Imperial College London

DrTimothyEbbels

Faculty of MedicineDepartment of Metabolism, Digestion and Reproduction

Reader in Computational Bioinformatics
 
 
 
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Contact

 

+44 (0)20 7594 3160t.ebbels Website

 
 
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Location

 

131Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Ye:2018:10.1007/s11306-018-1351-y,
author = {Ye, L and De, Iorio M and Ebbels, TMD},
doi = {10.1007/s11306-018-1351-y},
journal = {Metabolomics},
title = {Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data},
url = {http://dx.doi.org/10.1007/s11306-018-1351-y},
volume = {14},
year = {2018}
}

RIS format (EndNote, RefMan)

TY  - JOUR
AB - IntroductionTo aid the development of better algorithms for 1H 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.ObjectiveWe aim to use observed NMR chemical shift titration data to estimate the number of protonation sites for a range of urinary metabolites.MethodsA pool of urine from healthy subjects was titrated in the range pH 2–12, standard 1H 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.ResultsThe 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.ConclusionsGiven appropriate data, it is possible to estimate the number of protonation sites for many metabolites typically observed in 1H 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.
AU - Ye,L
AU - De,Iorio M
AU - Ebbels,TMD
DO - 10.1007/s11306-018-1351-y
PY - 2018///
SN - 1573-3882
TI - Bayesian estimation of the number of protonation sites for urinary metabolites from NMR spectroscopic data
T2 - Metabolomics
UR - http://dx.doi.org/10.1007/s11306-018-1351-y
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000431957900004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - http://hdl.handle.net/10044/1/60216
VL - 14
ER -