1
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Wang S, Lin C, Zhao L, Gong X, Zhang M, Zhang H, Hu P. Identifying isomers in Chinese traditional medicine via density functional theory and ion fragmentation simulation software QCxMS. J Chromatogr A 2024; 1730:465122. [PMID: 38941796 DOI: 10.1016/j.chroma.2024.465122] [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: 05/28/2024] [Revised: 06/22/2024] [Accepted: 06/24/2024] [Indexed: 06/30/2024]
Abstract
In the realm of electrospray ionization mass spectrometry (ESI-MS), distinguishing among isomers poses a significant challenge due to the minimal spectral differences that often arise from their subtle structural differences. This makes the accurate identification of these compounds through solely experimental spectra a daunting task. Computational chemistry has emerged as a pivotal tool in bridging the gap between experimental observations and theoretical understanding. This study used the MS fragmentation simulation software, QCxMS, to model the spectra of five groups of isomers, encompassing 11 compounds, found in the traditional Chinese medicine, Zhishi Xiebai Guizhi Decoction. By comparing the spectra predicted through computational methods with those derived from Ultra-high performance liquid chromatography-quadrupole-time of flight-mass spectrometry (UPLC-Q-TOF-MS) experiments, it was observed that, following the optimization of simulation parameters, QCxMS was capable of generating reliable spectra for all examined compounds. Notably, the data calculated under both GFN1-xTB and GFN2-xTB levels exhibited no significant discrepancies. Further analysis enabled the identification of the principal fragments of the 11 compounds from the theoretical data, facilitating the deduction of their fragmentation pathways. The Density Functional Theory (DFT) method was subsequently applied to compute the primary fragmentation energies of these compounds. The findings revealed a congruence between the energy data calculated using both thermodynamic and kinetic approaches and the observed fragment abundance of the isomers. This alignment providing a more precise theoretical framework for understanding the mechanisms underlying the generation of fragment ion differences among isomers.
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Affiliation(s)
- Shuai Wang
- Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Chuhui Lin
- Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Linghao Zhao
- Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Xueqing Gong
- School of Chemistry and Chemical Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China
| | - Min Zhang
- Shanghai Frontiers Science Center of Optogenetic Techniques for Cell Metabolism, School of Pharmacy, East China University of Science and Technology, Shanghai 200237, China
| | - Hongyang Zhang
- Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
| | - Ping Hu
- Shanghai Key Laboratory of Functional Materials Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China.
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2
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Wasowicz TJ, Jurkowski MK, Harris AL, Ljubić I. Unveiling the electron-induced ionization cross sections and fragmentation mechanisms of 3,4-dihydro-2H-pyran. J Chem Phys 2024; 161:064304. [PMID: 39120036 DOI: 10.1063/5.0218160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Accepted: 07/23/2024] [Indexed: 08/10/2024] Open
Abstract
The interactions of electrons with molecular systems under various conditions are essential to interdisciplinary research fields extending over the fundamental and applied sciences. In particular, investigating electron-induced ionization and dissociation of molecules may shed light on the radiation damage to living cells, the physicochemical processes in interstellar environments, and reaction mechanisms occurring in combustion or plasma. We have, therefore, studied electron-induced ionization and dissociation of the gas phase 3,4-dihydro-2H-pyran (DHP), a cyclic ether appearing to be a viable moiety for developing efficient clinical pharmacokinetics and revealing the mechanisms of biofuel combustion. The mass spectra in the m/z = 10-90 mass range were measured at several different energies of the ionizing electron beam using mass spectrometry. The mass spectra of DHP at the same energies were simulated using on-the-fly semi-classical molecular dynamics (MD) within the framework of the QCxMS formalism. The MD settings were suitably adjusted until a good agreement with the experimental mass spectra intensities was achieved, thus enabling a reliable assignment of cations and unraveling the plausible fragmentation channels. Based on the measurement of the absolute total ionization cross section of DHP (18.1 ± 0.9) × 10-16 cm2 at 100 eV energy, the absolute total and partial ionization cross sections of DHP were determined in the 5-140 eV electron energy. Moreover, a machine learning algorithm that was trained with measured cross sections from 25 different molecules was used to predict the total ionization cross section for DHP. Comparison of the machine learning simulation with the measured data showed acceptable agreement, similar to that achieved in past predictions of the algorithm.
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Affiliation(s)
- Tomasz J Wasowicz
- BioTechMed Center, Gdańsk University of Technology, ul. G. Narutowicza 11/12, 80-233 Gdańsk, Poland
- Division of Complex Systems Spectroscopy, Institute of Physics and Applied Computer Science, Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, ul. G. Narutowicza 11/122, 80-233 Gdańsk, Poland
| | - Michal K Jurkowski
- Division of Complex Systems Spectroscopy, Institute of Physics and Applied Computer Science, Faculty of Applied Physics and Mathematics, Gdańsk University of Technology, ul. G. Narutowicza 11/122, 80-233 Gdańsk, Poland
| | - Allison L Harris
- Physics Department, Illinois State University, Normal, Illinois 61790, USA
| | - Ivan Ljubić
- Department of Physical Chemistry, Ruđer Bošković Institute, Bijenička Cesta 54, HR-10000 Zagreb, Croatia
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3
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Lai Y, Koelmel JP, Walker DI, Price EJ, Papazian S, Manz KE, Castilla-Fernández D, Bowden JA, Nikiforov V, David A, Bessonneau V, Amer B, Seethapathy S, Hu X, Lin EZ, Jbebli A, McNeil BR, Barupal D, Cerasa M, Xie H, Kalia V, Nandakumar R, Singh R, Tian Z, Gao P, Zhao Y, Froment J, Rostkowski P, Dubey S, Coufalíková K, Seličová H, Hecht H, Liu S, Udhani HH, Restituito S, Tchou-Wong KM, Lu K, Martin JW, Warth B, Godri Pollitt KJ, Klánová J, Fiehn O, Metz TO, Pennell KD, Jones DP, Miller GW. High-Resolution Mass Spectrometry for Human Exposomics: Expanding Chemical Space Coverage. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2024; 58:12784-12822. [PMID: 38984754 PMCID: PMC11271014 DOI: 10.1021/acs.est.4c01156] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/01/2024] [Revised: 06/11/2024] [Accepted: 06/12/2024] [Indexed: 07/11/2024]
Abstract
In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.
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Affiliation(s)
- Yunjia Lai
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Jeremy P. Koelmel
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Douglas I. Walker
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elliott J. Price
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Stefano Papazian
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National
Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Katherine E. Manz
- Department
of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan 48109, United States
| | - Delia Castilla-Fernández
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - John A. Bowden
- Center for
Environmental and Human Toxicology, Department of Physiological Sciences,
College of Veterinary Medicine, University
of Florida, Gainesville, Florida 32611, United States
| | | | - Arthur David
- Univ Rennes,
Inserm, EHESP, Irset (Institut de recherche en santé, environnement
et travail) − UMR_S, 1085 Rennes, France
| | - Vincent Bessonneau
- Univ Rennes,
Inserm, EHESP, Irset (Institut de recherche en santé, environnement
et travail) − UMR_S, 1085 Rennes, France
| | - Bashar Amer
- Thermo
Fisher Scientific, San Jose, California 95134, United States
| | | | - Xin Hu
- Gangarosa
Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia 30322, United States
| | - Elizabeth Z. Lin
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Akrem Jbebli
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Brooklynn R. McNeil
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Dinesh Barupal
- Department
of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York 10029, United States
| | - Marina Cerasa
- Institute
of Atmospheric Pollution Research, Italian National Research Council, 00015 Monterotondo, Rome, Italy
| | - Hongyu Xie
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
| | - Vrinda Kalia
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Renu Nandakumar
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Randolph Singh
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Zhenyu Tian
- Department
of Chemistry and Chemical Biology, Northeastern
University, Boston, Massachusetts 02115, United States
| | - Peng Gao
- Department
of Environmental and Occupational Health, and Department of Civil
and Environmental Engineering, University
of Pittsburgh, Pittsburgh, Pennsylvania 15261, United States
- UPMC Hillman
Cancer Center, Pittsburgh, Pennsylvania 15232, United States
| | - Yujia Zhao
- Institute
for Risk Assessment Sciences, Utrecht University, Utrecht 3584CM, The Netherlands
| | | | | | - Saurabh Dubey
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Kateřina Coufalíková
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Hana Seličová
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Helge Hecht
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Sheng Liu
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Hanisha H. Udhani
- Biomarkers
Core Laboratory, Irving Institute for Clinical and Translational Research, Columbia University Irving Medical Center, New York, New York 10032, United States
| | - Sophie Restituito
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kam-Meng Tchou-Wong
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
| | - Kun Lu
- Department
of Environmental Sciences and Engineering, Gillings School of Global
Public Health, The University of North Carolina
at Chapel Hill, Chapel Hill, North Carolina 27599, United States
| | - Jonathan W. Martin
- Department
of Environmental Science, Science for Life Laboratory, Stockholm University, SE-106 91 Stockholm, Sweden
- National
Facility for Exposomics, Metabolomics Platform, Science for Life Laboratory, Stockholm University, Solna 171 65, Sweden
| | - Benedikt Warth
- Department
of Food Chemistry and Toxicology, Faculty of Chemistry, University of Vienna, 1010 Vienna, Austria
| | - Krystal J. Godri Pollitt
- Department
of Environmental Health Sciences, Yale School
of Public Health, New Haven, Connecticut 06520, United States
| | - Jana Klánová
- RECETOX,
Faculty of Science, Masaryk University, Kotlářská 2, 611 37 Brno, Czech Republic
| | - Oliver Fiehn
- West Coast
Metabolomics Center, University of California−Davis, Davis, California 95616, United States
| | - Thomas O. Metz
- Biological
Sciences Division, Pacific Northwest National
Laboratory, Richland, Washington 99354, United States
| | - Kurt D. Pennell
- School
of Engineering, Brown University, Providence, Rhode Island 02912, United States
| | - Dean P. Jones
- Department
of Medicine, School of Medicine, Emory University, Atlanta, Georgia 30322, United States
| | - Gary W. Miller
- Department
of Environmental Health Sciences, Mailman School of Public Health, Columbia University, New York, New York 10032, United States
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5
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Devata S, Cleaves HJ, Dimandja J, Heist CA, Meringer M. Comparative Evaluation of Electron Ionization Mass Spectral Prediction Methods. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2023. [PMID: 37390315 DOI: 10.1021/jasms.3c00059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/02/2023]
Abstract
During the past decade promising methods for computational prediction of electron ionization mass spectra have been developed. The most prominent ones are based on quantum chemistry (QCEIMS) and machine learning (CFM-EI, NEIMS). Here we provide a threefold comparison of these methods with respect to spectral prediction and compound identification. We found that there is no unambiguous way to determine the best of these three methods. Among other factors, we find that the choice of spectral distance functions play an important role regarding the performance for compound identification.
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Affiliation(s)
- Sriram Devata
- International Institute of Information Technology, Hyderabad 500 032, India
- Blue Marble Space Institute of Science, 1001 4th Ave, Suite 3201, Seattle, Washington 98154, United States
| | - Henderson James Cleaves
- Blue Marble Space Institute of Science, 1001 4th Ave, Suite 3201, Seattle, Washington 98154, United States
- Earth-Life Science Institute, Tokyo Institute of Technology, 2-12-1-IE-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan
| | - John Dimandja
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Christopher A Heist
- Georgia Tech Research Institute (GTRI), Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Markus Meringer
- Department of Atmospheric Processors, German Aerospace Center (DLR), Münchner Straße 20, 82234 Oberpfaffenhofen-Wessling, Germany
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