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Simultaneous Quantitation of Amino Acid Mixtures using Clustering Agents

A method that uses the abundances of large clusters formed in electrospray ionization to determine the solution-phase molar fractions of amino acids in multi-component mixtures is demonstrated. For solutions containing either four or 10 amino acids, the relative abundances of protonated molecules di...

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Detalles Bibliográficos
Autores principales: Leib, Ryan D., Williams, Evan R.
Formato: Texto
Lenguaje:English
Publicado: Springer-Verlag 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3062766/
https://www.ncbi.nlm.nih.gov/pubmed/21472601
http://dx.doi.org/10.1007/s13361-011-0081-4
Descripción
Sumario:A method that uses the abundances of large clusters formed in electrospray ionization to determine the solution-phase molar fractions of amino acids in multi-component mixtures is demonstrated. For solutions containing either four or 10 amino acids, the relative abundances of protonated molecules differed from their solution-phase molar fractions by up to 30-fold and 100-fold, respectively. For the four-component mixtures, the molar fractions determined from the abundances of larger clusters consisting of 19 or more molecules were within 25% of the solution-phase molar fractions, indicating that the abundances and compositions of these clusters reflect the relative concentrations of these amino acids in solution, and that ionization and detection biases are significantly reduced. Lower accuracy was obtained for the 10-component mixtures where values determined from the cluster abundances were typically within a factor of three of their solution molar fractions. The lower accuracy of this method with the more complex mixtures may be due to specific clustering effects owing to the heterogeneity as a result of significantly different physical properties of the components, or it may be the result of lower S/N for the more heterogeneous clusters and not including the low-abundance more highly heterogeneous clusters in this analysis. Although not as accurate as using traditional standards, this clustering method may find applications when suitable standards are not readily available. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s13361-011-0081-4) contains supplementary material, which is available to authorized users.