ArticleJournal of the American Society for Mass Spectrometry2014
MALDI efficiency of metabolites quantitatively associated with their structural properties: a quantitative structure-property relationship (QSPR) approach.
Article in Journal of the American Society for Mass Spectrometry, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- A power law distribution of metabolite abundance levels in mice regardless of the time and spatial scale of analysis.Scientific reports · 2018Article
- A Phytochemical-Sensing Strategy Based on Mass Spectrometry Imaging and Metabolic Profiling for Understanding the Functionality of the Medicinal Herb Green Tea.Molecules (Basel, Switzerland) · 2017Review
- A Chemometrics-driven Strategy for the Bioactivity Evaluation of Complex Multicomponent Systems and the Effective Selection of Bioactivity-predictive Chemical Combinations.Scientific reports · 2017Article
- Recommendations for quantitative analysis of small molecules by matrix-assisted laser desorption ionization mass spectrometry.Journal of chromatography. A · 2017Review
- High-mass-resolution MALDI mass spectrometry imaging of metabolites from formalin-fixed paraffin-embedded tissue.Nature protocols · 2016Article
- MALDI Mass Spectrometry Imaging for Visualizing In Situ Metabolism of Endogenous Metabolites and Dietary Phytochemicals.Metabolites · 2014Article
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Authors and funding
11 authors.
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Abstract
Matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) experiments require a suitable match of the matrix and target compounds to achieve a selective and sensitive analysis. However, it is still difficult to predict which metabolites are ionizable with a given matrix and which factors lead to an efficient ionization. In the present study, we extracted structural properties of metabolites that contribute to their ionization in MALDI-MS analyses exploiting our experimental data set. The MALDI-MS experiment was performed for 200 standard metabolites using 9-aminoacridine (9-AA) as the matrix. We then developed a prediction model for the ionization profiles (both the ionizability and ionization efficiency) of metabolites using a quantitative structure-property relationship (QSPR) approach. The classification model for the ionizability achieved a 91% accuracy, and the regression model for the ionization efficiency reached a rank correlation coefficient of 0.77. An analysis of the descriptors contributing to such model construction suggested that the proton affinity is a major determinant of the ionization, whereas some substructures hinder efficient ionization. This study will lead to the development of more rational and predictable MALDI-MS analyses.
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