1
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Nguyen J, Overstreet R, King E, Ciesielski D. Advancing the Prediction of MS/MS Spectra Using Machine Learning. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024. [PMID: 39258761 DOI: 10.1021/jasms.4c00154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2024]
Abstract
Tandem mass spectrometry (MS/MS) is an important tool for the identification of small molecules and metabolites where resultant spectra are most commonly identified by matching them with spectra in MS/MS reference libraries. While popular, this strategy is limited by the contents of existing reference libraries. In response to this limitation, various methods are being developed for the in silico generation of spectra to augment existing libraries. Recently, machine learning and deep learning techniques have been applied to predict spectra with greater speed and accuracy. Here, we investigate the challenges these algorithms face in achieving fast and accurate predictions on a wide range of small molecules. The challenges are often amplified by the use of generic machine learning benchmarking tactics, which lead to misleading accuracy scores. Curating data sets, only predicting spectra for sufficiently high collision energies, and working more closely with experimental mass spectrometrists are recommended strategies to improve overall prediction accuracy in this nuanced field.
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Affiliation(s)
- Julia Nguyen
- Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - 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
| | - Danielle Ciesielski
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
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2
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Sarkar S, Zheng X, Clair GC, Kwon YM, You Y, Swensen AC, Webb-Robertson BJM, Nakayasu ES, Qian WJ, Metz TO. Exploring new frontiers in type 1 diabetes through advanced mass-spectrometry-based molecular measurements. Trends Mol Med 2024:S1471-4914(24)00195-3. [PMID: 39152082 DOI: 10.1016/j.molmed.2024.07.009] [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: 05/23/2024] [Revised: 07/17/2024] [Accepted: 07/22/2024] [Indexed: 08/19/2024]
Abstract
Type 1 diabetes (T1D) is a devastating autoimmune disease for which advanced mass spectrometry (MS) methods are increasingly used to identify new biomarkers and better understand underlying mechanisms. For example, integration of MS analysis and machine learning has identified multimolecular biomarker panels. In mechanistic studies, MS has contributed to the discovery of neoepitopes, and pathways involved in disease development and identifying therapeutic targets. However, challenges remain in understanding the role of tissue microenvironments, spatial heterogeneity, and environmental factors in disease pathogenesis. Recent advancements in MS, such as ultra-fast ion-mobility separations, and single-cell and spatial omics, can play a central role in addressing these challenges. Here, we review recent advancements in MS-based molecular measurements and their role in understanding T1D.
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Affiliation(s)
- Soumyadeep Sarkar
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Xueyun Zheng
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Geremy C Clair
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Yu Mi Kwon
- Environmental Molecular Sciences Laboratory, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Youngki You
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | - Adam C Swensen
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA
| | | | - Ernesto S Nakayasu
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
| | - Wei-Jun Qian
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA, 99352, USA.
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3
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Metz TO, Chang CH, Gautam V, Anjum A, Tian S, Wang F, Colby SM, Nunez JR, Blumer MR, Edison AS, Fiehn O, Jones DP, Li S, Morgan ET, Patti GJ, Ross DH, Shapiro MR, Williams AJ, Wishart DS. Introducing 'identification probability' for automated and transferable assessment of metabolite identification confidence in metabolomics and related studies. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.30.605945. [PMID: 39131324 PMCID: PMC11312557 DOI: 10.1101/2024.07.30.605945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Methods for assessing compound identification confidence in metabolomics and related studies have been debated and actively researched for the past two decades. The earliest effort in 2007 focused primarily on mass spectrometry and nuclear magnetic resonance spectroscopy and resulted in four recommended levels of metabolite identification confidence - the Metabolite Standards Initiative (MSI) Levels. In 2014, the original MSI Levels were expanded to five levels (including two sublevels) to facilitate communication of compound identification confidence in high resolution mass spectrometry studies. Further refinement in identification levels have occurred, for example to accommodate use of ion mobility spectrometry in metabolomics workflows, and alternate approaches to communicate compound identification confidence also have been developed based on identification points schema. However, neither qualitative levels of identification confidence nor quantitative scoring systems address the degree of ambiguity in compound identifications in context of the chemical space being considered, are easily automated, or are transferable between analytical platforms. In this perspective, we propose that the metabolomics and related communities consider identification probability as an approach for automated and transferable assessment of compound identification and ambiguity in metabolomics and related studies. Identification probability is defined simply as 1/N, where N is the number of compounds in a reference library or chemical space that match to an experimentally measured molecule within user-defined measurement precision(s), for example mass measurement or retention time accuracy, etc. We demonstrate the utility of identification probability in an in silico analysis of multi-property reference libraries constructed from the Human Metabolome Database and computational property predictions, provide guidance to the community in transparent implementation of the concept, and invite the community to further evaluate this concept in parallel with their current preferred methods for assessing metabolite identification confidence.
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Affiliation(s)
- Thomas O. Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Christine H. Chang
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Afia Anjum
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Siyang Tian
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
| | - Fei Wang
- Department of Computing Science, University of Alberta, Edmonton, AB, Canada
- Alberta Machine Intelligence Institute, Edmonton, AB, Canada
| | - Sean M. Colby
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Jamie R. Nunez
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Madison R. Blumer
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Arthur S. Edison
- Department of Biochemistry & Molecular Biology, Complex Carbohydrate Research Center and Institute of Bioinformatics, University of Georgia, Athens, GA, USA
| | - Oliver Fiehn
- West Coast Metabolomics Center, University of California Davis, Davis, CA, USA
| | - Dean P. Jones
- Clinical Biomarkers Laboratory, Department of Medicine, Emory University, Atlanta, Georgia, USA
| | - Shuzhao Li
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Edward T. Morgan
- Department of Pharmacology and Chemical Biology, Emory University School of Medicine, Atlanta, Georgia, USA
| | - Gary J. Patti
- Center for Mass Spectrometry and Metabolic Tracing, Department of Chemistry, Department of Medicine, Washington University, Saint Louis, Missouri, USA
| | - Dylan H. Ross
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Madelyn R. Shapiro
- Artificial Intelligence & Data Analytics Division, Pacific Northwest National Laboratory, Richland, WA USA
| | - Antony J. Williams
- U.S. Environmental Protection Agency, Office of Research & Development, Center for Computational Toxicology & Exposure (CCTE), Research Triangle Park, NC USA
| | - David S. Wishart
- Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada
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4
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Hollerbach AL, Lin VS, Ibrahim YM, Ewing RG, Metz TO, Rodda KE. Elucidating the Gas-Phase Behavior of Nitazene Analog Protomers Using Structures for Lossless Ion Manipulations Ion Mobility-Orbitrap Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2024; 35:1609-1621. [PMID: 38907730 DOI: 10.1021/jasms.4c00200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/24/2024]
Abstract
2-Benzylbenzimidazoles, or "nitazenes", are a class of novel synthetic opioids (NSOs) that are increasingly being detected alongside fentanyl analogs and other opioids in drug overdose cases. Nitazenes can be 20× more potent than fentanyl but are not routinely tested for during postmortem or clinical toxicology drug screens; thus, their prevalence in drug overdose cases may be under-reported. Traditional analytical workflows utilizing liquid chromatography-tandem mass spectrometry (LC-MS/MS) often require additional confirmation with authentic reference standards to identify a novel nitazene. However, additional analytical measurements with ion mobility spectrometry (IMS) may provide a path toward reference-free identification, which would greatly accelerate NSO identification rates in toxicology laboratories. Presented here are the first IMS and collision cross section (CCS) measurements on a set of fourteen nitazene analogs using a structures for lossless ion manipulations (SLIM)-orbitrap MS. All nitazenes exhibited two high intensity baseline-separated IMS distributions, which fentanyls and other drug and druglike compounds also exhibit. Incorporating water into the electrospray ionization (ESI) solution caused the intensities of the higher mobility IMS distributions to increase and the intensities of the lower mobility IMS distributions to decrease. Nitazenes lacking a nitro group at the R1 position exhibited the greatest shifts in signal intensities due to water. Furthermore, IMS-MS/MS experiments showed that the higher mobility IMS distributions of all nitazenes possessing a triethylamine group produced fragment ions with m/z 72, 100, and other low intensity fragments while the lower mobility IMS distributions only produced fragment ions with m/z 72 and 100. The IMS, solvent, and fragmentation studies provide experimental evidence that nitazenes potentially exhibit three gas-phase protomers. The cyclic IMS capability of SLIM was also employed to partially resolve four sets of structurally similar nitazene isomers (e.g., protonitazene/isotonitazene, butonitazene/isobutonitazene/secbutonitazene), showcasing the potential of using high-resolution IMS separations in MS-based workflows for reference-free identification of emerging nitazenes and other NSOs.
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Affiliation(s)
- Adam L Hollerbach
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Vivian S Lin
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Yehia M Ibrahim
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Robert G Ewing
- Nuclear, Chemistry & Biology Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Thomas O Metz
- Biological Sciences Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Kabrena E Rodda
- Nuclear, Chemistry & Biology Division, Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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5
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Bouwmeester R, Richardson K, Denny R, Wilson ID, Degroeve S, Martens L, Vissers JPC. Predicting ion mobility collision cross sections and assessing prediction variation by combining conventional and data driven modeling. Talanta 2024; 274:125970. [PMID: 38621320 DOI: 10.1016/j.talanta.2024.125970] [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: 11/02/2023] [Revised: 03/01/2024] [Accepted: 03/20/2024] [Indexed: 04/17/2024]
Abstract
The use of collision cross section (CCS) values derived from ion mobility studies is proving to be an increasingly important tool in the characterization and identification of molecules detected in complex mixtures. Here, a novel machine learning (ML) based method for predicting CCS integrating both molecular modeling (MM) and ML methodologies has been devised and shown to be able to accurately predict CCS values for singly charged small molecular weight molecules from a broad range of chemical classes. The model performed favorably compared to existing models, improving compound identifications for isobaric analytes in terms of ranking and assigning identification probability values to the annotation. Furthermore, charge localization was seen to be correlated with CCS prediction accuracy and with gas-phase proton affinity demonstrating the potential to provide a proxy for prediction error based on chemical structural properties. The presented approach and findings represent a further step towards accurate prediction and application of computationally generated CCS values.
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Affiliation(s)
- Robbin Bouwmeester
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium.
| | | | | | - Ian D Wilson
- Computational & Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, United Kingdom
| | - Sven Degroeve
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
| | - Lennart Martens
- VIB-UGent Center for Medical Biotechnology, Ghent, Belgium; Department of Biomolecular Medicine, Ghent University, Ghent, Belgium
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6
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Huang S, Righetti L, Claassen FW, Krishna A, Ma M, van Beek TA, Chen B, Zuilhof H, Salentijn GIJ. Ultrafast, Selective, and Highly Sensitive Nonchromatographic Analysis of Fourteen Cannabinoids in Cannabis Extracts, Δ8-Tetrahydrocannabinol Synthetic Mixtures, and Edibles by Cyclic Ion Mobility Spectrometry-Mass Spectrometry. Anal Chem 2024; 96:10170-10181. [PMID: 38862388 PMCID: PMC11209660 DOI: 10.1021/acs.analchem.3c05879] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 05/28/2024] [Accepted: 05/30/2024] [Indexed: 06/13/2024]
Abstract
The diversity of cannabinoid isomers and complexity of Cannabis products pose significant challenges for analytical methodologies. In this study, we developed a method to analyze 14 different cannabinoid isomers in diverse samples within milliseconds by leveraging the unique adduct-forming behavior of silver ions in advanced cyclic ion mobility spectrometry-mass spectrometry. The developed method achieved the separation of isomers from four groups of cannabinoids: Δ3-tetrahydrocannabinol (THC) (1), Δ8-THC (2), Δ9-THC (3), cannabidiol (CBD) (4), Δ8-iso-THC (5), and Δ(4)8-iso-THC (6) (all MW = 314); 9α-hydroxyhexahydrocannabinol (7), 9β-hydroxyhexahydrocannabinol (8), and 8-hydroxy-iso-THC (9) (all MW = 332); tetrahydrocannabinolic acid (THCA) (10) and cannabidiolic acid (CBDA) (11) (both MW = 358); Δ8-tetrahydrocannabivarin (THCV) (12), Δ8-iso-THCV (13), and Δ9-THCV (14) (all MW = 286). Moreover, experimental and theoretical traveling wave collision cross section values in nitrogen (TWCCSN2) of cannabinoid-Ag(I) species were obtained for the first time with an average error between experimental and theoretical values of 2.6%. Furthermore, a workflow for the identification of cannabinoid isomers in Cannabis and Cannabis-derived samples was established based on three identification steps (m/z and isotope pattern of Ag(I) adducts, TWCCSN2, and MS/MS fragments). Afterward, calibration curves of three major cannabinoids were established with a linear range of 1-250 ng·ml-1 for Δ8-THC (2) (R2 = 0.9999), 0.1-25 ng·ml-1 for Δ9-THC (3) (R2 = 0.9987), and 0.04-10 ng·ml-1 for CBD (4) (R2 = 0.9986) as well as very low limits of detection (0.008-0.2 ng·ml-1). Finally, relative quantification of Δ8-THC (2), Δ9-THC (3), and CBD (4) in eight complex acid-treated CBD mixtures was achieved without chromatographic separation. The results showed good correspondence (R2 = 0.999) with those obtained by gas chromatography-flame ionization detection/mass spectrometry.
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Affiliation(s)
- Si Huang
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Laura Righetti
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
- Wageningen
Food Safety Research (WFSR), Wageningen
University & Research, P.O. Box 230, Wageningen 6700 AE, The Netherlands
| | - Frank W. Claassen
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Akash Krishna
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Ming Ma
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
| | - Teris A. van Beek
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Bo Chen
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
| | - Han Zuilhof
- Key
Laboratory of Phytochemical R&D of Hunan Province and Key Laboratory
of Chemical Biology & Traditional Chinese Medicine Research of
Ministry of Education, Hunan Normal University, No.36, Lushan Road, Changsha 410081, China
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
| | - Gert IJ. Salentijn
- Laboratory
of Organic Chemistry, Wageningen University, Stippeneng 4, Wageningen 6708 WE, The
Netherlands
- Wageningen
Food Safety Research (WFSR), Wageningen
University & Research, P.O. Box 230, Wageningen 6700 AE, The Netherlands
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7
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Zeng T, Chen X, van de Lavoir M, Robeyns R, Zhao L, Delgado Povedano MDM, van Nuijs ALN, Zhu L, Covaci A. Serum untargeted lipidomic characterization in a general Chinese cohort with residual per-/polyfluoroalkyl substances by liquid chromatography-drift tube ion mobility-mass spectrometry. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 929:172483. [PMID: 38631629 DOI: 10.1016/j.scitotenv.2024.172483] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/02/2024] [Revised: 03/16/2024] [Accepted: 04/12/2024] [Indexed: 04/19/2024]
Abstract
Per- and polyfluoroalkyl substances (PFAS) remain controversial due to their high persistency and potential human toxicity. Although occupational exposure to PFAS has been widely investigated, the implications of PFAS occurrence in the general population remain to be unraveled. Considering that serum from most people contains PFAS, the aim of this study was to characterize the lipidomic profile in human serum from a general cohort (n = 40) with residual PFAS levels. The geometric means of ∑PFAS (11.8 and 4.4 ng/mL) showed significant differences (p < 0.05) for the samples with the highest (n = 20) and lowest (n = 20) concentrations from the general population respectively. Reverse-phase liquid chromatography coupled to drift tube ion mobility and high-resolution mass spectrometry using dual polarity ionization was used to characterize the lipid profile in both groups. The structural elucidation involved the integration of various parameters, such as retention time, mass-to-charge ratio, tandem mass spectra and collision cross section values. This approach yielded a total of 20 potential biomarkers linked to the perturbed glycerophospholipid metabolism, energy metabolism and sphingolipid metabolism. Among these alterations, most lipids were down-regulated and some specific lipids (PC 36:5, PC 37:4 and PI O-34:2) exhibited a relatively strong Spearman correlation and predictive capacity for PFAS contamination. This study could support further toxicological assessments and mechanistic investigations into the effects of PFAS exposure on the lipidome.
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Affiliation(s)
- Ting Zeng
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Wilrijk 2610, Belgium
| | - Xin Chen
- Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Maria van de Lavoir
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Wilrijk 2610, Belgium
| | - Rani Robeyns
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Wilrijk 2610, Belgium
| | - Lu Zhao
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Wilrijk 2610, Belgium
| | | | - Alexander L N van Nuijs
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Wilrijk 2610, Belgium
| | - Lingyan Zhu
- Tianjin Key Laboratory of Environmental Remediation and Pollution Control, College of Environmental Science and Engineering, Nankai University, Tianjin 300350, China
| | - Adrian Covaci
- Toxicological Centre, Department of Pharmaceutical Sciences, University of Antwerp, Wilrijk 2610, Belgium.
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8
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Wang H, Zhang L, Li X, Sun M, Jiang M, Shi X, Xu X, Ding M, Chen B, Yu H, Li Z, Guo D, Yang W. Machine learning prediction for constructing a universal multidimensional information library of Panax saponins (ginsenosides). Food Chem 2024; 439:138106. [PMID: 38056336 DOI: 10.1016/j.foodchem.2023.138106] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 11/22/2023] [Accepted: 11/26/2023] [Indexed: 12/08/2023]
Abstract
Accurate characterization of Panax herb ginsenosides is challenging because of the isomers and lack of sufficient reference compounds. More structural information could help differentiate ginsenosides and their isomers, enabling more accurate identification. Based on the VionTM ion-mobility high-resolution LC-MS platform, a multidimensional information library for ginsenosides, namely GinMIL, was established by predicting retention time (tR) and collision cross section (CCS) through machine learning. Robustness validation experiments proved tR and CCS were suitable for database construction. Among three machine learning models we attempted, gradient boosting machine (GBM) exhibited the best prediction performance. GinMIL included the multidimensional information (m/z, molecular formula, tR, CCS, and some MS/MS fragments) for 579 known ginsenosides. Accuracy in identifying ginsenosides from diverse ginseng products was greatly improved by a unique LC-MS approach and searching GinMIL, demonstrating a universal Panax saponins library constructed based on hierarchical design. GinMIL could improve the accuracy of isomers identification by approximately 88%.
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Affiliation(s)
- Hongda Wang
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Lin Zhang
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Xiaohang Li
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Mengxiao Sun
- National Key Laboratory of Chinese Medicine Modernization, 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
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Xiaojian Shi
- Cellular & Molecular Physiology, Yale School of Medicine, 850 Yale West Campus, West Haven CT 06516, USA
| | - Xiaoyan Xu
- National Key Laboratory of Chinese Medicine Modernization, 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
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Boxue Chen
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Heshui Yu
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Zheng Li
- National Key Laboratory of Chinese Medicine Modernization, 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
| | - Dean Guo
- National Key Laboratory of Chinese Medicine Modernization, 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; Shanghai Research Center for Modernization of Traditional Chinese Medicine, National Engineering Laboratory for TCM Standardization Technology, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, 501 Haike Road, Shanghai 201203, China.
| | - Wenzhi Yang
- National Key Laboratory of Chinese Medicine Modernization, 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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9
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Cormanich RA, da Silva GD. Autobench V1.0: Benchmarking Automation for Electronic Structure Calculations. J Chem Inf Model 2024; 64:3322-3331. [PMID: 38536765 DOI: 10.1021/acs.jcim.4c00250] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/23/2024]
Abstract
This work reports on new software for automatic conformer energy benchmarking calculations for flexible molecules. The software workflow consists of four parts: conformational search, preoptimization, optimization, and frequency calculations at a higher level and last calculations using several theoretical levels. The software was written to be user-friendly and versatile to be used by nonexperts in computational chemistry. Any theoretical levels available in either Gaussian 16 or ORCA 5 may be applied in the benchmarking study. The workflow will automatically run conformational search calculations and deal with conformers that converge to the same minimum and those that show a negative frequency. At the end of the workflow, the user will have the mean absolute deviations and the most accurate method/DFT functional and basis set in comparison to the benchmark to be applied for the molecular system of interest. Case examples are given at the end of the paper that may help users to get insight into the software's main features.
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Affiliation(s)
- Rodrigo A Cormanich
- Instituto de Química, Departamento de Química Orgânica, Universidade Estadual de Campinas, PO Box 6154, Campinas 13083-970, São Paulo, Brazil
| | - Gabriel D da Silva
- Instituto de Química, Departamento de Química Orgânica, Universidade Estadual de Campinas, PO Box 6154, Campinas 13083-970, São Paulo, Brazil
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10
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Wisanpitayakorn P, Sartyoungkul S, Kurilung A, Sirivatanauksorn Y, Visessanguan W, Sathirapongsasuti N, Khoomrung S. Accurate Prediction of Ion Mobility Collision Cross-Section Using Ion's Polarizability and Molecular Mass with Limited Data. J Chem Inf Model 2024; 64:1533-1542. [PMID: 38393779 PMCID: PMC10934814 DOI: 10.1021/acs.jcim.3c01491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Revised: 01/26/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024]
Abstract
The rotationally averaged collision cross-section (CCS) determined by ion mobility-mass spectrometry (IM-MS) facilitates the identification of various biomolecules. Although machine learning (ML) models have recently emerged as a highly accurate approach for predicting CCS values, they rely on large data sets from various instruments, calibrants, and setups, which can introduce additional errors. In this study, we identified and validated that ion's polarizability and mass-to-charge ratio (m/z) have the most significant predictive power for traveling-wave IM CCS values in relation to other physicochemical properties of ions. Constructed solely based on these two physicochemical properties, our CCS prediction approach demonstrated high accuracy (mean relative error of <3.0%) even when trained with limited data (15 CCS values). Given its ability to excel with limited data, our approach harbors immense potential for constructing a precisely predicted CCS database tailored to each distinct experimental setup. A Python script for CCS prediction using our approach is freely available at https://github.com/MSBSiriraj/SVR_CCSPrediction under the GNU General Public License (GPL) version 3.
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Affiliation(s)
- Pattipong Wisanpitayakorn
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sitanan Sartyoungkul
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Alongkorn Kurilung
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Wonnop Visessanguan
- National
Center for Genetic Engineering and Biotechnology (BIOTEC), Pathumthani 12120, Thailand
| | - Nuankanya Sathirapongsasuti
- Section
of Translational Medicine, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Bangkok 10400, Thailand
- Research
Network of NANOTEC - MU Ramathibodi on Nanomedicine, Bangkok 12120, Thailand
| | - Sakda Khoomrung
- Siriraj
Center of Research Excellence in Metabolomics and Systems Biology
(SiCORE-MSB), Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Department
of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, 10700, Thailand
- Center
of Excellence for Innovation in Chemistry (PERCH−CIC), Faculty of Science Mahidol University, Bangkok 10400, Thailand
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11
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Baygi SF, Barupal DK. IDSL_MINT: a deep learning framework to predict molecular fingerprints from mass spectra. J Cheminform 2024; 16:8. [PMID: 38238779 PMCID: PMC10797927 DOI: 10.1186/s13321-024-00804-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 01/14/2024] [Indexed: 01/22/2024] Open
Abstract
The majority of tandem mass spectrometry (MS/MS) spectra in untargeted metabolomics and exposomics studies lack any annotation. Our deep learning framework, Integrated Data Science Laboratory for Metabolomics and Exposomics-Mass INTerpreter (IDSL_MINT) can translate MS/MS spectra into molecular fingerprint descriptors. IDSL_MINT allows users to leverage the power of the transformer model for mass spectrometry data, similar to the large language models. Models are trained on user-provided reference MS/MS libraries via any customizable molecular fingerprint descriptors. IDSL_MINT was benchmarked using the LipidMaps database and improved the annotation rate of a test study for MS/MS spectra that were not originally annotated using existing mass spectral libraries. IDSL_MINT may improve the overall annotation rates in untargeted metabolomics and exposomics studies. The IDSL_MINT framework and tutorials are available in the GitHub repository at https://github.com/idslme/IDSL_MINT .Scientific contribution statement.Structural annotation of MS/MS spectra from untargeted metabolomics and exposomics datasets is a major bottleneck in gaining new biological insights. Machine learning models to convert spectra into molecular fingerprints can help in the annotation process. Here, we present IDSL_MINT, a new, easy-to-use and customizable deep-learning framework to train and utilize new models to predict molecular fingerprints from spectra for the compound annotation workflows.
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Affiliation(s)
- Sadjad Fakouri Baygi
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, CAM Building, 3rd Floor, 17 E 102 St, New York, NY, 10029, USA
| | - Dinesh Kumar Barupal
- Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, CAM Building, 3rd Floor, 17 E 102 St, New York, NY, 10029, USA.
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12
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Song XC, Canellas E, Dreolin N, Goshawk J, Lv M, Qu G, Nerin C, Jiang G. Application of Ion Mobility Spectrometry and the Derived Collision Cross Section in the Analysis of Environmental Organic Micropollutants. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2023; 57:21485-21502. [PMID: 38091506 PMCID: PMC10753811 DOI: 10.1021/acs.est.3c03686] [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: 05/16/2023] [Revised: 11/09/2023] [Accepted: 11/09/2023] [Indexed: 12/27/2023]
Abstract
Ion mobility spectrometry (IMS) is a rapid gas-phase separation technique, which can distinguish ions on the basis of their size, shape, and charge. The IMS-derived collision cross section (CCS) can serve as additional identification evidence for the screening of environmental organic micropollutants (OMPs). In this work, we summarize the published experimental CCS values of environmental OMPs, introduce the current CCS prediction tools, summarize the use of IMS and CCS in the analysis of environmental OMPs, and finally discussed the benefits of IMS and CCS in environmental analysis. An up-to-date CCS compendium for environmental contaminants was produced by combining CCS databases and data sets of particular types of environmental OMPs, including pesticides, drugs, mycotoxins, steroids, plastic additives, per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and polybrominated diphenyl ethers (PBDEs), as well as their well-known transformation products. A total of 9407 experimental CCS values from 4170 OMPs were retrieved from 23 publications, which contain both drift tube CCS in nitrogen (DTCCSN2) and traveling wave CCS in nitrogen (TWCCSN2). A selection of publicly accessible and in-house CCS prediction tools were also investigated; the chemical space covered by the training set and the quality of CCS measurements seem to be vital factors affecting the CCS prediction accuracy. Then, the applications of IMS and the derived CCS in the screening of various OMPs were summarized, and the benefits of IMS and CCS, including increased peak capacity, the elimination of interfering ions, the separation of isomers, and the reduction of false positives and false negatives, were discussed in detail. With the improvement of the resolving power of IMS and enhancements of experimental CCS databases, the practicability of IMS in the analysis of environmental OMPs will continue to improve.
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Affiliation(s)
- Xue-Chao Song
- School
of the Environment, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou 310024, China
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Elena Canellas
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Nicola Dreolin
- Waters
Corporation, Stamford
Avenue, Altrincham Road, SK9 4AX Wilmslow, United Kingdom
| | - Jeff Goshawk
- Waters
Corporation, Stamford
Avenue, Altrincham Road, SK9 4AX Wilmslow, United Kingdom
| | - Meilin Lv
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Research
Center for Analytical Sciences, Department of Chemistry, College of
Sciences, Northeastern University, 110819 Shenyang, China
| | - Guangbo Qu
- School
of the Environment, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou 310024, China
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Institute
of Environment and Health, Jianghan University, Wuhan 430056, China
| | - Cristina Nerin
- Department
of Analytical Chemistry, Aragon Institute of Engineering Research
I3A, EINA, University of Zaragoza, Maria de Luna 3, 50018 Zaragoza, Spain
| | - Guibin Jiang
- School
of the Environment, Hangzhou Institute for Advanced Study, University of the Chinese Academy of Sciences, Hangzhou 310024, China
- State
Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese
Academy of Sciences, Beijing 100085, China
- Institute
of Environment and Health, Jianghan University, Wuhan 430056, China
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13
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Zhang H, Luo M, Wang H, Ren F, Yin Y, Zhu ZJ. AllCCS2: Curation of Ion Mobility Collision Cross-Section Atlas for Small Molecules Using Comprehensive Molecular Representations. Anal Chem 2023; 95:13913-13921. [PMID: 37664900 DOI: 10.1021/acs.analchem.3c02267] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
The development of ion mobility-mass spectrometry (IM-MS) has revolutionized the analysis of small molecules, such as metabolomics, lipidomics, and exposome studies. The curation of comprehensive reference collision cross-section (CCS) databases plays a pivotal role in the successful application of IM-MS for small-molecule analysis. In this study, we presented AllCCS2, an enhanced version of AllCCS, designed for the universal prediction of the ion mobility CCS values of small molecules. AllCCS2 incorporated newly available experimental CCS data, including 10,384 records and 7713 unified values, as training data. By leveraging a neural network trained on diverse molecular representations encompassing mass spectrometry features, molecular descriptors, and graph features extracted using a graph convolutional network, AllCCS2 achieved exceptional prediction accuracy. AllCCS2 achieved median relative error (MedRE) values of 0.31, 0.72, and 1.64% in the training, validation, and testing sets, respectively, surpassing existing CCS prediction tools in terms of accuracy and coverage. Furthermore, AllCCS2 exhibited excellent compatibility with different instrument platforms (DTIMS, TWIMS, and TIMS). The prediction uncertainties in AllCCS2 from the training data and the prediction model were comprehensively investigated by using representative structure similarity and model prediction variation. Notably, small molecules with high structural similarities to the training set and lower model prediction variation exhibited improved accuracy and lower relative errors. In summary, AllCCS2 serves as a valuable resource to support applications of IM-MS technologies. The AllCCS2 database and tools are freely accessible at http://allccs.zhulab.cn/.
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Affiliation(s)
- Haosong Zhang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Mingdu Luo
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hongmiao Wang
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Fandong Ren
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Yandong Yin
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
| | - Zheng-Jiang Zhu
- Interdisciplinary Research Center on Biology and Chemistry, Shanghai Institute of Organic Chemistry, Chinese Academy of Sciences, Shanghai 200032, China
- Shanghai Key Laboratory of Aging Studies, Shanghai 201210, China
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14
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Das S, Dinpazhoh L, Tanemura KA, Merz KM. Rapid and Automated Ab Initio Metabolite Collisional Cross Section Prediction from SMILES Input. J Chem Inf Model 2023; 63:4995-5000. [PMID: 37548575 DOI: 10.1021/acs.jcim.3c00890] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/08/2023]
Abstract
We implemented an ab initio CCS prediction workflow which incrementally refines generated structures using molecular mechanics, a deep learning potential, conformational clustering, and quantum mechanics (QM). Automating intermediate steps for a high performance computing (HPC) environment allows users to input the SMILES structure of small organic molecules and obtain a Boltzmann averaged collisional cross section (CCS) value as output. The CCS of a molecular species is a metric measured by ion mobility spectrometry (IMS) which can improve annotation of untargeted metabolomics experiments. We report only a minor drop in accuracy when we expedite the CCS calculation by replacing the QM geometry refinement step with a single-point energy calculation. Even though the workflow involves stochastic steps (i.e., conformation generation and clustering), the final CCS value was highly reproducible for multiple iterations on L-carnosine. Finally, we illustrate that the gas phase ensembles modeled for the workflow are intermediate files which can be used for the prediction of other properties such as aqueous phase nuclear magnetic resonance chemical shift prediction. The software is available at the following link: https://github.com/DasSusanta/snakemake_ccs.
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Affiliation(s)
- Susanta Das
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Laleh Dinpazhoh
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kiyoto Aramis Tanemura
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
| | - Kenneth M Merz
- Department of Chemistry, Michigan State University, 578 S. Shaw Lane, East Lansing, Michigan 48824, United States
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15
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Akhlaqi M, Wang WC, Möckel C, Kruve A. Complementary methods for structural assignment of isomeric candidate structures in non-target liquid chromatography ion mobility high-resolution mass spectrometric analysis. Anal Bioanal Chem 2023:10.1007/s00216-023-04852-y. [PMID: 37452839 PMCID: PMC10404200 DOI: 10.1007/s00216-023-04852-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Revised: 07/03/2023] [Accepted: 07/06/2023] [Indexed: 07/18/2023]
Abstract
Non-target screening with LC/IMS/HRMS is increasingly employed for detecting and identifying the structure of potentially hazardous chemicals in the environment and food. Structural assignment relies on a combination of multidimensional instrumental methods and computational methods. The candidate structures are often isomeric, and unfortunately, assigning the correct structure among a number of isomeric candidate structures still is a key challenge both instrumentally and computationally. While practicing non-target screening, it is usually impossible to evaluate separately the limitations arising from (1) the inability of LC/IMS/HRMS to resolve the isomeric candidate structures and (2) the uncertainty of in silico methods in predicting the analytical information of isomeric candidate structures due to the lack of analytical standards for all candidate structures. Here we evaluate the feasibility of structural assignment of isomeric candidate structures based on in silico-predicted retention time and database collision cross-section (CCS) values as well as based on matching the empirical analytical properties of the detected feature with those of the analytical standards. For this, we investigated 14 candidate structures corresponding to five features detected with LC/HRMS in a spiked surface water sample. Considering the predicted retention times and database CCS values with the accompanying uncertainty, only one of the isomeric candidate structures could be deemed as unlikely; therefore, the annotation of the LC/IMS/HRMS features remained ambiguous. To further investigate if unequivocal annotation is possible via analytical standards, the reversed-phase LC retention times and low- and high-resolution ion mobility spectrometry separation, as well as high-resolution MS2 spectra of analytical standards were studied. Reversed-phase LC separated the highest number of candidate structures while low-resolution ion mobility and high-resolution MS2 spectra provided little means for pinpointing the correct structure among the isomeric candidate structures even if analytical standards were available for comparison. Furthermore, the question arises which prediction accuracy is required from the in silico methods to par the analytical separation. Based on the experimental data of the isomeric candidate structures studied here and previously published in the literature (516 retention time and 569 CCS values), we estimate that to reduce the candidate list by 95% of the structures, the confidence interval of the predicted retention times would need to decrease to below 0.05 min for a 15-min gradient while that of CCS values would need to decrease to 0.15%. Hereby, we set a clear goal to the in silico methods for retention time and CCS prediction.
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Affiliation(s)
- Masoumeh Akhlaqi
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Wei-Chieh Wang
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Claudia Möckel
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Svante Arrhenius väg 16C, 114 18, Stockholm, Sweden.
- Department of Environmental Science, Svante Arrhenius väg 8, 114 18, Stockholm, Sweden.
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16
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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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17
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Cajahuaringa S, Caetano DLZ, Zanotto LN, Araujo G, Skaf MS. MassCCS: A High-Performance Collision Cross-Section Software for Large Macromolecular Assemblies. J Chem Inf Model 2023; 63:3557-3566. [PMID: 37184925 PMCID: PMC10269586 DOI: 10.1021/acs.jcim.3c00405] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2023] [Indexed: 05/16/2023]
Abstract
Ion mobility mass spectrometry (IM-MS) techniques have become highly valued as a tool for structural characterization of biomolecular systems since they yield accurate measurements of the rotationally averaged collision cross-section (CCS) against a buffer gas. Despite its enormous potential, IM-MS data interpretation is often challenging due to the conformational isomerism of metabolites, lipids, proteins, and other biomolecules in the gas phase. Therefore, reliable and fast CCS calculations are needed to help interpret IM-MS data. In this work, we present MassCCS, a parallelized open-source code for computing CCS of molecules ranging from small organic compounds to massive protein assemblies at the trajectory method level of description using atomic and molecular buffer gas particles. The performance of the code is comparable to other available software for small molecules and proteins but is significantly faster for larger macromolecular assemblies. We performed extensive tests regarding accuracy, performance, and scalability with system size and number of CPU cores. MassCCS has proven highly accurate and efficient, with execution times under a few minutes, even for large (84.87 MDa) virus capsid assemblies with very modest computational resources. MassCCS is freely available at https://github.com/cces-cepid/massccs.
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Affiliation(s)
- Samuel Cajahuaringa
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Daniel L. Z. Caetano
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
- Institute
of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
| | - Leandro N. Zanotto
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Guido Araujo
- Institute
of Computing, University of Campinas, Campinas, São Paulo 13083-852, Brazil
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
| | - Munir S. Skaf
- Center
for Computing in Engineering & Sciences, University of Campinas, Campinas, São Paulo 13083-861, Brazil
- Institute
of Chemistry, University of Campinas, Campinas, São Paulo 13083-970, Brazil
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18
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Houthuijs KJ, Berden G, Engelke UFH, Gautam V, Wishart DS, Wevers RA, Martens J, Oomens J. An In Silico Infrared Spectral Library of Molecular Ions for Metabolite Identification. Anal Chem 2023. [PMID: 37262385 DOI: 10.1021/acs.analchem.3c01078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Infrared ion spectroscopy (IRIS) continues to see increasing use as an analytical tool for small-molecule identification in conjunction with mass spectrometry (MS). The IR spectrum of an m/z selected population of ions constitutes a unique fingerprint that is specific to the molecular structure. However, direct translation of an IR spectrum to a molecular structure remains challenging, as reference libraries of IR spectra of molecular ions largely do not exist. Quantum-chemically computed spectra can reliably be used as reference, but the challenge of selecting the candidate structures remains. Here, we introduce an in silico library of vibrational spectra of common MS adducts of over 4500 compounds found in the human metabolome database. In total, the library currently contains more than 75,000 spectra computed at the DFT level that can be queried with an experimental IR spectrum. Moreover, we introduce a database of 189 experimental IRIS spectra, which is employed to validate the automated spectral matching routines. This demonstrates that 75% of the metabolites in the experimental data set are correctly identified, based solely on their exact m/z and IRIS spectrum. Additionally, we demonstrate an approach for specifically identifying substructures by performing a search without m/z constraints to find structural analogues. Such an unsupervised search paves the way toward the de novo identification of unknowns that are absent in spectral libraries. We apply the in silico spectral library to identify an unknown in a plasma sample as 3-hydroxyhexanoic acid, highlighting the potential of the method.
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Affiliation(s)
- Kas J Houthuijs
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
| | - Giel Berden
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
| | - Udo F H Engelke
- Department of Genetics, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Vasuk Gautam
- Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada
| | - David S Wishart
- Department of Biological Sciences, University of Alberta, Edmonton AB T6G 2E9, Canada
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada
- Department of Laboratory Medicine and Pathology, University of Alberta, Edmonton, AB T6G 2B7, Canada
- Faculty of Pharmacy and Pharmaceutical Sciences, University of Alberta, Edmonton, AB T6G 2H7, Canada
| | - Ron A Wevers
- Department of Genetics, Translational Metabolic Laboratory, Radboud University Medical Center, Nijmegen 6525 GA, The Netherlands
| | - Jonathan Martens
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
| | - Jos Oomens
- Institute for Molecules and Materials, FELIX Laboratory, Radboud University, Nijmegen 6525 ED, The Netherlands
- van 't Hoff Institute for Molecular Sciences, University of Amsterdam, Amsterdam 1098 XH, The Netherlands
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19
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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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20
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Brookhart A, Arora M, McCullagh M, Wilson ID, Plumb RS, Vissers JP, Tanna N. Understanding mobile phase buffer composition and chemical structure effects on electrospray ionization mass spectrometry response. J Chromatogr A 2023; 1696:463966. [PMID: 37054638 DOI: 10.1016/j.chroma.2023.463966] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2023] [Revised: 04/01/2023] [Accepted: 04/03/2023] [Indexed: 04/15/2023]
Abstract
Mobile phase selection is of critical importance in liquid chromatography - mass spectrometry (LC-MS) based studies, since it affects retention, chromatographic selectivity, ionization, limits of detection and quantification, and linear dynamic range. Generalized LC-MS mobile phase selection criteria, suitable for a broad class of chemical compounds, do not exist thus far. Here we have performed a large-scale qualitative assessment of the effect of solvent composition used for reversed-phase LC separations on electrospray ionization (ESI) response for 240 small molecular weight drugs, representing various chemical compound classes. Of these 240 analytes 224 were detectable using ESI. The main chemical structural features affecting ESI response were found to all be surface area or surface charge-related. Mobile phase composition was found to be less differentiating, although for some compounds a pH effect was noted. Unsurprisingly, chemical structure was found to be the dominant factor for ESI response for the majority of the investigated analytes, representing about 85% of the replicating detectable complement of the sample data set. A weak correlation between ESI response and structure complexity was observed. Solvents based on isopropanol, and those containing phosphoric or di- and trifluoracetic acids, performed relatively poorly in terms of chromatographic or ESI response, whilst the best performing 'generic' LC solvents were based on methanol, acetonitrile using formic acid and ammonium acetate as buffer components, consistent with current practice in many laboratories.
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Affiliation(s)
- Allison Brookhart
- Department of Biochemistry and Molecular Biology, University of Massachusetts Amherst, MA
| | - Mahika Arora
- Manning College of Information and Computer Sciences, University of Massachusetts Amherst, MA
| | | | - Ian D Wilson
- Computational & Systems Medicine, Department of Metabolism, Digestion and Reproduction, Imperial College, United Kingdom
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21
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Song Y, Song Q, Liu W, Li J, Tu P. High-confidence structural identification of metabolites relying on tandem mass spectrometry through isomeric identification: A tutorial. Trends Analyt Chem 2023. [DOI: 10.1016/j.trac.2023.116982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
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22
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Nielson FF, Kay B, Young SJ, Colby SM, Renslow RS, Metz TO. Similarity Downselection: Finding the n Most Dissimilar Molecular Conformers for Reference-Free Metabolomics. Metabolites 2023; 13:105. [PMID: 36677030 PMCID: PMC9864474 DOI: 10.3390/metabo13010105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 12/14/2022] [Accepted: 12/30/2022] [Indexed: 01/11/2023] Open
Abstract
Computational methods for creating in silico libraries of molecular descriptors (e.g., collision cross sections) are becoming increasingly prevalent due to the limited number of authentic reference materials available for traditional library building. These so-called "reference-free metabolomics" methods require sampling sets of molecular conformers in order to produce high accuracy property predictions. Due to the computational cost of the subsequent calculations for each conformer, there is a need to sample the most relevant subset and avoid repeating calculations on conformers that are nearly identical. The goal of this study is to introduce a heuristic method of finding the most dissimilar conformers from a larger population in order to help speed up reference-free calculation methods and maintain a high property prediction accuracy. Finding the set of the n items most dissimilar from each other out of a larger population becomes increasingly difficult and computationally expensive as either n or the population size grows large. Because there exists a pairwise relationship between each item and all other items in the population, finding the set of the n most dissimilar items is different than simply sorting an array of numbers. For instance, if you have a set of the most dissimilar n = 4 items, one or more of the items from n = 4 might not be in the set n = 5. An exact solution would have to search all possible combinations of size n in the population exhaustively. We present an open-source software called similarity downselection (SDS), written in Python and freely available on GitHub. SDS implements a heuristic algorithm for quickly finding the approximate set(s) of the n most dissimilar items. We benchmark SDS against a Monte Carlo method, which attempts to find the exact solution through repeated random sampling. We show that for SDS to find the set of n most dissimilar conformers, our method is not only orders of magnitude faster, but it is also more accurate than running Monte Carlo for 1,000,000 iterations, each searching for set sizes n = 3-7 out of a population of 50,000. We also benchmark SDS against the exact solution for example small populations, showing that SDS produces a solution close to the exact solution in these instances. Using theoretical approaches, we also demonstrate the constraints of the greedy algorithm and its efficacy as a ratio to the exact solution.
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Affiliation(s)
- Felicity F. Nielson
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| | - Bill Kay
- Pacific Northwest National Laboratory, Advanced Computing, Mathematics, and Data Division, Richland, WA 99354, USA
| | - Stephen J. Young
- Pacific Northwest National Laboratory, Advanced Computing, Mathematics, and Data Division, Richland, WA 99354, USA
| | - Sean M. Colby
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| | - Ryan S. Renslow
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
| | - Thomas O. Metz
- Pacific Northwest National Laboratory, Biological Sciences Division, Richland, WA 99354, USA
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23
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Hissong R, Evans KR, Evans CR. Compound Identification Strategies in Mass Spectrometry-Based Metabolomics and Pharmacometabolomics. Handb Exp Pharmacol 2023; 277:43-71. [PMID: 36409330 DOI: 10.1007/164_2022_617] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
The metabolome is composed of a vast array of molecules, including endogenous metabolites and lipids, diet- and microbiome-derived substances, pharmaceuticals and supplements, and exposome chemicals. Correct identification of compounds from this diversity of classes is essential to derive biologically relevant insights from metabolomics data. In this chapter, we aim to provide a practical overview of compound identification strategies for mass spectrometry-based metabolomics, with a particular eye toward pharmacologically-relevant studies. First, we describe routine compound identification strategies applicable to targeted metabolomics. Next, we discuss both experimental (data acquisition-focused) and computational (software-focused) strategies used to identify unknown compounds in untargeted metabolomics data. We then discuss the importance of, and methods for, assessing and reporting the level of confidence of compound identifications. Throughout the chapter, we discuss how these steps can be implemented using today's technology, but also highlight research underway to further improve accuracy and certainty of compound identification. For readers interested in interpreting metabolomics data already collected, this chapter will supply important context regarding the origin of the metabolite names assigned to features in the data and help them assess the certainty of the identifications. For those planning new data acquisition, the chapter supplies guidance for designing experiments and selecting analysis methods to enable accurate compound identification, and it will point the reader toward best-practice data analysis and reporting strategies to allow sound biological and pharmacological interpretation.
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24
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Rainey MA, Watson CA, Asef CK, Foster MR, Baker ES, Fernández FM. CCS Predictor 2.0: An Open-Source Jupyter Notebook Tool for Filtering Out False Positives in Metabolomics. Anal Chem 2022; 94:17456-17466. [PMID: 36473057 PMCID: PMC9772062 DOI: 10.1021/acs.analchem.2c03491] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Metabolite annotation continues to be the widely accepted bottleneck in nontargeted metabolomics workflows. Annotation of metabolites typically relies on a combination of high-resolution mass spectrometry (MS) with parent and tandem measurements, isotope cluster evaluations, and Kendrick mass defect (KMD) analysis. Chromatographic retention time matching with standards is often used at the later stages of the process, which can also be followed by metabolite isolation and structure confirmation utilizing nuclear magnetic resonance (NMR) spectroscopy. The measurement of gas-phase collision cross-section (CCS) values by ion mobility (IM) spectrometry also adds an important dimension to this workflow by generating an additional molecular parameter that can be used for filtering unlikely structures. The millisecond timescale of IM spectrometry allows the rapid measurement of CCS values and allows easy pairing with existing MS workflows. Here, we report on a highly accurate machine learning algorithm (CCSP 2.0) in an open-source Jupyter Notebook format to predict CCS values based on linear support vector regression models. This tool allows customization of the training set to the needs of the user, enabling the production of models for new adducts or previously unexplored molecular classes. CCSP produces predictions with accuracy equal to or greater than existing machine learning approaches such as CCSbase, DeepCCS, and AllCCS, while being better aligned with FAIR (Findable, Accessible, Interoperable, and Reusable) data principles. Another unique aspect of CCSP 2.0 is its inclusion of a large library of 1613 molecular descriptors via the Mordred Python package, further encoding the fine aspects of isomeric molecular structures. CCS prediction accuracy was tested using CCS values in the McLean CCS Compendium with median relative errors of 1.25, 1.73, and 1.87% for the 170 [M - H]-, 155 [M + H]+, and 138 [M + Na]+ adducts tested. For superclass-matched data sets, CCS predictions via CCSP allowed filtering of 36.1% of incorrect structures while retaining a total of 100% of the correct annotations using a ΔCCS threshold of 2.8% and a mass error of 10 ppm.
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Affiliation(s)
- Markace A. Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Chandler A. Watson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Carter K. Asef
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
| | - Makayla R. Foster
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Erin S. Baker
- Department of Chemistry and Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Facundo M. Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States; Petit Institute of Bioengineering and Biotechnology, Georgia Institute of Technology, Atlanta, Georgia 30332, United States
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25
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Celma A, Bade R, Sancho JV, Hernandez F, Humphries M, Bijlsma L. Prediction of Retention Time and Collision Cross Section (CCS H+, CCS H-, and CCS Na+) of Emerging Contaminants Using Multiple Adaptive Regression Splines. J Chem Inf Model 2022; 62:5425-5434. [PMID: 36280383 PMCID: PMC9709913 DOI: 10.1021/acs.jcim.2c00847] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Ultra-high performance liquid chromatography coupled to ion mobility separation and high-resolution mass spectrometry instruments have proven very valuable for screening of emerging contaminants in the aquatic environment. However, when applying suspect or nontarget approaches (i.e., when no reference standards are available), there is no information on retention time (RT) and collision cross-section (CCS) values to facilitate identification. In silico prediction tools of RT and CCS can therefore be of great utility to decrease the number of candidates to investigate. In this work, Multiple Adaptive Regression Splines (MARS) were evaluated for the prediction of both RT and CCS. MARS prediction models were developed and validated using a database of 477 protonated molecules, 169 deprotonated molecules, and 249 sodium adducts. Multivariate and univariate models were evaluated showing a better fit for univariate models to the experimental data. The RT model (R2 = 0.855) showed a deviation between predicted and experimental data of ±2.32 min (95% confidence intervals). The deviation observed for CCS data of protonated molecules using the CCSH model (R2 = 0.966) was ±4.05% with 95% confidence intervals. The CCSH model was also tested for the prediction of deprotonated molecules, resulting in deviations below ±5.86% for the 95% of the cases. Finally, a third model was developed for sodium adducts (CCSNa, R2 = 0.954) with deviation below ±5.25% for 95% of the cases. The developed models have been incorporated in an open-access and user-friendly online platform which represents a great advantage for third-party research laboratories for predicting both RT and CCS data.
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Affiliation(s)
- Alberto Celma
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain,Department
of Aquatic Sciences and Assessment, Swedish
University of Agricultural Sciences (SLU), SE-750 07Uppsala, Sweden
| | - Richard Bade
- University
of South Australia, Adelaide, UniSA: Clinical and Health Sciences,
Health and Biomedical Innovation, AdelaideSA-5000, South
Australia, Australia,Queensland
Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, 20 Cornwall Street, WoolloongabbaAUS-4102, Queensland, Australia
| | - Juan Vicente Sancho
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain
| | - Félix Hernandez
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain
| | - Melissa Humphries
- School
of Mathematical Sciences, University of
Adelaide, Ingkarni Wardli Building, North Terrace Campus, SA-5005Adelaide, Australia,
| | - Lubertus Bijlsma
- Environmental
and Public Health Analytical
Chemistry, Research Institute for Pesticides
and Water, University Jaume I, E-12071Castelló, Spain,
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26
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Le Maître J, Maillard JF, Hubert-Roux M, Afonso C, Giusti P. Prediction of Structures of Compounds Encountered in Complex Organic Matter with Highly Flexible Alkyl Chains Using Ion Mobility-Mass Spectrometry. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:2024-2031. [PMID: 36178343 DOI: 10.1021/jasms.2c00106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
The chemical structure of an organic molecule has a direct influence on its three-dimensional conformation. One way to obtain information on this conformation is to use ion mobility spectrometry. This technique allows the separation of different isomers according to their collision cross section (CCS) with an inert gas. Smaller or more compact molecules will have lower collision cross section values than larger molecules. The CCS is an intrinsic ion parameter for a specific gas and is thus predictable. Today, calculations of rigid molecules are commonly performed to obtain additional structural information on an ion. However, calculations are more complex with very flexible molecules. In particular, molecules presenting long alkyl chains can yield a high number of conformers. Each conformer is then associated with a CCS value that is specific to it. We report, here, a methodology to predict CCS of flexible molecules. The used approach is based on automatic conformers research followed by geometry optimization and CCS calculations. Determination of theoretical and experimental CCS values for a rigid polycyclic aromatic hydrocarbons (PAHs) standard was used to calibrate the Mobcal software. Then, 13 standard molecules ranging from 4 to 19 carbon alkyl chains, including three long alkyl chain isomers of C22H38, were analyzed on a TWIMS-ToF and calculated using our methodology. CCS deviations between experimental and theoretical values were found to be less than 1.5% over the whole studied CCS range. This method was finally applied for structural analysis of petroleum compounds refractory to the hydro-denitrogenation process.
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Affiliation(s)
- Johann Le Maître
- Normandie Université, COBRA, UMR 6014 et FR 3038, Université de Rouen, INSA de Rouen-Normandie, CNRS, IRCOF, 76130Mont Saint Aignan Cedex, France
- TotalEnergies OneTech R&D, Total Research & Technology Gonfreville, BP 27, 76700Harfleur, France
- International Joint Laboratory - iC2MC: Complex Matrices Molecular Characterization, TRTG, BP 27, 76700Harfleur, France
| | - Julien F Maillard
- Normandie Université, COBRA, UMR 6014 et FR 3038, Université de Rouen, INSA de Rouen-Normandie, CNRS, IRCOF, 76130Mont Saint Aignan Cedex, France
- International Joint Laboratory - iC2MC: Complex Matrices Molecular Characterization, TRTG, BP 27, 76700Harfleur, France
| | - Marie Hubert-Roux
- Normandie Université, COBRA, UMR 6014 et FR 3038, Université de Rouen, INSA de Rouen-Normandie, CNRS, IRCOF, 76130Mont Saint Aignan Cedex, France
- International Joint Laboratory - iC2MC: Complex Matrices Molecular Characterization, TRTG, BP 27, 76700Harfleur, France
| | - Carlos Afonso
- Normandie Université, COBRA, UMR 6014 et FR 3038, Université de Rouen, INSA de Rouen-Normandie, CNRS, IRCOF, 76130Mont Saint Aignan Cedex, France
- International Joint Laboratory - iC2MC: Complex Matrices Molecular Characterization, TRTG, BP 27, 76700Harfleur, France
| | - Pierre Giusti
- Normandie Université, COBRA, UMR 6014 et FR 3038, Université de Rouen, INSA de Rouen-Normandie, CNRS, IRCOF, 76130Mont Saint Aignan Cedex, France
- TotalEnergies OneTech R&D, Total Research & Technology Gonfreville, BP 27, 76700Harfleur, France
- International Joint Laboratory - iC2MC: Complex Matrices Molecular Characterization, TRTG, BP 27, 76700Harfleur, France
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27
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Belova L, Celma A, Van Haesendonck G, Lemière F, Sancho JV, Covaci A, van Nuijs ALN, Bijlsma L. Revealing the differences in collision cross section values of small organic molecules acquired by different instrumental designs and prediction models. Anal Chim Acta 2022; 1229:340361. [PMID: 36156233 DOI: 10.1016/j.aca.2022.340361] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Revised: 08/31/2022] [Accepted: 09/02/2022] [Indexed: 11/17/2022]
Abstract
The number of open access databases containing experimental and predicted collision cross section (CCS) values is rising and leads to their increased use for compound identification. However, the reproducibility of reference values with different instrumental designs and the comparison between predicted and experimental CCS values is still under evaluation. This study compared experimental CCS values of 56 small molecules (Contaminants of Emerging Concern) acquired by both drift tube (DT) and travelling wave (TW) ion mobility mass spectrometry (IM-MS). The TWIM-MS included two instrumental designs (Synapt G2 and VION). The experimental TWCCSN2 values obtained by the TWIM-MS systems showed absolute percent errors (APEs) < 2% in comparison to experimental DTIMS data, indicating a good correlation between the datasets. Furthermore, TWCCSN2 values of [M - H]- ions presented the lowest APEs. An influence of the compound class on APEs was observed. The applicability of prediction models based on artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), both built using TWIM-MS data, was investigated for the first time for the prediction of DTCCSN2 values. For [M+H]+ and [M - H]- ions, the 95th percentile confidence intervals of observed APEs were comparable to values reported for both models indicating a good applicability for DTIMS predictions. For the prediction of DTCCSN2 values of [M+Na]+ ions, the MARS based model provided the best results with 73.9% of the ions showing APEs below the threshold reported for [M+Na]+. Finally, recommendations for database transfer and applications of prediction models for future DTIMS studies are made.
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Affiliation(s)
- Lidia Belova
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium.
| | - Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avinguda de Vicent Sos Baynat, 12006, Castelló, Spain
| | - Glenn Van Haesendonck
- Biomolecular & Analytical Mass Spectrometry (BAMS) Group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Filip Lemière
- Biomolecular & Analytical Mass Spectrometry (BAMS) Group, University of Antwerp, Groenenborgerlaan 171, 2020, Antwerp, Belgium
| | - Juan Vicente Sancho
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avinguda de Vicent Sos Baynat, 12006, Castelló, Spain
| | - Adrian Covaci
- Toxicological Centre, University of Antwerp, Universiteitsplein 1, 2610, Wilrijk, Belgium
| | | | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Avinguda de Vicent Sos Baynat, 12006, Castelló, Spain.
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28
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Jariyasopit N, Limjiasahapong S, Kurilung A, Sartyoungkul S, Wisanpitayakorn P, Nuntasaen N, Kuhakarn C, Reutrakul V, Kittakoop P, Sirivatanauksorn Y, Khoomrung S. Traveling Wave Ion Mobility-Derived Collision Cross Section Database for Plant Specialized Metabolites: An Application to Ventilago harmandiana Pierre. J Proteome Res 2022; 21:2481-2492. [PMID: 36154058 PMCID: PMC9552781 DOI: 10.1021/acs.jproteome.2c00413] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Indexed: 11/29/2022]
Abstract
The combination of ion mobility mass spectrometry (IM-MS) and chromatography is a valuable tool for identifying compounds in natural products. In this study, using an ultra-performance liquid chromatography system coupled to a high-resolution quadrupole/traveling wave ion mobility spectrometry/time-of-flight MS (UPLC-TWIMS-QTOF), we have established and validated a comprehensive TWCCSN2 and MS database for 112 plant specialized metabolites. The database included 15 compounds that were isolated and purified in-house and are not commercially available. We obtained accurate m/z, retention times, fragment ions, and TWIMS-derived CCS (TWCCSN2) values for 207 adducts (ESI+ and ESI-). The database included novel 158 TWCCSN2 values from 79 specialized metabolites. In the presence of plant matrix, the CCS measurement was reproducible and robust. Finally, we demonstrated the application of the database to extend the metabolite coverage of Ventilago harmandiana Pierre. In addition to pyranonaphthoquinones, a group of known specialized metabolites in V. harmandiana, we identified flavonoids, xanthone, naphthofuran, and protocatechuic acid for the first time through targeted analysis. Interestingly, further investigation using IM-MS of unknown features suggested the presence of organonitrogen compounds and lipid and lipid-like molecules, which is also reported for the first time. Data are available on the MassIVE (https://massive.ucsd.edu, data set identifier MSV000090213).
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Affiliation(s)
- Narumol Jariyasopit
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Suphitcha Limjiasahapong
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Alongkorn Kurilung
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sitanan Sartyoungkul
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Pattipong Wisanpitayakorn
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Narong Nuntasaen
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| | - Chutima Kuhakarn
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| | - Vichai Reutrakul
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
| | - Prasat Kittakoop
- Chulabhorn
Graduate Institute, Program in Chemical Sciences, Chulabhorn Royal Academy, Laksi,
Bangkok 10210, Thailand
- Chulabhorn
Research Institute, Kamphaeng Phet 6 Road, Laksi, Bangkok 10210, Thailand
| | - Yongyut Sirivatanauksorn
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
| | - Sakda Khoomrung
- Metabolomics
and Systems Biology, Department of Biochemistry, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Siriraj
Metabolomics and Phenomics Center, Faculty
of Medicine Siriraj Hospital, Mahidol University, Bangkok 10700, Thailand
- Center
of Excellence for Innovation in Chemistry (PERCH-CIC), Faculty of Science, Mahidol University, Bangkok 10400 Thailand
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29
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Feuerstein ML, Hernández-Mesa M, Kiehne A, Le Bizec B, Hann S, Dervilly G, Causon T. Comparability of Steroid Collision Cross Sections Using Three Different IM-HRMS Technologies: An Interplatform Study. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1951-1959. [PMID: 36047677 PMCID: PMC9545150 DOI: 10.1021/jasms.2c00196] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Revised: 08/16/2022] [Accepted: 08/23/2022] [Indexed: 06/15/2023]
Abstract
Steroids play key roles in various biological processes and are characterized by many isomeric variants, which makes their unambiguous identification challenging. Ion mobility-mass spectrometry (IM-MS) has been proposed as a suitable platform for this application, particularly using collision cross section (CCS) databases obtained from different commercial IM-MS instruments. CCS is seen as an ideal additional identification parameter for steroids as long-term repeatability and interlaboratory reproducibility of this measurand are excellent and matrix effects are negligible. While excellent results were demonstrated for individual IM-MS technologies, a systematic comparison of CCS derived from all major commercial IM-MS technologies has not been performed. To address this gap, a comprehensive interlaboratory comparison of 142 CCS values derived from drift tube (DTIM-MS), traveling wave (TWIM-MS), and trapped ion mobility (TIM-MS) platforms using a set of 87 steroids was undertaken. Besides delivering three instrument-specific CCS databases, systematic comparisons revealed excellent interlaboratory performance for 95% of the ions with CCS biases within ±1% for TIM-MS and within ±2% for TWIM-MS with respect to DTIM-MS values. However, a small fraction of ions (<1.5%) showed larger biases of up to 7% indicating that differences in the ion conformation sampled on different instrument types need to be further investigated. Systematic differences between CCS derived from different IM-MS analyzers and implications on the applicability for nontargeted analysis are critically discussed. To the best of our knowledge, this is the most comprehensive interlaboratory study comparing CCS from three different IM-MS technologies for analysis of steroids and small molecules in general.
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Affiliation(s)
- Max L. Feuerstein
- Department
of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | | | - Andrea Kiehne
- Bruker
Daltonics GmbH & Co. KG, 28359 Bremen, Germany
| | | | - Stephan Hann
- Department
of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
| | | | - Tim Causon
- Department
of Chemistry, Institute of Analytical Chemistry, University of Natural Resources and Life Sciences, Vienna, Muthgasse 18, 1190 Vienna, Austria
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30
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Yesiltepe Y, Govind N, Metz TO, Renslow RS. An initial investigation of accuracy required for the identification of small molecules in complex samples using quantum chemical calculated NMR chemical shifts. J Cheminform 2022; 14:64. [PMID: 36138446 PMCID: PMC9499888 DOI: 10.1186/s13321-022-00587-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2020] [Accepted: 02/06/2022] [Indexed: 11/24/2022] Open
Abstract
The majority of primary and secondary metabolites in nature have yet to be identified, representing a major challenge for metabolomics studies that currently require reference libraries from analyses of authentic compounds. Using currently available analytical methods, complete chemical characterization of metabolomes is infeasible for both technical and economic reasons. For example, unambiguous identification of metabolites is limited by the availability of authentic chemical standards, which, for the majority of molecules, do not exist. Computationally predicted or calculated data are a viable solution to expand the currently limited metabolite reference libraries, if such methods are shown to be sufficiently accurate. For example, determining nuclear magnetic resonance (NMR) spectroscopy spectra in silico has shown promise in the identification and delineation of metabolite structures. Many researchers have been taking advantage of density functional theory (DFT), a computationally inexpensive yet reputable method for the prediction of carbon and proton NMR spectra of metabolites. However, such methods are expected to have some error in predicted 13C and 1H NMR spectra with respect to experimentally measured values. This leads us to the question-what accuracy is required in predicted 13C and 1H NMR chemical shifts for confident metabolite identification? Using the set of 11,716 small molecules found in the Human Metabolome Database (HMDB), we simulated both experimental and theoretical NMR chemical shift databases. We investigated the level of accuracy required for identification of metabolites in simulated pure and impure samples by matching predicted chemical shifts to experimental data. We found 90% or more of molecules in simulated pure samples can be successfully identified when errors of 1H and 13C chemical shifts in water are below 0.6 and 7.1 ppm, respectively, and below 0.5 and 4.6 ppm in chloroform solvation, respectively. In simulated complex mixtures, as the complexity of the mixture increased, greater accuracy of the calculated chemical shifts was required, as expected. However, if the number of molecules in the mixture is known, e.g., when NMR is combined with MS and sample complexity is low, the likelihood of confident molecular identification increased by 90%.
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Affiliation(s)
- Yasemin Yesiltepe
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Niranjan Govind
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA
| | - Thomas O Metz
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA
| | - Ryan S Renslow
- The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman, WA, USA.
- Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland, WA, USA.
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31
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Applications of ion mobility-mass spectrometry in the chemical analysis in traditional Chinese medicines. Se Pu 2022; 40:782-787. [PMID: 36156624 PMCID: PMC9516353 DOI: 10.3724/sp.j.1123.2022.01028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
离子淌度质谱(IM-MS)是一种将离子淌度分离与质谱分析相结合的新型分析技术。IM-MS的主要优势不仅是在质谱检测前提供了基于气相离子形状、大小、电荷数等因素的多一维分离,而且能够提供碰撞截面积、漂移时间等质谱信息进而辅助化合物鉴定。近年来,随着IM-MS技术的不断发展,该技术在中药化学成分分析中受到越来越多的关注。首先,IM-MS已成功应用于改善中药复杂成分尤其是同分异构体或等量异位素等成分的分离;其次,IM-MS可通过多重碎裂模式辅助高质量中药小分子质谱信息的获取;此外,IM-MS提供的高维质谱数据信息还可促进中药复杂体系多成分的整合分析。该文在对IM-MS分类和基本原理进行概述的基础上,从分离能力及分离策略、多重碎裂模式、多维质谱数据处理策略3个方面,重点综述了IM-MS在中药化学成分分析中的应用,以期为IM-MS在中药化学成分研究提供参考。
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32
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Paglia G, Smith AJ, Astarita G. Ion mobility mass spectrometry in the omics era: Challenges and opportunities for metabolomics and lipidomics. MASS SPECTROMETRY REVIEWS 2022; 41:722-765. [PMID: 33522625 DOI: 10.1002/mas.21686] [Citation(s) in RCA: 90] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/11/2020] [Revised: 01/17/2021] [Accepted: 01/17/2021] [Indexed: 06/12/2023]
Abstract
Researchers worldwide are taking advantage of novel, commercially available, technologies, such as ion mobility mass spectrometry (IM-MS), for metabolomics and lipidomics applications in a variety of fields including life, biomedical, and food sciences. IM-MS provides three main technical advantages over traditional LC-MS workflows. Firstly, in addition to mass, IM-MS allows collision cross-section values to be measured for metabolites and lipids, a physicochemical identifier related to the chemical shape of an analyte that increases the confidence of identification. Second, IM-MS increases peak capacity and the signal-to-noise, improving fingerprinting as well as quantification, and better defining the spatial localization of metabolites and lipids in biological and food samples. Third, IM-MS can be coupled with various fragmentation modes, adding new tools to improve structural characterization and molecular annotation. Here, we review the state-of-the-art in IM-MS technologies and approaches utilized to support metabolomics and lipidomics applications and we assess the challenges and opportunities in this growing field.
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Affiliation(s)
- Giuseppe Paglia
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Andrew J Smith
- School of Medicine and Surgery, University of Milano-Bicocca, Vedano al Lambro (MB), Italy
| | - Giuseppe Astarita
- Department of Biochemistry and Molecular & Cellular Biology, Georgetown University, Washington, District of Columbia, USA
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33
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Delvaux A, Rathahao-Paris E, Alves S. Different ion mobility-mass spectrometry coupling techniques to promote metabolomics. MASS SPECTROMETRY REVIEWS 2022; 41:695-721. [PMID: 33492707 DOI: 10.1002/mas.21685] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Metabolomics has become increasingly popular in recent years for many applications ranging from clinical diagnosis, human health to biotechnological questioning. Despite technological advances, metabolomic studies are still currently limited by the difficulty of identifying all metabolites, a class of compounds with great chemical diversity. Although lengthy chromatographic analyses are often used to obtain comprehensive data, many isobar and isomer metabolites still remain unresolved, which is a critical point for the compound identification. Currently, ion mobility spectrometry is being explored in metabolomics as a way to improve metabolome coverage, analysis throughput and isomer separation. In this review, all the steps of a typical workflow for untargeted metabolomics are discussed considering the use of an ion mobility instrument. An overview of metabolomics is first presented followed by a brief description of ion mobility instrumentation. The ion mobility potential for complex mixture analysis is discussed regarding its coupling with a mass spectrometer alone, providing gas-phase separation before mass analysis as well as its combination with different separation platforms (conventional hyphenation but also multidimensional ion mobility couplings), offering multidimensional separation. Various instrumental and analytical conditions for improving the ion mobility separation are also described. Finally, data mining, including software packages and visualization approaches, as well as the construction of ion mobility databases for the metabolite identification are examined.
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Affiliation(s)
- Aurélie Delvaux
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
| | - Estelle Rathahao-Paris
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
- Département Médicaments et Technologies pour la Santé (DMTS), SPI, Université Paris-Saclay, CEA, INRAE, Gif-sur-Yvette, 91191, France
| | - Sandra Alves
- Faculté des Sciences et de l'Ingénierie, Institut Parisien de Chimie Moléculaire (IPCM), Sorbonne Université, Paris, 75005, France
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34
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Wu F, Wu X, Chi C, Ding CF. Simultaneous Differentiation of C═C Position Isomerism in Fatty Acids through Ion Mobility and Theoretical Calculations. Anal Chem 2022; 94:12213-12220. [PMID: 36008361 DOI: 10.1021/acs.analchem.2c02706] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Fatty acids play a pivotal role in biological processes and have many isomers, particularly at the C═C position, that influence their biological function. Distinguishing between isomers is crucial to investigating their role in health and disease. However, separating the isomers poses a significant analytical challenge. In this study, we developed a simple and rapid strategy combining ion mobility spectrometry and theoretical chemical calculations to differentiate and quantify the C═C positional isomers in 2-/3-butenoic acid (BA), 2-/3-/4-pentenoic acid (PA), and 2-/3-/5-hexenoic acid (HA). C═C positional isomerism was mobility-differentiated by simple complexation with crown ethers (12C4, 15C5, and 18C6) and divalent metal ions (Mg2+, Ca2+, Mn2+, Fe2+, Co2+, Ni2+, Zn2+, Sr2+, and Ba2+), that is, converting C═C positional isomers with small structural differences into complexes with large structural differences through the interaction with metal ions and crown ethers. Metallized isomers were formed but could not be differentiated due to their complex and overlapping extracted ion mobiliograms (EIMs). Binary crown ether-isomer complexes were not observed, indicating that C═C positional isomers could not be separated by simple mixing with crown ethers. However, significant EIM differences were obtained for the formed ternary complexes, allowing baseline separation for the isomers. Notably, all crown ethers and metal ions have a separation effect with the isomers, with a calculated separation resolution (Rp-p) of 0.07-2.44. Theoretical chemical calculations were performed to provide in-depth structural information for the complexes and explain the separation principle. Theoretical conformational space showed that the divalent metal ions act as a bridge connecting the crown ether and the isomer. Additionally, the ternary complex becomes more compact as the distance between C═C and -COOH increases. Theoretical results can reflect the features of mobility experiments, with relative errors between the experiment collision cross-section (CCS) and theoretical CCS of no more than ±8.06%. This method was also evaluated in terms of quantification, accuracy, and precision repeatability. Overall, this study establishes that the crown ether-metal ion pair can function as a robust unit for differentiating C═C positional isomerism.
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Affiliation(s)
- Fangling Wu
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Xishi Wu
- Engineering Laboratory of Advanced Energy Materials, Ningbo Institute of Materials Technology & Engineering, Chinese Academy of Sciences, Ningo, Zhejiang 315201, China
| | - Chaoxian Chi
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China
| | - Chuan-Fan Ding
- Key Laboratory of Advanced Mass Spectrometry and Molecular Analysis of Zhejiang Province, Institute of Mass Spectrometry, School of Material Science and Chemical Engineering, Ningbo University, Ningbo, Zhejiang 315211, China
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35
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Xia J, Xiao W, Lin X, Zhou Y, Qiu P, Si H, Wu X, Niu S, Luo Z, Yang X. Ion Mobility-Derived Collision Cross-Sections Add Extra Capability in Distinguishing Isomers and Compounds with Similar Retention Times: The Case of Aphidicolanes. Mar Drugs 2022; 20:md20090541. [PMID: 36135730 PMCID: PMC9503386 DOI: 10.3390/md20090541] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/11/2022] [Accepted: 08/19/2022] [Indexed: 11/20/2022] Open
Abstract
The hyphenation of ion mobility spectrometry with high-resolution mass spectrometry has been widely used in the characterization of various metabolites. Nevertheless, such a powerful tool remains largely unexplored in natural products research, possibly mainly due to the lack of available compounds. To evaluate the ability of collision cross-sections (CCSs) in characterizing compounds, especially isomeric natural products, here we measured and compared the traveling-wave IMS-derived nitrogen CCS values for 75 marine-derived aphidicolanes. We established a CCS database for these compounds which contained 227 CCS values of different adducts. When comparing the CCS differences, 36 of 57 pairs (over 60%) of chromatographically neighboring compounds showed a ΔCCS over 2%. What is more, 64 of 104 isomeric pairs (over 60%) of aphidicolanes can be distinguished by their CCS values, and 13 of 18 pairs (over 70%) of chromatographically indistinguishable isomers can be differentiated from the mobility dimension. Our results strongly supported CCS as an important parameter with good orthogonality and complementarity with retention time. CCS is expected to play an important role in distinguishing complex and diverse marine natural products.
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Affiliation(s)
- Jinmei Xia
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Wenhai Xiao
- Key Laboratory of Systems Bioengineering (Ministry of Education), Frontiers Science Center for Synthetic Biology (Ministry of Education), Tianjin University, Tianjin 300072, China
| | - Xihuang Lin
- Analyzing and Testing Center, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Yiduo Zhou
- Institute of Food Science and Technology, Hebei Agricultural University, Baoding 071001, China
| | - Peng Qiu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Hongkun Si
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Xiaorong Wu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Siwen Niu
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Zhuhua Luo
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
| | - Xianwen Yang
- Key Laboratory of Marine Biogenetic Resources, Third Institute of Oceanography, Ministry of Natural Resources, Xiamen 361005, China
- Correspondence:
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36
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King E, Overstreet R, Nguyen J, Ciesielski D. Augmentation of MS/MS Libraries with Spectral Interpolation for Improved Identification. J Chem Inf Model 2022; 62:3724-3733. [PMID: 35905451 PMCID: PMC9400100 DOI: 10.1021/acs.jcim.2c00620] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
![]()
Tandem mass spectrometry (MS/MS) is a primary tool for
the identification
of small molecules and metabolites where resultant spectra are most
commonly identified by matching them with spectra in MS/MS reference
libraries. The high degree of variability in MS/MS spectrum acquisition
techniques and parameters creates a significant challenge for building
standardized reference libraries. Here we present a method to improve
the usefulness of existing MS/MS libraries by augmenting available
experimental spectra data sets with statistically interpolated spectra
at unreported collision energies. We find that highly accurate spectral
approximations can be interpolated from as few as three experimental
spectra and that the interpolated spectra will be consistent with
true spectra gathered from the same instrument as the experimental
spectra. Supplementing existing spectral databases with interpolated
spectra yields consistent improvements to identification accuracy
on a range of instruments and precursor types. Applying this method
yields significant improvements (∼10% more spectra correctly
identified) on large data sets (2000–10 000 spectra),
indicating this is a quick yet adept tool for improving spectral matching
in situations where available reference libraries are not yet sufficient.
We also find improvements of matching spectra across instrument types
(between an Agilent Q-TOF and an Orbitrap Elite), at high collision
energies (50–90 eV), and with smaller data sets available through
MassBank.
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Affiliation(s)
- Ethan King
- Computing and Analytics Division, Pacific Northwest National Laboratory, Richland, Washington 99352, United States
| | - Richard Overstreet
- Signature Science and Technology Division, Pacific Northwest National Laboratory, Richland, Washington 99352, 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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37
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Foster M, Rainey M, Watson C, Dodds JN, Kirkwood KI, Fernández FM, Baker ES. Uncovering PFAS and Other Xenobiotics in the Dark Metabolome Using Ion Mobility Spectrometry, Mass Defect Analysis, and Machine Learning. ENVIRONMENTAL SCIENCE & TECHNOLOGY 2022; 56:9133-9143. [PMID: 35653285 PMCID: PMC9474714 DOI: 10.1021/acs.est.2c00201] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/15/2023]
Abstract
The identification of xenobiotics in nontargeted metabolomic analyses is a vital step in understanding human exposure. Xenobiotic metabolism, transformation, excretion, and coexistence with other endogenous molecules, however, greatly complicate the interpretation of features detected in nontargeted studies. While mass spectrometry (MS)-based platforms are commonly used in metabolomic measurements, deconvoluting endogenous metabolites from xenobiotics is also often challenged by the lack of xenobiotic parent and metabolite standards as well as the numerous isomers possible for each small molecule m/z feature. Here, we evaluate a xenobiotic structural annotation workflow using ion mobility spectrometry coupled with MS (IMS-MS), mass defect filtering, and machine learning to uncover potential xenobiotic classes and species in large metabolomic feature lists. Xenobiotic classes examined included those of known high toxicities, including per- and polyfluoroalkyl substances (PFAS), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and pesticides. Specifically, when the workflow was applied to identify PFAS in the NIST SRM 1957 and 909c human serum samples, it greatly reduced the hundreds of detected liquid chromatography (LC)-IMS-MS features by utilizing both mass defect filtering and m/z versus IMS collision cross sections relationships. These potential PFAS features were then compared to the EPA CompTox entries, and while some matched within specific m/z tolerances, there were still many unknowns illustrating the importance of nontargeted studies for detecting new molecules with known chemical characteristics. Additionally, this workflow can also be utilized to evaluate other xenobiotics and enable more confident annotations from nontargeted studies.
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Affiliation(s)
- MaKayla Foster
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Markace Rainey
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, Georgia 30332, United States
| | - Chandler Watson
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, Georgia 30332, United States
| | - James N Dodds
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Kaylie I Kirkwood
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
| | - Facundo M Fernández
- School of Chemistry and Biochemistry, Georgia Institute of Technology, 901 Atlantic Drive NW, Atlanta, Georgia 30332, United States
| | - Erin S Baker
- Department of Chemistry, North Carolina State University, Raleigh, North Carolina 27695, United States
- Comparative Medicine Institute, North Carolina State University, Raleigh, North Carolina 27695, United States
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38
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Moran-Garrido M, Camunas-Alberca SM, Gil-de-la Fuente A, Mariscal A, Gradillas A, Barbas C, Sáiz J. Recent developments in data acquisition, treatment and analysis with ion mobility-mass spectrometry for lipidomics. Proteomics 2022; 22:e2100328. [PMID: 35653360 DOI: 10.1002/pmic.202100328] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Revised: 05/29/2022] [Accepted: 05/30/2022] [Indexed: 11/08/2022]
Abstract
Lipids are involved in many biological processes and their study is constantly increasing. To identify a lipid among thousand requires of reliable methods and techniques. Ion Mobility (IM) can be coupled with Mass Spectrometry (MS) to increase analytical selectivity in lipid analysis of lipids. IM-MS has experienced an enormous development in several aspects, including instrumentation, sensitivity, amount of information collected and lipid identification capabilities. This review summarizes the latest developments in IM-MS analyses for lipidomics and focusses on the current acquisition modes in IM-MS, the approaches for the pre-treatment of the acquired data and the subsequent data analysis. Methods and tools for the calculation of Collision Cross Section (CCS) values of analytes are also reviewed. CCS values are commonly studied to support the identification of lipids, providing a quasi-orthogonal property that increases the confidence level in the annotation of compounds and can be matched in CCS databases. The information contained in this review might be of help to new users of IM-MS to decide the adequate instrumentation and software to perform IM-MS experiments for lipid analyses, but also for other experienced researchers that can reconsider their routines and protocols. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- María Moran-Garrido
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Sandra M Camunas-Alberca
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Alberto Gil-de-la Fuente
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain.,Departamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Antonio Mariscal
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain.,Departamento de Tecnologías de la Información, Escuela Politécnica Superior, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660 Boadilla del Monte, Madrid, Spain
| | - Ana Gradillas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Coral Barbas
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
| | - Jorge Sáiz
- Centre for Metabolomics and Bioanalysis (CEMBIO), Departamento de Química y Bioquímica, Facultad de Farmacia, Universidad San Pablo-CEU, CEU Universities, Urbanización Montepríncipe, 28660, Boadilla del Monte, Madrid, Spain
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39
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Ross D, Seguin RP, Krinsky AM, Xu L. High-Throughput Measurement and Machine Learning-Based Prediction of Collision Cross Sections for Drugs and Drug Metabolites. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:1061-1072. [PMID: 35548857 PMCID: PMC9165597 DOI: 10.1021/jasms.2c00111] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 05/14/2023]
Abstract
Drug metabolite identification is a bottleneck of drug metabolism studies due to the need for time-consuming chromatographic separation and structural confirmation. Ion mobility-mass spectrometry (IM-MS), on the other hand, separates analytes on a rapid (millisecond) time scale and enables the measurement of collision cross section (CCS), a unique physical property related to an ion's gas-phase size and shape, which can be used as an additional parameter for identification of unknowns. A current limitation to the application of IM-MS to the identification of drug metabolites is the lack of reference CCS values. In this work, we assembled a large-scale database of drug and drug metabolite CCS values using high-throughput in vitro drug metabolite generation and a rapid IM-MS analysis with automated data processing. Subsequently, we used this database to train a machine learning-based CCS prediction model, employing a combination of conventional 2D molecular descriptors and novel 3D descriptors, achieving high prediction accuracies (0.8-2.2% median relative error on test set data). The inclusion of 3D information in the prediction model enables the prediction of different CCS values for different protomers, conformers, and positional isomers, which is not possible using conventional 2D descriptors. The prediction models, dmCCS, are available at https://CCSbase.net/dmccs_predictions.
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Affiliation(s)
| | | | | | - Libin Xu
- . Tel: (206) 543-1080. Fax: (206) 685-3252
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40
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te Brinke E, Arrizabalaga-Larrañaga A, Blokland MH. Insights of ion mobility spectrometry and its application on food safety and authenticity: A review. Anal Chim Acta 2022; 1222:340039. [DOI: 10.1016/j.aca.2022.340039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Revised: 06/01/2022] [Accepted: 06/03/2022] [Indexed: 11/01/2022]
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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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Costalunga R, Tshepelevitsh S, Sepman H, Kull M, Kruve A. Sodium adduct formation with graph-based machine learning can aid structural elucidation in non-targeted LC/ESI/HRMS. Anal Chim Acta 2022; 1204:339402. [PMID: 35397906 DOI: 10.1016/j.aca.2021.339402] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 12/07/2021] [Accepted: 12/23/2021] [Indexed: 11/01/2022]
Abstract
Non-targeted screening with LC/ESI/HRMS aims to identify the structure of the detected compounds using their retention time, exact mass, and fragmentation pattern. Challenges remain in differentiating between isomeric compounds. One untapped possibility to facilitate identification of isomers relies on different ionic species formed in electrospray. In positive ESI mode, both protonated molecules and adducts can be formed; however, not all isomeric structures form the same ionic species. The complicated mechanism of adduct formation has hindered the use of this molecular characteristic in the structural elucidation in non-targeted screening. Here, we have studied the adduct formation for 94 small molecules with ion mobility spectra and compared collision cross-sections of the respective ions. Based on the results we developed a fast support vector machine classifier with polynomial kernels for accurately predicting the sodium adduct formation in ESI/HRMS. The model is trained on five independent data sets from different laboratories and uses the graph-based connectivity of functional groups and PubChem fingerprints to predict the sodium adduct formation in ESI/HRMS. The validation of the model showed an accuracy of 74.7% (balanced accuracy 70.0%) on a dataset from an independent laboratory, which was not used in the training of the model. Lastly, we applied the classification algorithm to the SusDat database by NORMAN network to evaluate the proportion of isomeric compounds that could be distinguished based on predicted sodium adduct formation. It was observed that sodium adduct formation probability can provide additional selectivity for about one quarter of the exact masses and, therefore, shows practical utility for structural assignment in non-targeted screening.
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Affiliation(s)
- Riccardo Costalunga
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden; Department of Food and Drug, University of Parma, via Università, 12, I 43121, Parma, Italy
| | - Sofja Tshepelevitsh
- Institute of Chemistry, University of Tartu, Ravila 14a, Tartu, 50411, Estonia
| | - Helen Sepman
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden
| | - Meelis Kull
- Institute of Computer Science, University of Tartu, Narva mnt 18, 51009, Tartu, Estonia
| | - Anneli Kruve
- Department of Materials and Environmental Chemistry, Stockholm University, Svante Arrhenius väg 16, 106 91, Stockholm, Sweden.
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Molecular networking and collision cross section prediction for structural isomer and unknown compound identification in plant metabolomics: a case study applied to Zhanthoxylum heitzii extracts. Anal Bioanal Chem 2022; 414:4103-4118. [PMID: 35419692 DOI: 10.1007/s00216-022-04059-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/29/2022] [Accepted: 04/01/2022] [Indexed: 12/28/2022]
Abstract
Mass spectrometry-based plant metabolomics allow large-scale analysis of a wide range of compounds and the discovery of potential new active metabolites with minimal sample preparation. Despite recent tools for molecular networking, many metabolites remain unknown. Our objective is to show the complementarity of collision cross section (CCS) measurements and calculations for metabolite annotation in a real case study. Thus, a systematic and high-throughput investigation of root, bark, branch, and leaf of the Gabonese plant Zhanthoxylum heitzii was performed through ultra-high performance liquid chromatography high-resolution tandem mass spectrometry (UHPLC-QTOF/MS). A feature-based molecular network (FBMN) was employed to study the distribution of metabolites in the organs of the plants and discover potential new components. In total, 143 metabolites belonging to the family of alkaloids, lignans, polyphenols, fatty acids, and amino acids were detected and a semi-quantitative analysis in the different organs was performed. A large proportion of medical plant phytochemicals is often characterized by isomerism and, in the absence of reference compounds, an additional dimension of gas phase separation can result in improvements to both quantitation and compound annotation. The inclusion of ion mobility in the ultra-high performance liquid chromatography mass spectrometry workflow (UHPLC-IMS-MS) has been used to collect experimental CCS values in nitrogen and helium (CCSN2 and CCSHe) of Zhanthoxylum heitzii features. Due to a lack of reference data, the investigation of predicted collision cross section has enabled comparison with the experimental values, helping in dereplication and isomer identification. Moreover, in combination with mass spectra interpretation, the comparison of experimental and theoretical CCS values allowed annotation of unknown features. The study represents a practical example of the potential of modern mass spectrometry strategies in the identification of medicinal plant phytochemical components.
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Denis EH, Bade JL, Renslow RS, Morrison KA, Nims MK, Govind N, Ewing RG. Proton Affinity Values of Fentanyl and Fentanyl Analogues Pertinent to Ambient Ionization and Detection. JOURNAL OF THE AMERICAN SOCIETY FOR MASS SPECTROMETRY 2022; 33:482-490. [PMID: 35041405 DOI: 10.1021/jasms.1c00320] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Proton affinity is a major factor in the atmospheric pressure chemical ionization of illicit drugs. The detection of illicit drugs by mass spectrometry and ion mobility spectrometry relies on the analytes having greater proton affinities than background species. Evaluating proton affinities for fentanyl and its analogues is informative for predicting the likelihood of ionization in different environments and for optimizing the compounds' ionization and detection, such as through the addition of dopant chemicals. Herein, density functional theory was used to computationally determine the proton affinity and gas-phase basicity of 15 fentanyl compounds and several relevant molecules as a reference point. The range of proton affinities for the fentanyl compounds was from 1018 to 1078 kJ/mol. Fentanyl compounds with the higher proton affinity values appeared to form a bridge between the oxygen on the amide and the protonated nitrogen on the piperidine ring based on models and calculated bond distances. Experiments with fragmentation of proton-bound clusters using atmospheric flow tube-mass spectrometry (AFT-MS) provided estimates of relative proton affinities and showed proton affinity values of fentanyl compounds >1000 kJ/mol, which were consistent with the computational results. The high proton affinities of fentanyl compounds facilitate their detection by ambient ionization techniques in complex environments. The detection limits of the fentanyl compounds with AFT-MS are in the low femtogram range, which demonstrates the feasibility of trace vapor drug detection.
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Affiliation(s)
- Elizabeth H Denis
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Jessica L Bade
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Ryan S Renslow
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Kelsey A Morrison
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Megan K Nims
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Niranjan Govind
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
| | - Robert G Ewing
- Pacific Northwest National Laboratory, 902 Battelle Boulevard, Richland, Washington 99352, United States
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45
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Valdés A, Álvarez-Rivera G, Socas-Rodríguez B, Herrero M, Ibáñez E, Cifuentes A. Foodomics: Analytical Opportunities and Challenges. Anal Chem 2022; 94:366-381. [PMID: 34813295 PMCID: PMC8756396 DOI: 10.1021/acs.analchem.1c04678] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Affiliation(s)
- Alberto Valdés
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Gerardo Álvarez-Rivera
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Bárbara Socas-Rodríguez
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Miguel Herrero
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Elena Ibáñez
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
| | - Alejandro Cifuentes
- Laboratory of Foodomics, Institute
of Food Science Research, CIAL, CSIC, Nicolas Cabrera 9, Madrid, 28049, Spain
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46
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Blumer MR, Chang CH, Brayfindley E, Nunez JR, Colby SM, Renslow RS, Metz TO. Mass Spectrometry Adduct Calculator. J Chem Inf Model 2021; 61:5721-5725. [PMID: 34842435 DOI: 10.1021/acs.jcim.1c00579] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
We describe the Mass Spectrometry Adduct Calculator (MSAC), an automated Python tool to calculate the adduct ion masses of a parent molecule. Here, adduct refers to a version of a parent molecule [M] that is charged due to addition or loss of atoms and electrons resulting in a charged ion, for example, [M + H]+. MSAC includes a database of 147 potential adducts and adduct/neutral loss combinations and their mass-to-charge ratios (m/z) as extracted from the NIST/EPA/NIH Mass Spectral Library (NIST17), Global Natural Products Social Molecular Networking Public Spectral Libraries (GNPS), and MassBank of North America (MoNA). The calculator relies on user-selected subsets of the combined database to calculate expected m/z for adducts of molecules supplied as formulas. This tool is intended to help researchers create identification libraries to collect evidence for the presence of molecules in mass spectrometry data. While the included adduct database focuses on adducts typically detected during liquid chromatography-mass spectrometry analyses, users may supply their own lists of adducts and charge states for calculating expected m/z. We also analyzed statistics on adducts from spectra contained in the three selected mass spectral libraries. MSAC is freely available at https://github.com/pnnl/MSAC.
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Affiliation(s)
- Madison R Blumer
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Christine H Chang
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | | | - Jamie R Nunez
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Sean M Colby
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Ryan S Renslow
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
| | - Thomas O Metz
- Pacific Northwest National Laboratory, Richland, Washington 99354, United States
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Li Y, Kind T, Folz J, Vaniya A, Mehta SS, Fiehn O. Spectral entropy outperforms MS/MS dot product similarity for small-molecule compound identification. Nat Methods 2021; 18:1524-1531. [PMID: 34857935 DOI: 10.1038/s41592-021-01331-z] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Accepted: 10/25/2021] [Indexed: 11/09/2022]
Abstract
Compound identification in small-molecule research, such as untargeted metabolomics or exposome research, relies on matching tandem mass spectrometry (MS/MS) spectra against experimental or in silico mass spectral libraries. Most software programs use dot product similarity scores. Here we introduce the concept of MS/MS spectral entropy to improve scoring results in MS/MS similarity searches via library matching. Entropy similarity outperformed 42 alternative similarity algorithms, including dot product similarity, when searching 434,287 spectra against the high-quality NIST20 library. Entropy similarity scores proved to be highly robust even when we added different levels of noise ions. When we applied entropy levels to 37,299 experimental spectra of natural products, false discovery rates of less than 10% were observed at entropy similarity score 0.75. Experimental human gut metabolome data were used to confirm that entropy similarity largely improved the accuracy of MS-based annotations in small-molecule research to false discovery rates below 10%, annotated new compounds and provided the basis to automatically flag poor-quality, noisy spectra.
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Affiliation(s)
- Yuanyue Li
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA
| | - Tobias Kind
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA
| | - Jacob Folz
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA
| | - Arpana Vaniya
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA
| | - Sajjan Singh Mehta
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA.,Olobion, Parc Científic de Barcelona, Barcelona, Spain
| | - Oliver Fiehn
- West Coast Metabolomics Center, UC Davis Genome Center, University of California, Davis, CA, USA.
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48
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Patel MK, Pandey S, Kumar M, Haque MI, Pal S, Yadav NS. Plants Metabolome Study: Emerging Tools and Techniques. PLANTS (BASEL, SWITZERLAND) 2021; 10:2409. [PMID: 34834772 PMCID: PMC8621461 DOI: 10.3390/plants10112409] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 10/31/2021] [Accepted: 11/01/2021] [Indexed: 05/06/2023]
Abstract
Metabolomics is now considered a wide-ranging, sensitive and practical approach to acquire useful information on the composition of a metabolite pool present in any organism, including plants. Investigating metabolomic regulation in plants is essential to understand their adaptation, acclimation and defense responses to environmental stresses through the production of numerous metabolites. Moreover, metabolomics can be easily applied for the phenotyping of plants; and thus, it has great potential to be used in genome editing programs to develop superior next-generation crops. This review describes the recent analytical tools and techniques available to study plants metabolome, along with their significance of sample preparation using targeted and non-targeted methods. Advanced analytical tools, like gas chromatography-mass spectrometry (GC-MS), liquid chromatography mass-spectroscopy (LC-MS), capillary electrophoresis-mass spectrometry (CE-MS), fourier transform ion cyclotron resonance-mass spectrometry (FTICR-MS) matrix-assisted laser desorption/ionization (MALDI), ion mobility spectrometry (IMS) and nuclear magnetic resonance (NMR) have speed up precise metabolic profiling in plants. Further, we provide a complete overview of bioinformatics tools and plant metabolome database that can be utilized to advance our knowledge to plant biology.
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Affiliation(s)
- Manish Kumar Patel
- Department of Postharvest Science of Fresh Produce, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel
| | - Sonika Pandey
- Independent Researcher, Civil Line, Fathepur 212601, India;
| | - Manoj Kumar
- Institute of Plant Sciences, Agricultural Research Organization, Volcani Center, Rishon LeZion 7505101, Israel;
| | - Md Intesaful Haque
- Fruit Tree Science Department, Newe Ya’ar Research Center, Agriculture Research Organization, Volcani Center, Ramat Yishay 3009500, Israel;
| | - Sikander Pal
- Plant Physiology Laboratory, Department of Botany, University of Jammu, Jammu 180006, India;
| | - Narendra Singh Yadav
- Department of Biological Sciences, University of Lethbridge, Lethbridge, AB T1K 3M4, Canada
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Tsugawa H, Rai A, Saito K, Nakabayashi R. Metabolomics and complementary techniques to investigate the plant phytochemical cosmos. Nat Prod Rep 2021; 38:1729-1759. [PMID: 34668509 DOI: 10.1039/d1np00014d] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Covering: up to 2021Plants and their associated microbial communities are known to produce millions of metabolites, a majority of which are still not characterized and are speculated to possess novel bioactive properties. In addition to their role in plant physiology, these metabolites are also relevant as existing and next-generation medicine candidates. Elucidation of the plant metabolite diversity is thus valuable for the successful exploitation of natural resources for humankind. Herein, we present a comprehensive review on recent metabolomics approaches to illuminate molecular networks in plants, including chemical isolation and enzymatic production as well as the modern metabolomics approaches such as stable isotope labeling, ultrahigh-resolution mass spectrometry, metabolome imaging (spatial metabolomics), single-cell analysis, cheminformatics, and computational mass spectrometry. Mass spectrometry-based strategies to characterize plant metabolomes through metabolite identification and annotation are described in detail. We also highlight the use of phytochemical genomics to mine genes associated with specialized metabolites' biosynthesis. Understanding the metabolic diversity through biotechnological advances is fundamental to elucidate the functions of the plant-derived specialized metabolome.
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Affiliation(s)
- Hiroshi Tsugawa
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,RIKEN Center for Integrative Medical Sciences, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.,Department of Biotechnology and Life Science, Tokyo University of Agriculture and Technology, 2-24-16 Nakamachi, Koganei, Tokyo 184-8588, Japan.,Graduate School of Medical Life Science, Yokohama City University, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama 230-0045, Japan
| | - Amit Rai
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Kazuki Saito
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan. .,Plant Molecular Science Center, Chiba University, 1-8-1 Inohana, Chuo-ku, Chiba 260-8675, Japan
| | - Ryo Nakabayashi
- RIKEN Center for Sustainable Resource Science, 1-7-22 Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa 230-0045, Japan.
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50
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Celma A, Ahrens L, Gago-Ferrero P, Hernández F, López F, Lundqvist J, Pitarch E, Sancho JV, Wiberg K, Bijlsma L. The relevant role of ion mobility separation in LC-HRMS based screening strategies for contaminants of emerging concern in the aquatic environment. CHEMOSPHERE 2021; 280:130799. [PMID: 34162120 DOI: 10.1016/j.chemosphere.2021.130799] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Revised: 04/29/2021] [Accepted: 05/01/2021] [Indexed: 05/24/2023]
Abstract
Ion mobility separation (IMS) coupled to high resolution mass spectrometry (IMS-HRMS) is a promising technique for (non-)target/suspect analysis of micropollutants in complex matrices. IMS separates ionized compounds based on their charge, shape and size facilitating the removal of co-eluting isomeric/isobaric species. Additionally, IMS data can be translated into collision cross-section (CCS) values, which can be used to increase the identification reliability. However, IMS-HRMS for the screening of contaminants of emerging concern (CECs) have been scarcely explored. In this study, the role of IMS-HRMS for the identification of CECs in complex matrices is highlighted, with emphasis on when and with which purpose is of use. The utilization of IMS can result in much cleaner mass spectra, which considerably facilitates data interpretation and the obtaining of reliable identifications. Furthermore, the robustness of IMS measurements across matrices permits the use of CCS as an additional relevant parameter during the identification step even when reference standards are not available. Moreover, an effect on the number of true and false identifications could be demonstrated by including IMS restrictions within the identification workflow. Data shown in this work is of special interest for environmental researchers dealing with the detection of CECs with state-of-the-art IMS-HRMS instruments.
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Affiliation(s)
- Alberto Celma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Lutz Ahrens
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07, Uppsala, Sweden
| | - Pablo Gago-Ferrero
- Institute of Environmental Assessment and Water Research (IDAEA) Severo Ochoa Excellence Center, Spanish Council for Scientific Research (CSIC), Jordi Girona 18-26, E-08034, Barcelona, Spain
| | - Félix Hernández
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Francisco López
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Johan Lundqvist
- Department of Biomedicine and Veterinary Public Health, Swedish University of Agricultural Sciences, Box 7028, SE-750 07, Uppsala, Sweden
| | - Elena Pitarch
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Juan Vicente Sancho
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain
| | - Karin Wiberg
- Department of Aquatic Sciences and Assessment, Swedish University of Agricultural Sciences (SLU), Box 7050, SE-750 07, Uppsala, Sweden
| | - Lubertus Bijlsma
- Environmental and Public Health Analytical Chemistry, Research Institute for Pesticides and Water, University Jaume I, Castelló, E-12071, Spain.
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