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Overstreet R, King E, Clopton G, Nguyen J, Ciesielski D. QC-GN 2oMS 2: a Graph Neural Net for High Resolution Mass Spectra Prediction. J Chem Inf Model 2024. [PMID: 39013165 DOI: 10.1021/acs.jcim.4c00446] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2024]
Abstract
Predicting the mass spectrum of a molecular ion is often accomplished via three generalized approaches: rules-based methods for bond breaking, deep learning, or quantum chemical (QC) modeling. Rules-based approaches are often limited by the conditions for different chemical subspaces and perform poorly under chemical regimes with few defined rules. QC modeling is theoretically robust but requires significant amounts of computational time to produce a spectrum for a given target. Among deep learning techniques, graph neural networks (GNNs) have performed better than previous work with fingerprint-based neural networks in mass spectra prediction. To explore this technique further, we investigate the effects of including quantum chemically derived information as edge features in the GNN to increase predictive accuracy. The models we investigated include categorical bond order, bond force constants derived from extended tight-binding (xTB) quantum chemistry, and acyclic bond dissociation energies. We evaluated these models against a control GNN with no edge features in the input graphs. Bond dissociation enthalpies yielded the best improvement with a cosine similarity score of 0.462 relative to the baseline model (0.437). In this work we also apply dynamic graph attention which improves performance on benchmark problems and supports the inclusion of edge features. Between implementations, we investigate the nature of the molecular embedding for spectra prediction and discuss the recognition of fragment topographies in distinct chemistries for further development in tandem mass spectrometry prediction.
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Affiliation(s)
- Richard Overstreet
- Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Ethan King
- Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Grady Clopton
- Department of Chemistry, Tennessee State University, Nashville, Tennessee 37209, United States
| | - Julia Nguyen
- Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Danielle Ciesielski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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2
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Wang F, Xiang L, Sze-Yin Leung K, Elsner M, Zhang Y, Guo Y, Pan B, Sun H, An T, Ying G, Brooks BW, Hou D, Helbling DE, Sun J, Qiu H, Vogel TM, Zhang W, Gao Y, Simpson MJ, Luo Y, Chang SX, Su G, Wong BM, Fu TM, Zhu D, Jobst KJ, Ge C, Coulon F, Harindintwali JD, Zeng X, Wang H, Fu Y, Wei Z, Lohmann R, Chen C, Song Y, Sanchez-Cid C, Wang Y, El-Naggar A, Yao Y, Huang Y, Cheuk-Fung Law J, Gu C, Shen H, Gao Y, Qin C, Li H, Zhang T, Corcoll N, Liu M, Alessi DS, Li H, Brandt KK, Pico Y, Gu C, Guo J, Su J, Corvini P, Ye M, Rocha-Santos T, He H, Yang Y, Tong M, Zhang W, Suanon F, Brahushi F, Wang Z, Hashsham SA, Virta M, Yuan Q, Jiang G, Tremblay LA, Bu Q, Wu J, Peijnenburg W, Topp E, Cao X, Jiang X, Zheng M, Zhang T, Luo Y, Zhu L, Li X, Barceló D, Chen J, Xing B, Amelung W, Cai Z, Naidu R, Shen Q, Pawliszyn J, Zhu YG, Schaeffer A, Rillig MC, Wu F, Yu G, Tiedje JM. Emerging contaminants: A One Health perspective. Innovation (N Y) 2024; 5:100612. [PMID: 38756954 PMCID: PMC11096751 DOI: 10.1016/j.xinn.2024.100612] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 03/10/2024] [Indexed: 05/18/2024] Open
Abstract
Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary health. Despite global efforts to mitigate legacy pollutants, the continuous introduction of new substances remains a major threat to both people and the planet. In response, global initiatives are focusing on risk assessment and regulation of emerging contaminants, as demonstrated by the ongoing efforts to establish the UN's Intergovernmental Science-Policy Panel on Chemicals, Waste, and Pollution Prevention. This review identifies the sources and impacts of emerging contaminants on planetary health, emphasizing the importance of adopting a One Health approach. Strategies for monitoring and addressing these pollutants are discussed, underscoring the need for robust and socially equitable environmental policies at both regional and international levels. Urgent actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.
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Affiliation(s)
- Fang Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Leilei Xiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Kelvin Sze-Yin Leung
- Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
- HKBU Institute of Research and Continuing Education, Shenzhen Virtual University Park, Shenzhen, China
| | - Martin Elsner
- Technical University of Munich, TUM School of Natural Sciences, Institute of Hydrochemistry, 85748 Garching, Germany
| | - Ying Zhang
- School of Resources & Environment, Northeast Agricultural University, Harbin 150030, China
| | - Yuming Guo
- Climate, Air Quality Research Unit, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
| | - Bo Pan
- Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| | - Hongwen Sun
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Taicheng An
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Guangguo Ying
- Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, Guangdong 510006, China
| | - Bryan W. Brooks
- Department of Environmental Science, Baylor University, Waco, TX, USA
- Center for Reservoir and Aquatic Systems Research (CRASR), Baylor University, Waco, TX, USA
| | - Deyi Hou
- School of Environment, Tsinghua University, Beijing 100084, China
| | - Damian E. Helbling
- School of Civil and Environmental Engineering, Cornell University, Ithaca, NY 14853, USA
| | - Jianqiang Sun
- Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province, Zhejiang University of Technology, Hangzhou 310014, China
| | - Hao Qiu
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Timothy M. Vogel
- Laboratoire d’Ecologie Microbienne, Universite Claude Bernard Lyon 1, UMR CNRS 5557, UMR INRAE 1418, VetAgro Sup, 69622 Villeurbanne, France
| | - Wei Zhang
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Yanzheng Gao
- Institute of Organic Contaminant Control and Soil Remediation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang Road 1, Nanjing 210095, China
| | - Myrna J. Simpson
- Environmental NMR Centre and Department of Physical and Environmental Sciences, University of Toronto Scarborough, 1265 Military Trail, Toronto, ON M1C 1A4, Canada
| | - Yi Luo
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Scott X. Chang
- Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, AB T6G 2E3, Canada
| | - Guanyong Su
- Jiangsu Key Laboratory of Chemical Pollution Control and Resources Reuse, School of Environmental and Biological Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
| | - Bryan M. Wong
- Materials Science & Engineering Program, Department of Chemistry, and Department of Physics & Astronomy, University of California-Riverside, Riverside, CA, USA
| | - Tzung-May Fu
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Dong Zhu
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Karl J. Jobst
- Department of Chemistry, Memorial University of Newfoundland, 45 Arctic Avenue, St. John’s, NL A1C 5S7, Canada
| | - Chengjun Ge
- Key Laboratory of Agro-Forestry Environmental Processes and Ecological Regulation of Hainan Province, School of Ecological and Environmental Sciences, Hainan University, Haikou 570228, China
| | - Frederic Coulon
- School of Water, Energy and Environment, Cranfield University, Cranfield MK43 0AL, UK
| | - Jean Damascene Harindintwali
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiankui Zeng
- Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Haijun Wang
- Institute for Ecological Research and Pollution Control of Plateau Lakes, School of Ecology and Environmental Science, Yunnan University, Kunming 650504, China
| | - Yuhao Fu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Zhong Wei
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Rainer Lohmann
- Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, USA
| | - Changer Chen
- Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, South China Normal University, Guangzhou, Guangdong 510006, China
| | - Yang Song
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Concepcion Sanchez-Cid
- Environmental Microbial Genomics, UMR 5005 Laboratoire Ampère, CNRS, École Centrale de Lyon, Université de Lyon, Écully, France
| | - Yu Wang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ali El-Naggar
- Department of Renewable Resources, University of Alberta, 442 Earth Sciences Building, Edmonton, AB T6G 2E3, Canada
- Department of Soil Sciences, Faculty of Agriculture, Ain Shams University, Cairo 11241, Egypt
| | - Yiming Yao
- Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Yanran Huang
- Applied Biology and Chemical Technology, Hong Kong Polytechnic University, Hong Kong, China
| | | | - Chenggang Gu
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Huizhong Shen
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yanpeng Gao
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Chao Qin
- Institute of Organic Contaminant Control and Soil Remediation, College of Resources and Environmental Sciences, Nanjing Agricultural University, Weigang Road 1, Nanjing 210095, China
| | - Hao Li
- Faculty of Environmental Science & Engineering, Kunming University of Science & Technology, Kunming 650500, China
| | - Tong Zhang
- Environmental Microbiome Engineering and Biotechnology Laboratory, Center for Environmental Engineering Research, Department of Civil Engineering, The University of Hong Kong, Hong Kong, China
| | - Natàlia Corcoll
- Department of Biological and Environmental Sciences, University of Gothenburg, Gothenburg, Sweden
| | - Min Liu
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Daniel S. Alessi
- Department of Earth and Atmospheric Sciences, University of Alberta, Edmonton, AB T6G 2E3, Canada
| | - Hui Li
- Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
| | - Kristian K. Brandt
- Section for Microbial Ecology and Biotechnology, Department of Plant and Environmental Sciences, University of Copenhagen, Thorvaldsensvej 40, 1871 Frederiksberg C, Denmark
- Sino-Danish Center (SDC), Beijing, China
| | - Yolanda Pico
- Food and Environmental Safety Research Group of the University of Valencia (SAMA-UV), Desertification Research Centre - CIDE (CSIC-UV-GV), Road CV-315 km 10.7, 46113 Moncada, Valencia, Spain
| | - Cheng Gu
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Jianhua Guo
- Australian Centre for Water and Environmental Biotechnology, The University of Queensland, Brisbane, QLD 4072, Australia
| | - Jianqiang Su
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
| | - Philippe Corvini
- School of Life Sciences, University of Applied Sciences and Arts Northwestern Switzerland, 4132 Muttenz, Switzerland
| | - Mao Ye
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Teresa Rocha-Santos
- Centre for Environmental and Marine Studies (CESAM) & Department of Chemistry, University of Aveiro, 3810-193 Aveiro, Portugal
| | - Huan He
- Jiangsu Engineering Laboratory of Water and Soil Eco-remediation, School of Environment, Nanjing Normal University, Nanjing 210023, China
| | - Yi Yang
- Key Laboratory of Geographic Information Science of the Ministry of Education, School of Geographic Sciences, East China Normal University, Shanghai 200241, China
| | - Meiping Tong
- College of Environmental Sciences and Engineering, Peking University, Beijing 100871, China
| | - Weina Zhang
- Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control, School of Environmental Science and Engineering, Institute of Environmental Health and Pollution Control, Guangdong University of Technology, Guangzhou 510006, China
| | - Fidèle Suanon
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- Laboratory of Physical Chemistry, Materials and Molecular Modeling (LCP3M), University of Abomey-Calavi, Republic of Benin, Cotonou 01 BP 526, Benin
| | - Ferdi Brahushi
- Department of Environment and Natural Resources, Agricultural University of Tirana, 1029 Tirana, Albania
| | - Zhenyu Wang
- Institute of Environmental Processes and Pollution Control, and School of Environment & Ecology, Jiangnan University, Wuxi 214122, China
| | - Syed A. Hashsham
- Center for Microbial Ecology, Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
- Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Marko Virta
- Department of Microbiology, University of Helsinki, 00010 Helsinki, Finland
| | - Qingbin Yuan
- State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210093, China
| | - Gaofei Jiang
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Louis A. Tremblay
- School of Biological Sciences, University of Auckland, Auckland, Aotearoa 1142, New Zealand
| | - Qingwei Bu
- School of Chemical & Environmental Engineering, China University of Mining & Technology - Beijing, Beijing 100083, China
| | - Jichun Wu
- Key Laboratory of Surficial Geochemistry, Ministry of Education, School of Earth Sciences and Engineering, Nanjing University, Nanjing 210023, China
| | - Willie Peijnenburg
- National Institute of Public Health and the Environment, Center for the Safety of Substances and Products, 3720 BA Bilthoven, The Netherlands
- Leiden University, Center for Environmental Studies, Leiden, the Netherlands
| | - Edward Topp
- Agroecology Mixed Research Unit, INRAE, 17 rue Sully, 21065 Dijon Cedex, France
| | - Xinde Cao
- School of Environmental Science and Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Xin Jiang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Minghui Zheng
- State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Taolin Zhang
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
| | - Yongming Luo
- State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Lizhong Zhu
- Department of Environmental Science, Zhejiang University, Hangzhou, Zhejiang 310058, China
| | - Xiangdong Li
- Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China
| | - Damià Barceló
- Chemistry and Physics Department, University of Almeria, 04120 Almeria, Spain
| | - Jianmin Chen
- Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention (LAP3), Department of Environmental Science and Engineering, Fudan University, Shanghai 200433, China
| | - Baoshan Xing
- Stockbridge School of Agriculture, University of Massachusetts, Amherst, MA 01003, USA
| | - Wulf Amelung
- Institute of Crop Science and Resource Conservation (INRES), Soil Science and Soil Ecology, University of Bonn, 53115 Bonn, Germany
- Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Zongwei Cai
- State Key Laboratory of Environmental and Biological Analysis, Department of Chemistry, Hong Kong Baptist University, Hong Kong, China
| | - Ravi Naidu
- Global Centre for Environmental Remediation (GCER), The University of Newcastle (UON), Newcastle, NSW 2308, Australia
- Cooperative Research Centre for Contamination Assessment and Remediation of the Environment (CRC CARE), The University of Newcastle (UON), Newcastle, NSW 2308, Australia
| | - Qirong Shen
- Jiangsu Provincial Key Lab for Organic Solid Waste Utilization, Jiangsu Collaborative Innovation Center for Solid Organic Waste Resource Utilization, Nanjing Agricultural University, Nanjing 210095, China
| | - Janusz Pawliszyn
- Department of Chemistry, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Yong-guan Zhu
- University of Chinese Academy of Sciences, Beijing 100049, China
- Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Andreas Schaeffer
- Institute for Environmental Research, RWTH Aachen University, 52074 Aachen, Germany
| | - Matthias C. Rillig
- Institute of Biology, Freie Universität Berlin, Berlin, Germany
- Berlin-Brandenburg Institute of Advanced Biodiversity Research (BBIB), Berlin, Germany
| | - Fengchang Wu
- State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing 100012, China
| | - Gang Yu
- Advanced Interdisciplinary Institute of Environment and Ecology, Beijing Normal University, Zhuhai, China
| | - James M. Tiedje
- Center for Microbial Ecology, Department of Plant, Soil, and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA
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Kirschbaum C, Greis K, Torres-Boy AY, Riedel J, Gewinner S, Schöllkopf W, Meijer G, Helden GV, Pagel K. Studying the Intrinsic Reactivity of Chromanes by Gas-Phase Infrared Spectroscopy. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 38950388 DOI: 10.1021/jasms.4c00216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Tandem mass spectrometry is routinely used for the structural analysis of organic molecules, but many fragmentation reactions are not well understood. Because several potential structures can correspond to a measured mass, the assignment of product ions is ambiguous using mass spectrometry alone. Here, we combine mass spectrometry with high-resolution gas-phase infrared spectroscopy and computational chemistry tools to identify product ion structures and derive collision-induced fragmentation mechanisms of the chromane derivatives Trolox and Methyltrolox. We find that protonated Trolox and Methyltrolox fragment identically via dehydration and decarbonylation, while deprotonated ions display substantially diverging reactivities. For deprotonated Methyltrolox, we observe unusual radical fragmentation reactions and suggest a [1,2]-Wittig rearrangement involving aryl migration in the gas phase. Overall, the combined experimental and theoretical approach presented here revealed complex proton dynamics and intramolecular rearrangement reactions, which expand our understanding on structure-reactivity relationships of isolated molecules in different protonation states.
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Affiliation(s)
- Carla Kirschbaum
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, 14195 Berlin, Germany
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Kim Greis
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, 14195 Berlin, Germany
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | | | - Jerome Riedel
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, 14195 Berlin, Germany
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Sandy Gewinner
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | | | - Gerard Meijer
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Gert von Helden
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
| | - Kevin Pagel
- Freie Universität Berlin, Institute of Chemistry and Biochemistry, 14195 Berlin, Germany
- Fritz Haber Institute of the Max Planck Society, 14195 Berlin, Germany
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4
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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] [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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5
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Kostyukevich Y, Osipenko S, Borisova L, Kireev A. In-Electrospray source Hydrogen/Deuterium exchange coupled to multistage fragmentation for the investigation of the protonation and fragmentation pathways of gas phase ions. JOURNAL OF MASS SPECTROMETRY : JMS 2024; 59:e5032. [PMID: 38736146 DOI: 10.1002/jms.5032] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 04/02/2024] [Indexed: 05/14/2024]
Abstract
Identification of molecules in complex natural matrices relies on matching the fragmentation spectra of ions under investigation and the spectra acquired for the corresponding analytical standards. Currently, there are many databases of experimentally measured tandem mass spectrometry spectra (such as NIST, MzCloud, and Metlin), and considerable progress has been made in the development of software for predicting tandem mass spectrometry fragments in silico using combinatorial, machine learning, and quantum chemistry approaches (such as MetFrag, CFM-ID, and QCxMS). However, the electrospray ionization molecules can be ionized at different sites (protonated or deprotonated), and the fragmentation spectra of such ions are different. Here, we are using the combination of the in-ESI source hydrogen/deuterium exchange reaction and MSn fragmentation for the investigation of the fragmentation pathways for different protomers of organic molecules. It is shown that the distribution of the deuterium in the fragment ions reflects the presence of different protomers. For several molecules, the distribution of deuterium was traced up to the MS5 level of fragmentation revealing many unusual and unexpected effects. For example, we investigated the loss of HF from the ciprofloxacin and norfloxacin ions and observed that for ions protonated at -COOH group, the eliminating hydrogen always comes from -NH group. When ions are protonated at another site, the elimination of hydrogen with a probability of 30% occurs from the -NH group, and with a probability of 70%, it originates from other sites on the molecule. Such effects were not described previously. Quantum chemical simulation was used for the verification of the protonated structures and simulation of the corresponding fragmentation spectra.
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Affiliation(s)
| | - Sergey Osipenko
- Skolkovo Institute of Science and Technology, Moscow, Russia
| | | | - Albert Kireev
- Skolkovo Institute of Science and Technology, Moscow, Russia
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6
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Mahajan P, Fiehn O, Barupal D. IDSL.GOA: gene ontology analysis for interpreting metabolomic datasets. Sci Rep 2024; 14:1299. [PMID: 38221536 PMCID: PMC10788336 DOI: 10.1038/s41598-024-51992-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Accepted: 01/11/2024] [Indexed: 01/16/2024] Open
Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2393 metabolic GO terms and associated 3144 genes, 1,492 EC annotations, and 2621 metabolites. IDSL.GOA analysis of a case study of older versus young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR < 0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/ .
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Affiliation(s)
- Priyanka Mahajan
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California, Davis, CA, 95616, USA
| | - Dinesh Barupal
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, 10954, USA.
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7
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Paenurk E, Chen P. Robustness of Threshold Collision-Induced Dissociation Simulations for Bond Dissociation Energies. J Phys Chem A 2024; 128:333-342. [PMID: 38155581 DOI: 10.1021/acs.jpca.3c06862] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2023]
Abstract
The threshold collision-induced dissociation (T-CID) method is the workhorse for gas-phase bond dissociation energy (BDE) measurements. However, T-CID does not measure BDEs directly; instead, BDEs are obtained by fitting simulated data to the experimental data. We previously observed several large discrepancies between the computed and experimental BDEs. To analyze the reliability of the experimental values, we previously reported a study of the dissociation rate models in the simulation. Here, we report a study of the collision simulation part, specifically in the L-CID (ligand CID) program. We show that the BDE values are robust even to intentionally introduced mistakes in the simulations, varying in most cases by less than 3 kcal mol-1. The most significant exception is the collisional energy transfer (CET) simulation, which led to deviations larger than 10 kcal mol-1. However, we found that the BDEs obtained with explicitly simulated CET distributions deviated by only 3 kcal mol-1 from those simulated with the original model. Collectively, our results suggest that the T-CID-derived BDE values are robust and are likely to be accurate.
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Affiliation(s)
- Eno Paenurk
- Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich 8093, Switzerland
- Department of Chemistry, Yale University, New Haven, Connecticut 06520, United States
| | - Peter Chen
- Department of Chemistry and Applied Biosciences, ETH Zürich, Zürich 8093, Switzerland
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8
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Mahajan P, Fiehn O, Barupal D. IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.03.25.534225. [PMID: 37034715 PMCID: PMC10081191 DOI: 10.1101/2023.03.25.534225] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/17/2023]
Abstract
Biological interpretation of metabolomic datasets often ends at a pathway analysis step to find the over-represented metabolic pathways in the list of statistically significant metabolites. However, definitions of biochemical pathways and metabolite coverage vary among different curated databases, leading to missed interpretations. For the lists of genes, transcripts and proteins, Gene Ontology (GO) terms over-presentation analysis has become a standardized approach for biological interpretation. But, GO analysis has not been achieved for metabolomic datasets. We present a new knowledgebase (KB) and the online tool, Gene Ontology Analysis by the Integrated Data Science Laboratory for Metabolomics and Exposomics (IDSL.GOA) to conduct GO over-representation analysis for a metabolite list. The IDSL.GOA KB covers 2,393 metabolic GO terms and associated 3,144 genes, 1,492 EC annotations, and 2,621 metabolites. IDSL.GOA analysis of a case study of older vs young female brain cortex metabolome highlighted 82 GO terms being significantly overrepresented (FDR <0.05). We showed how IDSL.GOA identified key and relevant GO metabolic processes that were not yet covered in other pathway databases. Overall, we suggest that interpretation of metabolite lists should not be limited to only pathway maps and can also leverage GO terms as well. IDSL.GOA provides a useful tool for this purpose, allowing for a more comprehensive and accurate analysis of metabolite pathway data. IDSL.GOA tool can be accessed at https://goa.idsl.me/.
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Affiliation(s)
- Priyanka Mahajan
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA 10954
| | - Oliver Fiehn
- NIH-West Coast Metabolomics Center, University of California, Davis, California, 95616, USA
| | - Dinesh Barupal
- Integrated Data Science Laboratory for Metabolomics and Exposomics, Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, USA 10954
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9
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Elser D, Pflieger D, Villette C, Moegle B, Miesch L, Gaquerel E. Evolutionary metabolomics of specialized metabolism diversification in the genus Nicotiana highlights N-acylnornicotine innovations. SCIENCE ADVANCES 2023; 9:eade8984. [PMID: 37624884 PMCID: PMC10456844 DOI: 10.1126/sciadv.ade8984] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2022] [Accepted: 07/25/2023] [Indexed: 08/27/2023]
Abstract
Specialized metabolite (SM) diversification is a core process to plants' adaptation to diverse ecological niches. Here, we implemented a computational mass spectrometry-based metabolomics approach to exploring SM diversification in tissues of 20 species covering Nicotiana phylogenetics sections. To markedly increase metabolite annotation, we created a large in silico fragmentation database, comprising >1 million structures, and scripts for connecting class prediction to consensus substructures. Together, the approach provides an unprecedented cartography of SM diversity and section-specific innovations in this genus. As a case study and in combination with nuclear magnetic resonance and mass spectrometry imaging, we explored the distribution of N-acylnornicotines, alkaloids predicted to be specific to Repandae allopolyploids, and revealed their prevalence in the genus, albeit at much lower magnitude, as well as a greater structural diversity than previously thought. Together, the data integration approaches provided here should act as a resource for future research in plant SM evolution.
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Affiliation(s)
- David Elser
- Institut de Biologie Moléculaire des Plantes du CNRS, Université de Strasbourg, Strasbourg, France
| | - David Pflieger
- Institut de Biologie Moléculaire des Plantes du CNRS, Université de Strasbourg, Strasbourg, France
| | - Claire Villette
- Institut de Biologie Moléculaire des Plantes du CNRS, Université de Strasbourg, Strasbourg, France
| | - Baptiste Moegle
- Institut de Chimie du CNRS UMR 7177, Université de Strasbourg, Strasbourg, France
| | - Laurence Miesch
- Institut de Chimie du CNRS UMR 7177, Université de Strasbourg, Strasbourg, France
| | - Emmanuel Gaquerel
- Institut de Biologie Moléculaire des Plantes du CNRS, Université de Strasbourg, Strasbourg, France
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10
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Guo R, Zhang Y, Liao Y, Yang Q, Xie T, Fan X, Lin Z, Chen Y, Lu H, Zhang Z. Highly accurate and large-scale collision cross sections prediction with graph neural networks. Commun Chem 2023; 6:139. [PMID: 37402835 DOI: 10.1038/s42004-023-00939-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 06/23/2023] [Indexed: 07/06/2023] Open
Abstract
The collision cross section (CCS) values derived from ion mobility spectrometry can be used to improve the accuracy of compound identification. Here, we have developed the Structure included graph merging with adduct method for CCS prediction (SigmaCCS) based on graph neural networks using 3D conformers as inputs. A model was trained, evaluated, and tested with >5,000 experimental CCS values. It achieved a coefficient of determination of 0.9945 and a median relative error of 1.1751% on the test set. The model-agnostic interpretation method and the visualization of the learned representations were used to investigate the chemical rationality of SigmaCCS. An in-silico database with 282 million CCS values was generated for three different adduct types of 94 million compounds. Its source code is publicly available at https://github.com/zmzhang/SigmaCCS . Altogether, SigmaCCS is an accurate, rational, and off-the-shelf method to directly predict CCS values from molecular structures.
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Affiliation(s)
- Renfeng Guo
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Youjia Zhang
- School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, China
| | - Yuxuan Liao
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Qiong Yang
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Ting Xie
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Xiaqiong Fan
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China
| | - Zhonglong Lin
- Yunnan Academy of Tobacco Agricultural Sciences, 650021, Kunming, Yunnan, China
| | - Yi Chen
- Yunnan Academy of Tobacco Agricultural Sciences, 650021, Kunming, Yunnan, China.
| | - Hongmei Lu
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China.
| | - Zhimin Zhang
- College of Chemistry and Chemical Engineering, Central South University, 410083, Changsha, China.
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11
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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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12
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Li X, Wang H, Jiang M, Ding M, Xu X, Xu B, Zou Y, Yu Y, Yang W. Collision Cross Section Prediction Based on Machine Learning. Molecules 2023; 28:molecules28104050. [PMID: 37241791 DOI: 10.3390/molecules28104050] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Revised: 05/10/2023] [Accepted: 05/10/2023] [Indexed: 05/28/2023] Open
Abstract
Ion mobility-mass spectrometry (IM-MS) is a powerful separation technique providing an additional dimension of separation to support the enhanced separation and characterization of complex components from the tissue metabolome and medicinal herbs. The integration of machine learning (ML) with IM-MS can overcome the barrier to the lack of reference standards, promoting the creation of a large number of proprietary collision cross section (CCS) databases, which help to achieve the rapid, comprehensive, and accurate characterization of the contained chemical components. In this review, advances in CCS prediction using ML in the past 2 decades are summarized. The advantages of ion mobility-mass spectrometers and the commercially available ion mobility technologies with different principles (e.g., time dispersive, confinement and selective release, and space dispersive) are introduced and compared. The general procedures involved in CCS prediction based on ML (acquisition and optimization of the independent and dependent variables, model construction and evaluation, etc.) are highlighted. In addition, quantum chemistry, molecular dynamics, and CCS theoretical calculations are also described. Finally, the applications of CCS prediction in metabolomics, natural products, foods, and the other research fields are reflected.
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Affiliation(s)
- Xiaohang Li
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Hongda Wang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Meiting Jiang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Mengxiang Ding
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Xiaoyan Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Bei Xu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yadan Zou
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Yuetong Yu
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
| | - Wenzhi Yang
- State Key Laboratory of Component-Based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
- Haihe Laboratory of Modern Chinese Medicine, Tianjin University of Traditional Chinese Medicine, 10 Poyanghu Road, Tianjin 301617, China
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13
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Zhang B, Hao M, Xiong J, Li X, Koopman J. Ab initio molecular dynamics calculations on electron ionization induced fragmentations of C 4F 7N and C 5F 10O for understanding their decompositions under discharge conditions. Phys Chem Chem Phys 2023; 25:7540-7549. [PMID: 36857631 DOI: 10.1039/d2cp03498k] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
C4F7N and C5F10O are the most promising SF6 alternatives as eco-friendly insulating gaseous mediums in electrical engineering. It is necessary to clarify their electrical stability and decomposition mechanisms. In this work, we first introduced our experimental results for decomposition products of C4F7N/CO2 and C5F10O/synthetic air mixtures under partial discharge and spark discharge conditions. Then, we performed ab initio molecular dynamics (AIMD) simulations on the typical decomposition products. The simulations were performed under standard electron impact mass spectrometry (EI-MS); thus, the statistical results of the mass spectra were compared with those of the experimentally obtained standard mass spectra from the NIST database. The AIMD simulation method in simulating the electron-induced ionization process was verified and found to be reliable. Finally, the calculations were also performed for C4F7N and C5F10O with incident electron energies of 20 eV and 70 eV, respectively. The dominant pathway for both gases is the formation of CF3+ with the fracture of the C-C bond. The AIMD simulation is able to predict the decomposition channels after electron-impact ionization without any preconceived knowledge of fragmentation pathways, which provides a novel insight into understanding the decomposition mechanisms of C4F7N and C5F10O under different discharge conditions with different energies.
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Affiliation(s)
- Boya Zhang
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China.
| | - Mai Hao
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China.
| | - Jiayu Xiong
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China. .,State Grid Sichuan Electric Power Research Institute, Chengdu, Sichuan Province 610041, China
| | - Xingwen Li
- State Key Laboratory of Electrical Insulation and Power Equipment, Xi'an Jiaotong University, Xi'an, Shaanxi Province 710049, China.
| | - Jeroen Koopman
- Mulliken Center for Theoretical Chemistry, Institute of Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115 Bonn, Germany
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14
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Tupertsev B, Osipenko S, Kireev A, Nikolaev E, Kostyukevich Y. Simple In Vitro 18O Labeling for Improved Mass Spectrometry-Based Drug Metabolites Identification: Deep Drug Metabolism Study. Int J Mol Sci 2023; 24:ijms24054569. [PMID: 36902002 PMCID: PMC10002766 DOI: 10.3390/ijms24054569] [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: 02/06/2023] [Revised: 02/21/2023] [Accepted: 02/23/2023] [Indexed: 03/03/2023] Open
Abstract
The identification of drug metabolites formed with different in vitro systems by HPLC-MS is a standard step in preclinical research. In vitro systems allow modeling of real metabolic pathways of a drug candidate. Despite the emergence of various software and databases, identification of compounds is still a complex task. Measurement of the accurate mass, correlation of chromatographic retention times and fragmentation spectra are often insufficient for identification of compounds especially in the absence of reference materials. Metabolites can "slip under the nose", since it is often not possible to reliably confirm that a signal belongs to a metabolite and not to other compounds in complex systems. Isotope labeling has proved to be a tool that aids in small molecule identification. The introduction of heavy isotopes is done with isotope exchange reactions or with complicated synthetic schemes. Here, we present an approach based on the biocatalytic insertion of oxygen-18 isotope under the action of liver microsomes enzymes in the presence of 18O2. Using the local anesthetic bupivacaine as an example, more than 20 previously unknown metabolites were reliably discovered and annotated in the absence of the reference materials. In combination with high-resolution mass spectrometry and modern methods of mass spectrometric metabolism data processing, we demonstrated the ability of the proposed approach to increase the degree of confidence in interpretating metabolism data.
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Affiliation(s)
- Boris Tupertsev
- Center of Molecular and Cellular Biology (CMCB), Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
- Moscow Institute of Physics and Technology, Phystech School of Biological and Medical Physics, Institutskiy per., 9, Dolgoprudny, 141701 Moscow, Russia
| | - Sergey Osipenko
- Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
| | - Albert Kireev
- Center of Molecular and Cellular Biology (CMCB), Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
| | - Eugene Nikolaev
- Center of Molecular and Cellular Biology (CMCB), Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
| | - Yury Kostyukevich
- Center of Molecular and Cellular Biology (CMCB), Skolkovo Institute of Science and Technology, Nobel Str., 3, 121205 Moscow, Russia
- Correspondence:
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15
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Fu D, Habtegabir SG, Wang H, Feng S, Han Y. Understanding of protomers/deprotomers by combining mass spectrometry and computation. Anal Bioanal Chem 2023:10.1007/s00216-023-04574-1. [PMID: 36737499 DOI: 10.1007/s00216-023-04574-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/19/2023] [Accepted: 01/27/2023] [Indexed: 02/05/2023]
Abstract
Multifunctional compounds may form different prototropic isomers under different conditions, which are known as protomers/deprotomers. In biological systems, these protomer/deprotomer isomers affect the interaction modes and conformational landscape between compounds and enzymes and thus present different biological activities. Study on protomers/deprotomers is essentially the study on the acidity/basicity of each intramolecular functional group and its effect on molecular structure. In recent years, the combination of mass spectrometry (MS) and computational chemistry has been proven to be a powerful and effective means to study prototropic isomers. MS-based technologies are developed to discriminate and characterize protomers/deprotomers to provide structural information and monitor transformations, showing great superiority than other experimental methods. Computational chemistry is used to predict the thermodynamic stability of protomers/deprotomers, provide the simulated MS/MS spectra, infrared spectra, and calculate collision cross-section values. By comparing the theoretical data with the corresponding experimental results, the researchers can not only determine the protomer/deprotomer structure, but also investigate the structure-activity relationship in a given system. This review covers various MS methods and theoretical calculations and their devotion to isomer discrimination, structure identification, conformational transformation, and phase transition investigation of protomers/deprotomers.
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Affiliation(s)
- Dali Fu
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering and Environment, China University of Petroleum-Beijing, Beijing, 102249, People's Republic of China
| | - Sara Girmay Habtegabir
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering and Environment, China University of Petroleum-Beijing, Beijing, 102249, People's Republic of China
| | - Haodong Wang
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering and Environment, China University of Petroleum-Beijing, Beijing, 102249, People's Republic of China
| | - Shijie Feng
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering and Environment, China University of Petroleum-Beijing, Beijing, 102249, People's Republic of China
| | - Yehua Han
- State Key Laboratory of Heavy Oil Processing, College of Chemical Engineering and Environment, China University of Petroleum-Beijing, Beijing, 102249, People's Republic of China.
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16
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Koopman J, Grimme S. Calculation of Mass Spectra with the QCxMS Method for Negatively and Multiply Charged Molecules. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:2226-2242. [PMID: 36343304 DOI: 10.1021/jasms.2c00209] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Analysis and validation of a mass spectrometry (MS) experiment are usually performed by comparison to reference spectra. However, if references are missing, measured spectra cannot be properly matched. To close this gap, the Quantum Chemical Mass Spectrometry (QCxMS) program has been developed. It enables fully automatic calculations of electron ionization (EI) and positive ion collision-induced dissociation (CID) mass spectra of singly charged molecular ions. In this work, the extension to negative and multiple ion charge for the CID run mode is presented. QCxMS is now capable of calculating structures carrying any charge, without the need for pretabulated fragmentation pathways or machine learning of database spectra. Mass spectra of four single negatively charged and two multiple positively charged organic ions with molecular sizes from 12 to 92 atoms were computed and compared to reference spectra. The underlying Born-Oppenheimer molecular dynamics (MD) calculations were conducted using the semiempirical quantum mechanical GFN2-xTB method, while for some small molecules, ab initio DFT-based MD simulations were performed. Detailed insights into the fragmentation pathways were gained, and the effects of the computed charge assignments on the resulting spectrum are discussed. Especially for the negative ion mode, the influence of the deprotonation site to create the anion was found to be substantial. Doubly charged fragments could successfully be calculated fully automatically for the first time, while higher charged structures introduced severe assignment problems. Overall, this extension of the QCxMS program further enhances its applicability and underlines its value as a sophisticated toolkit for CID-based tandem MS structure elucidation.
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Affiliation(s)
- Jeroen Koopman
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115Bonn, Germany
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115Bonn, Germany
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17
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Wang S, Kind T, Bremer PL, Tantillo DJ, Fiehn O. Beyond the Ground State: Predicting Electron Ionization Mass Spectra Using Excited-State Molecular Dynamics. J Chem Inf Model 2022; 62:4403-4410. [DOI: 10.1021/acs.jcim.2c00597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Shunyang Wang
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
- Department of Chemistry, University of California, 1 Shields Avenue, Davis, California 95616, United States
| | - Tobias Kind
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
| | - Parker Ladd Bremer
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
- Department of Chemistry, University of California, 1 Shields Avenue, Davis, California 95616, United States
| | - Dean J. Tantillo
- Department of Chemistry, University of California, 1 Shields Avenue, Davis, California 95616, United States
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
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18
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Manathunga M, Götz AW, Merz KM. Computer-aided drug design, quantum-mechanical methods for biological problems. Curr Opin Struct Biol 2022; 75:102417. [PMID: 35779437 DOI: 10.1016/j.sbi.2022.102417] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2022] [Revised: 05/14/2022] [Accepted: 05/16/2022] [Indexed: 11/28/2022]
Abstract
Quantum chemistry enables to study systems with chemical accuracy (<1 kcal/mol from experiment) but is restricted to a handful of atoms due to its computational expense. This has led to ongoing interest to optimize and simplify these methods while retaining accuracy. Implementing quantum mechanical (QM) methods on modern hardware such as multiple-GPUs is one example of how the field is optimizing performance. Multiscale approaches like the so-called QM/molecular mechanical method are gaining popularity in drug discovery because they focus the application of QM methods on the region of choice (e.g., the binding site), while using efficient MM models to represent less relevant areas. The creation of simplified QM methods is another example, including the use of machine learning to create ultra-fast and accurate QM models. Herein, we summarize recent advancements in the development of optimized QM methods that enhance our ability to use these methods in computer aided drug discovery.
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Affiliation(s)
- Madushanka Manathunga
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States. https://twitter.com/@MaduManathunga
| | - Andreas W Götz
- San Diego Supercomputer Center, University of California San Diego, La Jolla, CA 92093, United States. https://twitter.com/@awgoetz
| | - Kenneth M Merz
- Department of Chemistry and Department of Biochemistry and Molecular Biology, Michigan State University, East Lansing, MI 48824, United States.
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19
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Schnegotzki R, Koopman J, Grimme S, Süssmuth RD. Quantum Chemistry-based Molecular Dynamics Simulations as a Tool for the Assignment of ESI-MS/MS Spectra of Drug Molecules. Chemistry 2022; 28:e202200318. [PMID: 35235707 PMCID: PMC9325386 DOI: 10.1002/chem.202200318] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Indexed: 11/08/2022]
Abstract
In organic mass spectrometry, fragment ions provide important information on the analyte as a central part of its structure elucidation. With increasing molecular size and possible protonation sites, the potential energy surface (PES) of the analyte can become very complex, which results in a large number of possible fragmentation patterns. Quantum chemical (QC) calculations can help here, enabling the fast calculation of the PES and thus enhancing the mass spectrometry-based structure elucidation processes. In this work, the previously unknown fragmentation pathways of the two drug molecules Nateglinide (45 atoms) and Zopiclone (51 atoms) were investigated using a combination of generic formalisms and calculations conducted with the Quantum Chemical Mass Spectrometry (QCxMS) program. The computations of the de novo fragment spectra were conducted with the semi-empirical GFNn-xTB (n=1, 2) methods and compared against Orbitrap measured electrospray ionization (ESI) spectra in positive ion mode. It was found that the unbiased QC calculations are particularly suitable to predict non-evident fragment ion structures, sometimes contrasting the accepted generic formulation of fragment ion structures from electron migration rules, where the "true" ion fragment structures are approximated. For the first time, all fragment and intermediate structures of these large-sized molecules could be elucidated completely and routinely using this merger of methods, finding new undocumented mechanisms, that are not considered in common rules published so far. Given the importance of ESI for medicinal chemistry, pharmacokinetics, and metabolomics, this approach can significantly enhance the mass spectrometry-based structure elucidation processes and contribute to the understanding of previously unknown fragmentation pathways.
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Affiliation(s)
- Romina Schnegotzki
- Institut für Chemie, Technische Universität Berlin, Straße des 17. Juni 124, 10623, Berlin, Germany
| | - Jeroen Koopman
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115, Bonn, Germany
| | - Stefan Grimme
- Mulliken Center for Theoretical Chemistry, Institute for Physical and Theoretical Chemistry, University of Bonn, Beringstr. 4, 53115, Bonn, Germany
| | - Roderich D Süssmuth
- Institut für Chemie, Technische Universität Berlin, Straße des 17. Juni 124, 10623, Berlin, Germany
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20
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Das S, Tanemura KA, Dinpazhoh L, Keng M, Schumm C, Leahy L, Asef CK, Rainey M, Edison AS, Fernández FM, Merz KM. In Silico Collision Cross Section Calculations to Aid Metabolite Annotation. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:750-759. [PMID: 35378036 PMCID: PMC9277703 DOI: 10.1021/jasms.1c00315] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/28/2023]
Abstract
The interpretation of ion mobility coupled to mass spectrometry (IM-MS) data to predict unknown structures is challenging and depends on accurate theoretical estimates of the molecular ion collision cross section (CCS) against a buffer gas in a low or atmospheric pressure drift chamber. The sensitivity and reliability of computational prediction of CCS values depend on accurately modeling the molecular state over accessible conformations. In this work, we developed an efficient CCS computational workflow using a machine learning model in conjunction with standard DFT methods and CCS calculations. Furthermore, we have performed Traveling Wave IM-MS (TWIMS) experiments to validate the extant experimental values and assess uncertainties in experimentally measured CCS values. The developed workflow yielded accurate structural predictions and provides unique insights into the likely preferred conformation analyzed using IM-MS experiments. The complete workflow makes the computation of CCS values tractable for a large number of conformationally flexible metabolites with complex molecular structures.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Mithony Keng
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Christina Schumm
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Lydia Leahy
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
| | - Carter K Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Markace Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Arthur S Edison
- Departments of Genetics and Biochemistry, Institute of Bioinformatics and Complex Carbohydrate Center, University of Georgia, 315 Riverbend Road, Athens, Georgia 30602, United States
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
- Petit Institute for Bioengineering and Bioscience, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 South Shaw Lane, East Lansing, Michigan 48824, United States
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21
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Kostyukevich Y, Sosnin S, Osipenko S, Kovaleva O, Rumiantseva L, Kireev A, Zherebker A, Fedorov M, Nikolaev EN. PyFragMS-A Web Tool for the Investigation of the Collision-Induced Fragmentation Pathways. ACS OMEGA 2022; 7:9710-9719. [PMID: 35350354 PMCID: PMC8945079 DOI: 10.1021/acsomega.1c07272] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Accepted: 02/28/2022] [Indexed: 05/13/2023]
Abstract
Dissociation induced by the accumulation of internal energy via collisions of ions with neutral molecules is one of the most important fragmentation techniques in mass spectrometry (MS), and the identification of small singly charged molecules is based mainly on the consideration of the fragmentation spectrum. Many research studies have been dedicated to the creation of databases of experimentally measured tandem mass spectrometry (MS/MS) spectra (such as MzCloud, Metlin, etc.) and developing software for predicting MS/MS fragments in silico from the molecular structure (such as MetFrag, CFM-ID, CSI:FingerID, etc.). However, the fragmentation mechanisms and pathways are still not fully understood. One of the limiting obstacles is that protomers (positive ions protonated at different sites) produce different fragmentation spectra, and these spectra overlap in the case of the presence of different protomers. Here, we are proposing to use a combination of two powerful approaches: computing fragmentation trees that carry information of all consecutive fragmentations and consideration of the MS/MS data of isotopically labeled compounds. We have created PyFragMS-a web tool consisting of a database of annotated MS/MS spectra of isotopically labeled molecules (after H/D and/or 16O/18O exchange) and a collection of instruments for computing fragmentation trees for an arbitrary molecule. Using PyFragMS, we investigated how the site of protonation influences the fragmentation pathway for small molecules. Also, PyFragMS offers capabilities for performing database search when MS/MS data of the isotopically labeled compounds are taken into account.
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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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23
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Wang S, Kind T, Bremer PL, Tantillo DJ, Fiehn O. Quantum Chemical Prediction of Electron Ionization Mass Spectra of Trimethylsilylated Metabolites. Anal Chem 2022; 94:1559-1566. [DOI: 10.1021/acs.analchem.1c02838] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Affiliation(s)
- Shunyang Wang
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
- Department of Chemistry, University of California, 1 Shields Ave., Davis, California 95616, United States
| | - Tobias Kind
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
| | - Parker Ladd Bremer
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
- Department of Chemistry, University of California, 1 Shields Ave., Davis, California 95616, United States
| | - Dean J. Tantillo
- Department of Chemistry, University of California, 1 Shields Ave., Davis, California 95616, United States
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, 451 Health Sciences Drive, Davis, California 95616, United States
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Asakawa D, Saikusa K. Fragmentation efficiency of phenethylamines in electrospray ionization source estimated by theoretical chemistry calculation. JOURNAL OF MASS SPECTROMETRY : JMS 2021; 57:e4802. [PMID: 34929756 DOI: 10.1002/jms.4802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 11/08/2021] [Accepted: 11/25/2021] [Indexed: 06/14/2023]
Abstract
Small molecules with polar functional groups, including substituted phenethylamines, are commonly analyzed by liquid chromatography-mass spectrometry (LC-MS) with electrospray ionization (ESI). Analyte molecules are mostly detected in protonated and cation-adducted forms through positive-ion electrospray ionization-mass spectrometry (ESI-MS). However, the ESI of substituted phenethylamines commonly provides an intense signal of fragment ions by ESI in-source collision-induced dissociation (IS-CID), which hinders the unambiguous identification of phenethylamines. This phenomenon was approximated as a unimolecular dissociation model, and the dissociation efficiency was evaluated by various quantum chemistry calculations to determine the ESI IS-CID efficiency. The calculated results were consistent with the experimental data, when the dissociation threshold energy of phenethylamines was calculated using the post-Hartree-Fock (post-HF) method, CCSD(t)/cc-pVTZ//MP2(full)/6-311++G(d,p). In contrast to post-HF methods, the utilization of density functional theory calculations with a suitable functional is recognized as an accurate and competitive low-cost approach. In particular, ωB97-XD, M06-2X-D3, and recently developed Minnesota functionals, such as M11, MN12-SX, and MN15, provided reliable results, as in the case of the post-HF method. The results obtained by the recently developed double hybrid functionals, DSD-PEBP86-D3(BJ), PBE0-DH, and PBE-QIDH, were also reliable. The consideration of ESI IS-CID can facilitate the identification of analyte molecules because most phenethylamines, except for N-methylated analogs, provide an intense signal in the ESI mass spectrum.
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Affiliation(s)
- Daiki Asakawa
- Research Institute for Measurement and Analytical Instrumentation, National Institute of Advanced Industrial Science and Technology, National Metrology Institute of Japan, Tsukuba, Japan
| | - Kazumi Saikusa
- Research Institute for Material and Chemical Measurement, National Institute of Advanced Industrial Science and Technology, National Metrology Institute of Japan, Tsukuba, Japan
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Nontargeted Screening Using Gas Chromatography-Atmospheric Pressure Ionization Mass Spectrometry: Recent Trends and Emerging Potential. Molecules 2021; 26:molecules26226911. [PMID: 34834002 PMCID: PMC8624013 DOI: 10.3390/molecules26226911] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2021] [Revised: 11/10/2021] [Accepted: 11/12/2021] [Indexed: 01/02/2023] Open
Abstract
Gas chromatography–high-resolution mass spectrometry (GC–HRMS) is a powerful nontargeted screening technique that promises to accelerate the identification of environmental pollutants. Currently, most GC–HRMS instruments are equipped with electron ionization (EI), but atmospheric pressure ionization (API) ion sources have attracted renewed interest because: (i) collisional cooling at atmospheric pressure minimizes fragmentation, resulting in an increased yield of molecular ions for elemental composition determination and improved detection limits; (ii) a wide range of sophisticated tandem (ion mobility) mass spectrometers can be easily adapted for operation with GC–API; and (iii) the conditions of an atmospheric pressure ion source can promote structure diagnostic ion–molecule reactions that are otherwise difficult to perform using conventional GC–MS instrumentation. This literature review addresses the merits of GC–API for nontargeted screening while summarizing recent applications using various GC–API techniques. One perceived drawback of GC–API is the paucity of spectral libraries that can be used to guide structure elucidation. Herein, novel data acquisition, deconvolution and spectral prediction tools will be reviewed. With continued development, it is anticipated that API may eventually supplant EI as the de facto GC–MS ion source used to identify unknowns.
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