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Cited by 41Metabolomics and Mass Spectrometry StudiesMass Spectrometry Techniques and ApplicationsComputational Drug Discovery Methods

Evaluation of an artificial neural network retention index model for chemical structure identification in nontargeted metabolomics

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Samaraweera, Milinda A., Hall, L. Mark, Hill, Dennis W., Grant, David F.·2018·Analytical Chemistry
DOI 10.1021/acs.analchem.8b03118PMC PMC8378237
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Abstract

Liquid chromatography coupled with electrospray ionization tandem mass spectrometry (LC-ESI-MS/MS) is a major analytical technique used for nontargeted identification of metabolites in biological fluids. Typically, in LC-ESI-MS/MS based database assisted structure elucidation pipelines, the exact mass of an unknown compound is used to mine a chemical structure database to acquire an initial set of possible candidates. Subsequent matching of the collision induced dissociation (CID) spectrum of the unknown to the CID spectra of candidate structures facilitates identification. However, this approach often fails because of the large numbers of potential candidates (i.e., false positives) for which CID spectra are not available. To overcome this problem, CID fragmentation predication programs have been developed, but these also have limited success if large numbers of isomers with similar CID spectra are present in the candidate set. In this study, we investigated the use of a retention index (RI) predictive model as an orthogonal method to help improve identification rates. The model was used to eliminate candidate structures whose predicted RI values differed significantly from the experimentally determined RI value of the unknown compound. We tested this approach using a set of ninety-one endogenous metabolites and four in silico CID fragmentation algorithms: CFM-ID, CSI:FingerID, Mass Frontier, and MetFrag. Candidate sets obtained from PubChem and the Human Metabolite Database (HMDB) were ranked with and without RI filtering followed by in silico spectral matching. Upon RI filtering, 12 of the ninety-one metabolites were eliminated from their respective candidate sets, i.e., were scored incorrectly as negatives. For the remaining seventy-nine compounds, we show that RI filtering eliminated an average of 58% from PubChem candidate sets. This resulted in an approximately 2-fold improvement in average rankings when using CFM-ID, Mass Frontier, and MetFrag. In addition, RI filtering slightly increased the occurrence of number one rankings for all 4 fragmentation algorithms. However, RI filtering did not significantly improve average rankings when HMDB was used as the candidate database, nor did it significantly improve average rankings when using CSI:FingerID. Overall, we show that the current RI model incorrectly eliminated more true positives (12) than were expected (4-5) on the basis of the filtering method. However, it slightly improved the number of correct first place rankings and improved overall average rankings when using CFM-ID, Mass Frontier, and MetFrag.

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  • Manual verification of metabolites following identification scoring is required for shotgun metabolomics because scoring methods are much less mature than for other omics data types.

    “manual verification of metabolites following identification scoring is required for shotgun metabolomics, as scoring methods are much less mature than for other ‘omics data types (Stein and Scott, 1994; Samaraweera et al., 2018).”

    From Missing data in multi-omics integration: Recent advances through artificial intelligence (2023) · cites this paper as Samaraweera et al., 2018

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Research works printed but not linked: 5

  1. [2]Fiehn O Plant Mol. Biol. 2002, 48 (1–2), 155– 171. 11860207
  2. [6]Milman BL; Zhurkovich IK TrAC, Trends Anal. Chem. 2016, 80, 636– 640.
  3. [17]Hufsky F; Scheubert K; Böcker S TrAC, Trends Anal. Chem. 2014, 53, 41– 48.
  4. [23]Allen F; Greiner R; Wishart D Metabolomics 2015, 11 (1), 98– 110.
  5. [32]Bolton EE; Wang Y; Thiessen PA; Bryant SH Annu. Rep. Comput. Chem. 2008, 4, 217– 241.

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Authors
Samaraweera, M. A., Hall, L. M., Hill, D. W., & Grant, D. F.
Affiliations
  • University of Connecticut (US)
  • Hall Associates Consulting, 2 Davis Street, Quincy, Massachusetts 02170, United States
Corresponding author
Grant, David F.
ORCID
Venue
Analytical Chemistry
Publisher
American Chemical Society (ACS)
Published
2018-10-15
Volume
90
Issue
21
Pages
12752-12760
ISSN
  • 0003-2700 (ISSN-L)
  • 1520-6882
DOI
10.1021/acs.analchem.8b03118
PMC
PMC8378237
OpenAlex
W2896473185
Cited by
41
References
39
Citations by year
2026: 2 · 2025: 1 · 2024: 1 · 2023: 6 · 2022: 7 · 2021: 4 · 2020: 12 · 2019: 8
FWCI
2.13
Type
journal article
Language
en
Open access
Locations
  • Analytical Chemistry (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of General Medical Sciences (GM087714)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
  • Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
Keywords
retention index prediction, artificial neural networks, untargeted metabolomics, chemical structure identification, LC-ESI-MS/MS, in silico fragmentation, false positives, endogenous metabolites, PubChem, MetFrag
MeSH terms
Algorithms, Chromatography, Liquid, Computer Simulation, Models, Chemical, Molecular Structure, Neural Networks, Computer, Spectrometry, Mass, Electrospray Ionization, Metabolomics, Databases, Chemical

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