Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data

Title: Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data
Authors: Ye, L
De Iorio, M
Ebbels, TMD
Item Type: Journal Article
Abstract: Introduction To aid the development of better algorithms for 1 H NMR data analysis, such as alignment or peak-fitting, it is important to characterise and model chemical shift changes caused by variation in pH. The number of protonation sites, a key parameter in the theoretical relationship between pH and chemical shift, is traditionally estimated from the molecular structure, which is often unknown in untargeted metabolomics applications. Objective We aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites. Methods A pool of urine from healthy subjects was titrated in the range pH 2–12, standard 1 H NMR spectra were acquired and positions of 51 peaks (corresponding to 32 identified metabolites) were recorded. A theoretical model of chemical shift was fit to the data using a Bayesian statistical framework, using model selection procedures in a Markov Chain Monte Carlo algorithm to estimate the number of protonation sites for each molecule. Results The estimated number of protonation sites was found to be correct for 41 out of 51 peaks. In some cases, the number of sites was incorrectly estimated, due to very close pKa values or a limited amount of data in the required pH range. Conclusions Given appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in 1 H NMR metabolomics without knowledge of the molecular structure. This approach may be a valuable resource for the development of future automated metabolite alignment, annotation and peak fitting algorithms.
Issue Date: 1-May-2018
Date of Acceptance: 16-Mar-2018
ISSN: 1573-3882
Publisher: Springer Verlag
Journal / Book Title: Metabolomics
Volume: 14
Issue: 5
Copyright Statement: © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Keywords: Science & Technology
Life Sciences & Biomedicine
Endocrinology & Metabolism
Peak shift changes
Protonation site
Bayesian model selection
0301 Analytical Chemistry
1103 Clinical Sciences
0601 Biochemistry And Cell Biology
Analytical Chemistry
Publication Status: Published
Article Number: ARTN 56
Online Publication Date: 2018-03-26
Appears in Collections:Division of Surgery
Faculty of Medicine

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