 07/12/2019] [Indexed: 06/10/2023]
Abstract
Developing of theoretical tools can be very helpful for supporting new pollutant detection. Nowadays, a combination of mass spectrometry and chromatographic techniques are the most basic environmental monitoring methods. In this paper, two organochlorine compound mass spectra classification systems were proposed. The classification models were developed within the framework of artificial neural networks (ANNs) and fast 1D and 2D molecular descriptor calculations. Based on the intensities of two characteristic MS peaks, namely, [M] and [M-35], two classification criterions were proposed. According to criterion I, class 1 comprises [M] signals with the intensity higher than 800 NIST units, while class 2 consists of signals with the intensity lower or equal than 800. According to criterion II, class 1 consists of [M-35] signals with the intensity higher than 100, while signals with the intensity lower or equal than 100 belong to class 2. As a result of ANNs learning stage, five models for both classification criterions were generated. The external model validation showed that all ANNs are characterized by high predicting power; however, criterion I-based ANNs are much more accurate and therefore are more suitable for analytical purposes. In order to obtain another confirmation, selected ANNs were tested against additional dataset comprising popular sunscreen agents disinfection by-products reported in previous works.
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Affiliation(s)
- Maciej Przybyłek
- Chair and Department of Physical Chemistry, Pharmacy Faculty, Collegium Medicum of Bydgoszcz, Nicolaus Copernicus University in Toruń, Kurpińskiego 5, 85-950, Bydgoszcz, Poland.
| | - Waldemar Studziński
- Faculty of Chemical Technology and Engineering, University of Technology and Life Science, Seminaryjna 3, 85-326, Bydgoszcz, Poland
| | - Alicja Gackowska
- Faculty of Chemical Technology and Engineering, University of Technology and Life Science, Seminaryjna 3, 85-326, Bydgoszcz, Poland
| | - Jerzy Gaca
- Faculty of Chemical Technology and Engineering, University of Technology and Life Science, Seminaryjna 3, 85-326, Bydgoszcz, Poland
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14
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Jiang R, Qi Y, Gao A, Zhong H. Ion fragmentations via photoelectron activated radical relays and competed hole oxidization on semiconductor nanoparticles for mass spectrometry. Anal Chim Acta 2018; 1044:1-11. [DOI: 10.1016/j.aca.2018.06.033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2018] [Revised: 06/09/2018] [Accepted: 06/12/2018] [Indexed: 11/17/2022]
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15
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Schüler JA, Neumann S, Müller-Hannemann M, Brandt W. ChemFrag: Chemically meaningful annotation of fragment ion mass spectra. JOURNAL OF MASS SPECTROMETRY : JMS 2018; 53:1104-1115. [PMID: 30103269 DOI: 10.1002/jms.4278] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/20/2018] [Revised: 07/23/2018] [Accepted: 07/25/2018] [Indexed: 05/26/2023]
Abstract
Identification and structural determination of small molecules by mass spectrometry is an important step in chemistry and biochemistry. However, the chemically realistic annotation of a fragment ion spectrum can be a difficult challenge. We developed ChemFrag, for the detection of fragmentation pathways and the annotation of fragment ions with chemically reasonable structures. ChemFrag combines a quantum chemical with a rule-based approach. For different doping substances as test instances, ChemFrag correctly annotates fragment ions. In most cases, the predicted fragments are chemically more realistic than those from purely combinatorial approaches, or approaches based on machine learning. The annotation generated by ChemFrag often coincides with spectra that have been manually annotated by experts. This is a major advance in peak annotation and allows a more precise automatic interpretation of mass spectra.
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Affiliation(s)
- Jördis-Ann Schüler
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, Halle (Saale), 06120, Germany
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle (Saale), 06120, Germany
| | - Steffen Neumann
- Department of Stress and Development Biology, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle (Saale), 06120, Germany
- German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Deutscher Platz 5e, Leipzig, 04103, Germany
| | - Matthias Müller-Hannemann
- Institute of Computer Science, Martin Luther University Halle-Wittenberg, Von-Seckendorff-Platz 1, Halle (Saale), 06120, Germany
| | - Wolfgang Brandt
- Department of Bioorganic Chemistry, Leibniz Institute of Plant Biochemistry, Weinberg 3, Halle (Saale), 06120, Germany
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16
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Kind T, Tsugawa H, Cajka T, Ma Y, Lai Z, Mehta SS, Wohlgemuth G, Barupal DK, Showalter MR, Arita M, Fiehn O. Identification of small molecules using accurate mass MS/MS search. MASS SPECTROMETRY REVIEWS 2018; 37:513-532. [PMID: 28436590 PMCID: PMC8106966 DOI: 10.1002/mas.21535] [Citation(s) in RCA: 249] [Impact Index Per Article: 41.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/27/2016] [Revised: 03/17/2017] [Accepted: 03/18/2017] [Indexed: 05/03/2023]
Abstract
Tandem mass spectral library search (MS/MS) is the fastest way to correctly annotate MS/MS spectra from screening small molecules in fields such as environmental analysis, drug screening, lipid analysis, and metabolomics. The confidence in MS/MS-based annotation of chemical structures is impacted by instrumental settings and requirements, data acquisition modes including data-dependent and data-independent methods, library scoring algorithms, as well as post-curation steps. We critically discuss parameters that influence search results, such as mass accuracy, precursor ion isolation width, intensity thresholds, centroiding algorithms, and acquisition speed. A range of publicly and commercially available MS/MS databases such as NIST, MassBank, MoNA, LipidBlast, Wiley MSforID, and METLIN are surveyed. In addition, software tools including NIST MS Search, MS-DIAL, Mass Frontier, SmileMS, Mass++, and XCMS2 to perform fast MS/MS search are discussed. MS/MS scoring algorithms and challenges during compound annotation are reviewed. Advanced methods such as the in silico generation of tandem mass spectra using quantum chemistry and machine learning methods are covered. Community efforts for curation and sharing of tandem mass spectra that will allow for faster distribution of scientific discoveries are discussed.
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Affiliation(s)
- Tobias Kind
- Genome Center, Metabolomics, UC Davis, Davis, California
| | - Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| | - Tomas Cajka
- Genome Center, Metabolomics, UC Davis, Davis, California
| | - Yan Ma
- National Institute of Biological Sciences, Beijing, People’s Republic of China
| | - Zijuan Lai
- Genome Center, Metabolomics, UC Davis, Davis, California
| | | | | | | | | | - Masanori Arita
- RIKEN Center for Sustainable Resource Science, Yokohama, Kanagawa, Japan
| | - Oliver Fiehn
- Genome Center, Metabolomics, UC Davis, Davis, California
- Faculty of Sciences, Department of Biochemistry, King Abdulaziz University, Jeddah, Saudi Arabia
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Blaženović I, Kind T, Ji J, Fiehn O. Software Tools and Approaches for Compound Identification of LC-MS/MS Data in Metabolomics. Metabolites 2018; 8:E31. [PMID: 29748461 PMCID: PMC6027441 DOI: 10.3390/metabo8020031] [Citation(s) in RCA: 392] [Impact Index Per Article: 65.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2018] [Revised: 04/26/2018] [Accepted: 05/06/2018] [Indexed: 01/17/2023] Open
Abstract
The annotation of small molecules remains a major challenge in untargeted mass spectrometry-based metabolomics. We here critically discuss structured elucidation approaches and software that are designed to help during the annotation of unknown compounds. Only by elucidating unknown metabolites first is it possible to biologically interpret complex systems, to map compounds to pathways and to create reliable predictive metabolic models for translational and clinical research. These strategies include the construction and quality of tandem mass spectral databases such as the coalition of MassBank repositories and investigations of MS/MS matching confidence. We present in silico fragmentation tools such as MS-FINDER, CFM-ID, MetFrag, ChemDistiller and CSI:FingerID that can annotate compounds from existing structure databases and that have been used in the CASMI (critical assessment of small molecule identification) contests. Furthermore, the use of retention time models from liquid chromatography and the utility of collision cross-section modelling from ion mobility experiments are covered. Workflows and published examples of successfully annotated unknown compounds are included.
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Affiliation(s)
- Ivana Blaženović
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
| | - Tobias Kind
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
| | - Jian Ji
- State Key Laboratory of Food Science and Technology, School of Food Science of Jiangnan University, School of Food Science Synergetic Innovation Center of Food Safety and Nutrition, Wuxi 214122, China.
| | - Oliver Fiehn
- NIH West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA 95616, USA.
- Department of Biochemistry, Faculty of Sciences, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
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Janesko BG, Li L, Mensing R. Quantum Chemical Fragment Precursor Tests: Accelerating de novo annotation of tandem mass spectra. Anal Chim Acta 2017; 995:52-64. [DOI: 10.1016/j.aca.2017.09.034] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2017] [Revised: 09/14/2017] [Accepted: 09/16/2017] [Indexed: 01/23/2023]
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Cautereels J, Blockhuys F. Quantum Chemical Mass Spectrometry: Verification and Extension of the Mobile Proton Model for Histidine. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2017; 28:1227-1235. [PMID: 28349436 DOI: 10.1007/s13361-017-1636-9] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Revised: 02/15/2017] [Accepted: 02/21/2017] [Indexed: 06/06/2023]
Abstract
The quantum chemical mass spectrometry for materials science (QCMS2) method is used to verify the proposed mechanism for proton transfer - the Mobile Proton Model (MPM) - by histidine for ten XHS tripeptides, based on quantum chemical calculations at the DFT/B3LYP/6-311+G* level of theory. The fragmentations of the different intermediate structures in the MPM mechanism are studied within the QCMS2 framework, and the energetics of the proposed mechanism itself and those of the fragmentations of the intermediate structures are compared, leading to the computational confirmation of the MPM. In addition, the calculations suggest that the mechanism should be extended from considering only the formation of five-membered ring intermediates to include larger-ring intermediates. Graphical Abstract ᅟ.
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Affiliation(s)
- Julie Cautereels
- Department of Chemistry, University of Antwerp, Groenenborgerlaan 171, B-2020, Antwerp, Belgium
| | - Frank Blockhuys
- Department of Chemistry, University of Antwerp, Groenenborgerlaan 171, B-2020, Antwerp, Belgium.
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Böcker S. Searching molecular structure databases using tandem MS data: are we there yet? Curr Opin Chem Biol 2017; 36:1-6. [DOI: 10.1016/j.cbpa.2016.12.010] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2016] [Revised: 12/06/2016] [Accepted: 12/07/2016] [Indexed: 10/20/2022]
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