Number Of Protonic Sites Research Articles

Methylation as a strategy for enhancing sensitivity and selectivity in peptide analysis with Differential Mobility Spectrometry (DMS)

Benefits and drawbacks of combining in-vacuo/solution methylation with ESI-DMS-MS analysis are demonstrated in studies with the peptides pentaalanine and [Glu1] fibrinopeptide B. Methylation introduces a quaternary ammonium at the N-terminus of the peptide and changes the number of protonation sites. This has allowed the comparative quantification of sensitivity and selectivity with and without methylation. The transformation of the protonation site in pentaalanine to a quaternary ammonium was observed to reduce DMS selectivity and this was attributed to a weaker ion/modifier interaction. On the other hand, sensitivity was increased and this was attributed to the elimination of ion loss by proton transfer to the modifier. Methylation of [Glu1] fibrinopeptide B was observed not to change the charge state as one protonation site is displaced by a quaternary ammonium. Sensitivity was increased due to suppression of proton transfer to the modifier. Selectivity was not significantly affected in this case as ion/modifier interaction was maintained through the additional protonation site. Our study was conducted with 5 different modifiers and some of their mixtures.

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

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.